Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

July 2006

SHARED-USE PATHS

FINAL REPORT

 Evaluation of Safety, Design, and Operation of
FOREWORD 

Shared paths are paved, off-road facilities designed for travel by a variety of nonmotorized users, 

including bicyclists, pedestrians, skaters, joggers, and others. Shared-path planners and designers 

face a serious challenge in determining how wide paths should be and whether the various modes of 

travel should be separated from each other. Currently, there is very little substantive guidance 

available to aid in those decisions. 

This document describes the development of a new method to analyze the quality of service 

provided by shared paths of various widths and the accommodation of various travel-mode splits. 

The researchers assembled the new method using new theoretical traffic-flow concepts, a large set 

of operational data from 15 paths in 10 cities across the United States, and the perceptions of more 

than 100 path users. Given a count or estimate of the overall path user volume in the design-hour, 

the new method described here can provide the level of service for path widths from 2.44 to 

6.1 meters (8 to 20 feet). 

The information in this document should be of interest to planners, engineers, parks and recreation 

professionals, and to others involved in the planning, design, operation, and/or maintenance of 

shared paths. In addition, this document will be of interest to researchers investigating how to 

analyze multiple modes of travelers in a finite space with minimal traffic control. This document 

describes a spreadsheet calculation tool called SUPLOS that was also developed as part of the same 

effort, and this tool is being circulated by the Federal Highway Administration (FHWA). 

Michael F. Trentacoste 

Director, Office of Safety 

 Research and Development 

NOTICE 

This document is disseminated under the sponsorship of the U.S. Department of Transportation in 

the interest of information exchange. The U.S. Government assumes no liability for the use of the 

information contained in this document. This report does not constitute a standard, specification, or 

regulation. 

The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ 

names appear in this report only because they are considered essential to the objective of the 

document. 

QUALITY ASSURANCE STATEMENT 

FHWA provides high-quality information to serve Government, industry, and the public in a 

manner that promotes public understanding. Standards and policies are used to ensure and 

maximize the quality, objectivity, utility, and integrity of its information. FHWA periodically 

reviews quality issues and adjusts its programs and processes to ensure continuous quality 

improvement. 
Technical Report Documentation Page

1. Report No. 

FHWA-HRT-05-137 

2. Government Accession No. 3. Recipient’s Catalog No. 

5. Report Date 

July 2006 

4. Title and Subtitle 

Evaluation of Safety, Design, and Operation of Shared-Use Paths—

Final Report 6. Performing Organization Code 

7. Author(s) 

J.E. Hummer, N.M. Rouphail, J.L. Toole, R.S. Patten, 

R.J. Schneider, J.S. Green, R.G. Hughes, and S.J. Fain 

8. Performing Organization Report No. 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

Department of Civil, Construction, 

and Environmental Engineering

North Carolina State University 

Raleigh, NC 27695

11. Contract or Grant No. 

DTFH61-00-R-00070 

13. Type of Report and Period Covered 

Final Report 

September 2000 – May 2005

12. Sponsoring Agency Name and Address 

Office of Safety Research and Development 

Federal Highway Administration 

6300 Georgetown Pike 

McLean, VA 22101 14. Sponsoring Agency Code 

15. Supplementary Notes: 

Contracting Officer’s Technical Representative (COTR): Ann Do, HRDS-06 

16. Abstract 

Shared-use paths are becoming increasingly busy in many places in the United States. Path designers and 

operators need guidance on how wide to make new or rebuilt paths, and on whether to separate the 

different types of users. The current guidance is not very specific; it has not been calibrated to conditions 

in the United States, and does not accommodate the range of modes found on a typical U.S. path. The 

purpose of this project was to develop a level of service (LOS) estimation method for shared-use paths 

that overcomes these limitations. The research included the development of the theory of traffic flow on a 

path, an extensive effort to collect data on path operations, and a survey through which path users 

expressed their degree of satisfaction with the paths shown in a series of videos. 

Based on the theory developed and the data collected, the researchers developed an LOS estimation 

method for bicyclists that requires minimal input and produces a simple and useful result. Factors 

involved in the estimation of an LOS for a path include the number of times a typical bicyclist meets or 

passes another path user, the number of those passings that are delayed, the path width, and whether the 

path has a centerline. The method considers four other types of path users besides the adult bicyclists for 

whom the LOS is calculated—pedestrians, joggers, child bicyclists, and skaters. 

This report documents the research conducted during the project. Other products of the effort include 

Report No. FHWA-HRT-05-138, Shared-Use Path Level of Service Calculator: A User’s Guide (for the 

LOS procedure and the spreadsheet calculation tool); and a TechBrief, Publication No. FHWA-HRT-05-

139, Evaluation of Safety, Design, and Operation of Shared-Use Paths.

17. Key Words 

Path, trail, bicycle, shared use, level of 

service, width, pedestrian, skater. 

18. Distribution Statement 

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161. 

19. Security Classif. (of this report) 

Unclassified 

20. Security Classif. (of this page) 

Unclassified 

21. No. of Pages 

161 

22. Price 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
ii 

SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol 

LENGTH 

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

AREA 

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha

mi2 square miles 2.59 square kilometers km2

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3 

yd3 cubic yards 0.765 cubic meters m3 

NOTE: volumes greater than 1000 L shall be shown in m3

MASS 

oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t") 

TEMPERATURE (exact degrees) o

F Fahrenheit 5 (F-32)/9 Celsius oC 

or (F-32)/1.8 

ILLUMINATION 

fc foot-candles 10.76 lux lx

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

APPROXIMATE CONVERSIONS FROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH

mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

AREA mm2

 square millimeters 0.0016 square inches in2 

m2 square meters 10.764 square feet ft2 

m2 square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3 

m3 cubic meters 1.307 cubic yards yd3 

MASS 

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T 

TEMPERATURE (exact degrees) o

C Celsius 1.8C+32 Fahrenheit oF 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2 candela/m2 0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for th International System of Units. Approp e riate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003)
iii 

TABLE OF CONTENTS 

1. Introduction..................................................................................................................................1 

Shared-Use Paths .........................................................................................................................1 

Definition .................................................................................................................................1 

Gaining Popularity...................................................................................................................1 

Problems Facing Designers..........................................................................................................1 

Existing Level of Service (LOS) Method ................................................................................2 

Limitations of Current LOS Method........................................................................................2 

Project Objective..........................................................................................................................4 

Project Methods ...........................................................................................................................4 

Development of Theory ...........................................................................................................5 

Operational Data Collection ....................................................................................................5 

Perceptual Data Collection ......................................................................................................6 

A Usable Procedure .................................................................................................................6 

Project Scope Limited..................................................................................................................7 

Research Products........................................................................................................................8 

Description of the Products......................................................................................................8 

Intended Users .........................................................................................................................8 

Report Format ............................................................................................................................10 

2. Literature Review.......................................................................................................................11 

Introduction................................................................................................................................11 

Path User Characteristics ...........................................................................................................11 

Pedestrian Characteristics ......................................................................................................11 

Bicyclist Characteristics.........................................................................................................13 

Other Path Users ....................................................................................................................16 

Measuring Path User Quality of Service ...................................................................................17 

Hindrance...............................................................................................................................17 

Density ...................................................................................................................................20 

Space......................................................................................................................................21 

Stress......................................................................................................................................21 

Current LOS Scales................................................................................................................23 

Other Ways To Set the LOS Scale.........................................................................................25 

Summary....................................................................................................................................26 

3. Development of Theory .............................................................................................................29 

Introduction................................................................................................................................29 

Estimating the Number of Events..............................................................................................29 

Glossary of Variables.............................................................................................................30 

Estimating Active Passing Events .............................................................................................30 

Extension to Exclude Marginal Active Passing Events.........................................................32 

Estimating the Number of Passive Passing Events................................................................34 

Extension to Exclude Marginal Passive Passing Events........................................................37 
iv 

Estimating the Number of Meetings..........................................................................................37 

Sensitivity of Passing Events to Key Parameters ..................................................................41 

Estimating the Probability of Delayed Passing..........................................................................43 

Delayed Passing on Three-Lane Paths...................................................................................46 

Delayed Passing on Four-Lane Paths ....................................................................................48 

Multimodal Delayed Passing Probability ..............................................................................49 

Numerical Application of the Delayed Passing Models........................................................49 

4. Operational Data Collection ......................................................................................................53 

Introduction................................................................................................................................53 

Data Collection Method.............................................................................................................53 

Equipment..............................................................................................................................54 

Site Selection .............................................................................................................................55 

Data Collection Execution .........................................................................................................60 

5. Operational Data Analysis .........................................................................................................63 

Introduction................................................................................................................................63 

Average and Default Values ......................................................................................................63 

Speed......................................................................................................................................63 

Volume and Mode Split.........................................................................................................64 

Peak-Hour Factor...................................................................................................................66 

Users Occupying Two Lanes.................................................................................................68 

Distance Needed to Pass ........................................................................................................69 

Validating Theory ......................................................................................................................71 

Speeds Normally Distributed.................................................................................................72 

Comparing Predicted Meetings and Passings to Field Data ..................................................77 

6. Perception Data Collection ........................................................................................................87 

General Rationale.......................................................................................................................87 

Participants.................................................................................................................................87 

Collection of User Perception Data ...........................................................................................89 

Structure and Content of the Survey Instrument....................................................................89 

Format of Participants’ Responses.........................................................................................91 

7. Analysis of Perception Survey Responses.................................................................................93 

Introduction................................................................................................................................93 

Data Overview ...........................................................................................................................93 

Demographic Variables .........................................................................................................96 

Path Design Variables............................................................................................................99 

Events on the Path................................................................................................................100 

Video Quality.......................................................................................................................103 

Model Creation ........................................................................................................................104 

Interactions...........................................................................................................................104 

Choice of Overall Response.................................................................................................105 

Fitting the Model..................................................................................................................106 
v 

8. LOS Procedure.........................................................................................................................109 

Introduction..............................................................................................................................109 

Types of Shared-Use Paths To Which This Study Applies .....................................................110 

LOS Defined ............................................................................................................................110 

The Perception Survey Response Scale...................................................................................111 

Developing the LOS Procedure ...............................................................................................113 

Start With the Model from Chapter 7 ..................................................................................114 

Model Does Not Cover All Combinations...........................................................................114 

Delayed Passing Adjustment ...................................................................................................115 

Adjustment for Low Number of Events...............................................................................117 

Putting It All Together.........................................................................................................117 

Comparison to HCM Method ..............................................................................................119 

Applying the Model .................................................................................................................121 

Link Analysis.......................................................................................................................121 

Data Requirements...............................................................................................................123 

Assumptions and Default Values.........................................................................................126 

LOS Lookup Tables.................................................................................................................127 

Instructions for Using the LOS Calculator ..........................................................................127 

Implications for Trail Design...............................................................................................129 

Trail Width...........................................................................................................................129 

Centerline Striping...............................................................................................................130 

Multiple Treadways .............................................................................................................130 

Trail Operations and Management.......................................................................................130 

Case Studies.............................................................................................................................131 

9. Conclusions and Recommendations ........................................................................................133 

Introduction..............................................................................................................................133 

Conclusions..............................................................................................................................134 

Recommendations....................................................................................................................135 

Marketing the New LOS Procedure.....................................................................................136 

Future Research Needs ........................................................................................................136 

Appendix A. Perception Survey Fact Sheet and Informed Consent Form ..................................139 

Appendix B. Perception Survey Background Information Form ................................................141 

Appendix C. Screen Shots From Perception Study Video ..........................................................143 

References....................................................................................................................................149 
vi 

LIST OF FIGURES 

Figure 1. Schematic for active passing estimation........................................................................ 31 

Figure 2. Schematic for passive passing estimation. .................................................................... 34 

Figure 3. Schematic for meeting event estimation........................................................................ 37 

Figure 4. Sensitivity of hourly passing rates to individual bicyclist speed................................... 42 

Figure 5. Sensitivity of hourly passing rates to directional flow rates.......................................... 43 

Figure 6. Delayed passing cases on a two-lane path..................................................................... 45 

Figure 7. Schematic of delayed passing on a three-lane path....................................................... 47 

Figure 8. Schematic of delayed passing on a four-lane path. ....................................................... 49 

Figure 9. PHF as a function of hourly volume.............................................................................. 68 

Figure 10. Distribution of bicycle speed data. .............................................................................. 73 

Figure 11. Distribution of pedestrian speed data. ......................................................................... 73 

Figure 12. Distribution of skater speed data. ................................................................................ 74 

Figure 13. Distribution of jogger speed data. ............................................................................... 74 

Figure 14. Distribution of child bicyclist speed data. ................................................................... 75 

Figure 15. Average meetings and passings per trail related to average user volume. .................. 79 

Figure 16. Model prediction versus field data for meetings based on volume groups. ................ 80 

Figure 17. Model prediction versus field data for meetings based on speed groups.................... 80 

Figure 18. Model prediction versus field data for passings based on volume groups. ................. 81 

Figure 19. Model prediction versus field data for passings based on speed groups..................... 81 

Figure 20. Representative response format................................................................................... 92 

Figure 21. Effects of respondent age on overall rating................................................................. 96 

Figure 22. Effects of respondent gender on overall rating............................................................ 97 

Figure 23. Effects of path user type on overall rating................................................................... 98 

Figure 24. Effects of respondent health status on overall rating................................................... 98 

Figure 25. Effects of respondent path use on overall rating. ........................................................ 99 

Figure 26. Effects of path width on overall rating...................................................................... 100 

Figure 27. Effects of the number of meetings on overall rating. ................................................ 102 

Figure 28. Effects of the number of active passings on overall rating. ...................................... 102 

Figure 29. Comparison of recommended new LOS procedure to 2000 HCM procedure for a 

2.44-m- (8-ft-) wide (two-lane) path with no centerline........................................... 120 

Figure 30. Comparison of recommended new LOS procedure to 2000 HCM procedure for a 

3.66-m- (12-ft-) wide (three-lane) path with no centerline....................................... 121 

Figure 31. Fact sheet for conformed consent.............................................................................. 139 

Figure 32. Lake Johnson Trail. ................................................................................................... 143 

Figure 33. Sammamish River Trail............................................................................................. 143 

Figure 34. Mill Valley-Sausalito Pathway.................................................................................. 144 

Figure 35 White Rock Lake Trail. .............................................................................................. 144 

Figure 36. Lakefront Trail........................................................................................................... 145 

Figure 37. South Bay Trail.......................................................................................................... 145 

Figure 38. Forest Park Trail........................................................................................................ 146 

Figure 39. Honeymoon Island Trail............................................................................................ 146 

Figure 40. Minuteman Bikeway. ................................................................................................ 147 

Figure 41. Dr. Paul Dudley Bicycle Path.................................................................................... 147 
vii 

LIST OF TABLES 

Table 1. LOS examples for a two-way, shared-use path. ............................................................. 20 

Table 2. Bicycle LOS criteria for shared-use paths in the 2000 HCM......................................... 24 

Table 3. Pedestrian LOS criteria for 2.4-m- (8-ft-) wide shared-use paths in the 2000 HCM. .... 24 

Table 4. Average flow pedestrian walkway LOS criteria from the 2000 HCM.(4)....................... 25 

Table 5. Computational spreadsheet for active passing events..................................................... 33 

Table 6. Computational spreadsheet for passive passing events. ................................................. 36 

Table 7. Computational spreadsheet for meeting events. ............................................................. 40 

Table 8. Numerical illustration of path-width effect on delayed passing..................................... 50 

Table 9. Characteristics of operational study sites........................................................................ 58 

Table 10. Additional characteristics of operational study sites. ................................................... 59 

Table 11. Number of successful data collection runs by trail....................................................... 61 

Table 12. Speeds by mode and trail (all speeds in mi/h). ............................................................. 64 

Table 13. Volumes and mode splits by trail. ............................................................................... 65 

Table 14. PHF data. ...................................................................................................................... 67 

Table 15. Distance needed for a bicycle to pass a pedestrian....................................................... 70 

Table 16. Summary of distance needed to pass values................................................................. 71 

Table 17. Chi-square test results comparing field and normal distributions for speed data......... 76 

Table 18. Average meetings and passings on each trail. .............................................................. 78 

Table 19. Statistical test comparing meetings estimated by model to field data 

for volume groups. ....................................................................................................... 83 

Table 20. Statistical test comparing meetings estimated by model to field data 

for speed groups ........................................................................................................... 84 

Table 21. Statistical test comparing passings estimated by model to field data 

or volume groups.......................................................................................................... 85 

Table 22. Statistical test comparing passings estimated by model to field data 

for speed groups. .......................................................................................................... 86 

Table 23. Distribution of subjects by age. .................................................................................... 88 

Table 24. Distribution of reports of individual health status. ....................................................... 88 

Table 25. Distribution of individuals’ estimated frequency of riding and/or walking for either 

recreational and/or fitness purposes............................................................................. 88 

Table 26. Estimates of shared-path use......................................................................................... 89 

Table 27. Characteristics of the 36 perception data collection video clips................................... 91 

Table 28. Average ratings of each video clip. .............................................................................. 95 

Table 29. Effects of other path design variables on average ratings. ......................................... 101 

Table 30. Effects of video quality on average ratings. ............................................................... 103 

Table 31. Correlation between the four perception measures..................................................... 106 

Table 32. Analysis of variance table for the final model............................................................ 108 

Table 33. Parameter estimate table for the final model. ............................................................. 108 

Table 34. Correspondence between perception score and LOS. ................................................ 112 

Table 35. Scores and levels of service based only on equation 61............................................. 115 

Table 36. Scores and grades based on complete LOS procedure. .............................................. 119 

1 

1. INTRODUCTION 

SHARED-USE PATHS 

Definition 

 

Shared-use paths are paved, off-street travel ways designed to serve nonmotorized travelers. 

Across the United States, bicyclists are typically the most common users of shared-use paths. 

However, in many places, shared-use paths are frequently used by pedestrians, inline skaters, 

roller skaters, skateboarders, wheelchair users, and users of many other modes. In many places, 

Segway® Human Transporters (Segway HT) are allowed on shared-use paths and blur the line 

between motorized and nonmotorized modes. In the United States, there are very few paths 

limited exclusively to bicyclists. Most off-street paths in this country fall into the shared-use path 

category. We should note that the term “trail” is used interchangeably with the term “shared-use 

path” in this report. 

Most shared-use paths in the United States are constructed to provide recreational opportunities. 

Some are also intended to serve commuters. Shared-use paths are also very common on 

university campuses because motor vehicle traffic and parking are often heavily restricted. 

Gaining Popularity 

Shared-use paths are gaining popularity in two different ways in recent years in the United 

States. First, new path segments are opening across the United States all of the time. Whether 

they are in old railroad rights-of-way, on creekside and riverside flood plains, on the banks of 

reservoirs and lakes, or in rights-of-way set aside by developers, almost every medium-sized and 

large urban area in the United States has some shared-use paths and has plans for more. Funding 

for path construction is being provided by Federal, State, and local governments and by private 

sources. There is no sign that the pace of construction of new shared-use paths is slowing. 

The greatest testimony to the success of the trails movement in the United States is the enormous 

amount of use they have attracted. Some urban trails attract thousands of users per hour during 

peak periods. Many trails are experiencing morning rush hours on weekdays and traffic jams on 

weekend afternoons. Trail managers in many parts of the country are becoming increasingly 

concerned about user conflicts and injuries. Some are also concerned that potential users are 

deciding not to use a trail because of crowding. 

PROBLEMS FACING DESIGNERS 

During the design of every shared-use path, someone eventually asks how wide should a 

pathway be. That question nearly always raises even more questions: What types of users can we 

reasonably expect? When will we need to widen the path? Do we need to separate different types 

of users from each other? These are very difficult questions for designers. They face that classic 

design dilemma of overbuilding versus obsolescence. If the designer specifies a trail wider than 

future use justifies, money is wasted that could have otherwise gone to construct more miles of 

trail elsewhere. If the designer specifies a trail that proves to be too narrow for the future volume 
2 

and mix of users, there will be more user conflicts and collisions, greater unhappiness among 

users, and the need to consider expensive trail widening. 

At this time, conventional design manuals do little to help designers resolve their dilemmas. The 

1999 American Association of State Highway and Transportation Officials (AASHTO) Guide 

for the Development of Bicycle Facilities states, “Under most conditions, a recommended paved 

width for a two-directional shared-use path is 3 meters [m] (10 feet [ft])... Under certain 

conditions, it may be necessary or desirable to increase the width of a shared-use path to 3.6 m 

(12 ft) or even 4.2 m (14 ft), due to substantial use by bicycles, joggers, skaters, and 

pedestrians.”(1) No further guidance is given to determine what specific levels of use—or mixture 

of uses—warrant a wider pathway or a separation of users. 

Existing Level of Service (LOS) Method 

Versions of the Highway Capacity Manual (HCM) prior to the year 2000 contained no help for 

trail designers.(2) There were no quality-of-service procedures for shared-use paths. 

A recent research effort, conducted by several of the authors of this report and sponsored by the 

Federal Highway Administration (FHWA), attempted to fill this information gap. Rouphail, 

et al., recommended an analytical procedure to determine the LOS for bicyclists on shared offstreet paths for inclusion in the 2000 edition of the HCM.(2-3) The Transportation Research Board 

(TRB) Highway Capacity and Quality of Flow Committee, which oversees the HCM, agreed 

with the recommendation and the 2000 edition contained the procedure.(4) Rouphail, et al., 

adapted the procedure that was originally developed by Hein Botma (also a member of this 

research team), based on simulations and field studies from The Netherlands.(2,5) 

Botma’s model, which is discussed in depth in the literature review in chapter 2 of this report, is 

based on fundamental traffic-flow theory. The Botma model works much like a model of 

vehicular traffic on a roadway in that a shared-use path also has perceived lanes of travel. The 

model estimates the number of passings and meetings by a test bicyclist traveling at the mean 

speed of bicyclists on the trail. “Meetings” refer to users traveling in the opposite direction of the 

test bicyclist, and “passings” occur when the test bicyclist overtakes users traveling in the same 

direction. The Botma procedure, as adopted in the 2000 HCM, compiles the numbers of 

bicyclists and pedestrians who are met and who are passed. The LOS of bicycles is determined 

by adding the number of meetings estimated to twice the number of passings estimated and 

comparing this number of weighted events to an LOS scale. For an LOS scale, Rouphail, et al., 

recommended the use of the A through F scale, which is familiar from other chapters of the 

HCM, with essentially arbitrary boundaries between levels.(3)

Limitations of Current LOS Method 

As described above, Botma’s procedure, which bases LOS on the estimated number of meetings 

and passings for bicyclists, is an attractive framework. There can be little debate that, in general, 

paths where bicyclists incur more meetings and passings should be less desirable than trails with 

fewer meetings and passings. However, the LOS procedure in the 2000 HCM has a number of 
3 

serious limitations that make it difficult for designers to use in resolving their path design 

dilemmas. These limitations include: 

• The procedure needs to be calibrated and validated for U.S. conditions. As detailed in 

chapter 2, Botma’s equations are based on sound theory and they are based, in part, on 

field data from The Netherlands.(5) However, they have never been compared to U.S. field 

data. U.S. paths are typically wider than European paths. U.S. bicyclists are generally not 

as experienced. U.S. bicyclists tend to ride more often for recreation and less often for 

commuting. And U.S. bicycles are different from European bicycles. Among the parts of 

the model that need to be calibrated is the relative weighting of passings to meetings. 

• The procedure does not account for “passive passings.” This is an event when the test 

bicyclist is passed by a faster path user. Passive passings are probably undesirable from 

the test bicyclist’s perspective and should be considered in an LOS procedure. 

• The procedure assumes that path users do not impede each other’s movements (i.e., that 

there is always adequate room for the test bicyclist to pass with no change in speed or 

lateral positioning). This is true only if: (1) the path is wide enough, and/or (2) there is no 

opposing traffic during the passing maneuver. If passing is restricted, there will be a 

number of “delayed overtakings.” The significance of this limitation cannot be 

overstated. When passing and meeting become restricted, the procedure cannot predict 

that the LOS will worsen, because the number of events actually decreases. The 

procedure also assumes that bicyclists always want to pass any encountered bicyclists or 

pedestrians who are going at a slower rate. However, if the speed difference is small, or if 

the test bicyclist is near the end of his or her time on the path, this will not be true. 

• The current LOS procedure for shared-use paths accounts for pedestrians and bicycles 

only. However, in his original model, Botma simulated other path users, including 

mopeds and tandem bicycles.(5) Shared-use paths in the United States currently 

accommodate large numbers of joggers, inline skaters, skateboarders, and other types of 

users. The addition of other path users can be represented analytically in one of two 

ways. If a path user group appears to have a similar mean speed to another group, then 

such groups can be combined into one larger group that has a common mean and 

standard deviation. However, if a group is quite different from the others, then all events 

associated with this group must be estimated using separate equations. 

• The current procedure is based on single values of mean bicycle and pedestrian speed. 

Designers in areas where bicycles and pedestrians may travel faster or slower should 

have the ability to incorporate that information into their LOS estimates. 

• The 2000 HCM method is limited to the analysis of two-lane and three-lane paths. 

Furthermore, two-lane paths are specified as 2.44 m (8 ft) wide, and three-lane paths are 

specified as 3.05 m (10 ft) wide. Designers considering other widths and numbers of 

lanes have no current guidance. 
4 

• A stronger basis for the LOS criteria is clearly needed. While most of the LOS criteria in 

the 2000 HCM were set based on the expert opinion of the members of the Highway 

Capacity and Quality of Service Committee, there is recent research in some areas that 

bases the criteria on user surveys. In the pedestrian and bicycle arena, Harkey, et al., and 

Landis, et al., both members of this research team, have developed LOS criteria for onstreet bicycle paths that are validated against user perceptions of the quality of service.(6-7) 

A set of LOS criteria that is well grounded in user perceptions would be more credible 

than a set based on expert opinion alone. 

• There is a great need to effectively convey the procedure and criteria to shared-use path 

designers and operators. The HCM should certainly remain as one way to convey the 

procedure and criteria. However, the HCM is not a prominent document among shareduse path designers and operators. Also, the next version of the HCM may not be issued 

for many years. We need to convey any new procedure to the users in an effect manner 

and sooner. 

With all of these limitations on the current LOS procedure, the need is clear for a substantial 

research effort to refine the method and to provide designers with a new procedure. 

PROJECT OBJECTIVE 

 

The overall project objective was the production of a tool that professionals can use to evaluate 

the operational effectiveness of a shared-use path, given a traffic forecast or observation at an 

existing path along with some geometric parameters. The project adopted Botma’s method as the 

basic framework for the LOS procedure.(5) In particular, the objective was to produce a tool that 

would overcome the major limitations in the current LOS procedure noted above. It was 

desirable that the procedure emerging from this project would: 

• Be calibrated and validated. 

• Be based on U.S. data. 

• Have LOS criteria based on user input for a typical mix of trip purposes. 

• Include more modes. 

• Include the ability to change key parameters such as mean speeds. 

• Account for delayed passing. 

• Analyze the full range of existing and possible path widths. 

• Be in a form ready for use by path designers. 

PROJECT METHODS 

The four major activities needed to achieve the project objective described were: 

1. Development of the additional theoretical framework necessary to overcome the 

limitations of the existing procedure noted above. 

2. Collection of field data on path operations to calibrate and validate the theoretical 

equations for U.S. conditions. 
5 

3. Collection of path user perception data to establish LOS criteria. 

4. Development of an LOS estimation tool that professionals working with shared-use paths 

could use, and a plan to distribute that tool. 

Development of Theory 

To achieve its objective, the project team had to develop the theory of traffic flow on shared-use 

paths in two important ways. First, the team had to determine a way to calculate the number of 

passive passings that occurred on a typical path. As noted above, a passive passing is an occasion 

when the test bicyclist is passed by a faster path user. Because bicyclists are typically the fastest 

users on a path, the number of passive passings is probably small in most cases; however, it 

should contribute to an LOS estimate. Passive passings were not used in the 2000 HCM

procedure. 

Furthermore, the team had to find a way to calculate the number of delayed passings. These are 

times when the test bicyclist would arrive behind a slower path user and not be able to pass 

because of the lack of an adequate-sized gap in the next lane to the left (oncoming or same 

direction). Obviously, delayed passings are undesirable for bicyclists since they would have to 

slow down and then expend energy accelerating when an adequate gap appears. Delayed 

passings are also critical because they are so closely related to path width. Prior to this project, 

there were some delayed passing calculations in the literature related to two-lane highway 

operation and similar facilities; however, nothing in the literature related to shared-path 

operation. 

Operational Data Collection 

The objective of the operational data collection portion of this project was to collect the field 

data needed to calibrate and validate the LOS model for shared-use paths. To calibrate and 

validate an LOS model, the main variables that needed to be collected were meetings and desired 

and actual passings by path users. Other data that need to be collected are the mean speed and 

speed range of the different user groups. In addition, trail characteristics must be recorded at 

each site. To ensure later flexibility, it was desirable that scenes on paths of interest be recorded 

from different perspectives so that additional data could be obtained by viewing videotapes if 

needed. 

The project proposal identified three methods of data collection: (1) a one-camera method, (2) a 

two-camera method, and (3) a moving-bicycle method. The one-camera method placed a camera 

at an elevated position where it could record scenes on a long path segment. The two-camera 

method recorded when path users entered and exited a path segment of interest, inferring 

meetings and passings on that segment. The moving-bicycle method, by contrast, collected 

meetings and passings from the perspective of a test bicyclist using a camera mounted on the 

bicyclist’s helmet. 

After careful consideration of all of the pros and cons for all three methods, the team chose to 

use the moving-bicycle method. Vantage points for the one-camera method would be rare (tall 
6 

buildings and hills with unobstructed views of qualifying shared-use paths are not common in the 

United States). The two-camera method would not be able to identify the difference between 

actual passings and desired passings because only path users would know whether they wanted 

to pass and were unable to do so and why. For example, a bicyclist may not have been able to 

pass because of inadequate path width or congestion. The moving-bicycle method can collect the 

needed data without these problems. The moving-bicycle method can be supplemented with a 

stationary camera on the side of the path. It can also be used to record path user volumes and the 

characteristics of different user groups, such as mean speed. Consequently, the moving-bicycle 

method, supplemented by a stationary camera, was the primary operational data collection 

method. 

Perceptual Data Collection 

A major part of this effort was to help set the LOS criteria by collecting data on user perceptions 

of multi-use trail design and operations. From a user perception standpoint, the intent of the 

present study was to quantify the effect of selected operational trail parameters on bicyclist and 

pedestrian judgments of the perceived adequacy of the trail facility. It is recognized that user 

responses will differ, depending on the individuals’ own reasons for using the trail (e.g., whether 

they were seeking a casual and relaxed activity or a rigorous individual workout unencumbered 

by users with more relaxed intentions). It was beyond the scope of this study to collect data on 

user perceptions as a function of users’ individual intentions or needs. However, an effort was 

made to obtain the opinions of a variety of users. 

The research team believed that it was possible to define the LOS for a trail in operational terms, 

independent of the factors governing the capacity of the trail. For example, a two-lane trail will 

obviously have less capacity than a four-lane trail; however, both, under different demand 

conditions, may be described as operating at the same LOS. In the present study, LOS is 

assumed to vary as a function of operational trail conditions that can be specified largely in terms 

of meeting and passing events. Depending upon the capacity of a trail and its particular level of 

use, each trail can be described in terms of the frequency of these meeting and passing events. If 

it could be shown that users’ judgments of the adequacy of a trail vary as a function of such 

events, it would be possible to predict user response to trail conditions and designs beyond the 

limited set of paths addressed by the study. 

A Usable Procedure 

As noted above, an important element of this project was that the procedure developed was 

usable by trail design professionals and that it would be distributed in a manner that would reach 

them. The research team included trail design professionals who carefully crafted the products 

for their colleagues. In addition, the researchers developed products that could be adopted in 

future versions of the HCM, as well as distributed in other ways. A section later in this chapter 

describes the research products in more detail. 
7 

PROJECT SCOPE LIMITED 

The scope of the project and, therefore, the products emerging from the project, was limited in 

several important ways. First, the project was limited to selected nonmotorized travel modes. We 

set out to expand the current procedure to those nonmotorized modes that are common on typical 

U.S. shared-use paths. In the end, we collected data on adult bicyclists, child bicyclists, walking 

pedestrians, running pedestrians, and inline skaters, and included these in our LOS method. 

Other modes of travel seen occasionally on shared-use paths, such as roller skaters, scooters, 

wheelchairs, Segways, and tandem bicycles, were not included because we did not see enough of 

them during our data collection for inclusion. Riders on horseback and snowmobiles are 

examples of other occasional path users that were outside the scope of this effort because they 

did not use the paths of interest in large numbers year-round. The LOS estimation procedure 

could be expanded to include any of these modes, or any other mode, if the analyst possessed 

some basic data about the mode, such as mean speed. 

The scope of the project was also limited to off-street, paved paths. Although the methodology 

developed could apply to paths used exclusively by bicyclists and to one-way paths, the bulk of 

the attention in this research was centered on two-way paths serving pedestrians, bicyclists, and 

other users because they are the vast majority of the off-street paths in the United States. Since 

most paths with gravel, dirt, wood chips, or other loose material on the surface do not attract 

much bicycle volume, project data collection and analysis were limited to paths that were paved 

or had hard surfaces. A designer who is working on a path that has a hard-packed gravel or 

granular stone surface on which bicyclists operate in a very similar manner to paved paths may 

be able to apply the methodology we developed for that path with minimal additional error. 

Also, the project scope was limited in that the LOS produced was from the bicyclist’s point of 

view. The researchers collected some perception data from the pedestrian’s point of view, but not 

enough to establish their own LOS scale. Chapter 9 will recommend future research targeted at 

estimating path LOS from the points of view of pedestrians, skaters, and others. 

Furthermore, the project scope was limited to the analysis of trail segments at least 

0.40 kilometers (km) (0.25 miles (mi)) long, uninterrupted by stop signs, signals, important 

intersections, or other similar features. Analysts will need other ways to find the LOS at these 

points. 

Finally, the project results were not intended for forecasting the number of future users of a path. 

While there may be some overlap between operational/design and forecasting methods, the 

premise behind this effort is that user volumes are an input rather than an output. The intent of 

this project was to answer questions regarding how wide the path should be to satisfy current or 

future demand, rather than to estimate how many users would be attracted to a path of a certain 

design. 
8 

RESEARCH PRODUCTS 

Description of the Products 

The three final products of this study are: (1) this report, (2) Share-Use Path Level of Service 

Calculator: A User’s Guide (User’s Guide) (Publication No. FHWA-HRT-05-138), and (3) 

Evaluation of Safety, Design, and Operation of Shared-Use Paths, a TechBrief (Publication No. 

FHWA-HRT-05-139). These products will be distributed primarily via the U.S. Department of 

Transportation (USDOT) Pedestrian and Bicycle Information Center (PBIC) Web site. The 

User’s Guide provides detailed, step-by-step instructions on how to use the LOS procedure and 

spreadsheet calculation tool, which can be downloaded from the Turner-Fairbank Highway 

Research Center Web site at www.tfhrc.gov. The User’s Guide and TechBrief can also be 

downloaded from the Web site. 

The widespread use and application of the LOS methodology is ultimately dependent upon how 

easy it is to use, whether it is considered applicable to trail design scenarios, and whether trail 

designers are able to gather the data needed to use the model. At this time, there are two main 

applications of the model: (1) to determine the appropriate width of a new trail, and (2) to 

determine how much width to add to an existing trail to accommodate current or projected levels 

of use. Determining whether to separate modes or directions of travel is also emerging as a key 

application. 

The availability of data and its ease of collection are often key components in the success of an 

LOS model. We tried to ensure that the data items collected for the model would be relatively 

easy for a trail designer to obtain. We also recommended default values for most of the needed 

inputs. In addition, the User’s Guide describes how to effectively collect data for use in the 

model. 

Our idea for the LOS calculator was that it should be some type of spreadsheet application or 

self-executing graphical user interface software. The team was inspired by the League of Illinois 

Bicyclists, which developed online graphical user interface software that calculates a bicycle 

LOS for a roadway using the Bicycle Compatibility Index and the Bicycle LOS model (see 

http://www.bikelib.org/roads/blos/losform.htm).(6-7)

The interface is easy to use, is accessible directly from the League’s Web site, and suggests 

default values if an analyst does not have all of the necessary data. A user can simply click on the 

calculate button, and an LOS result for each model is displayed. We attempted to create a 

calculator for our shared-use path LOS model that would be made available in a similar format 

and would allow users to easily calculate an LOS for a shared-use path. 

Intended Users 

Unlike the roadway environment, which is almost exclusively the domain of civil engineers, 

shared-use paths are designed by a wide variety of practitioners. Some of the most creative and 

unique trails in the country are the direct result of the diverse skills of these designers. Since 
9 

these professionals look to a variety of different sources for design guidance, establishing 

national guidelines is difficult. 

We identified three main target audiences for the marketing of our shared-use path LOS model: 

(1) transportation professionals, (2) trail designers/coordinators, and (3) pedestrian, bicycle, and 

trail advocates and organizations: 

• Transportation Professionals. These individuals are engineers, planners, or designers. 

They work in planning, engineering, and public works departments at all levels of 

government. They may also work for consulting firms or at research institutes. These 

individuals often rely on roadway design manuals such as the AASHTO Guide for the 

Development of Bicycle Facilities, the Highway Capacity Manual, the AASHTO Policy 

on Geometric Design of Highways and Streets (the Green Book), and the Manual on 

Uniform Traffic Control Devices (MUTCD), along with other State and local roadway 

design manuals. (See references 1, 4, 8, and 9.) Transportation professionals are more 

likely to possess a technical background in standard LOS applications, roadway cross 

sections, and in the design of roadways and/or bicycle and pedestrian facilities. 

• Trail Designers/Coordinators. These individuals are planners, landscape architects, or 

other professionals who are involved in the design, development, and maintenance of 

trails; however, they may not have a technical background in transportation planning. 

They work in planning, parks and recreation, environmental protection, or greenway and 

trail departments at all levels of government, and they also may be consultants hired by 

governments to design shared-use paths. They often rely on park and recreation design 

manuals, information about the design of trails obtained from various clearinghouses, and 

past professional experience. This group is important to reach because they often make 

decisions about the design, location, and development of shared-use paths. 

• Pedestrian, Bicycle, and Trail Advocates and Organizations. These groups often play 

an essential role in making shared-use paths a reality. Organizations such as Rails-toTrails Conservancy and East Coast Greenway Alliance often provide technical guidance 

and/or serve as clearinghouses for innovative design approaches. Local trail 

alliance/advocacy groups are often influential, and many times they make major 

decisions in the development of shared-use paths. It is important that the shared-use path 

LOS model address their concerns and that it be embraced as a useful tool by these 

groups as well. These groups frequently set a vision for trails in a community, provide 

input into the kinds of trails to be created, and provide coordination among the many 

players who will develop, own, and manage the trail. 

Through this report, the User’s Guide, and the TechBrief, we tried to reach all three of these 

groups. It should be noted, however, that the primary intent of this report is to provide technical 

details with regard to our methods and data. Unlike the User’s Guide and the TechBrief, this 

report is not intended for wide distribution. 
10 

REPORT FORMAT 

This report includes eight chapters in addition to the introductory chapter. Chapter 2 is a review 

of the literature pertaining to LOS estimation for shared-use paths. Chapter 3 describes the 

development of the theoretical background that we needed for the procedure. Chapter 4 discusses 

the methods we used to collect the field data on path operations. Chapter 5 shows how we used 

the field data to calibrate and validate our LOS model. Chapter 6 describes how we collected 

data on user perception of shared-use paths having various geometric and operational 

characteristics. Chapter 7 shows how we analyzed the perception data in order to develop the 

LOS criteria. Chapter 8 presents the highlights from the LOS procedure. (A much more 

comprehensive guide to the procedure, written for the audiences we described above, is available 

in the User’s Guide.) Finally, chapter 9 provides a summary of the project and our 

recommendations for future research to improve technical capabilities in this area. At the end of 

the report are a set of appendixes and a complete list of references. 
11

2. LITERATURE REVIEW 

INTRODUCTION 

This chapter provides a thorough, critical review of the major, relevant research to date on the 

topic of LOS estimation for shared-use paths. The chapter contains sections on path user 

characteristics, ways to measure the quality of the performance of the shared path for the users, 

and ways to establish an LOS scale. The review will indicate why path designers and others 

needed a research project on this topic, and will show which directions the project had to take. 

Much of the material in this document is from previous recent work by members of the project 

team for the FHWA, including Rouphail, et al.,(10) and Allen, et al.(11) The document also 

includes results from searches of computerized indexes and manual searches by the project team 

of available library resources. 

PATH USER CHARACTERISTICS 

Pedestrian Characteristics 

There is a wide variety of users within the pedestrian population and, therefore, a large variety of 

needs within this population that have to be addressed by path designers. One can classify 

pedestrians by gender, age, or trip purpose, among other typologies. In addition, disabled 

pedestrians have unique requirements that the profession must address in order to adequately 

serve this group. 

Gender is an important factor where pedestrians are concerned. Fruin notes that despite the 

consistency in walking gait across both sexes and all ages, differences in other aspects of 

pedestrian walking and standing exist among these groups.(12) For example, adult male 

pedestrians consume more area than their female counterparts, and female pedestrians exhibit a 

higher level of pelvic rotation for a given length of stride. 

Polis, et al., observed differences in walking speeds between male and female pedestrians in 

Israel.(13) The authors attributed this primarily to the typically greater physical size and stride of 

males; they also noted a second hypothesis—that a greater number of males than females were 

walking to and from work in Israel. 

Another dividing factor is age. Aging reduces the length of the stride of a pedestrian and results 

in a commensurate reduction in walking speed.(12) Very young pedestrians will also walk at a 

slower gait than other groups.(14) There may also be differences between pedestrians of different 

ages, including perception, reaction time, and risk-taking, which are important considerations in 

evaluating passing, although there has been limited attention paid to these aspects in the 

pedestrian literature. 

Knoblauch, et al., note that the current traffic environment is “not well adapted to the needs of 

the older pedestrian” and reports that older pedestrians have the highest pedestrian fatality rate of 

any age group.(15) With decreases in visual acuity accelerating after age 60, and with reductions 
12

in walking speeds prevalent among the elderly, the transportation professional faces unique 

challenges in attempting to service this segment of the population. 

One can also divide pedestrians into groups by trip purpose. Commuting pedestrians exhibit 

higher pedestrian speeds than do shoppers.(16) By stopping to window-shop, the latter group also 

consumes more of the walkway width.(16) Students exhibit different characteristics than other 

groups.(17)

Impaired users are a critical concern for the designers of pedestrian facilities. In a recent report 

for FHWA, Kirschbaum, et al.,(18) describe many different types of users who the designers of 

pedestrian facilities should consider, including: 

• Stroller users. 

• Wheelchair users. 

• Individuals with limited balance. 

• Individuals with a vision impairment. 

• Older adults. 

• Children. 

• Individuals who are obese. 

• Crutch or support cane users. 

• Individuals with low fitness levels. 

• Individuals with cognitive impairments. 

• Individuals with emotional impairments. 

Kirschbaum, et al., cite Census Bureau statistics from 1994 that “approximately 20 percent of 

Americans have a disability and the percentage of people with disabilities is increasing.”(18)

In terms of pedestrian space requirements, designers of pedestrian facilities (considering only 

unimpaired pedestrians) use body depth and shoulder breadth, at least implicitly, for minimum 

space standards. In addition, pedestrians require a certain minimum space for comfort. Fruin 

described these concepts as the “body ellipse” and the “body buffer zone.”(12) All recent editions 

of the HCM applied the concepts of pedestrian space as a measure of effectiveness with regard to 

pedestrian facility analysis procedures. (See references 2, 4, 14, and 19.) As noted by 

Tanaboriboon and Guyano, cultural attitudes and prevailing pedestrian characteristics may affect 

space requirements.(20) For example, they note that Asians are typically smaller than Westerners, 

and that Asian pedestrians require less personal space than Americans. 

Fruin notes that the average adult male body occupies an area of about 0.14 square meters (m2

) 

(1.5 square feet (ft2

)).(12) Given the existence of body sway in both idle and moving persons, as 

well as the typical preference to avoid contact with others, Fruin presents the concept of a 45.7-

centimeter (cm) by 61.0-cm (18-inch by 24-inch) body ellipse, with a total area of 0.279 m2 

(3 ft2), as the practical minimum standing area. Davis and Braaksma use a similar body ellipse of 

46.0 cm by 61.0 cm (18 inches by 24 inches) in their study of Canadian transportation 

terminals.(21)
13

Pushkarev and Zupan note that pedestrians can occupy as little as 0.09 m2

 (0.97 ft2) per woman 

and 0.14 m2

 (1.50 ft2) per man, but require about 0.22 to 0.26 m2 (2.4 to 2.8 ft2) per person to 

avoid touching, and prefer a body buffer zone of 0.27 to 0.84 m2 (2.9 to 9.0 ft2) to avoid 

“emotional discomfort in the presence of strangers.” Flow involving “unnatural shuffling” begins 

when space falls below 0.75 m2 (8.1 ft2).(22)

Navin and Wheeler(23) state that an individual’s “domain” (clear space around the individual) 

could be defined by a parabolic curve with its apex about 0.75 m (2.4 ft) in front of the 

pedestrian and the edges about 0.40 m (1.3 ft) to the pedestrian’s sides. 

There is little information in the technical literature on joggers on shared-use paths. Most of the 

recreational literature on joggers relates to optimizing performance rather than characterizing 

typical joggers. 

Bicyclist Characteristics 

There are estimated to be more than 100 million bicyclists in the United States; however, less 

than 1 percent of travel trips are made by bicycling in this country.(24) According to one source, 

there are three general categories of bicycle users: (1) the child bicyclist, (2) the casual or 

inexperienced adult bicyclist, and (3) the experienced adult bicyclist.(25) A report released by 

FHWA divides bicyclists into three similar categories: (1) Group A: Advanced Bicyclists, 

(2) Group B: Basic Bicyclists, and (3) Group C: Children.(26) The behavior and attributes of these 

three groups differ; however, most bicycle facilities cater to all three types of bicyclists. 

The child bicyclist (group C) is defined as a bicyclist who is too young to obtain a motor vehicle 

operator’s license (age 16 in most States). Approximately three-quarters of all children under age 

16 ride bicycles, and this group makes up a little less than half of all bicyclists.(25) A high 

percentage of children are forced to ride bicycles because they have no other transportation 

alternatives. This group tends to prefer residential streets with low motor vehicle speed limits 

and volumes, well-defined bicycle lanes on arterials and collectors, and/or separate bicycle paths. 

The casual or inexperienced adult bicyclist (group B) is defined as someone who is old enough to 

possess a motor vehicle operator’s license, is moderately skilled, and has a basic, but not 

extensive, knowledge of bicycling. For this group, bicycling is mostly a recreational activity that 

is done on residential streets and bicycle paths. However, this group occasionally will make 

purposeful trips and/or use major streets. It is estimated that this group makes up approximately 

40 percent of the overall bicycling population.(25)

The experienced or advanced adult bicyclist (group A) is defined as an experienced, 

knowledgeable, and skilled bicyclist who is old enough to possess a motor vehicle operator’s 

license. This group tends to use the bicycle for longer trips and more often for purposeful trips 

than the casual adult bicyclist. It is estimated that this group makes up approximately 10 percent 

of the overall bicycling population.(25) This group normally prefers to use the most direct route to 

its destination, and riders are willing to use a variety of different types of streets with or without 

designated bicycle facilities. 
14

FHWA reports that more than 50 percent of bicycle trips in the United States are taken for 

social/recreational purposes.(24) The other trip categories were personal or family business, school 

and church, work, and “other” trips. Bicycle trips are often divided into just two categories for 

operational analysis: (1) recreational trips and (2) purposeful trips. Purposeful trips include all 

categories other than social/recreational. The fact that more than half of all bicycling trips may 

be recreational must be considered when analyzing bicycle traffic, because the same is generally 

not true for motor vehicles. 

Hunter, et al., conducted a study in several U.S. cities and found that bicycle traffic volumes 

between the hours of 7 a.m. and 7 p.m. on weekdays were fairly constant, with peak-hour 

volumes being approximately 11⁄3 times the average hourly volumes.(27) They also found that the 

peak hours on weekdays typically corresponded with local commuter schedules. In one city, they 

measured peak-hour volumes as 10 to 15 percent of total daily volume. The proportion of 

weekday to weekend traffic varied greatly, depending on the recreational uses of the bicycle 

facilities. Seasonally, they found that volumes were generally highest in the summer and lowest 

in the winter. 

A study conducted in the Seattle area by Niemeier analyzed bicycle volume data collected over 

1 year at five separate locations.(28) The study showed that bicycle volumes were higher during 

the p.m. peak than during the morning peak at all but one location, which was slightly lower. 

Three of the locations had more than double the volume during the p.m. peak. Bicycle peak-hour 

factors (PHFs) between 0.52 and 0.82 were observed during the morning peaks at the various 

locations, and PHFs between 0.58 and 0.80 were observed during the p.m. peaks. The study 

showed significant variability in volumes over the year. This variability suggested that a single 

volume count could be biased by as much as ±15 percent, depending on the time of year that the 

count was taken. Volumes were much lower during adverse weather because bicyclists are 

exposed to the elements. 

Because of the recreational and social nature of bicycling, bicycle users often ride in pairs. A 

study found that in The Netherlands, the number of paired bicycles was a function of bicycle 

volume.(5) However, the dependence differed with location. As expected, the study also found 

that paired riding was more common during recreational bicycle trips than purposeful trips. The 

fact that bicycle users often ride in pairs has been noted by others; however, no other attempt has 

been made to quantify this phenomenon. 

Other than the bicyclists, the bicycle itself has known properties that have to be taken into 

account. With regard to space requirements, a typical bicycle in the United States is 1.75 m 

(5.75 ft) in length, with a handlebar width of 0.60 m (2 ft).(25) In The Netherlands, it has been 

reported that 95 percent of bicycles are less than 1.90 m (6.25 ft) in length and that 100 percent 

of bicycle handlebar widths are less than 0.75 m (2.5 ft). 

In addition, a bicyclist needs a certain amount of operating space. No bicyclist, at any speed, can 

ride a bicycle in a perfectly straight line. One U.S. source reports that a typical bicycle needs 

between 0.75 m (2.5 ft) and 1.40 m (4.5 ft) of width in which to operate.(25) This amount of space 

can also be referred to as the effective lane width for a bicycle. An older study in Davis, CA, 

recommends a minimum width of 1.28 m (4.2 ft) for bicycles, with additional width at higher 
15

volumes.(29) In The Netherlands, 1.00 m (3.3 ft) of clear space is generally recommended for 

bicycles.(30) In Germany, 1.00 m (3.3 ft) is reported as the normal width of one bicycle lane.(31) In 

Sweden, 1.20 m (3.95 ft) is reported as a typical bicycle lane width.(32) A Chinese study reports 

that the width of a two-lane bicycle path in China is generally 2.5 m (8.2 ft), with an additional 

1.0 m (3.3 ft) added for each additional lane.(33) The Norwegian Public Roads Administration 

states, “One meter is not enough,” and recommends a width of 1.6 m (5.3 ft) for single-lane 

bicycle lanes.(34)

Overall space requirements for bicycles can also be defined in terms of density. A Canadian 

study found that bicycle operating space greater than 9.3 m2

/bicycle (100.1 ft2/bicycle) provided 

for free-flow bicycling conditions.(35) The study also found that when less than 3.0 m2/bicycle 

(32.3 ft2

/bicycle) of operating space is provided, there was no freedom for bicycles to maneuver. 

A study in China(33) found that bicycle operating space greater than 10 m2/bicycle 

(107.6 ft2

/bicycle) provided very comfortable operations, and that less than 2.2 m2/bicycle 

(23.7 ft2

/bicycle) forced most cyclists to dismount and walk their bicycles. The older study in 

Davis, CA, found that bicycle operating space greater than 20 m2

/bicycle (200 ft2/bicycle) 

provided free-flow conditions and that less than 3.7 m2/bicycle (40 ft2/bicycle) represented 

congestion.(36)

Free-flow speed is also important in the study of bicycle operations. The Davis, CA, study 

reported a mean velocity of approximately 19 kilometers per hour (km/h) (11.8 miles per hour 

(mi/h)) for class I bicycle facilities and mean bicycle velocities between approximately 

17.7 km/h (11.0 mi/h) to 20.1 km/h (12.5 mi/h) for class II facilities.(29) Class I facilities are offstreet paths and class II facilities are designated as on-street bicycle lanes. 

Another study conducted in Davis, CA, reports that the free-flow speed of bicycles is usually 

above 17.7 km/h (11.0 mi/h).(36) A study conducted primarily in Michigan on university 

campuses reported average observed speeds of 24.9 km/h (15.5 mi/h) on bicycle lanes and 

20.3 km/h (12.6 mi/h) on bicycle paths.(37) A manual released by FHWA(38) reported that the 85th 

percentile speed of bicycles is approximately 24 km/h (15 mi/h), and that a design speed of 

32 km/h (20 mi/h) on level terrain would allow for nearly all bicyclists to travel at their desired 

speeds. 

In Sweden, the 85th percentile free-flow speed of bicycles is reported to be between 16 km/h 

(10 mi/h) and 28 km/h (17.4 mi/h).(32) A Canadian study found a free-flow speed of 25 km/h 

(15.5 mi/h).(35) One study in China reported observed average bicycle speeds at various locations 

between 10 km/h (6.2 mi/h) and 16 km/h (10 mi/h), with an overall mean of approximately 

12 km/h (7.5 mi/h).(39) Another Chinese study reported observed average bicycle speeds between 

12 km/h (7.5 mi/h) and 16.3 km/h (10.1 mi/h), with an overall mean of approximately 14 km/h 

(8.7 mi/h).(40) A more recent Chinese study reported peak-hour free-flow speeds of 18.2 km/h 

(11.3 mi/h), where bicycle traffic was separated from motor vehicles by a barrier, and 13.9 km/h 

(8.6 mi/h) at locations without a lane barrier.(41) A Dutch study reported a mean bicycle speed of 

18 km/h (11.2 mi/h), with a standard deviation of 3 km/h (1.9 mi/h).(42) The Dutch study also 

reported that the observed average speed appeared to be unaffected by path width. 
16

Virkler and Balasubramanian(43) conducted the most detailed field study on shared-use paths 

using North American data collected prior to this study. Their study focused on operational data 

collected along two multi-use facilities (one in Columbia, MO, and the other in Brisbane, 

Australia). Mean speeds were recorded for hikers, joggers, and bicyclists. Results from the speed 

study showed that the mean speed of joggers was roughly twice that of hikers, and the mean 

speed of bicyclists was roughly twice that of joggers. As expected, the standard deviation of 

speed within each user group increased as the mean speed increased. The mean speeds collected 

at the Missouri site were similar to those collected at the Brisbane site; however, the standard 

deviation of speeds was much higher at the Missouri site for the jogger and bicyclist user groups. 

In addition, passing data for each user-group combination were collected at the two sites. Passing 

data recorded at the two sites showed significant differences with respect to the mean passing 

time. On average, passing times at the Columbia site were approximately twice as long as 

passing times at the Brisbane site. This is to be expected given the larger standard deviation of 

speeds observed at the Missouri site. A total of 206 passing maneuvers were recorded at the 

Brisbane site compared to 49 passing maneuvers at the Columbia site. 

In an earlier effort, Botma and Papendrecht(5) collected operational data on four paths within a 

town and on one tour (race) path outside of town. The narrowest town path was 1.8 m wide, 

while the other three were approximately 2.5 m (8.2 ft) wide. The paths selected for the data 

collection contained only bicycle and moped traffic. No pedestrians were observed. Speed and 

lateral clearance data were collected for all bicycles and mopeds. Passing data (i.e., frequency, 

passing time, and lateral clearance) were also collected. The average bicycle speed recorded at 

each of the sites was 19 km/h (11.8 mi/h), which is comparable to Virkler and 

Balasubramanian’s observations.(43) However, the standard deviation found by Botma and 

Papendrecht (4.8 km/h (3 mi/h)) was much lower than that found by Virkler and 

Balasubramanian (4.8 to 7.6 km/h (3 to 4.7 mi/h)).(5,43) Interestingly, curb height, which varied 

from 3 to 10 cm (1.1 to 3.9 inches), did not affect the lateral position of bicyclists; however, it 

did affect that of mopeds. Bicycle passing data collected in this study indicate that on the 

narrower 1.8-m path, the distance over which passing occurred was quite short (about 24 m 

(79 ft)) and took less than 5 seconds (s). On the 2.5-m path, passing took place over a much 

longer distance (about 63 m (206 ft)) and consumed more than 11 s. Therefore, based on this 

study, it appears that the wider paths provided a more comfortable transition for passing 

maneuvers as cyclists became less concerned about negotiating opposing traffic. 

In summary, we know a good deal about bicyclist characteristics, although there is a rather large 

range for some key parameters. For example, free-flow bicycle speed appears to be somewhere 

between 10 km/h (6.2 mi/h) and 28 km/h (17.4 mi/h), with a majority of the observations being 

between 12 km/h (7.5 mi/h) and 20 km/h (12.4 mi/h). Meanwhile, the design speed 

recommended by AASHTO for bicycle facilities in the United States is 32 km/h (20 mi/h), which 

is the same as that recommended by FHWA.(1)

Other Path Users 

Bicyclists and pedestrians are typically not the only regular users of shared-use paths in the 

United States. Until recently, there has not been any significant quantitative research completed 
17

in the United States that addresses the effects that other path users (i.e., inline skaters, 

skateboarders, scooters, etc.) have on the performance of the path. This changed with the recent 

publication of a final report from FHWA on the characteristics of many of these other path 

users.(44) The research team studied the characteristics of 811 users of 14 emerging devices in 

three States. They measured the physical dimensions of the devices, the space required for a 

three-point turn, the lateral operating space (sweep width), turning radii, acceleration 

capabilities, speed, and stopping sight distances. One of their most important findings that relates 

to the objectives of this study was that inline skaters had a sweep width of 1.5 m (5 ft). This is 

larger than the minimum bicycle lane width of 1.2 m (4 ft) recommended by AASHTO.(1) The 

team also found that for horizontal curve radius and stopping sight distance, the emerging 

devices that had the highest values were recumbent bicycles. Segways were included among the 

emerging devices studies during this effort, incidentally; however, the team found that Segway 

users would not be the critical users for any of the design criteria evaluated. 

In Europe, interest is rising in the role played by skaters on shared-use paths. The main concern 

seems to be safety, especially in city centers. Most of the current expressions of this concern are 

to blame the skaters and admonish them to behave better. There is no new information about 

adequate facilities that would incorporate skaters with other users. In fact, some observers 

believe that the number of inline skaters will rise in the short term, but will stabilize or fall in the 

medium and long term. 

MEASURING PATH USER QUALITY OF SERVICE 

Hindrance 

On shared-use facilities, the presence of pedestrians can be detrimental to bicycle quality of 

service because pedestrians move at much lower speeds. However, it is very difficult to establish 

a single bicycle/pedestrian equivalent value because the relationship between the two modes 

differs depending on their respective volumes, directional splits, and other conditions. Botma 

developed the concept of hindrance on shared-use paths to overcome this difficulty and to allow 

a meaningful quality of service to be computed.(42)

Botma’s procedure for determining bicyclist LOS on a shared-use path is founded on the concept 

of the hindrance experienced by path users as they travel a unit length of the path.(42) Based on 

earlier field studies conducted at four sites in The Netherlands, he found that the correlation 

between bicycle volume and speed was very weak (R2 = 0.20).(5) Density, the ratio of volume and 

speed, has been used in Germany as the LOS indicator on trails; however, the selection of 

densities to delineate LOS can be rather subjective. Botma therefore concluded that LOS for 

bicycle paths should not be based on speed or density, but rather on the freedom to maneuver and 

the ability to make unrestricted passing maneuvers.(42)

Hindrance reflects the degree to which a user is restricted from the freedom to maneuver. Such 

restrictions occur when a bicyclist passes a slower bicyclist or a pedestrian, or when he or she 

meets a bicyclist or pedestrian traveling in the opposite direction. LOS is then defined on the 

basis of the fraction of the path users that experience hindrance. For example, LOS A is said to 

occur when fewer than 10 percent of all users experience hindrance over a 1-km (0.62-mi) path. 
18

On the other extreme, the LOS E/F boundary is reached when all users experience hindrance and 

when the average path user is expending about two-thirds of his or her time maneuvering around 

other users. It is important to note that the LOS E/F boundary occurs well before the path 

capacity is reached. Capacity in one lane on a bicycle path has been reported to be somewhere 

between 1,500 and 5,000 bicycles per hour, as will be shown later. 

Using a simple simulation model, Botma and Papendrecht were able to relate the percentage of 

hindrance to directional volumes.(45) Furthermore, because hindrance is difficult to measure 

directly in the field, the frequency of meeting and passing events was used as a proxy variable 

for hindrance. Of course, the conversion from events to hindrance requires some judgment about 

the “relative impedance” of each event from the user perspective. Since there was no guidance in 

the literature, Botma assumed that all meeting events are half as severe as all passing events.(42) 

Therefore, the total hindrance is calculated as the weighted frequency of all meeting and passing 

events. The computation of the various event-type frequencies is described next. From the 

bicyclist’s perspective, the number of events experienced by the average bicyclist depends on: 

(1) whether the path is one way or two way, (2) whether the path is exclusive (bicycles only) or 

shared (bicycles, pedestrians, inline skaters, etc.), and (3) the directional volumes of all path 

users. For a two-way shared-use path serving primarily bicycles and pedestrians, there are four 

types of events, including: 

A. Bicycle passing a bicycle. 

B. Bicycle passing a pedestrian. 

C. Bicycle meeting a bicycle. 

D. Bicycle meeting a pedestrian. 

For case A, assume that bicyclists do not impede each other and that bicycle speeds are normally 

distributed with mean (Ub) and standard deviation (σb). The desired frequency of passing events 

(per hour and unit length) experienced by the average bicyclist in a directional bicycle flow of Qb

per hour can be estimated:(46)

π

σ

b

b b

U

Q F A 2 ( ) =

 (1) 

For case B, the frequency of pedestrians that the average bicyclist passes, assuming a directional 

pedestrian flow of Qp per hour with mean speed Up, is estimated by the following equation: 

( ) (1 )

b

p

p U

U

F B = Q −

 (2) 

In case C, the frequency of opposing bicycles that the average bicyclist meets, assuming an 

opposing bicycle flow of Qbo, with mean speed Ubo, is estimated as: 
19

 (3) 

In the event that the mean bicycle speeds are equal in both directions, F(C) simplifies to 2Qbo. 

Finally, for case D, the frequency of opposing pedestrians that the average bicyclist meets, 

assuming an opposing pedestrian flow of Qpo, with mean speed Upo, is estimated as: 

( ) (1 )

po

b

po U

U F D = Q +

 (4) 

From the pedestrian’s perspective, Botma’s method assumes (probably unrealistically) that 

pedestrians do not impede each other on a shared-use path. Therefore, the relevant events are 

only those involving the interactions of pedestrians and bicycles. For a two-way, shared-use path 

that serves primarily bicycles and pedestrians, there are two such events (making the reasonable 

assumption that pedestrians never pass bicyclists): 

E. Pedestrian overtaken by bicycles traveling in the same direction. 

F. Pedestrian meeting bicycles traveling in the opposite direction. 

For case E, the hourly frequency of bicycles passing the average pedestrian on the path, 

assuming a directional bicycle flow of Qb per hour, and assuming pedestrian and bicycle mean 

speeds of Up and Ub, respectively, is estimated from: 

( ) (1 )

b

p

b U

U

F E = Q −

 (5) 

In case F, the hourly frequency of opposing bicycles meeting an average pedestrian on the path, 

assuming an opposing directional bicycle flow of Qbo per hour, and assuming pedestrian and 

opposing bicycle mean speeds of Up and Ubo, respectively, is estimated from: 

( ) (1 )

bo

p

b o U

U

F F = Q +

 (6)

A path-wide hindrance or LOS can be obtained by adding all the “impedance weighted” events 

for each path user, and then calculating the path average using the volume for all path users. The 

procedure reveals a high sensitivity of bicycle LOS to pedestrian volumes and a much lower 

sensitivity of pedestrian LOS to bicycle volumes. The two examples in table 1, taken from 

Botma, illustrate the point.(42) The LOS indicated in parentheses is the one predicted if the path 

were to be used exclusively by pedestrians or bicycles. 

 

( ) (1 )

bo

b

bo U

U

F C = Q +
20

Table 1. LOS examples for a two-way, shared-use path. 

Ex. Two-way, 

pedestrians/h 

Two-way, 

bicycles/h LOS-Ped LOS-Bicycle LOS-Combined 

1 40 200 A (A) F (D) F 

2 200 40 A (A) F (A) D 

*Assuming a 50/50 split for all volumes. 

 

Until very recently, the HCM had no substantive material on bicycle operations and much less on 

shared-use paths.(2,14,19) Based on previous research performed for FHWA by some members of 

this research team, much of Botma’s work on shared-use paths (modified for U.S. conditions) 

was incorporated into the 2000 HCM.(3-4,7) This material appears in three chapters. Chapter 11 

deals with pedestrian and bicycle concepts and provides descriptive information on the various 

facility types. Chapter 18 on pedestrians presents the effects of bicycles on pedestrian LOS on 

shared-use paths. Finally, chapter 19 on bicycles provides the most comprehensive treatment of 

shared-use paths. Analysis methods are provided for any combination of pedestrians and bicycles 

for one-way and two-way paths, and for two-lane and three-lane paths. Examples illustrating the 

LOS benefits of separating pedestrians and bicycles are provided. All of this material was 

reflected in the Highway Capacity software and in other software replicating HCM calculations. 

The one published effort to date that attempted to validate Botma’s method was by Virkler and 

Balasubramanian.(43) The bicycle passing data they collected on a shared-use path in Brisbane, 

Australia, were compared to predictions calculated using Botma’s hindrance models. The mean 

speed and standard deviation data from the Brisbane site were applied to Botma’s overtaking 

frequency model and were compared to the actual observations for each six passing 

combinations: (1) bicycle passing bicycle, (2) jogger passing jogger, (3) hiker passing hiker, 

(4) bicycle passing jogger, (5) bicycle passing hiker, and (6) jogger passing hiker. Results from 

the prediction model were very similar to the actual observations for all passing combinations, 

with the exception of the hiker passing hiker and jogger passing hiker combinations. The 

predicted frequencies for these two passing types were much higher than those observed in the 

field. Similarly, the predicted delayed overtakings for these two passing combinations were 

much higher than the actual delayed overtakings, while the rest of the combinations were quite 

comparable. 

Density 

Density has often been proposed as a measure of effectiveness (MOE) for bicycle facilities. 

Previous studies in California, (36) Germany,(31) and China(33) all proposed levels of service based 

on density. The California study reported that bicycle capacity occurs at approximately 2,600 

bicycles per hour per 1.0-m (3.3-ft) lane. That study proposed that LOS A was at a density 

greater than 20 m2/bicycle (215 ft2/bicycle) while LOS F was at a density less than 3.7 

m

2

/bicycle (40 ft2/bicycle). The German study proposed that LOS A was at a density greater 

than 200 m2

/bicycle (2150 ft2/bicycle) while LOS F was at a density less than 10 m2/bicycle (108 

ft2

/bicycle). Instead of LOS of an A through F scale, the Chinese study proposed the following 

seven “states of bicycle traffic”: 
21

• Very comfortable. 

• Comfortable. 

• Cannot overtake. 

• More dense. 

• Very crowded. 

• Prepare to dismount. 

• Dismount. 

The highest state, very comfortable, was at a density greater than 10 m2

/bicycle (108 ft2/bicycle) 

while lowest state, dismount, was at a density less than 2.2 m2

/bicycle (24 ft2/bicycle). Note that 

a density just over 10 m2/bicycle (108 ft2/bicycle) would produce an LOS E in Germany and 

would produce a “very comfortable” rating on the Chinese scale. No capacities were reported in 

the Chinese study. However, based on the speeds and densities reported, it appears that the 

capacity for a 2-m- (6.6-ft-) wide path is between 4,400 and 4,500 bicycles per hour, and the 

capacity of a 3-m- (9.9-ft-) wide path is between 6,600 and 6,700 bicycles per hour. 

Space 

The 2000 HCM uses space (m2

/ped) as the primary measurement of effectiveness for pedestrian 

LOS on uninterrupted facilities.(4) The HCM uses that measurement because space dictates the 

pedestrians’ ease, speed, and freedom of movement. Because pedestrian movements are affected 

by the presence and relative location of other pedestrians, space is a viable measurement. 

Stress 

There are many components to the bicycling environment that need to be considered when 

thinking about the potential attractiveness of a bicycle facility or determining the best locations 

for new construction and improvements that would benefit bicycles. Researchers have identified 

many of those factors and have shown how they can help estimate the quality of service for a 

facility. To this point, such work has applied only to on-street bicycle facilities. 

Based on other studies, Northwestern University(25) has assigned stress levels between 1 and 5 to 

three primary factors and to three secondary factors that contribute to the relative attractiveness 

of an on-street bicycle facility. Level 5 suggests a high level of stress, and level 1 suggests a low 

level of stress. The assignment of an overall stress level to a facility is based on an average of the 

values for the three primary factors: 

• Motor vehicle volume in the adjacent lane. 

• Curb lane width in meters or feet. 

• Speed of the motor vehicles in the adjacent lane. 

Stress level 1 is associated with motor vehicle volumes of 50 vehicles per hour per lane, curb 

lane widths of 4.6 meters (15 feet), and motor vehicle speeds of 40 km/h (25 mi/h). Meanwhile, 

stress level 5 is associated with motor vehicle volumes of 450 vehicles per hour per lane, curb 

lane widths of 3.4 meters (11 feet), and motor vehicle speeds of 72 km/h (45 mi/h). 
22

After the average of the three primary factors is found, the stress level of the facility can be 

subjectively rated based on the values of three specified secondary factors or on any other 

secondary factors that are felt to be important. The three specified secondary factors were: 

• Driveways per kilometer or per mile. 

• Percentage of heavy vehicles. 

• Parking turnover per hour per block. 

Stress level 1 is associated with 5 percent heavy vehicles, 6 driveways per kilometer (10 

driveways per mile), and no parking allowed. Stress level 5 is associated with 15 percent heavy 

vehicles, 31 driveways per kilometer (50 driveways per mile), and 20 parking movements per 

hour per block. Other secondary factors mentioned for possible consideration include bicycle 

volume, pavement condition, sight distance, bus routes, presence of drainage grates, intersection 

turning volumes, and street grade. If an analyst felt that one or more of these additional 

secondary factors were important, a subjective decision could be made as to the degradation 

contributed to the stress level of the facility. 

Several local and State transportation agencies have developed their own stress level methods for 

on-street bicycle facilities. These are generally based on or look very similar to the Northwestern 

University method described above, using similar primary and secondary factors with slightly 

different values. 

SIMULATION MODELS 

Analysts could obtain performance measures on shared-use paths from computer simulation 

models. However, to date, there is no widely used computer simulation software in the United 

States that is capable of describing user interactions on shared-use paths in a realistic manner. 

The earliest attempt was a numerical, microscopic simulation model by Botma and 

Papendrecht.(45) The model included three types of users: solo bicyclists, bicyclists riding in 

pairs, and mopeds (no pedestrians). It is essentially a Monte Carlo simulator of individual entities 

whose speeds follow the normal distribution with exponentially distributed headway. The model 

assumes no impedance between users and is used to capture the number of passing and meeting 

events of various users for a variety of volume and speed ranges. Given that much of the model 

outputs are already captured by Botma’s analytical equations, the utility of such a model is 

questionable. 

There are obvious similarities between operations of nonmotorized traffic on shared-use paths, 

and motorized traffic on two-lane highways. Both facilities experience passing and meeting 

events, delayed passing, and conflicts with opposing traffic. In that respect, there may be lessons 

to be learned from simulation models such TWOPAS in the United States(48) and TRAAR in 

Australia.(49) The TWOPAS model was extensively used in developing a new two-lane highway 

procedure for the 2000 HCM.(4) Of specific relevance to this study is the comparison of desired 

versus actual passing, and how the frequency varies according to directional volumes and speeds. 

In fact, Morrall and Werner proposed using the ratio of actual versus desired passing as the 

measure of two-lane highway LOS.(50) This may be one way of overcoming the deficiency in 

Botma’s method that was alluded to earlier in this report. 
23

A review of the most popular microscopic traffic simulation software, including CORSIM,(51)

VISSIM,(52) and INTEGRATION,(53) showed that they will not help much for pedestrian and 

bicycle modeling. CORSIM does not model pedestrians explicitly. It basically adds vehicular 

delay based on the level of pedestrian impact. The pedestrian impact can be modeled in 

CORSIM as a statistical distribution. No bicycle impact is considered in CORSIM. CORSIM 

does allow users to customize the model for certain vehicle types and, therefore, could be 

“tricked” into modeling pedestrians, bicycles, and other users. However, the lack of an algorithm 

to make same-direction passings on a two-lane highway (CORSIM uses one-way links 

exclusively) would make modeling a two-way, shared-use path (by far the most popular type) 

untenable. Similarly, INTEGRATION does not consider bicycles or pedestrians. VISSIM can 

explicitly model both pedestrians and bicycles. Pedestrians and bicycles simply follow their 

routes. However, from its manual and our experience with the model, it appears that VISSIM 

does not model pedestrian and bicycle interactions. The conclusion from this review is that these 

popular existing microscopic traffic simulation models would not be helpful in this research. 

SETTING THE LOS SCALE 

Capacity 

Many LOS scales in the HCM and elsewhere adopt capacity as the LOS E to F boundary. This is 

especially true of uninterrupted facilities such as shared-use paths. However, the capacity or 

saturation flow of bicycle facilities is rarely observed in practice, especially in the United States. 

The 1994 HCM(14) listed the following ranges of reported capacities: 

• One-way, one-lane bicycle lane or path, 1,700 to 2,350 bicycles per hour. 

• Two-way, one-lane bicycle path, 850 to 1,000 bicycles per hour. 

• Two-way, two-lane bicycle lane or path, 500 to 2,000 bicycles per hour. 

Lane widths corresponding to the observations above were from 0.9 to 1.2 meters (3 to 4 feet). 

Current LOS Scales 

Versions of the HCM prior to 2000 did not have an LOS scale for shared-use paths. In 2000, the 

HCM presented hindrance as the MOE for shared-use paths, based on Botma’s procedure. The 

2000 HCM(4) also established LOS scales for bicycles and pedestrians on shared-use paths, again 

based on Botma’s suggestions, as shown in tables 2 and 3. The LOS on a shared-use facility is 

not the same from the viewpoint of pedestrians and bicycles. 

As noted previously, for bicyclists, LOS F refers to a situation where an average user 

experiences hindrance more than one time in a 1.0-km (0.62-mi) trail segment. Perhaps the most 

important thing to note when viewing the bicycle LOS scale (tables 2) is that LOS F is not 

equivalent to capacity for the facility. An unacceptable number of events is always reached prior 

to capacity and, in some cases, capacity can be almost twice the volume at which LOS F is 

reached. The procedures in the 2000 HCM are based on frequencies of events and on perceived 

LOS, not on the carrying capacity of the facility. 
24

Table 2. Bicycle LOS criteria for shared-use paths in the 2000 HCM.(4) 

LOS Frequency of events, 2-way, 

2-lane paths1

 (events/hour) 

Frequency of events, 2-way, 

3-lane paths2 (events/hour) 

A <40 <90 

B >40–60 >90–140 

C >60–100 >140–210 

D >100–150 >210–300 

E >150–195 >300–375 

F >195 >375 

Notes: 

1. 2.4-m- (8-ft-) wide paths. 

2. 2-m- (10-ft-) wide paths. 

Table 3. Pedestrian LOS criteria for 2.4-m- (8-ft-) wide shared-use paths 

in the 2000 HCM.(4) 

LOS Number of events per hour1

Corresponding bicycle 

service volume per 

direction2 (bicycles/hour) 

A <38 <28 

B >38–60 >28–44 

C >60–103 >44–75 

D >103–144 >75–105 

E >144–180 >105–131 

F >180 >131 

Notes: 

1. An event is a bicycle meeting or passing a pedestrian. 

2. Assuming 50/50 directional split of bicycles. 

Regarding tables 2 and 3, it is also important to note that all service volumes given in the 2000 

HCM for shared-use paths assume ideal geometric and traffic conditions. Lateral obstructions, 

extended sections with appreciable grades, and other local factors may reduce the LOS for a 

facility. Such factors have not been sufficiently documented to date to make a quantitative 

assessment of their effects. 

The key assumption in the LOS scale for pedestrians on shared-use paths is that pedestrians do 

not hinder other pedestrians. In Botma’s discussion of his own work,(42) he questions this 

assumption. 

Botma’s expression describing the total number of overtakings of pedestrians by bicyclists, Nf/s, 

is: 

 (7) 

where: 

X = Length of site, m 

T = Time period considered, s 

Nf/s = X T Qf Qs (1/Us – 1/Uf) 
25

Qf = Flow of faster group in subject direction, bicyclists/s 

Qs = Flow of slower group in subject direction, pedestrians/s 

Uf = Mean speed of faster group, m/s (for bicyclists) 

Us = Mean speed of slower group, m/s (for pedestrians) 

Using an average pedestrian speed of 1.25 m/s (4.1 ft/s) and an average bicyclist speed of 5 m/s 

(16.4 ft/s), Botma developed an LOS table for pedestrians traveling on two-lane, two-way, 

shared-use paths. Table 3, substituting time period for frequency, showed Botma’s service levels. 

The 2000 HCM only provides LOS criteria for a 2.4-m- (8-ft-) wide (two-lane) path. 

Note that if one applied table 3 to an exclusive pedestrian trail, one would always have a service 

level of A, regardless of pedestrian volume, since the tables depend entirely on bicycle volume. 

Using existing walkway LOS standards based on space (presented below) certainly seems more 

reasonable than the use of Botma’s method for an exclusive or predominant pedestrian facility. 

For exclusive pedestrian walkways, as noted above, the 2000 edition of the HCM (as well as 

previous editions) uses space as the primary MOE.(4) At LOS A, the pedestrian has 5.6 m2

(60 ft2

) or more of space. Under these conditions, pedestrians are capable of walking freely, 

without having to alter their path because of other pedestrians. The HCM defines capacity and 

LOS F to be at 0.74 m2 (8 ft2

) or less. Under these conditions, pedestrian walking speeds are 

greatly decreased. Pedestrians are merely capable of “shuffling” and there are frequent 

encounters with other pedestrians. Pedestrian mobility is severely lessened under these 

circumstances. Table 4 presents the levels of service for pedestrian walkways from the 2000 

HCM. Speed and volume to capacity (v/c) ratios are used as supplementary LOS criteria. 

Table 4. Average flow pedestrian walkway LOS criteria from the 2000 HCM.(4)

LOS Space 

(ft2

/p) 

Flow rate 

(p/min/ft)

Speed (ft/s) v/c ratio 

A >60 ≤5 >4.25 ≤0.21 

B >40–60 >5–7 >4.17–4.25 >0.21–0.31 

C >24–40 >7–10 >4.00–4.17 >0.31–0.44 

D >15–24 >10–15 >3.75–4.00 >0.44–0.65 

E >8–15 >15–23 >2.50–3.75 >0.65–1.0 

F ≤8 variable ≤2.50 variable 

1 ft = 0.3 m 

Other Ways To Set the LOS Scale 

The experiences of past researchers may be helpful to this research team in recommending and 

establishing valid LOS criteria. Harkey, et al.,(6) and Landis, et al.,(7) both conducted surveys of 

bicyclists to develop LOS criteria for on-street bicycle facilities. Harkey, et al., conducted their 

surveys based on video images, while Landis, et al., conducted their surveys based on bicyclists 

riding sample facility segments. These are among the options available for gathering user 

perception of quality-of-service data. 
26

The stress levels discussed previously have been turned into levels of service by some agencies. 

However, in this regard, the LOS scales have typically been set quite arbitrarily. 

Past work by the TRB Highway Capacity and Quality of Flow Committee on setting LOS 

criteria has focused on the A through F scale, and primarily on finding a valid E/F boundary. For 

signalized intersections, this boundary was based on a large database collected for a National 

Cooperative Highway Research Program (NCHRP) study and on a judgment as to when delays 

became unacceptable. Criteria for other chapters were based on the chances that frustrated 

motorists would violate the traffic control and would endanger safety, relative to the chances at a 

signalized intersection. Pecheux, et al.,(59) explored the signalized intersection LOS criteria more 

systematically, exposing subjects in a laboratory study to red signal delays of various lengths and 

asking for reactions on a 1 to 10 scale. After analyzing the results, Pecheux, et al., concluded that 

their subjects had difficulty discerning more than two or three levels of service, basically 

merging levels A through C or D together. 

SUMMARY 

There are many sources of guidance for shared-use path designers. However, the current sources 

available to designers provide strictly qualitative guidance. The sources provide no guidance on 

how many of each different type of user will cause a path with given geometric conditions to 

provide a poor quality of service. Botma’s procedure remains the best, thus far, to attempt to 

provide that quantitative guidance. 

As presented, Botma’s models relating events to volumes are limited to shared-use paths serving 

pedestrians and bicycles only. However, his original micro-simulation model(5) could simulate 

multiple path users, including mopeds and tandem bicycles. The addition of other path users can 

be represented analytically in one of two ways. If a path user group (e.g., joggers) appears to 

have a similar mean speed to another group, then such groups can be lumped into one larger 

group that has a common mean and standard deviation. However, if a group is quite different 

from the others, then all events associated with this group must be described separately using 

equations 1 through 6 as appropriate. 

The second limiting assumption is that path users do not impede each other’s movements. A 

good example is the predicted number of bicycle overtakings given by equation 1. This equation 

assumes that there is always adequate room to pass, with no change in speed or lateral 

positioning. This is true only if: (1) the path is wide enough, and/or (2) there is no opposing 

traffic during the passing maneuver. If passing is restricted, then there will be a number of 

“delayed overtakings.” Virkler and Balasubramanian(43) used concepts of probability theory that 

were originally applied to two-lane highway operations to estimate the probability of delayed 

overtakings. The significance of this limitation cannot be overstated. When passing and meeting 

events become restricted, the procedure cannot predict that LOS will worsen when the number of 

events actually decreases. 
27

In summary, while the Botma procedure is based, in part, on field data, there are a number of 

reasons why the procedures should be validated for North American conditions: 

• There is no statistically valid database in North America that will enable direct 

comparisons with Botma’s procedure without new field data collection. 

• The minimum shared-use path widths in the United States are typically greater than in 

Europe. In addition, the current AASHTO standards for bicycle path widths do not match 

the bicycle paths observed by Botma in The Netherlands. 

• Bicycle riders in the United States are not generally as experienced as their European 

counterparts. Their expectations about quality of service on a path may be different. 

• The recreational to purposeful bicycle trip ratio differs between the United States and 

overseas. This may have an impact on the mean and standard deviation of speeds 

observed on shared-use paths. Initial evidence of that disparity appeared in Virkler and 

Balasubramanian’s research. 

• Bicycles themselves differ between Europe and North America. 

The available literature provides some help; however, a substantial effort involving several types 

of field data collection was necessary to overcome the limitations in the Botma procedure and to 

calibrate an LOS scale that will help U.S. path designers. 

29 

3. DEVELOPMENT OF THEORY 

INTRODUCTION 

This chapter discusses the theoretical underpinnings of the proposed operational models. The 

first model (discussed in the next section) was developed for the purpose of estimating the 

desired number of active passing, passive passing, and meeting events on a shared path from the 

perspective of the bicyclist. In calculating the desired number of events, it is assumed that the 

path has adequate width, so that no passing or other events are constrained by the path geometry. 

Active passing refers to the situation where the average bicyclist (traveling at the average bicycle 

speed) desires to pass slower moving vehicles on the path (i.e., bicycles, pedestrians, inline 

skaters, etc.). Passive passing refers to the average bicyclist being overtaken by faster moving 

bicyclists or other modes. Meeting refers to the number of opposing vehicles that are met while 

the average bicyclist is on the path. This model is demand oriented and uses primarily the 

attributes of the mixed traffic flow that are on the path. These attributes consist of the modal 

volumes and their respective means and standard deviations of speeds over the path. As will be 

indicated in chapters 4 and 5 on field data collection and analysis, the modal attributes pertaining 

to speed are based on the field-verified assumption that speeds are normally distributed with a 

given mean and standard deviation that may vary from mode to mode. 

The second model discussed in this chapter imposes constraints on the number or fraction of 

passing maneuvers that can be executed because of the geometry of the path. The motivation for 

focusing on active passing maneuvers is founded on the results of the perception data analysis 

(chapter 7), where the primary impedance reported is related to the inability of the bicyclist to 

pass other users on the path. In this model, the path width is converted into an equivalent number 

of lanes in each direction, and a probabilistic model of the delayed passing maneuvers for each 

lane configuration is estimated. The LOS method described in chapter 8 uses the actual number 

of delayed passings per hour (which is the product of the desired passing maneuvers and the 

probability of a delayed passing) in determining the path LOS. 

ESTIMATING THE NUMBER OF EVENTS 

The estimates shown in this chapter are founded on the original work done by Botma(5,43) and are 

partially documented in the 2000 HCM.(4) The original work by Botma was confined to two 

modes, namely pedestrians and bicycles. The proposed model extends Botma’s calculations to 

estimate the desired number of meetings and passings by any vehicle (bicycle, pedestrian, inline 

skater, etc.) at any desired speed. As stated in the previous section, the speeds of the various 

modes are normally distributed, and this assumption was verified with field data. A second 

difference between the proposed approach and Botma’s is the numerical nature of the model. 

This is because the area under the normal distribution curve (representing modal speed) is 

needed to calculate the estimated number of events. For simpler speed distributions (e.g., 

uniform distribution), closed-form solutions can be derived. However, because the method is 

numerical, it should be able to cope with any (or more than one) speed distribution for each of 

the modes using the path. The same type of models can be used to look at cross-modal meetings 

and passings (e.g., bicycles passing pedestrians, etc.). The model is meant to be applied for one 

pair of modes at a time. This means that the procedure first predicts the number of events 
30 

encountered by the average bicyclist when considering other bicyclists only. The model is then 

re-applied when considering another mode (e.g., pedestrians), and so forth. The total number of 

events is then summed across all modes, and provides an overall picture of the multimodal 

passing and meeting demand on the path. 

The following derivations are therefore limited to a pair of modes. The “test unit” in this case 

refers to the bicycle mode. Modal flow rates and speed apply to the mode that is impeding the 

bicycle mode on the path. 

Glossary of Variables 

L = Length of path, mi 

Q = Modal flow rate (units past a point) in subject direction, per hour 

Qo = Modal flow rate in opposite direction (units/h) 

μ = Modal mean speed in subject direction (mi/h) 

μo = Modal mean speed in opposite direction (mi/h) 

σ = Modal standard deviation of speed in subject direction (mi/h) 

σo = Modal standard deviation of speed in opposite direction (mi/h) 

k = Modal density in subject direction (units/mi) 

ko = Modal density in opposite direction (units/mi) 

N = Number of modal units on the path in subject direction (N = kL) 

No = Number of modal units on the path in the opposite direction (No = koL) 

U = Speed of the test unit (mi/h) 

β = Threshold speed ratio for initiating an active passing (default = 1) 

γ = Threshold speed ratio for initiating a passive passing (default = 1) 

ESTIMATING ACTIVE PASSING EVENTS 

Active passing is defined as the desired number of passing maneuvers for the test vehicle (in our 

case, a bicycle, but it could be any mode), which is traveling at a constant speed, U, and 

encountering an impeding modal stream (other bicycles, pedestrians, etc.), which is traveling in 

the same direction at a speed (v) that is normally distributed in space with N(μ,σ

2

). This situation 

is depicted graphically in figure 1. 
31 

 

 U x dx

 

 

Figure 1. Schematic for active passing estimation. 

In our example, the test vehicle is a bicycle that is traveling at speed U. Let x be the distance 

from the location of the test bicycle to a strip on the path of length dx. By definition, the 

expected number of modal units in dx is k * dx. The desired number of passings by the test 

bicycle of those units in dx can be calculated as follows. The test bicycle will pass only those 

units in dx that will exit the segment L after the test bicycle has exited. Mathematically, this is 

defined as: 

 (8) 

which, with some manipulation, gives: 

 (9) 

Therefore, the expected number of active passings for strip dx will be: 

 (10) 

Since v is distributed with N(μ,σ

2

), then the stated probability in equation 10 can be easily 

calculated from the integral under the standard normal curve. By dividing the full length of the 

shared path into small discrete slices, each of length dx, the cumulative probability, F(x), and the 

expected number of active passing maneuvers can be calculated as the average of the probability 

at the start and end of each slice, as shown in equation 11 below: 

 (11) 

The number of active passing events is then summed over all slices to produce the desired 

passings for the entire path. 

As an illustration, the following numerical example demonstrates the model application: 

Let L = 1 mi, Q = 400 bicycles per hour, x = 0.2 mi, dx = 0.01 mi, U = 15.5 mi/h, μ = 12.5 mi/h, 

and σ = 3 mi/h. The density of modal users is estimated at k = 400/12.5 = 32 vehicles/mi (1 mi = 

1.61 km, 1 mi/h = 1.61 km/h). Applying equation 11, 

L 

P (bicycle U passing units in dx) = 

P (exit time for units in dx > path travel time for bicycle U) 

= P [(L – x)/v > L/U] 

P [v < U (1 – x/L)] 

E (pa) = k * dx * P [v < U (1 – x/L)] 

P [v] = 0.50 {F (x – dx) + F (x)} 
32 

(12) 

where Z is the standard normal variable. Similarly, at the downstream end of the slice: 

(13) 

which, according to equation 11, gives: P [ ] = (0.507 + 0.487)/2 = 0.497 and E (pa) = 32 * 0.01 

* 0.497 = 0.159 desired passing maneuvers. 

By numerically repeating this process for x = 0.01 to 1 mi in increments of 0.01 mi (1 mi = 

1.61 km), the total number of active passings on the path can be estimated. For this example, the 

total number of desired passing maneuvers can be shown to be 6.71 for the test bicycle. A 

portion of a spreadsheet illustrating the above computations for active passing events is shown in 

table 5. The above computations are bolded in the table. It should be noted that since the number 

of passing events is random, the standard deviation is computed as the square root of the mean 

number of events, based on the Poisson property. 

Extension to Exclude Marginal Active Passing Events 

The stated probability formulation in the preceding section is quite restrictive in that a desired 

passing is considered to take place even when there are very small differences in speed between 

the test unit and slower moving units ahead of it. This assumption can be relaxed to restrict the 

count of active passing to cases where the ratio of the speed of the passed vehicle to the passing 

vehicle is below a certain threshold. Let that threshold be β (see table 5). In this case, the 

probability of passing described in equation 9 can be restated as: 

P [ ] = P [v < min (Uβ, U(1 – x/L))] (14) 

When β = 1, the number of active passings estimated by equation 12 reverts to the original 

formulation in equation 9. As β drops below 1, the probability decreases, and so does the number 

of desired passings. For the above example, a beta threshold of 0.80 (meaning that a desired 

passing occurs only if the passed vehicle speed is below 80 percent of the passing vehicle speed) 

will result in a drop of active passings from 6.71 to 5.66, a reduction of about 16 percent. 

F (x – dx) = F (0.2 – 0.01) = 0.19 

F (x) = 0.20 

P [v < 15.5 * (1 – 0.19/1)] = P [v < 12.55] = P [Z < (12.55 – 12.5)/3] = P [Z < 0.017] = 0.507 

P [v < 15.5 * (1 – 0.2/1)] = P [Z < -0.033] = 0.487 
33 

Table 5. Computational spreadsheet for active passing events. 

Input Output 

Q = 400 vph Travel time= 3.87 min 

MU = 12.5 mi/h K = 32 vpm 

SIGMA = 3 mi/h N = 32 veh 

U = 15.5 mi/h Mean active passings, path totals = 6.71 

L = 1 miles Standard deviation = 2.59 

BETA = 1 (<= 1.0) Passing rate per hour = 104 

X ALPHA V(X) F(VX) F(Vx+DX) F 

N PASSACTIVE 

 15.50 

1 0.01 15.35 0.841 0.829 0.835 0.267 

2 0.02 15.19 0.829 0.815 0.822 0.263 

3 0.03 15.04 0.815 0.801 0.808 0.259 

4 0.04 14.88 0.801 0.786 0.794 0.254 

5 0.05 14.73 0.786 0.771 0.779 0.249 

6 0.06 14.57 0.771 0.755 0.763 0.244 

7 0.07 14.42 0.755 0.738 0.747 0.239 

8 0.08 14.26 0.738 0.721 0.730 0.234 

9 0.09 14.11 0.721 0.704 0.712 0.228 

10 0.10 13.95 0.704 0.686 0.695 0.222 

11 0.11 13.80 0.686 0.667 0.676 0.216 

12 0.12 13.64 0.667 0.648 0.658 0.210 

13 0.13 13.49 0.648 0.629 0.638 0.204 

14 0.14 13.33 0.629 0.609 0.619 0.198 

15 0.15 13.18 0.609 0.589 0.599 0.192 

16 0.16 13.02 0.589 0.569 0.579 0.185 

17 0.17 12.87 0.569 0.548 0.559 0.179 

18 0.18 12.71 0.548 0.528 0.538 0.172 

19 0.19 12.56 0.528 0.507 0.518 0.166 

20 0.20 12.40 0.507 0.487 0.497 0.159 

21 0.21 12.25 0.487 0.466 0.476 0.152 

22 0.22 12.09 0.466 0.446 0.456 0.146 

23 0.23 11.94 0.446 0.425 0.435 0.139 

24 0.24 11.78 0.425 0.405 0.415 0.133 

25 0.25 11.63 0.405 0.385 0.395 0.126 

26 0.26 11.47 0.385 0.366 0.375 0.120 

27 0.27 11.32 0.366 0.346 0.356 0.114 

28 0.28 11.16 0.346 0.328 0.337 0.108 

29 0.29 11.01 0.328 0.309 0.318 0.102 

1 mi = 1.61 km 

1 mi/h = 1.61 km/h 
34 

Estimating the Number of Passive Passing Events 

By definition, passive passing events refer to the number of units that will overtake the test 

vehicle as it travels at speed U over the path. The test vehicle is overtaken by other units 

traveling in the same direction at speeds that are characterized by a normal distribution N(μ,σ

2

). 

Units that are already in the path cannot overtake a test vehicle that is about to enter the path. 

Passive passing events are thus calculated for units that are about to enter the path, based on their 

travel time relationship to that of the test unit. Again, a unit will pass the test unit if: (1) it is 

behind the test unit when the test unit enters the path, and (2) it will exit the path prior to the test 

unit. This process is illustrated in figure 2 below. 

 

 x L 

 dx 

 U 

Figure 2. Schematic for passive passing estimation. 

In this case, the probability that a modal unit at location dx (e.g., faster bicycle) will pass the test 

bicycle, which is traveling at speed U while on path L, is the probability that the unit will exit the 

path prior to the test bicycle, or: 

(15) 

which can be rewritten as: 

 (16) 

The expected number of passive passings because of the units that are in dx at time zero is: 

 (17) 

An important question arises regarding the length x needed to capture most of the passive 

passing events. It is evident that the slower the test unit or the higher the average modal user 

speed, the longer x should be. Define δ as the lowest probability of passing to be included in the 

computations. Note that as x increases, the probability of a passive passing event decreases. 

Then, the objective is to find the lowest x, x*

, such that: 

(18) 

P [(L + x)/v < L/U] 

P [v > U (1 + x/L)] 

P [v > U (1 + x*/L)] < δ

 E (pp) = (k)(dx)P [v > U (1 + x/L)] 
35 

Assuming a normal distribution of speeds over space, the standard normal variable Z that 

corresponds to δ is Zδ and P (Z > Zδ) = δ. Then, the minimum value of x is computed as: 

(19) 

As an example, let L = 1 mi, x = 0.2 mi, dx = 0.01 mi, Q = 400 vehicles per hour, μ = 12.5, σ = 

3, U = 9.5 mi/h, and δ = 0.01 (1 mi = 1.61 km, 1 mi/h = 1.61 km/h). From a standard normal 

distribution table, Z0.01 = +2.326. Substituting into equation 19 gives: 

(20) 

This is slightly longer than the actual path length. If a more accurate estimate is needed—say 

only probabilities less than 0.005 are ignored—this will require a longer analysis length of 

1.8 km (1.129 mi). 

Similar to the active passing procedure, the individual slice passings are aggregated over each dx 

and summed over L to calculate the total number of passive passings on the path. For the 

example above, it can be shown that for x = 0.20 mi (0.32 km): 

(21) 

and the total passive passing events over the entire path are estimated at 10.95. Portions of a 

computational spreadsheet that executes the above equations are shown in table 6. 

x

* = L [(Zδ σ + μ)/U – 1] 

x*= 1 [(2.326(3)+12.5)/9.5-1]= 1.049 miles (1.69 km) 

E[pp]=32(0.01)(0.649)= 0.2077 
36 

Table 6. Computational spreadsheet for passive passing events. 

Input Output 

Q = 400 vph Travel time= 6.316 min 

MU = 12.5 mi/h K = 32 vpm 

SIGMA = 3 mi/h N = 32 vpm 

U = 9.5 mi/h X minimum = 1.049 miles

X = 1 miles Mean passive passings, 

path totals = 

 10.95 

GAMMA = 1 (>=1) Standard deviation = 3.31 

DELTA = 0.01 Passing rate per hour = 104 

X ALPHA V(X) F(VX) F(Vx+DX) F 

N PASSPASSIVE 

 9.500 

 1 0.01 9.595 0.841 0.834 0.837 0.268 

 2 0.02 9.690 0.834 0.826 0.830 0.265 

 3 0.03 9.785 0.826 0.817 0.821 0.263 

 4 0.04 9.880 0.817 0.809 0.813 0.260 

 5 0.05 9.975 0.809 0.800 0.804 0.257 

 6 0.06 10.070 0.800 0.791 0.796 0.255 

 7 0.07 10.165 0.791 0.782 0.786 0.252 

 8 0.08 10.260 0.782 0.772 0.777 0.249 

 9 0.09 10.355 0.772 0.763 0.768 0.246 

10 0.10 10.450 0.763 0.753 0.758 0.242 

11 0.11 10.545 0.753 0.743 0.748 0.239 

12 0.12 10.640 0.743 0.732 0.738 0.236 

13 0.13 10.735 0.732 0.722 0.727 0.233 

14 0.14 10.830 0.722 0.711 0.716 0.229 

15 0.15 10.925 0.711 0.700 0.706 0.226 

16 0.16 11.020 0.700 0.689 0.695 0.222 

17 0.17 11.115 0.689 0.678 0.683 0.219 

18 0.18 11.210 0.678 0.666 0.672 0.215 

19 0.19 11.305 0.666 0.655 0.661 0.211 

20 0.20 11.400 0.655 0.643 0.649 0.208 

21 0.21 11.495 0.643 0.631 0.637 0.204 

22 0.22 11.590 0.631 0.619 0.625 0.200 

23 0.23 11.685 0.619 0.607 0.613 0.196 

24 0.24 11.780 0.607 0.595 0.601 0.192 

25 0.25 11.875 0.595 0.583 0.589 0.188 

26 0.26 11.970 0.583 0.570 0.576 0.184 

27 0.27 12.065 0.570 0.558 0.564 0.180 

28 0.28 12.160 0.558 0.545 0.551 0.176 

29 0.29 12.255 0.545 0.533 0.539 0.172 

1 mi = 1.61 km 

1 mi/h = 1.61 km/h 
37 

Extension to Exclude Marginal Passive Passing Events 

Similar to the active passing condition, the above formulation can be extended to restrict passive 

passing to cases where the ratio of the speed of the passing vehicle to the speed of the passed 

vehicle is above a certain threshold. Let that threshold be γ; therefore, the probability of passing 

can be restated as: 

 (22) 

For γ = 1, the number of passive passings in equation 18 reverts to the original formulation 

(equation 16). As γ increases above 1, the probability decreases, and so does the number of 

desired passings. For the above example, a Gamma threshold of 1.25 (meaning that a desired 

passing occurs only if the passing vehicle speed is 25 percent above the slower vehicle speed) 

will result in a drop of active passings from 10.95 to 9.96 (about 9.1 percent). 

ESTIMATING THE NUMBER OF MEETINGS 

Using the same concepts outlined in the previous section for passive passing events, let x be 

defined as a segment of the path upstream from the segment of interest in the opposing direction. 

It is obvious from the schematic in figure 3 that every opposing vehicle that is present on the 

path when the test unit enters it will meet the test unit regardless of speed, assuming, of course, 

that no vehicle enters or exits the path at an intermediate point. In addition, all opposing vehicles 

that enter the segment before the test vehicle exits it will also meet the test unit. The 

computations in this section focus on this second term. 

The probability of a meeting because of units that are present at location dx at time zero (when 

the test vehicle enters the path) is estimated as: 

 

P [ ] = P [X/vo < L/U] (23) 

 

 L Note: Opposing vehicles in L are “meetings.” X dx

 

 U 

Figure 3. Schematic for meeting event estimation. 

P [v > max (Uγ, U (1 + x/L))] 
38 

Which can be reorganized as: 

(24) 

and the corresponding average number of meeting events because of units in dx is: 

 (25) 

Similar to the case of passive passings, the minimum length x* to guarantee a minimum 

probability δ of inclusion into the computations of meetings can be calculated from the equation: 

(26) 

where Z is the standard normal variable. The total number of meetings is then calculated as the 

sum of the meetings with opposing vehicles that are already on the path when the test unit enters 

it (this is simply the equivalent of the opposing density times the path length) and meeting with 

vehicles that have yet to enter the path (as described by equation 25). Therefore, the total 

meeting events are computed as: 

 (27) 

For a numerical example of the calculation of the number of meetings, assume that there is a 

vehicle positioned between x = 0.19 mi and x = 0.20 mi when the test bicycle enters the path. 

Also assume L = 1 mi, x = 0.20 mi, dx = 0.01 mi, U = 15.5 mi/h, Qo = 400 vehicles per hour, μo = 

12.5 mi/h, σo = 3 mi/h, and δ = 0.01 (1 mi = 1.61 km, 1 mi/h = 1.61 km/h). We first determine 

the minimum virtual path length needed to capture 99 percent of all meetings. From equation 26: 

(28) 

so that 124 segments, each 0.01 mi in length, would be required to numerically compute the 

meetings. From equation 24: 

(29) 

(30) 

so that P[0.19 – 0.20] = (0.9993 + 0.999276)/2 = 0.999206. The expected number of meetings 

per equation 25 is computed as: 

(31) 

E [m] = ko L + ∑ x E [m2] 

P [ ] = P [vo > XU/L]

E [m2]= ko.(dx)P [vo > XU/L] 

x*= L/U(Zδ σo+μo) 

x*= 1/15.5(2.326(3)+12.5)= 1.238 mi (1.99 km) 

P [vo > 0.19(15.5)/1]= P [vo > 2.945]= 0.999276 

P [vo >0.20(15.5)/1]=P [vo > 3.10] = 0.999136 

E [m2]= (400/12.5)(0.01)0.999206=0.319746 
39 

Finally, the total expected number of meetings per equation 27 is: 

(32) 

A partial computational spreadsheet that executes the above equations for estimating the number 

of meeting events is shown in figure 6. For comparison purposes, table 7 contrasts the numerical 

estimates of meetings developed in this work with Hein Botma’s(5) analytical estimate given in 

equation 33: 

 (33) 

In most cases (assuming the minimum x value is satisfied), the two estimates are identical. 

E [m] from Botma = Qo L [1/U + 1/μo] 

E [m]= (400/12.5)(1) + ∑x E (m2) = 32+25.81= 57.81 meeting events on the path 
40 

Table 7. Computational spreadsheet for meeting events. 

Input Output 

Q = 400 vph Travel time= 3.87 min 

MU = 12.5 mi/h Ko = 32 vpm 

SIGMA = 3 mi/h No = 32 veh 

U = 9.5 mi/h X minimum = 1.2566 miles

X = 1 miles Average meetings, path totals = 57.81 

DELTA = 0.01 Standard deviation = 7.60 

 Botma’s analytical estimate = 57.81 

 Meeting rate per hour = 896 

X ALPHA V(X) F(VX) F(Vx+DX) F 

N 

MEETINGS 

 0.000 32.000 on path 

 1 0.01 0.155 1.000 1.000 1.000 0.320 

 2 0.02 0.310 1.000 1.000 1.000 0.320 

 3 0.03 0.465 1.000 1.000 1.000 0.320 

 4 0.04 0.620 1.000 1.000 1.000 0.320 

 5 0.05 0.775 1.000 1.000 1.000 0.320 

 6 0.06 0.930 1.000 1.000 1.000 0.320 

 7 0.07 1.085 1.000 1.000 1.000 0.320 

 8 0.08 1.240 1.000 1.000 1.000 0.320 

 9 0.09 1.395 1.000 1.000 1.000 0.320 

10 0.10 1.550 1.000 1.000 1.000 0.320 

11 0.11 1.705 1.000 1.000 1.000 0.320 

12 0.12 1.860 1.000 1.000 1.000 0.320 

13 0.13 2.015 1.000 1.000 1.000 0.320 

14 0.14 2.170 1.000 1.000 1.000 0.320 

15 0.15 2.325 1.000 1.000 1.000 0.320 

16 0.16 2.480 1.000 1.000 1.000 0.320 

17 0.17 2.635 1.000 0.999 1.000 0.320 

18 0.18 2.790 0.999 0.999 0.999 0.320 

19 0.19 2.945 0.999 0.999 0.999 0.320 

20 0.20 3.100 0.999 0.999 0.999 0.320 

21 0.21 3.255 0.999 0.999 0.999 0.320 

22 0.22 3.410 0.999 0.999 0.999 0.320 

23 0.23 3.565 0.999 0.999 0.999 0.320 

24 0.24 3.720 0.999 0.998 0.998 0.319 

25 0.25 3.875 0.998 0.998 0.998 0.319 

26 0.26 4.030 0.998 0.998 0.998 0.319 

27 0.27 4.185 0.998 0.997 0.997 0.319 

28 0.28 4.340 0.997 0.997 0.997 0.319 

29 0.29 4.495 0.997 0.996 0.996 0.319 

1 mi = 1.61 km 

1 mi/h = 1.61 km/h 
41 

Sensitivity of Passing Events to Key Parameters 

In this section, we present the results of a limited sensitivity analysis of the rate of desired 

passing events. We conducted two experiments in which the speed of the test unit and the 

directional flow rate, respectively, were allowed to vary while keeping all other parameters fixed. 

These analyses were important as a first step in understanding the basis for setting LOS 

thresholds for shared paths. In both experiments, the following parameters were kept fixed: 

stream speed = 20.1 km/h (12.5 mi/h), standard deviation of speed = 4.8 km/h (3 mi/h), and path 

travel time = 1 h (to obtain hourly passing rates). 

In the first experiment, the test bicyclist speed was allowed to vary from 15.3 to 24.9 km/h 

(9.5 to 15.5 mi/h) in 1.6-km/h (1-mi/h) increments. This was done to compare the passing 

requirements for bicyclists on either end of the speed distribution. The results are summarized in 

figure 7. As expected, active passing demand increased with the test bicyclist speed, while 

passive passing demand decreased. For a test bicyclist traveling at the average stream speed 

(20.1 km/h (12.5 mi/h) in the figure), the number of active and passive passing events is exactly 

the same, as expected. It is important to note from the figure that the total lowest passing demand 

occurs at that point as well. In other words, the application of the model will always assume the 

case of the average bicyclist. 

It should be remembered, however, that trip purpose could have an impact on which test bicyclist 

speed to use. It is obvious, for example, that based on the trend in figure 4, those cyclists at either 

end of the speed spectrum will have to contend with many more events than the average bicyclist 

does. Not shown are the effects of speed on meetings. These events are much more common, 

ranging from a low of 704 meetings per hour at a speed of 15.3 km/h (9.5 mi/h) to 896 meetings 

per hour at a speed of 24.9 km/h (15.5 mi/h), an increase of 28.5 percent. The effect on impeding 

bicycles in the opposite direction should be considered minimal, and is discussed further in 

chapter 7. 
42 

1 mi/h = 1.61 km/h 

Figure 4. Sensitivity of hourly passing rates to individual bicyclist speed. 

In the second experiment, the directional flow rate was allowed to vary from 100 to 700 vehicles 

per hour, in 150-vehicle per hour increments. The test bicyclist speed was fixed at 25 km/h (15.5 

mi/h). The results are summarized in figure 5. As expected, all passing rates increased as flow 

rate increased, with the trend close to linear. It should also be noted that the rate of passive 

passing events is low given that the test bicyclist speed is, on average, 4.8 km/h (3 mi/h) higher 

than the average traffic stream speed. On the other hand, the active passing events are quite 

frequent. The analysis indicates that at an exclusive bicycle path operating with 700 vehicles per 

hour per direction in which one would like to maintain a speed of 25 km/h (15.5 mi/h) requires 

an active passing maneuver on average every 3,600/196 = 18 s (obviously a very intolerable 

situation). By comparison, such maneuvers will occur very infrequently (every 105 s or so) when 

directional flow is 100 vehicles per hour. 

0

20

40

60

80

100

120

9 10 11 12 13 14 15 16

Test Unit Speed (mi/h)

Passing Events

Active Passing/hr

Passive Passing/hr

Total Passing/hr

Stream Flow Rate= 400 vph; Stream Speed= 12.5 mi/h 
43 

In conclusion, the preceding analysis established a mechanism for identifying passing and 

meeting event demand rates under a variety of operating conditions. However, the analysis did 

not account for the supply or capacity side of the path, in particular, how the width of the path or 

the number of lanes impact the ability to carry out the desired meeting and passing maneuvers. 

This is explained next, and provides a fundamental approach to determining LOS on multimodal 

shared-use paths. 

1 mi/h = 1.61 km/h 

Figure 5. Sensitivity of hourly passing rates to directional flow rates. 

ESTIMATING THE PROBABILITY OF DELAYED PASSING 

The preceding section focused on estimating the demand for passing maneuvers. In this section, 

constraints on performing such maneuvers are introduced in the form of delayed passing 

probabilities. These constraints depend heavily on the geometry of the path, and are analyzed for 

two-, three-, and four-lane path configurations. There is no attempt to precisely translate width 

into the number of lanes because much of that translation really depends on how the units 

actually use the path. However, there are some clear assignments. A 2.4-m- (8-ft-) wide path 

should always be analyzed as a two-lane path, a 3.6-m (12-ft) path as a three-lane path, and a 

0

50

100

150

200

250

0 100 200 300 400 500 600 700 800

Flow Rate in Subject Direction (vph)

Passing Events/hr

Active Pass/hr

Passive Pass/hr

Total Pass/hr

Stream Speed= 12.5 mph; Standard Deviation = 3mph; Test Unit Speed= 15.5 mph
44 

4.8-m (16-ft) path as a four-lane path. Intermediate widths, however, are much more difficult to 

gauge, and the number of effective lanes in those cases will depend very much on the actual use. 

In general, and irrespective of configuration, a delayed passing maneuver emerges when there is 

a bicycle or pedestrian ahead of the overtaking bicycle in the subject direction in conjunction 

with another opposing bicycle(s) or pedestrian(s) in the opposing direction. This presence of 

impeding vehicles is computed for a “required passing distance” that is dependent on the type of 

passing vehicle and type of passed vehicle. For example, chapter 5 shows that a bicycle passing 

another bicycle requires 33.5 m (110 ft) of clear space, on average, to pass, while only 20.7 m 

(68 ft) are required for a bicycle to pass a pedestrian. For multi-lane paths (three or more lanes), 

the procedure below accounts for multiple user groups that may block multiple lanes. Each path 

configuration is treated separately. Common calibration features are also highlighted as needed. 

Glossary of Variables (1 mi = 1.61 km, 1 mi/h = 1.61 km/h) 

d = Index of a delayed passing maneuver 

s = Index for the subject direction 

o = Index for the opposing direction 

b = Index for blocking two lanes in one direction 

n = Index for blocking single lane in one direction 

X = Distance to complete a passing maneuver (mi)* 

v = Index for vacant path in one direction 

P = Probability 

Q = Flow rate for overtaken vehicles in one direction (vehicles per hour) 

K = Density for overtaken vehicles in one direction (vehicles per mile) 

U = Space mean speed for overtaken vehicles in one direction (mi/h) 

Delayed Passing on Two-Lane Paths 

In this case, a delayed passing in the subject direction occurs when, within the distance required 

to complete a pass (x*), the passing bicycle encounters: (1) traffic in both directions, each 

blocking a single lane; or (2) no traffic in the subject direction in conjunction with traffic in the 

opposing direction that is being overtaken by an opposing bicycle. These two cases are 

illustrated in figure 6. 
45 

 

 

 

 x* x* 

 (a) Traffic in both directions. (b) Opposing traffic only. 

x* = Required passing distance Subject bicycle Opposing bicycle Other user 

Figure 6. Delayed passing cases on a two-lane path. 

The delayed passing probability in the subject direction can be expressed as: 

(34) 

The first term in equation 34 is simply the joint probability of blocking a lane in each of the 

directions. The other terms are the joint probability of no traffic in the subject lane (1 – Pns), 

traffic in the opposing lane (Pno), and a nondelayed passing maneuver in the opposing direction 

(1 – Pdo). 

 

Similarly, for the opposing direction, it can be shown that: 

(35) 

Solving equations 34 and 35 for Pds yields the following expression: 

(36)

Substituting back into equation 35 yields the corresponding value for Pdo. It is evident from this 

model that the likelihood of delay for the two-lane case is highly sensitive to the level of 

opposing traffic. 

The above model requires the calibration of two parameters, Pns and Pno. We now assume that the 

overtaken traffic in the subject direction has a density Ks in vehicles per mile. Thus, for a passing 

distance x as indicated in figure 9, and assuming Poisson counts, the probability of observing no 

vehicles over that length (or a vacant segment of length X) is computed as: 

(37) 

where Ks is estimated as the ratio of flow rate Qs to space mean speed Us for the overtaken traffic 

in the subject direction. Note that if Ks is computed in units of vehicles per mile, then x in the 

above equation should be entered in miles as well. Similarly, for the opposing direction: 

Pds = Pno Pns + Pno(1 – Pns)(1 – Pdo) 

Pdo = Pno Pns + Pns(1 – Pno)(1 – Pds) 

Pds = [Pno Pns + Pno(1 – Pns)

2

] / [1 – PnoPns(1 – Pno)(1 – Pns)] 

Pvs= e(-X)K

s 
46 

(38) 

From equations 37 and 38, the probability of having one or more vehicles in segment x is simply 

computed as: 

(39) 

 (40)

It is important to reiterate that the distance x depends on the mode being passed (i.e., other 

bicycles, pedestrians, inline skaters, etc.) and is not a constant. Values for x are calibrated from 

field data and are further explained in chapter 5. 

Delayed Passing on Three-Lane Paths 

Three-lane paths represent a more difficult challenge for modeling than two-lane paths because 

the use of the middle lane could create a variety of operational scenarios. To limit the number of 

possible scenarios, several assumptions were made during the development of the procedure as 

to how users will behave on the path. The principal three assumptions are: 

1. Movements in the subject direction can use the rightmost two lanes, while opposing 

traffic can use the leftmost two lanes (that means the middle lane can be shared over 

time). 

2. Bicycles passing other vehicles in the same direction must perform that maneuver in the 

middle lane only; no bicycles are allowed to use the leftmost lane for passing. 

3. Groups of users (e.g., pedestrians side by side) can block the two lanes allocated to each 

direction, but cannot block the leftmost lane allocated to the other direction. 

As a result of the above assumptions, the middle lane can be used by: 

a. Side-by-side pedestrians, bicyclists, etc., blocking their right and middle lanes. 

b. Side-by-side opposing pedestrians, bicyclists, etc., blocking their right and middle lanes, 

or 

c. An opposing bicycle that is overtaking opposing pedestrians, bicyclists, etc., that are 

blocking their rightmost lane only. 

 

In the three-lane case, a delayed passing in the subject direction occurs when encountering: 

(a) traffic in the subject direction blocking the rightmost lane only, in conjunction with opposing 

traffic occupying the other two lanes; or (b) side-by-side users blocking the two rightmost lanes 

in the subject direction (see assumption 2 above). These cases are illustrated in figure 7. The 

delay passing probability in the subject direction can be expressed as: 

Pds = Pns Po + Pbs (41) 

Pns = 1 – Pvs 

Pvo= e

(-X)Ko

Pno= 1–Pvo 
47 

 

where the probability of opposing traffic occupying the two leftmost lanes is estimated from: 

 

Po = Pbo + Pno (1 – Pdo) (42) 

 

 x* x* x* 

(a) Impeding traffic in (b) Opposing bicycle in (c) Side-by-side traffic 

 both directions. middle lane. in subject direction. 

x* = Passing distance Subject bicycle Opposing bicycle Other side-by-side users 

 Other single-lane user 

Figure 7. Schematic of delayed passing on a three-lane path. 

Equation 33 states that opposing traffic occupying the two leftmost lanes can occur in two ways: 

(1) by having opposing side-by-side users occupying the two lanes (first term of the right side of 

equation 33), or (2) by having an undelayed opposing passing maneuver (which can occur only if 

there is an opposing group of users blocking the leftmost lane). Combining equations 41 and 42 

gives: 

(43) 

Since the reciprocal case occurs in the opposing direction, it can be shown that: 

 

(44) 

Equations 43 and 44 are simultaneous equations with two unknowns, Pds and Pdo. A closed-form 

solution can be obtained as follows. First, define: 

(45) 

By manipulating equations 44 and 45, it can be shown that: 

 

(46) 

Substituting in equation 43 gives: 

 

(47) 

Pds = Pns [Pbo + Pno (1 – Pdo)] + Pbs

Pdo = Pno [Pbs + Pns (1 – Pds)] + Pbo

D = Pds – Pdo

Pds= [Pns(Pbo+ Pno(1+D)) + Pbs] / (1 + PnsPno) 

D = [( Pbs – Pbo) + (PnsPbo – PnoPbs)] / (1 – Pns Pno) 
48 

 

Substituting the value of Pds in equation 38 and D in equation 46 back into equation 45 gives: 

(48) 

 

Thus, the above model will estimate the probability of delayed passing in both directions 

simultaneously. From the above derivation, it is apparent that the model requires the calibration 

of four probability parameters, two for the subject and two for the opposing directions, namely 

Pn and Pb in each direction. This is discussed next. 

We start by estimating Pb. From field observations of three-lane paths, the fraction of all events 

in which both lanes are blocked by traffic from the subject direction (case c in figure 10) can be 

estimated (note that an event is considered to occur only when there is traffic in the subject 

direction). Let that fraction of events be Fbs. This value is essentially the marginal distribution of 

Pbs when traffic is present in x. Therefore, we can estimate Pbs in the subject direction as: 

(49) 

Next, the probability of single-lane blockage Pns is estimated as: 

 

 (50) 

Note that Pvs, the probability of a vacant segment x, is estimated from equation 37 in exactly the 

same manner as for the two-lane paths. The same procedure is applied for the opposing traffic 

calibration parameters, yielding the equations below: 

 (51) 

(52) 

Delayed Passing on Four-Lane Paths 

In the case of four-lane paths, the assumption is that the path is divided in a manner similar to a 

divided four-lane highway. Therefore, the probability of delayed passing is taken to be 

independent of the amount of opposing traffic since no passing will occur in the two leftmost 

lanes. This is a conservative assumption, but one that it is necessary to make given the lack of 

national data on four-lane paths at the volume levels needed to test that assumption. Referring to 

equation 32, the value of Po is set to zero, and the delayed passing probability is simply equal to 

Pbs (equation 49 for the subject direction, and Pbo (equation 51) for the opposing direction. Figure 

8 depicts the delayed passing concept. 

Pdo = Pds – D 

Pns = 1 – Pvs – Pbs = (1 – Pvs)(1 – Fbs) 

Pbo = (1 – Pvo) Fbo

Pno = 1 – Pvo – Pbo = (1 – Pvo)(1 – Fbo) 

Pbs = (1 – Pvs)Fbs 
49 

 x* 

 x* = Passing distance Subject bicycle Other side-by-side users 

Figure 8. Schematic of delayed passing on a four-lane path. 

Multimodal Delayed Passing Probability 

The probability estimates given in the preceding sections dealt exclusively with one pair of 

modal users, namely a bicycle passing another mode (other bicycles, pedestrians, etc.). 

Assuming that the speeds and flows of various modes on the path are independent, the 

probability of delayed passing under the combination of modes that interfere with the bicycle can 

be calculated as the probability of no delay from all modes combined. Assuming independence 

between scenarios (possibly a strong assumption), the aggregate delayed passing probability 

when encountering m different modes on the path can be shown as: 

(53) 

(54) 

Numerical Application of the Delayed Passing Models 

To illustrate the concepts defined in this chapter, a numerical example of a path serving two 

modes—bicycles and pedestrians—is presented. The data include: 

Bicycle Data 

• Two-way flow rate (Q) = 500 bicycles per hour. 

• Average speed (U) = 20.6 km/h (12.8 mi/h). 

• Speed standard deviation = 4.8 km/h (3 mi/h). 

• Fraction of bicycle events with bicycles traveling side by side = 0.1. 

• Passing distance for bicycle passing bicycle = 32 m (106 ft). 

• 50/50 directional split with symmetrical parameters for the opposing flow. 

Pedestrian Data 

• Two-way flow rate (Q) = 200 pedestrians per hour. 

• Average speed (U) = 5.5 km/h (3.4 mi/h). 

• Speed standard deviation = 1.6 km/h (1 mi/h). 

Pds = 1 – ∏m (1 – Pds/m) 

Pdo = 1 – ∏m (1 – Pdo/m) 
50 

• Fraction of pedestrians events with pedestrians traveling side by side = 0.35. 

• Passing distance for bicycle passing pedestrian = 18 m (60 ft). 

• 50/50 directional split with symmetrical parameters for the opposing flow. 

The example illustrates the difference in the probability of delayed passing for a two-, three-, or 

four-lane paths. Since flows and other parameters on the path are fully symmetrical, only the 

results for the subject direction are reported. Table 8 below summarizes all of the computations 

by mode and references the source equation for the calculation when applicable. 

Table 8. Numerical illustration of path-width effect on delayed passing. 

Two-Lane 

Path 

Three-Lane

Path 

Four-Lane 

Variables of Interest Path 

Bikes Peds. Bikes Peds. Bikes Peds. 

Bicycle travel time (min) 4.7 4.7 4.7 4.7 4.7 4.7 

Bicycle active passing events 1.83 21.6 1.83 21.6 1.83 21.6 

Bicycle active passing rate per 

hour 23 276 23 276 23 276 

Total desired passing rate per 

hour 299 299 299 

Passing distance (Xm) in ft 

(assumed) 106 60 106 60 106 60 

Density (Ksm) vehicles per mile 

= Q/U 19.5 29.4 19.5 29.4 19.5 29.4 

Pvs/m (equation 37) 0.676 0.715 0.676 0.715 0.676 0.715

Fbs/m (assumed) 0.1 0.35 0.1 0.35 0.10 0.35 

Pbs/m (equation 49) 0 0 0.033 0.10 0.033 0.10 

Pns/m (equations 39 and 50) 0.324 0.285 0.291 0.185 0.291 0.185

Pds/m, % (equations 36, 47, and 

49) 26.6 23.6 11.7 14.7 3.3 10.0 

Pds, % (equation 53) 43.9 24.7 13.0 

Number of delayed passings 

per hour 131 73.8 38.8 

Mean time between delayed 

pass, s 27.5 48.8 93.0 

1 ft = 0.3 m 

1 vehicle per mile = 0.621 vehicles per kilometer 

The first four rows in the table pertain to the calculation of desired passing events as described 

earlier in the chapter. The analysis yields a total of about 24 desired passing maneuvers (of 

slower bicycles and pedestrians) by a bicyclist traveling at the average speed while on the path. 

The effect that path widening would have on the quality of service is shown in table 8 as 

exemplified by the delayed passing probability and the average time between delayed passing 

events. By going from two to three lanes, the delayed passing probability is reduced by about 
51 

44 percent, and the time between delayed passing events increases from 27 to 49 s. Further 

widening to develop a four-lane path will result in an additional reduction of 48 percent in 

delayed passing and a comfortable time between delayed passing events of more than 1.5 min. 

The analysis shown can be extended for any combination of modes that may occur in the subject 

and opposing direction. Based on the field data gathered in this study, we applied the delayed 

passing method to 25 various combinations of modes in the subject and opposing directions. The 

combinations included a test bicycle passing each of the five modes that were at all common on 

the paths we studied (bicycles, child bicyclists, joggers, inline skaters, and pedestrians), while the 

opposing traffic consisted of each of those same five modes. Additional details on the application 

for multimodal users can be found in chapter 8 and the User’s Guide. 

53

4. OPERATIONAL DATA COLLECTION 

INTRODUCTION 

One of the major objectives of this research was that the LOS methodology be calibrated and 

validated using data collected from the United States. The challenges facing the project team 

included a choice of methodology to allow the efficient collection of high-quality data on the 

operations of shared-use paths and the selection of study paths to provide a nationwide sample of 

representative paths. This chapter describes how the researchers met those challenges. Chapter 5 

provides the results from the operational data collection effort. 

DATA COLLECTION METHOD 

The model for estimating the number of meetings and passings experienced by a test bicyclist 

developed in depth in chapter 3 uses the volume, average speed, and standard deviation around 

the average speed of each mode on the path as inputs. The data collection to calibrate and 

validate this model must therefore involve all of these variables. Of course, trail characteristics 

must also be recorded at each site. To ensure later flexibility, it was also desirable that scenes on 

paths of interest be recorded from different perspectives so that additional data could be obtained 

later by viewing videotapes if needed. 

The project team identified three possible methods of operational data collection, which included 

a one-camera method, a two-camera method, and a moving-bicycle method. The one-camera 

method involved a camera on a high perch that was able to record activity, including meetings 

and passings, on a long segment of path. The two-camera method involved two cameras set up 

several thousand feet apart along a path. From each camera, a sequence of users could be 

determined and, from those sequences, meetings and passings could be discerned. In the end, 

however, we concluded that the one-camera and two-camera methods would not provide 

adequate data, so we chose the moving-bicycle method (described below). Vantage points for the 

one-camera method would have been rare; tall buildings and hills with unobstructed views of 

qualifying shared-use paths are not common in the United States. The two-camera method would 

not have been able to identify the difference between actual passings and desired passings, 

because only path users would have known whether they wanted to pass and were unable to do 

so and why. For example, a bicyclist may not have been able to pass because of inadequate path 

width or congestion. 

We chose the moving-bicycle method as our primary operational data collection method because 

it overcomes the flaws in the other methods. It is not restricted to places where special camera 

vantage points are available, and it can determine desired passings. The method works by 

collecting data from the perspective of the bicyclist, and is analogous to the moving-vehicle 

method of collecting volumes and travel times on the highway (such as described in chapter 15 

of the HCM(4)). In the moving-bicycle method, a member of the project team rides a bicycle 

along a path segment of interest at a predetermined constant speed. The team member attempts to 

maintain that speed as closely as possible, passing when encountering slower same-direction 

users when there is sufficient room to do so safely while maintaining that constant speed. The 

team member wore a video camera on his or her helmet that recorded the number of meetings, 
54

the number of passings accomplished, and the number of passings delayed or not accomplished 

(i.e., reached the end of the segment before the opportunity to pass presented itself). At the same 

time, as the bicyclist was making his or her ride, a colleague was counting the number of users of 

each mode in each direction moving past the midway point of the segment. This provided the 

needed volume data. 

A potential bias with the moving-bicycle method is that data collector judgments determine the 

difference between a desired pass, a following maneuver with no desire to pass, and a completed 

pass. To prevent this bias from affecting the results, we equipped the bicycle with a minicomputer (a SpecializedTM Speed Zone P.Brain) that displayed the bicycle speed to the nearest 

0.16 km/h (0.1 mi/h). In addition, the team recorded the time needed to ride the segment from 

start to finish to verify that the desired speed was maintained. 

Moving-bicycle desired speeds were determined for each path based on the results of a prior 

study of the bicycle speed distribution on the path segment of interest. Typically, the data 

collection team conducted the prior study on the day before the moving-bicycle study was to 

commence. Therefore, the data collection process typically took 2 days per trail. On the first day, 

the team collected an adequate sample of speeds of the free-flowing bicycles (30 minimum) and 

of other path users using a stopwatch and a clearly marked distance. Then, the team calculated a 

mean and standard deviation for the bicycle speed sample. The moving-bicycle runs on the 

second day were typically made at three speed levels—high, medium, and low—that 

corresponded to the mean speed plus one standard deviation, the mean speed, and the mean 

speed minus one standard deviation from this prior sample. 

On the second day, the team collected the moving-bicycle data. We used a stationary camera on 

the side of the path at the midpoint, next to the volume data collector, to provide backup for the 

volume count, to provide additional speed observations, and to provide other data that may have 

proved necessary later. To avoid fatigue, the two data collectors traded bicycle duty and 

stationary volume counting duty occasionally. 

During the second day of data collection, the team set a goal to collect at least 20 runs at each of 

the three different speeds, or a total of 60 runs conducted at each path. Since we collected data 

for both directions along the trail, 60 runs actually provided 10 runs in each direction along the 

path at the three different speeds. Higher sample sizes would have been desirable, but were not 

usually possible, because user volumes on the paths of interest did not usually stay high for many 

hours of the day. We could only collect during daylight hours, and the bicycle riders became 

fatigued. 

Equipment 

The main equipment for this data collection included a stationary camera/recorder, a bicycle, a 

mini video camera/recorder for the bicyclist’s helmet, and a bicycle speedometer. The bicyclist’s 

recorder was carried in a handlebar pouch, where the display was visible to the bicyclist, so that 

he or she could be sure that the system was recording. A microphone taped to the bicyclist’s shirt 

was incorporated into the mini-camera system to allow the bicyclist to record comments during a 

run The most helpful of these comments was whether a particular event was a delayed passing or 

not. 
55

We used a hybrid bicycle, which is a combination of a mountain bicycle and a road bicycle, 

during our data collection. Hybrid bicycles have a smoother and wider tire than mountain 

bicycles in order to obtain the higher speeds and increased stability that we needed. The bicycle 

we used was also easy to disassemble and reassemble for travel by plane, because we attempted 

to use the same bicycle for all of the different data collection sites to ensure more consistency 

during the data collection process. In the end, mechanical problems with the bicycle meant that 

we used a rented bicycle during one of our data collection trips (to Saint Louis, MO). 

The mobile camera and recorder system we chose are generally used for surveillance operations. 

The mini-camera was approximately 50 millimeters (mm) (2 inches) long and 25 mm (1 inch) in 

diameter. The camera had 360 lines of resolution and a 3.6-mm-wide lens. The recorder was 

supposedly the world’s smallest VCR at the time we purchased it, with an LCD monitor that was 

about 190.5 mm (7.5 inches) by 114.3 mm (4.5 inches) by 88.9 mm (3.5 inches), weighing about 

0.68 kilograms (kg) (1.5 pounds (lb)). The rechargeable camera battery lasted about 2 h. The 

research team soon developed a routine of changing all batteries and cassette tapes on all 

cameras and recorders every 2 h or 10 runs to ensure that we kept recording when desired. Total 

equipment costs for the cameras, bicycle, and accessories were about $3,000. 

SITE SELECTION 

The project budget allowed for operational data collection for up to 20 trails in 10 cities across 

the United States. This was likely to provide a large enough sample to calibrate and validate the 

procedure in a credible manner. The project team sought operational data collection sites that 

met a strict set of criteria to ensure project success. These criteria included: 

1. Sites in most regions of the United States. 

2. Two or more sites in a city in order to reduce travel costs and meet the goal of 20 path 

segments in 10 cities. 

3. Sites that were well known to trail planners and designers in order to build credibility in 

the results. 

4. Sites that had moderate to high traffic levels for at least some portions of some days. 

5. Sites that had long segments with no intersections or turnouts (ensuring uninterrupted 

flow). 

6. Sites where the project team could unload equipment easily from a vehicle. 

7. Sites that had a wide variety of geometric characteristics. 

8. Sites with managers who were willing to cooperate. 

Based on the knowledge of the researchers and input from FHWA staff at the February 2001 

briefing, the team assembled a preliminary list of possible data collection sites that may have met 

some or all of these criteria. The sites included: 

1. Raleigh, NC: Shelly Lake Trail, Lake Johnson Trail, and Apex Lake Trail 

2. Hilton Head Island, SC 

3. Jekyll Island, GA 

4. Pinellas County, FL: Pinellas Trail 

5. Gainesville, FL: Gainesville-Hawthorne Trail 
56

6. Tallahassee, FL: St. Mark’s Trail 

7. Winter Garden and Apopka, FL: West Orange Trail 

8. Orlando, FL: Cady Way Trail 

9. Boston and Cambridge, MA: Charles River Trail 

10. Arlington, Bedford, and Lexington, MA: Minuteman Bikeway 

11. White Plains, NY: North County Trailway and South County Trailway 

12. Manhattan, NY: Hudson River Trail from Battery Park to 125th Street 

13. Brooklyn, NY: Shore Parkway and Ocean Parkway 

14. Philadelphia, PA: Schuylkill River Trail 

15. Washington, DC: Capital Crescent Trail and Washington and Old Dominion Trail 

16. Virginia Beach, VA 

17. Chicago, IL: Lakefront Path 

18. Mackinac Island, MI: M-185 

19. Madison, WI 

20. St. Louis, MO: Forest Park Bike Path and Grant’s Trail 

21. Columbia, MO 

22. Phoenix, AZ 

23. Tucson, AZ 

24. Denver, CO: Platte River Greenway and Cherry Creek Path 

25. Houston, TX: Harrisburg Rail Trail, West White Oak Bayou Trail, West Brays Bayou 

Trail System, and Buffalo Bayou Trail 

26. Dallas, TX 

27. Davis, CA 

28. Huntington Beach, CA: Bolsa Chica-Huntington Beach 

29. Los Angeles, CA: South Bay Trail 

30. Santa Ana, CA: Santa Ana River Trail 

31. Santa Monica, CA: Ocean Front Walk 

32. San Diego, CA: Ocean Front Walk 

33. San Francisco and Oakland, CA: San Francisco Bay Trail 

34. Portland, OR 

35. Seattle, WA: Burke-Gilman Trail 

36. Boulder, CO 

37. Portland, ME 

The project team developed a questionnaire for the owners or managers of the paths listed above 

to determine the suitability of a particular path for data collection. The questionnaire asked: 

1. Is the trail paved? 

2. What is the length of the shortest uninterrupted segment? 

3. Which user modes use this trail? 

4. What is the predominant user mode? 

5. Are the trail users mainly recreational users or commuters? 

6. Is the trail divided by a median, berm, or pavement striping? 

7. What is the width of the trail? 

8. Would you consider the trail volumes to be high, medium, or low? 

9. What is the peak month of the year? 
57

10. What is the peak day of the week for the trail? 

11. What is the peak time of the day for the trail? 

12. How would you describe the trail grades (level, rolling, or mountainous)? 

13. Would we be able to park a vehicle near the trail? 

14. Do we need permission to conduct our study along your trail? 

The project team sent the questionnaire to the owners or managers of the 37 trails via regular 

mail and e-mail and received 26 responses. From the responses, the researchers identified a list 

of 10 cities and an alternate city that provided the best possible opportunities to satisfy the 

criteria. The list included cities in all regions of the United States and cities with many of the 

best-known trails in the United States. The final list of sites approved by FHWA was: 

1. Seattle, WA 

2. San Francisco, CA 

3. Boston, MA 

4. Chicago, IL 

5. St. Petersburg, FL 

6. St. Louis, MO 

7. Raleigh, NC 

8. Dallas, TX 

9. Washington, DC 

10. Denver, CO 

11. Los Angeles, CA (alternate) 

In the end, we used our alternate city, Los Angeles, and did not collect data in Denver because of 

travel and weather difficulties. 

In the course of this study, the team collected data from 15 trails and 10 cities scattered across 

the United States. Some cities only had one usable trail. The data collection sites were: 

1. Lake Johnson Trail in Raleigh, NC 

2. Pinellas Trail and Honeymoon Island Trail near St. Petersburg, FL 

3. White Creek Trail and White Rock Lake Trail in Dallas, TX 

4. Mill Valley-Sausalito Pathway near San Francisco, CA 

5. South Bay Trail in Santa Monica, CA 

6. Sammamish River Trail near Seattle, WA 

7. Forest Park Trail and Grant’s Farm Trail in St. Louis, MO 

8. Lakefront Trail in Chicago, IL 

9. Dr. Paul Dudley Bike Path and Minuteman Bikeway in and near Boston, MA 

10. Capital Crescent Trail and Washington and Old Dominion Trail near the District of Columbia 

The most restrictive criteria in terms of locating usable trail segments were the segment length 

and the need for moderate to high volumes of traffic. Trails with moderate to high volumes of 

traffic tend to be in areas with many intersections and trail connections; however, we wanted 

segments at least 0.8 km (0.5 mi) long between intersections to gather unbiased data using the 

moving-bicycle method. In the end, we compromised on segment length in a couple of places (a 
58

0.40-km (0.25-mi) segment for the South Bay Trail and a 0.64-km (0.4-mi) segment for the 

Forest Park Trail). We settled for segments in other places that did not have very high volumes, 

as shown in chapter 5. 

Tables 9 and 10 provides some details on the chosen study trails. The study trails were located in 

urban and suburban areas. The study trail environments included parks, lakes, beaches, 

highways, and downtown areas. There was a nice range of trail widths from 2.44 m to 6.1 m 

(8 to 20 ft). The study trails were sometimes marked with centerlines, and sometimes there were 

other adjacent treadways that accommodated some users. Few trails had significant horizontal or 

vertical curvature. Most trails had good sight distances, as judged qualitatively by the research 

team after riding them numerous times on a bicycle. 

Table 9. Characteristics of operational study sites. 

Location Path name 

Community 

context Trail type Study location 

Area 

landscape 

Width 

(ft) Centerline 

Raleigh, NC 

Lake 

Johnson 

Trail Suburban Park loop 

0.25 mile point to 

the 0.75 mile point 

Wooded 

park, lake 8–8.5 None 

Redmond, 

WA 

Sammamish 

River Trail Suburban 

Linear 

riverside 

greenway 

In Sixty Acre Park, 

about 0.5 mi from 

NE 116th St. 

Grass, 

ballfields 10 None 

Marin 

County, CA 

Mill 

ValleySausalito 

Pathway Suburban Rail-trail 

At Bothin Marsh, 

north of the U.S. 

101 bridge 

Marsh, 

highway, 

bay 

9.5–

10.5 None 

Dallas, TX 

White Rock 

Lake Trail Urban Park loop 

Just south of the E. 

Lawther/Emerald 

Is. park access, 

near Winfrey Point 

Grass, 

lake, park 

road 14 Solid 

Chicago, IL 

Lakefront 

Trail Urban 

Lakefront 

beach trail 

Near trail 

intersection with 

North Avenue 

Grass and 

beach 20 Solid 

Santa 

Monica, CA 

South Bay 

Trail Suburban 

Oceanfront 

beach trail 

About a mile north 

of the Santa 

Monica Pier Beach 14 Dashed 

St. Louis, 

MO 

Forest Park 

Trail Urban Park loop 

On the north edge 

of the park, along 

Lindell Blvd., 

between mile 5.25 

and 5.75 

Grass and 

street 10 Solid 

Dunedin, 

FL 

Honeymoon 

Is. Trail 

(Dunedin 

Causeway) Suburban 

Hwy. 

sidepath/ 

greenway 

West of the 

drawbridge 

Beach, 

roadway 12 None 

Arlington, 

MA 

Minuteman 

Bikeway Suburban Rail-trail 

Mile marker 7.5 

near the bike shop Wooded 12 Dashed 

Boston, MA 

Dr. Paul 

Dudley 

Bike Path Urban 

Linear 

riverside 

greenway 

South of the River, 

just east of the 

Harvard Br. 

River, 

highway 8 Dashed 
59

Table 9. Characteristics of operational study sites (continued). 

Location Path name 

Community 

context Trail type Study location 

Area 

landscape 

Width 

(ft) Centerline 

Vienna, VA 

W&OD 

Trail Suburban Rail-trail 

Near downtown 

Vienna 

Grass and 

trees 10 Solid 

Washington, 

DC 

Capital 

Crescent 

Trail Urban Rail-trail 

Between K St. and 

Fletcher's 

Boathouse Wooded 10 Dashed 

St. Louis 

Co., MO 

Grant's 

Trail Suburban Rail-trail 

Near I-55 and 

Union Road 

Grass and 

trees 12 Solid 

Dunedin, 

FL 

Pinellas 

Trail Suburban Rail-trail 

North of Curlew 

Road 

Golf 

course and 

hwy. 15 Solid 

Dallas, TX 

White 

Creek Trail Suburban 

Linear 

streamside 

greenway 

North of the Fair 

Oaks Tennis 

Center, on both 

sides of the 

overpass 

Grass, 

stream 8 None 

1 ft = 0.305 m 

1 mi = 1.61 km 

Table 10. Additional characteristics of operational study sites. 

Location Path name Surface Shoulder 

Other 

treadways 

Clear 

zone (ft) 

Sight 

distance 

Horizontal 

curvature 

Vertical 

curvature 

Raleigh, NC 

Lake 

Johnson 

Trail 

Asphalt 

(in poor 

condition) No None 1 to 4 Poor Medium Low 

Redmond, 

WA 

Sammamish 

River Trail Asphalt No Horse trail 6 to 10 Good Low Low 

Marin 

County, CA 

Mill 

ValleySausalito 

Pathway Asphalt 

Yes, 5-7 

ft gravel None 5 to 7 Unlimited Low No 

Dallas, TX 

White Rock 

Lake Trail 

12 ft of 

asphalt 

with 1 ft 

concrete 

edges No 

Some 

bicyclists 

use park 

road 10 to 20 Unlimited Low No 

Chicago, IL 

Lakefront 

Trail Concrete No None Unlimited Good None No 

Santa 

Monica, CA 

South Bay 

Trail Concrete No None Unlimited Unlimited Low No 

St. Louis, 

MO 

Forest Park 

Trail Asphalt 

Yes, 4 ft 

dirt on 

one side 

Joggers 

use 4 ft 

dirt 

shoulder 4 Good None No 

Dunedin, 

FL 

Honeymoon 

Is. Trail 

(Dunedin 

Causeway) Asphalt No None 

0 on one 

side, 4 on 

the other Unlimited Low No 
60

Table 10. Additional characteristics of operational study sites (continued). 

Location Path name Surface Shoulder 

Other 

treadways 

Clear 

zone (ft) 

Sight 

distance 

Horizontal 

curvature 

Vertical 

curvature 

Arlington, 

MA 

Minuteman 

Bikeway Asphalt No None 2 to 4 Fair None No 

Boston, MA 

Dr. Paul 

Dudley 

Bike Path Asphalt No 

Separate 

ped. paths None Poor Medium No 

Vienna, VA 

W&OD 

Trail Asphalt No None Unlimited Excellent Low No 

Washington, 

DC 

Capital 

Crescent 

Trail Asphalt No 

Peds. Use 

adjacent 

towpath 2 to 4 Unlimited Low No 

St. Louis 

Co., MO 

Grant's 

Trail Asphalt No None 2 to 4 Good Low No 

Dunedin, 

FL 

Pinellas 

Trail Asphalt No 

Some ped. 

paths 6 to 10 Unlimited None No 

Dallas, TX 

White 

Creek Trail Asphalt No None 30 to 75 Unlimited None No 

1 ft = 0.305 m 

1 mi = 1.61 km 

DATA COLLECTION EXECUTION 

Data collection occurred from July 2001 to March 2002. Peak hours and peak times were 

identified for each trail location. The peak days were generally Saturdays and Sundays. Peak 

hours varied by location. There often appeared to be two peaks during the weekend days on most 

trails; one volume peak in the morning and a second volume peak in the afternoon. Trail users 

who appeared to be using the trail for fitness purposes appeared most often during the morning 

hours. Recreational and casual users, consisting of tourists and families, appeared more often 

during the afternoons. At data collection sites where there were commuters, the commuter peak 

hours were generally the weekday mornings. The data collection process generally lasted from 

early morning until dusk; consequently, a great variation in volume was typically collected at 

each site. 
61

The data collection team attempted to collect 60 trials at each trail. Because of inclement weather 

and mechanical failures, it was not possible to obtain 60 trials at each trail location. In total, 771 

runs were successfully completed. Table 11 shows the sample size by trail. Because it was such a 

high-quality site and there were no other candidate sites in the city, the team collected extra data 

at the Lakefront Trail in Chicago. The most disappointing data collection trip was to 

Washington, DC, during October 2001, when bad weather prevented all but a handful of runs at 

what should have been excellent data collection sites. 

Table 11. Number of successful data collection runs by trail. 

Trail Successful Runs 

Lake Johnson 58 

Sammamish River 58 

Mill Valley-Sausalito 60 

White Rock Lake 60 

White Creek 60 

Lakefront 90 

South Bay 60 

Grant’s 30 

Forest Park 57 

Honeymoon Island 48 

Pinellas 57 

Minuteman 60 

Dr. Paul Dudley 60 

Capital Crescent 9 

Washington & Old Dominion 4 

Total 771 

63 

5. OPERATIONAL DATA ANALYSIS 

INTRODUCTION 

After the operational data collection process described in chapter 4, the research team analyzed 

the data from both the static and mobile vantage points. The analysis had two main objectives. 

First, the team wanted to develop a series of average and default values for key parameters that it 

could use as it developed the LOS estimation procedure. The speed samples collected were 

obviously an important part of this. Other important values to be determined from the data 

included the PHF and the percentage of each user group that traveled in groups of two or more 

mode users. The second objective of the analysis was to compare the measured number of 

meetings and passings against the number of meetings and passings predicted from the theory 

developed in chapter 3. This comparison would validate the theory applicable to typical U.S. 

shared-path conditions. This chapter describes how the project team met both of these main 

objectives. 

AVERAGE AND DEFAULT VALUES 

Speed 

As was noted in chapter 4, the project team recorded speed samples at each trail for each 

representative user group. The measurement was manual, with a stopwatch, and the team’s goal 

was a sample of at least 30 free-flowing bicyclists and as many other path users as appeared 

during the recording of the bicyclists’ speeds. In the cases of the Lakefront and Sammamish 

River Trails, since the team collected data at two different places along the trail, we collected 

more than 30 bicycle speeds for the sample. The team did not collect usable speed data on the 

Washington, DC, area trails because of poor weather conditions. Since most bicyclists and other 

path users traveled the sample paths as individuals instead of in groups (as will be seen below), 

these free-flowing average speeds are probably quite similar to the overall average speeds. 

Table 14 shows a summary of the speed data we collected. The table shows the sample size, 

average speed, and standard deviation around that average for each mode at each trail. The table 

also shows, for each mode, the average of the 13 trail average speeds and the average and 

standard deviation of the total set of observations. The average speed for the 443 adult bicyclists 

observed was 20.62 km/h (12.81 mi/h), with a standard deviation of 5.49 km/h (3.41 mi/h). 

Average bicycle speeds on the paths ranged from almost 24.15 km/h (15 mi/h) on the Pinellas 

Trail in Florida—a wide, straight, and flat path used by many fitness riders—to more than 

16.1 km/h (10 mi/h) on the Lake Johnson Trail in Raleigh, NC—a narrow, curvy, bumpy path. 

The 20.60 km/h (12.8 mi/h) average is just slightly higher than values reported in the literature—

perhaps because most segments studied had at least 0.80 km (0.5 mi) uninterrupted by 

intersections or turnouts, and because we broke out child bicyclists as a separate mode. The 

average speed for the 275 pedestrians observed was 5.42 km/h (3.37 mi/h); pedestrian speeds 

were more uniform than bicycle speeds across trails (discounting the sites with only a handful of 

observations). Based on samples of more than 200 users for each mode, the average inline skater 

speed was 16.30 km/h (10.13 mi/h), while the average jogger speed was 10.40 km/h (6.46 mi/h). 

The average child bicycle speed was 12.64 km/h (7.85 mi/h), but this was based on a sample of 
64 

only 29 users. Overall, the speed data in table 12 are sufficient for use in an LOS procedure in 

cases where the trail designer or manager does not have a sample for the specific trail. 

Table 12. Speeds by mode and trail (all speeds in mi/h). 

Mode: Bicycle Pedestrian Inline skater Runner Child Bicyclist 

Trail N Mean 

Std.

Dev. N Mean 

Std.

Dev. N Mean 

Std.

Dev. N Mean 

Std.

Dev. N Mean 

Std.

Dev. 

PD 29 12.74 2.60 5 4.06 0.50 24 9.37 1.76 9 7.68 1.23 0 

M 29 14.32 3.40 8 3.59 0.35 21 11.22 2.72 5 6.91 1.24 1 9.98 

P 25 14.96 3.98 2 4.79 0.28 6 13.68 5.48 3 7.18 1.38 0 

HI 30 11.22 3.57 29 3.49 0.52 8 8.61 2.39 15 6.43 0.65 0 

FP 26 13.78 3.18 16 3.64 0.63 15 11.15 3.14 8 7.07 1.09 0 

G 30 12.30 3.28 16 3.55 0.48 15 10.11 1.41 8 6.59 1.17 0 

SB 30 10.66 3.22 30 3.17 0.35 30 9.71 2.06 29 5.83 0.85 2 7.19 0.87 

LF 60 12.60 3.15 46 3.16 0.57 53 10.94 2.68 46 6.99 0.97 0 

WC 30 11.87 2.29 31 3.07 0.49 31 8.20 2.11 31 6.33 1.18 10 7.56 1.80 

WR 31 14.27 3.01 25 3.02 0.43 6 8.86 3.62 33 5.44 0.89 6 9.46 0.84 

MV 30 14.58 3.08 30 3.52 0.54 0 30 6.63 1.23 2 4.88 0.83 

SR 63 13.23 3.35 7 3.09 0.48 9 11.41 2.17 13 5.80 1.44 5 8.16 1.84 

LJ 30 10.40 2.80 30 3.83 0.60 1 7.25 30 6.99 1.09 3 6.83 2.78 

All* 13 12.84 1.51 13 3.54 0.49 12 10.04 1.75 13 6.61 0.63 7 7.72 1.71 

All** 443 12.82 3.41 275 3.37 0.59 219 10.13 2.75 260 6.46 1.20 29 7.85 1.95 

* Over trails 

** Over samples 

Key to trail names: PD = Paul Dudley, M = Minuteman, P = Pinellas, HI = Honeymoon Is., FP = Forest Park, G = Grant’s, SB = 

South Bay, LF = Lakefront, WC = White Creek, WR = White Rock, MV = Mill Valley, SR = Sammamish River, LGJ = Lake 

Johnson. 

1 mi/h = 1.61 km/h 

Volume and Mode Split 

Some trail designers and managers will have detailed counts or forecasts of the volumes of users 

by mode expected on their paths. However, most will not have such detailed data and will need 

to rely on default values for those inputs. Our data set, based on up to 8 h of observation at 

relatively busy (if not peak) times on each of 15 well-known paths across the United States, may 

serve well in providing those default values. 

 

Table 13 shows a summary of our volume and mode-split data by trail. This table includes all 15 

trails, from the data recorded manually at the midpoint of the segment of interest; the volumes 

are from both directions of travel on the path. From table 13, we note that adult bicyclists made 

up the majority of the trail users overall and on most trails, with 56 percent of the user share 

overall. This ranged from a high of 81 percent on the Pinellas Trail to a low of 14 percent on the 

Lake Johnson Trail; these are the same trails that were the extremes for bicycle speeds as well. 

Pedestrians made up 18 percent of the trail users overall, with a range of 63 percent on the Lake 

Johnson Trail to 3 percent on the Sammamish River Trail. Inline skaters were 10 percent of the 

users observed, joggers were 13 percent of the users observed, and child bicyclists were 

3 percent of the users observed. Some trails did not have any skaters, and others did not have any 

child bicyclists. There was a wide range of volumes exhibited across trails, ranging from an 

average of more than 2,300 users per hour on the four-lane Chicago Lakefront Trail during a 
65 

sunny summer weekend to just 44 users per hour on the Washington and Old Dominion Trail in 

cold, windy, and rainy conditions. The average of the average volumes per trail was 426 users 

per hour, although there was a large standard deviation around that average. 

Table 13. Volumes and mode splits by trail. 

Bicycles Pedestrians Skaters Runners Child Bikes 

Trail Per 

hour 

% of 

total 

Per 

hour 

% of 

total 

Per 

hour 

% of 

total 

Per 

hour 

% of 

total 

Per 

hour 

% of 

total 

Total 

per 

hour 

PD 317 72 36 8 64 15 17 4 5 1 438 

M 229 52 28 6 80 18 69 16 36 8 442 

P 98 81 6 5 14 12 3 2 0 0 120 

HI 25 23 60 54 9 8 14 13 2 2 110 

FP 99 33 73 24 42 14 83 28 3 1 299 

G 73 59 20 16 13 10 5 4 13 10 123 

SB 248 40 107 17 154 25 77 12 30 5 616 

LF 1131 49 474 20 286 12 410 18 15 1 2317 

WC 140 65 21 10 31 14 14 7 10 4 217 

WR 180 72 34 14 9 3 20 8 9 3 252 

MV 402 63 50 8 0 0 178 28 11 2 641 

SR 330 79 14 3 25 6 14 3 35 8 418 

LJ 29 14 130 63 0 0 45 22 2 1 205 

CC 89 59 27 18 5 4 30 20 0 0 151 

WO 32 74 2 5 2 5 7 16 0 0 44 

Ave. 228 56 72 18 49 10 66 13 11 3 426 

Std. 

dev. 277 20 117 18 77 7 106 9 13 3 554 

Key to trail names: PD = Paul Dudley, M = Minuteman, P = Pinellas, HI = Honeymoon Is., FP = Forest Park, 

G = Grant’s, SB = South Bay, LF = Lakefront, WC = White Creek, WR = White Rock, MV = Mill Valley, 

SR = Sammamish River, LJ = Lake Johnson, CC = Capital Crescent , WO = W&OD 

1 mi/h = 1.61 km/h 

The research team was concerned about the wide variations in volume and mode split observed 

at the sample trails because users may lack confidence in using these results as default values in 

their analyses when local data are unavailable. In particular, the Lake Johnson Trail had a very 

different mode split than the other trails, the Washington and Old Dominion had much lower 

volumes than the other trails, and the Lakefront Trail had much higher volumes than the other 

trails. However, recomputing the average mode splits and volumes without these trails made 

very little difference in the averages, except for the case of the Lakefront Trail. Dropping the 

Lakefront Trail meant that the average volume declined to 291 users per hour. In the end, it 

appears that mode split and volume are simply parameters that will vary widely from trail to 

trail; consequently, trail designers and managers are going to have to consider that variation in 

their analyses. The User’s Guide provides some suggestions as to how this can be done in some 

typical analyses. 
66 

Peak-Hour Factor 

The PHF is an important consideration used in capacity and LOS calculations to adjust for 

peaking of traffic within the hour of interest. Since a 15-minute (min) timeframe is used for 

determining an LOS in the HCM,(4) the PHF for this procedure will also be based on 15 min. The 

equation for determining the PHF is:(4)

PHF = v / (4 * V) (55) 

where v is the hourly volume and V is the volume of users in the peak 15-min time period within 

the hour. Values of PHF for shared-use paths are not readily available in practice or in the 

literature; therefore, using our data to find an average or default PHF should be helpful. 

The volume data collected manually and summarized in table 13 were not from continuous 

counts; instead, the data collector only counted while the bicyclist with the helmet camera was 

moving. This made computation of a PHF from those data very difficult. To compute an average 

PHF, we turned to the images recorded by the static camera. We selected tapes in which good 

weather and good equipment performance allowed continuous 1-h counts. Table 14 shows data 

that we collected from the static camera for this purpose. 
67 

Table 14. PHF data. 

Trail Time Users per 

Hour 

Peak-Hour 

Factor 

10:30 a.m. 192 0.889 

11:35 a.m. 203 0.875 

White Rock 

12:35 p.m. 173 0.901 

12:50 p.m. 311 0.770 

 1:50 p.m. 361 0.771 

 3:38 p.m. 320 0.920 

Sammamish River 

 5:40 p.m. 187 0.698 

 9:50 a.m. 352 0.815 

12:30 p.m. 402 0.831 

Minuteman 

 2:30 p.m. 401 0.928 

 2:30 p.m. 319 0.798 

 4:30 p.m. 333 0.876 

Paul Dudley 

 7:00 p.m. 190 0.819 

10:30 a.m. 163 0.728 

12:30 p.m. 166 0.988 

Lake Johnson 

 3:00 p.m. 229 0.806 

11:00 a.m. 1766 0.901 

 1:00 p.m. 2056 0.948 

Lakefront 

 6:00 p.m. 1132 0.901 

10:00 a.m. 112 0.885 

12:00 p.m. 310 0.765 

Mill Valley-Sausalito 

 2:00 p.m. 197 0.723 

South Bay 11:00 a.m. 296 0.903 

 1:00 p.m. 321 0.912 

Average 437 0.848 

Standard Deviation 496 0.078 

Table 14 shows that the average hourly volume for the time periods sampled was almost equal to 

the average hourly volume from the full data set in table 13. Based on those time periods, an 

average PHF was about 0.85. The standard deviation of about 0.08 around that average PHF 

shows that there was some variation from trail to trail and hour to hour. One trend worth noting 

was that, as expected, PHF tended to rise as the hourly volume rose. Figure 9 shows the trend, 

which was heavily influenced by the high-volume Chicago observations. 
68 

Figure 9. PHF as a function of hourly volume. 

Users Occupying Two Lanes 

The researchers needed the proportion of users moving along the path while occupying two lanes 

as an input to the delayed passing procedure developed in chapter 3. Usually, a group of two 

users moving together would occupy two lanes. However, there were many cases where two 

users moving together occupied less than two lanes because they moved in single file, one 

walked or rode off the path, or they walked or rode very close to each other. There were also 

cases when a single path user occupied two lanes. This was typically an inline skater swinging 

his or her arms and legs widely, although there were also cases when a weaving, wobbling 

bicyclist used two lanes. Thus, to collect data on this proportion, we had to carefully examine the 

movements of each user or group of users. The static camera did not provide a perspective that 

showed how much of the path a user or group of users was occupying; therefore, we had to use 

the video recorded by the mobile (helmet) camera. 

We reviewed 21 h of runs on seven paths (all the times and trails listed in table 14, except the 

Chicago Lakefront, where these data were too difficult to collect). In that sample, we observed 

the following proportions: 

0.6

0.7

0.8

0.9

1.0

0 500 1000 1500 2000 2500

User volume per hour

Peak-Hour Factor
69 

• Bicyclist groups occupied two lanes in 140 of the 2,691 cases observed (5 percent). 

• Pedestrian groups occupied two lanes in 252 of the 698 cases observed (36 percent). 

• Jogger groups occupied two lanes in 66 of the 536 cases observed (12 percent). 

• Skater groups occupied two lanes in 39 of the 493 cases observed (8 percent). 

• Child bicycle groups occupied two lanes in 1 of the 202 cases observed (0.5 percent). 

We note that a “group” in this context consisted of one or more path users who seemed to be 

moving along the path together. 

Distance Needed to Pass 

Another factor needed as an input to the delayed passing procedure developed in chapter 3 is the 

distance needed to pass. To collect these data, we needed high-quality images with many passing 

maneuvers on paths that were of moderate width. In the end, we reviewed 50 runs in which highquality video with passing maneuvers was available on five paths (Grant’s, Forest Park, 

Minuteman, Sammamish River, and Honeymoon Island). These paths are 3 to 3.6 m (10 to 12 ft) 

wide. Table 15 shows the data for the case of the experiment bicycle passing a pedestrian. The 

overall average distance is 29.89 m (98 ft), and the average did not change much when the 

experiment bicycle was traveling from 17.71 to 22.54 km/h (11 to 14 mi/h) (near the overall 

average bicycle speed). We did not have nearly as large sample sizes for the bicycle passing 

other modes; however, the samples we recorded were: 

• 49.7-m (163-ft) average distance to pass a bicycle, based on four observations. 

• 48.1-m (158-ft) average distance to pass an inline skater, based on four observations. 

• 31.7-m (104-ft) average distance to pass a jogger, based on seven observations. 

We did not examine a case where the experiment bicycle passed a child bicyclist during the 50 

runs examined. 

The field data for the distance needed to pass were difficult to collect and may not be 

representative of actual passings for several reasons. First, our data collectors were trying to 

maintain a constant speed, even while passing, while normal bicyclists would probably speed up 

during the pass. Second, our data collectors were probably much more conservative in making a 

passing than were the regular bicyclists, who would probably use longer gaps before and after 

making the pass. Third, since our runs were only 0.80 km (0.5 mi) long (or shorter in a few 

cases), we were only passing the slowest bicyclists. 
70 

Table 15. Distance needed for a bicycle to pass a pedestrian. 

Passing a ped. group occupying one lane Passing a ped. group occupying two lanes 

Time, sec Speed, mi/h Distance, ft Time, sec Speed, mi/h Distance, ft 

5.81 7.8 66 9.16 7.8 105 

7.22 8.3 88 9.35 7.8 107 

5.53 8.3 67 9.75 8.0 114 

4.28 8.4 53 10.41 8.5 130 

4.66 8.4 57 5.53 8.6 70 

8.06 8.5 100 5.00 10.0 73 

6.24 9.3 85 7.69 10.1 114 

9.15 9.4 126 6.65 10.3 100 

3.28 9.4 45 5.31 11.8 92 

4.07 9.4 56 8.00 11.8 138 

5.31 10.1 79 6.94 12.2 124 

5.04 10.3 76 5.91 13.3 115 

7.62 10.7 120 4.94 13.4 97 

7.94 10.9 127 5.10 14.6 109 

6.75 11.3 112 5.97 14.8 130 

5.59 11.3 93 6.93 14.8 150 

5.59 12.1 99 4.35 14.8 94 

4.75 12.2 85 5.19 15.3 116 

4.56 12.2 82 4.97 15.3 112 

5.50 12.3 99 – – – 

5.56 12.6 103 – – – 

4.50 13.4 88 – – – 

3.62 14.5 77 – – – 

4.09 14.8 89 – – – 

4.78 16.4 115 – – – 

4.72 17.3 120 – – – 

Overall average 89 Overall average 110 

Ave. for speeds 11–14 mi/h 95 Ave. for speeds 11–14 mi/h 113 

1 ft = 0.305 m 

1 mi/h = 1.61 km/h 

To supplement the small samples of somewhat questionable field data on the distance needed to 

pass, the project team developed a model. The basis of the model is the simple idea that the 

distance needed to pass is equal to the gap before the pass, plus the gap after the pass, plus the 

distance traveled during the passing by the user being passed. The terms of this model are in 

equation 56: 

(56) 

where DP is the distance needed to complete a passing (in ft), BG is the gap between the passer 

and the user to be passed at the beginning of the passing (in ft), EG is the gap between the passer 

and the user that was passed at the end of the passing (in ft), PS is the speed of the passer 

DP = (BG + EG) * PS/(PS – SS) 
71 

(in ft/s), and SS is the speed of the user being passed (in ft/s). Since the mean speeds of the 

modes, other than bicycles given in table 14 above, are so much less than the mean bicycle 

speed, it is a safe assumption to use the mean speeds of those other modes as the appropriate 

values for SS in equation 47. However, the mean speeds of bicycles being passed would be 

considerably less than the overall bicycle mean speed if the passing bicycle is assumed to travel 

at the mean speed. To estimate SS for bicycles, then, the researchers developed a simulation of 

operations on a path that randomly assigned speeds to bicyclists along the path, based on the 

assumption that bicycle speeds are normally distributed (see later in the chapter); the researchers 

then tracked the speeds of bicycles that were passed by a test bicycle traveling at the overall 

mean speed. Based on a test bicycle and an overall bicycle mean speed of 20.6 km/h (12.8 mi/h), 

a standard deviation of 5.49 km/h (3.41 mi/h) around that mean speed, and a volume of 115 

bicycles per hour in one direction (the average volume across all paths from table 15), the SS for 

passed bicycles was about 12.88 km/h (8 mi/h) or 3.66 m/s (12 ft/s). Using PS = 20.61 km/h 

(12.8 mi/h) or 5.73 m/s (18.8 ft/s), SS as described above, and BG = EG = 6.1 m (20 ft) (which 

we believe to be reasonable for many passing situations), the researchers produced the values in 

the column labeled “Estimated” in table 18. As one can see, the estimated values in table 18 are 

about two-thirds of the field average values for bicycles, skaters, and joggers, and about one-half 

of the field average value for pedestrians. 

Table 16. Summary of distance needed to pass values. 

Distance needed for bicycles to pass, ft 

Mode Field average Estimated Assumed 

Bicycles 163 110 100 

Pedestrians 98 49 60 

Skaters 158 87 100 

Runners 104 61 70 

Child Bikes None 69 70 

1 ft = 0.305 m 

The column labeled “Assumed” in table 16 shows the values of distance to pass that the team 

used in the delayed passing spreadsheet that became a part of the LOS procedure (see chapter 8). 

We assumed a lower value for passing bicycles because we believe that BG and EG are typically 

lower than 6.1 m (20 ft) when the passing user is traveling nearly the same speed as the user 

being passed since path users try to minimize the time that they are in the middle or opposing 

lane. For pedestrians, skaters, and joggers, we assumed values between the field average and 

estimated values, but closer to the estimated values, because the field data were based on very 

conservative bicycling practices. Finally, with no relevant field data on hand, we assumed that 

the distance required to pass a child bicyclist was nearly the same as the estimated value. 

VALIDATING THEORY 

Besides providing the important average and default values described above, the other major 

purpose of the operational data collection was to validate the theory developed in chapter 3 on 

predicting the number of meetings and passings along a path. In this section, we first show that 

the key assumption in chapter 3—that user speeds are normally distributed—was a sound one. 
72 

We then compare the theoretical predictions of meetings and passings to the field data for a 

variety of paths and show that the theory generally matched the field data reasonably well. 

Speeds Normally Distributed 

The assumption that user speeds are normally distributed was key in chapter 3, and the field data 

we collected generally prove that the assumption was sound. Figures 10 through 14 show the 

distributions of the speeds observed during our data collection (the same data set as described in 

table 12 above) by mode. All of the distributions look normal, with the exception of a couple of 

points. More convincing evidence is provided in table 16 for all modes except for child 

bicyclists, for whom the sample size was too small. Table 16 shows the results of chi-square tests 

comparing the distribution of speeds collected in the field to a theoretical normal distribution of 

speeds with a mean and standard deviation as reported in table 12. The chi-square test is well 

known and commonly used for this purpose (e.g., see May’s classic text, Traffic Flow 

Fundamentals).(60) Table 17 shows that the field and theoretical normal distributions were not 

significantly different at the 0.05 level for the cases of the bicycle, pedestrian, and skater modes. 

Joggers were the only mode for which there was a significant difference. Table 17 shows that the 

difference was only because of an overabundance of relatively slow (8.05 km/h (5 mi/h)) 

joggers. Overall, the field data provide convincing evidence that user speeds on the shared-use 

paths that we studied were normally distributed. 
73 

1 mi/h – 1.61 km/h 

Figure 10. Distribution of bicycle speed data. 

 1 mi/h = 1.61 km/h 

Figure 11. Distribution of pedestrian speed data. 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

0 1 2 3 4 5 

Speed, mi/h 

Number of observations 

0

10

20

30

40

50

60

0 5 10 15 20 25

Speed, mi/h

Number of observations
74 

 1 mi/h = 1.61 km/h 

Figure 12. Distribution of skater speed data. 

 1 mi/h = 1.61 km/h 

Figure 13. Distribution of jogger speed data. 

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25

Speed, mi/h

Number of observations

0

10

20

30

40

50

60

70

80

90

0 5 10 15

Speed, mi/h

Number of observations
75 

 1 mi/h = 1.61 km/h 

Figure 14. Distribution of child bicyclist speed data. 

0

1

2

3

4

5

6

7

8

9

10 

0 5 10 15

Speed, mi/h

Number of observations
76 

Table 17. Chi-square test results comparing field and 

normal distributions for speed data. 

Frequency 

Mode Speed, mi/h Counted Theoretical Chi-square value

6 and lower 5 14 6.0 

 7 16 12 1.3 

 8 23 20 0.6 

 9 34 28 1.5 

10 39 36 0.2 

11 53 46 1.1 

12 44 50 0.6 

13 50 53 0.1 

14 47 48 0.0 

15 44 42 0.1 

16 27 34 1.5 

17 21 24 0.4 

18 15 17 0.1 

19 9 10 0.1 

20 5 6 0.1 

21 7 3 5.5 

22 and higher 4 2 1.2 

Sum 20.4 

Critical chi-square value at 0.05 level 23.7 

Bicycle 

Significant difference? No 

 2 8 8 0.0 

 2.5 27 33 0.9 

 3 85 75 1.3 

 3.5 83 88 0.2 

 4 54 53 0.0 

 4.5 14 16 0.3 

 5 4 3 0.7 

Sum 3.4 

Critical chi-square value at 0.05 level 9.5 

Pedestrian 

Significant difference? No 

 1 mi/h = 1.61 km/h 
77 

Table 17. Chi-square test results comparing field and 

normal distributions for speed data (continued).

Frequency 

Mode Speed, mi/h Counted Theoretical Chi-square value

5 and lower 6 10 1.8 

 6 10 10 0.0 

 7 18 17 0.1 

 8 24 24 0.0 

 9 37 29 2.4 

10 38 32 1.1 

11 24 30 1.0 

12 21 25 0.5 

13 15 18 0.7 

14 13 11 0.2 

15 4 6 1.0 

16 4 3 0.2 

17 and higher 4 2 1.6 

Sum 10.6 

Critical chi-square value at 0.05 level 18.3 

Skater 

Significant difference? No 

4 & lower 6 10 1.9 

 5 53 36 7.8 

 6 81 76 0.4 

 7 69 80 1.4 

 8 40 44 0.4 

 9 8 13 1.8 

10 and higher 4 2 1.9 

Sum 15.7 

Critical chi-square value at 0.05 level 9.5 

Runner 

Significant difference? No 

 1 mi/h = 1.61 km/h 

Comparing Predicted Meetings and Passings to Field Data 

Chapter 3 described the development of a theory on how to estimate the number of meetings and 

passings by a test bicyclist on a path, given the traffic volumes and speeds of the various modes 

on the path. To gather the field data against which those estimates will be compared, a team 

member viewed the videotapes recorded from the helmet camera and documented the number of 

meetings and active passing events that occurred during each trial. The researchers did not 

document passive passing events since very few of them were seen on the mobile video. 

Table 18 shows a summary of the meetings and passings (completed and desired) data in terms 

of average values per trail. Table 18 does not report data from the two Washington, DC, area 

trails since those sample sizes were so small and the data were so unstable that they were not 

helpful. Obviously, there was good variation in meetings and passings per mile. The test bicycle 
78 

on the Lakefront Trail in Chicago met an average of 120 trail users per mile, while the test 

bicycle only met an average of nine trail users per mile on the Pinellas and Grant’s trails. The 

test bicycle on the Lakefront Trail passed an average of 21 trail users per mile, while passing an 

average of 2 trail users per mile on the Pinellas and Grant’s Trails. 

Table 18. Average meetings and passings on each trail. 

Trail 

Average user 

volume per hour 

Average meetings 

per mile 

Average passings 

per mile 

Paul Dudley 438 31 3 

Minuteman 442 27 5 

Pinellas 120 9 2 

Honeymoon Is. 110 13 5 

Forest Park 299 22 6 

Grant's 123 9 2 

South Bay 616 61 12 

Lakefront 2317 120 21 

White Creek 217 16 3 

White Rock 252 17 4 

Mill Valley 641 29 6 

Sammamish 418 28 3 

Lake Johnson 205 21 11 

Average 477 31 6.4 

Standard Deviation 581 30 5.4 

1 mi = 1.61 km 

A quick look at table 18 shows that the number of meetings and passings were related to the user 

volumes on the trail. Figure 15 shows that relationship more clearly, without calculating the 

averages from the Lakefront Trail, which would be way off the chart. There are some variations 

in the relationship between meetings and volume, and passings and volume; however, generally, 

as volume rises, so do the meetings and passings. 
79 

0

10

20

30

40

50

60

70

0 200 400 600 800

Average users per hour

Average meetings or passings 

per mile

Meetings Passings

 1 mi = 1.61 km 

Figure 15. Average meetings and passings per trail related to average user volume. 

Comparing the number of meetings and (completed plus desired) passings estimated by the 

model developed in chapter 3 to the number recorded by the helmet camera during the 

operational data collection runs was a fairly straightforward task. The researchers bundled the 

runs from each path into two or three groups based on total user volume and test bicyclist speed 

because the variation in meetings and passings between the individual runs was so great. The 

team used the actual speed of the test bicycle during the run to make its prediction, though, rather 

than using the average bicycle speed for the path or for the set of paths. Figures 16 and 17 show 

the results for meetings, while figures 18 and 19 show the results for passings. 
80 

0

100

200

300

400

500

600

0 100 200 300 400 500 600

Model prediction

Field data

Figure 16. Model prediction versus field data for meetings based on volume groups. 

0

100

200

300

400

500

600

700

0 100 200 300 400 500 600 700

Model prediction

Field data

Figure 17. Model prediction versus field data for meetings based on speed groups. 
81 

0

20

40

60

80

100

120

0 20 40 60 80 100 120

Model prediction

Field data

Figure 18. Model prediction versus field data for passings based on volume groups. 

0

20

40

60

80

100

120

140

160

180

0 20 40 60 80 100 120 140 160 180

Model prediction

Field data

Figure 19. Model prediction versus field data for passings based on speed groups. 

Figures 16 through 19 show that, in general, the model estimates fit the field data quite well. On 

figures 16 and 17 for meetings, and figure 19 for passings, there is only one point that 

particularly stands out for which the model estimate and the field data do not match. That point 
82 

was for the Mill Valley-Sausalito Trail with high volumes or high speeds. The reason for the 

mismatch probably relates to the fact that there were a large number of fast joggers on that path 

during meeting and passing data collection, and these joggers were not recorded during speed 

data collection. 

To provide more detail on how well the model estimates matched the field data, tables 19 

through 22, show the data and a statistical test on the fit of the model estimate to the field data 

for each bundle tested. The test was whether the field data fell within two standard deviations 

(shown as σ in tables 19 through 22) of the mean estimated by the model. This was actually a 

demanding test for the higher numbers of meetings because, for a Poisson-distributed variable 

such as the estimated mean number of meetings or passings, the standard deviation was equal to 

the square root of the estimated mean. Thus, two times the standard deviation for such a variable 

becomes a proportionally narrower range as the estimated mean gets larger. The worst fit on the 

four tables was in table 19 for meetings by volume group, where only four of the 23 cases fell 

within the limits. However, in table 19, there was a nice balance between the 9 cases where the 

field data were above the limits and the 10 cases where the field data were below the limits, 

showing no bias. Table 20 had a better fit with 11 of the 23 cases within the limits. The balance 

between the cases above and below the limits was not as good for table 20, with 2 cases above 

the limits and 10 below. Although a look at the data in table20 shows several cases in which the 

field data just missed the lower limit. Tables 21 and 22 for passings show better matches 

between the model estimates and field data than for meetings, as might be expected, since the 

mean values were much lower. In the cases in tables 21 and 22 where the field data fell outside 

the limits, there was a good balance between lower and higher values, which showed little bias. 

Overall, tables 19 through 22 should reinforce the idea that the models developed in chapter 3 

did well in predicting the number of meetings and passings recorded during our operational data 

collection effort. 
83 

Table 19. Statistical test comparing meetings estimated by model to field data 

for volume groups. 

Meetings estimated by 

model 

Trail 

Volume 

group 

Length, 

miles 

Mean 

Mean – 

2 * σ

Mean 

+ 2 * σ

Meetings 

counted 

from 

data 

Within 

+ 2 * σ? 

Too 

high 

or 

low? 

Low 10 53 38 68 112 No High 

Med. 10 202 174 230 177 Yes – 

White Rock 

High 10 387 348 426 229 No Low 

Low 9.5 120 98 142 174 No High 

Med. 9.5 226 196 256 210 Yes – 

Sammamish 

River 

High 10 469 426 512 345 No Low 

Mill Valley Low 8.2 254 222 286 170 No Low 

High 8.2 627 577 677 298 No Low 

Low 10 81 63 99 112 No High 

Med. 10 183 156 210 169 Yes – 

White 

Creek 

High 10 302 267 337 188 No Low 

Low 5 160 135 185 208 No High 

Med. 5 344 307 381 303 No Low 

South Bay 

High 5 524 478 570 402 No Low 

Low 8 23 13 33 85 No High 

Med. 8 122 100 144 106 Yes – 

Honeymoon 

Island 

High 8 145 121 169 112 No High 

Low 9.5 74 57 91 52 No Low 

Med. 9.5 61 45 77 84 No High 

Pinellas 

High 9.5 83 65 101 115 No High 

Low 11 249 217 281 366 No High 

Med. 11 430 389 471 370 No Low 

Minuteman 

High 11.5 559 512 606 420 No Low 

1 mi = 1.61 km 
84 

Table 20. Statistical test comparing meetings estimated by model to field data 

for speed groups 

Meetings estimated by 

model 

Trail 

Speed 

group 

Length, 

miles 

Mean 

Mean – 

2 * σ

Mean 

+ 2 * σ

Meetings 

counted 

from 

data 

Within 

+ 2 * 

σ? 

Too 

high 

or 

low? 

Low 10 262 230 294 188 No Low 

Med. 10 242 211 273 220 Yes – 

White Rock 

High 10 119 97 141 110 Yes – 

Low 9.5 239 208 270 238 Yes – 

Med. 9.5 277 244 310 250 Yes – 

Sammamish 

River 

High 10 303 268 338 241 No Low 

Mill Valley Low 8.2 331 295 367 213 No Low 

High 8.2 554 507 601 255 No Low 

Low 10 184 157 211 152 No Low 

Med. 10 215 186 244 185 No Low 

White 

Creek 

High 10 169 143 195 132 No Low 

Low 5 397 357 437 317 No Low 

Med. 5 299 264 334 287 Yes – 

South Bay 

High 5 310 275 345 309 Yes – 

Low 8 117 95 139 113 Yes – 

Med. 8 121 99 143 84 No Low 

Honeymoon 

Island 

High 8 63 47 79 106 No High 

Low 9.5 80 62 98 96 Yes – 

Med. 9.5 60 45 75 78 No High 

Pinellas 

High 9.5 74 57 91 77 Yes – 

Low 11 366 328 404 378 Yes – 

Med. 11 476 432 520 410 No Low 

Minuteman 

High 11.5 408 368 448 368 Yes – 

1 mi = 1.61 km 
85 

Table 21. Statistical test comparing passings estimated by model to field data 

or volume groups. 

Passings estimated by 

model 

Trail 

Volume 

group 

Length, 

miles 

Mean 

Mean – 

2 * σ

Mean 

+ 2 * σ

Passings 

counted 

from 

data 

Within 

+ 2 * 

σ? 

Too 

high 

or 

low? 

Low 10 29 18 40 33 Yes – 

Med. 10 58 43 73 31 No Low 

White Rock 

High 10 106 85 127 49 No Low 

Low 9.5 23 13 33 21 Yes – 

Med. 9.5 29 18 40 23 Yes – 

Sammamish 

River 

High 10 37 25 49 39 Yes – 

Mill Valley Low 8.2 59 44 74 59 Yes – 

High 8.2 173 147 199 43 No Low 

Low 10 35 23 47 25 Yes – 

Med. 10 49 35 63 40 Yes – 

White 

Creek 

High 10 57 42 72 57 Yes – 

Low 5 51 37 65 53 Yes – 

Med. 5 73 56 90 73 Yes – 

South Bay 

High 5 133 110 156 89 No Low 

Low 8 20 11 29 34 No High 

Med. 8 37 25 49 47 Yes – 

Honeymoon 

Island 

High 8 43 30 56 44 Yes – 

Low 9.5 5 1 9 3 Yes – 

Med. 9.5 7 2 12 12 Yes – 

Pinellas 

High 9.5 5 1 9 30 No High 

Low 11 47 33 61 76 No High 

Med. 11 71 54 88 82 Yes – 

Minuteman 

High 11.5 76 59 93 107 No High 

1 mi = 1.61 km 
86 

Table 22. Statistical test comparing passings estimated by model to field data 

for speed groups. 

Passings estimated by 

Model 

Trail 

Speed 

group 

Length, 

miles 

Mean 

Mean – 

2 * σ

Mean + 

2 * σ

Passings 

counted 

from 

data 

Within 

+ 2 * σ? 

Too 

high 

or 

low? 

Low 10 56 41 71 25 No Low 

Med. 10 79 61 97 54 No Low 

White Rock 

High 10 54 39 69 34 No Low 

Low 9.5 22 13 31 20 Yes – 

Med. 9.5 24 14 34 30 Yes – 

Sammamish 

River 

High 10 54 39 69 33 No Low 

Mill Valley Low 8.2 114 93 135 59 No Low 

High 8.2 99 79 119 43 No Low 

Low 10 47 33 61 52 Yes – 

Med. 10 57 42 72 37 No Low 

White 

Creek 

High 10 33 22 44 34 Yes – 

Low 5 69 52 86 61 Yes – 

Med. 5 77 59 95 63 Yes – 

South Bay 

High 5 110 89 131 91 Yes – 

Low 8 30 19 41 37 Yes – 

Med. 8 31 20 42 44 No High 

Honeymoon 

Island 

High 8 48 34 62 44 Yes – 

Low 9.5 3 0 6 7 No High 

Med. 9.5 4 0 8 17 No High 

Pinellas 

High 9.5 15 7 23 21 Yes – 

Low 11 45 32 58 94 No High 

Med. 11 76 59 93 84 Yes – 

Minuteman 

High 11.5 97 77 117 87 Yes – 

1 mi = 1.61 km 
87 

6. PERCEPTION DATA COLLECTION 

GENERAL RATIONALE 

The purpose of this phase of the study was to quantify users’ perceptions of the essential 

structural and operational characteristics of shared-path facilities and their interaction for the 

purpose of developing a better understanding of how such perceptions, along with objective 

measures of facility performance, might be used to construct a model of shared-path LOS. 

The basic approach was to have participants view selected segments of imagery collected by a 

bicyclist wearing a helmet-mounted video camera as he or she rode through a shared-path 

environment at a set, predetermined speed. We were able to use the imagery that we collected 

from a number of trails across the United States as part of the operational data collection effort as 

described in chapters 4 and 5. 

User perceptions were quantified in terms of their ratings of lateral separation, longitudinal 

separation, and the perceived ability to pass other trail users. Participants were also asked to 

provide an overall rating of how satisfied they would be using the trail segment that they were 

viewing. 

Participants ranged in age and in the level of familiarity/use of shared-path facilities. There was 

also a wide range in terms of estimates of their own health status and in terms of the extent to 

which participants engaged in walking and/or riding for recreational and/or fitness purposes. 

More information is provided on user attributes below. 

All data were collected during fall 2002 and winter 2003. The imagery shown to the respondents 

in the perception phase of the study was collected during summer and fall 2001 and winter 2002. 

PARTICIPANTS 

A total of 105 individuals served as volunteer participants in the perception data collection phase 

of the study. Participants were recruited from the Raleigh-Durham-Chapel Hill, NC, area and 

from the greater Washington, DC, metropolitan area. The data were collected in group settings, 

with the size of the individual groups based on subject availability. The volunteers were recruited 

primarily from bicycle user groups in the two areas. The fact that the respondents were willing to 

give up a portion of their evening or their lunch period for a volunteer effort to aid future 

bicycling indicates the level of commitment that the respondents had regarding the project. 

Thirty-four percent of the participants were female and 66 percent were male. The distribution of 

subjects by age range is given in table 23. We were very pleased by the wide range of respondent 

ages. 
88 

Table 23. Distribution of subjects by age. 

Age range, years Percent Cumulative percent 

18–24 6.5 6.5 

25–31 15.0 21.5 

32–38 14.0 35.5 

39–45 24.3 59.8 

46–51 12.1 72.0 

52–58 9.3 81.3 

59–65 14.0 95.3 

Over 65 4.7 100.0 

The distribution of self-reported individual health status is given in table 24, while the 

distribution of the participants’ estimates of their frequency of walking or bicycle riding for 

recreational and/or fitness purposes is given in table 25. As one might expect, those interested in 

shared-use paths generally believed themselves to be in good health and generally walked or 

bicycled often for recreational and fitness uses. Note that the walking and bicycling reported in 

table 25 was not necessarily on shared-use paths. 

Table 24. Distribution of reports of individual health status. 

Reported health status Percent Cumulative percent 

Fair 4.1 4.1 

Good 40.2 44.3 

Excellent 55.7 100.0 

Table 25. Distribution of individuals’ estimated frequency of riding and/or walking for 

either recreational and/or fitness purposes. 

Walking or bicycling 

frequency Percent Cumulative percent 

Never 6.6 6.6 

A few times a year 22.6 29.2 

More than once a month 30.2 59.4 

More than twice a week 39.6 99.1 

Almost daily 0.9 100.0 

In response to a question on how frequently respondents used shared-path facilities (either as 

pedestrians or as bicyclists), the estimates are provided in table 26 below. These data confirm 

that our respondents were generally very experienced shared-path users. 
89 

Table 26. Estimates of shared-path use. 

Estimates of shared 

path use 

Percent Cumulative 

percent 

Never 1.9 1.9 

Rarely 7.6 9.5 

Occasionally 18.1 27.6 

Regularly 24.8 52.4 

1 to 3 times a week 34.3 86.7 

Daily 13.3 100.0 

In response to a question on the respondents’ most frequent reasons for trips (either as 

pedestrians or bicyclists) on shared-path facilities, the following characterizes the range of 

responses: 

• Commuting to work: 18.9 percent. 

• Commuting to school: 1.2 percent. 

• Utilitarian trips: 9.4 percent. 

• Socialize with friends: 5.9 percent. 

• Recreation: 33.5 percent. 

• Fitness: 31.2 percent. 

Compared to nationwide shared-path users, this respondent sample was probably overrepresented for commuters to work and under-represented for commuters to school. 

COLLECTION OF USER PERCEPTION DATA 

Individual user perception data were collected by responses provided to items on a paper-andpencil survey developed by the project team. More details on the nature of the survey instrument 

and on the data collection procedure itself are given below. 

Structure and Content of the Survey Instrument 

A paper-and-pencil survey instrument was developed by the University of North Carolina 

Highway Safety Research Center (HSRC). The instrument was similar to instruments developed 

and used by HSRC to collect user perception data for the FHWA Bike Index Study(6) and to that 

developed and used by Hughes and Harkey.(61)

For the present study, thirty-six 60-s video sequences were selected from the head-mounted 

video camera images collected during the operational data collection phase of the study. The 

project team thought that this was about the upper limit of the effort that we could expect from 

our volunteer respondents without fatigue having a major impact on the results. All video 

sequences were black-and-white and were limited to the field of view of the camera selected for 

use in the study. Video quality, especially after digitizing, ranged from good to marginal because 

some images had less than optimal camera angles and some were quite dark. Nonetheless, the 

project team selected real video over staged video or still images for this study because the real 

video best conveyed actual path operations. The images were good enough and were displayed 
90 

for long enough to give the respondents a realistic view of operations on the trail at that time. 

After digitizing, the video sequences did not include sound. 

Selection of the individual video sequences was based on a review of the structural and 

operational facility characteristics of the trails used in the operational data collection phase of the 

study. Basically, we began by selecting 10 of the trails on which we collected operational data 

that had the best video quality and that best spanned the range of geographic locations, trail 

widths, and trail geometrics. Then, for each of these trails, we selected three trials (six for the 

higher volume Chicago and Seattle trails) at the proper bicycle speed (see below) that 

represented high, medium, and low user volumes. Finally, the team selected and digitized 60-s 

clips from within the longer trials that best represented the volume levels desired and that did not 

contain unusual events that could cause bias in the response, such as a passive pass. Table 27 

shows a summary of the 36 video clips that we used. 

Whereas operational data were collected under three different desired bicycle speeds (trail mean, 

trail mean plus one standard deviation, and trail mean minus one standard deviation), the 

sequences used in the perception data collection phase were all from trials where the test bicycle 

speed was between 15.29 and 20.93 km/h (9.5 and 13 mi/h). We thus tried to remove the speed at 

which the bicycle was moving as a variable for the respondents. 

Subjects viewed each of the 36 conditions while seated in a group setting. Video sequences were 

projected from a laptop computer using a liquid crystal display (LCD) projector and a projected 

screen size of approximately 1.8 m (6 ft) high by 3.0 m (10 ft) wide. All video sequences were 

stored in a CD-ROM format. 

After the respondents entered the testing room, they were seated, welcomed, asked to fill out the 

informed consent form (appendix A), asked to read an introduction (also in appendix A), and 

asked to fill in their background information (appendix B). They responded to a warmup 

sequence of three 60-s trials. We did not analyze the data from these three warmup trials. After 

all respondents’ questions about the format were answered, the main testing began. 
91 

Table 27. Characteristics of the 36 perception data collection video clips.

Trial No. of events 

no. 

Location 

Meet APa

 PPb

Speed, 

mi/h 

Width, 

ft 

General 

description 

Centerline Clear 

zone, 

ft 

Sight 

dist. 

Vert. 

tilt 

Glare Focusc

001 

002 

003 

Lake 

Johnson 

 1 

 5 

 4 

 0 

 3 

 5 

0 

0 

0 

10.7 

 9.9 

 9.6 

 8 Rural 

wooded 

No 2 Poor Large 

No 

Med. 

High 

High 

High 

In 

Mod. Out 

In 

004 

005 

006 

007 

008 

009 

Sammamish 

River 

 2 

 6 

 2 

 3 

10 

14 

 1 

 0 

 3 

 0 

 0 

 1 

0 

1 

0 

1 

0 

0 

11.1 

11.7 

11.9 

 9.6 

10.7 

10.7 

10 Rural grass No 8 Good No 

No 

No 

No 

No 

No 

Low 

No 

Low 

High 

No 

No 

In 

In 

In 

Sl. Out 

Sl. Out 

Sl. Out 

010 

011 

012 

Mill ValleySausalito  7 

 7 

 8 

 1 

 0 

 9 

0 

0 

2 

11.5 

11.9 

12.0 

 9.5 Suburban 

marsh 

No 6 Unlimited Small 

Small 

No 

No 

No 

No 

Mod. Out 

In 

Sl. Out 

013 

014 

015 

White Rock 

Lake 

 2 

 9 

 9 

 2 

 1 

 3 

0 

0 

0 

12.6 

12.7 

12.8 

14 Urban lake Solid 15 Unlimited No 

Med. 

Med. 

Low 

No 

Low 

Sl. Out 

In 

Sl. Out 

016 

017 

018 

019 

020 

021 

Lakefront 36 

47 

73 

28 

45 

60 

 7 

14 

16 

11 

11 

15 

0 

0 

0 

0 

1 

2 

12.6 

12.3 

10.1 

11.3 

11.5 

11.8 

20 Urban beach Solid 20 Poor No 

No 

No 

No 

No 

No 

Low 

Med. 

Low 

No 

High 

Med. 

In 

In 

Sl. Out 

In 

In 

Sl. Out 

022 

023 

024 

South Bay 4 

 9 

17 

 2 

 0 

 4 

0 

0 

0 

11.3 

11.0 

10.7 

14 Urban beach Dashed 20 Unlimited Small 

Small 

No 

No 

No 

High 

Sl. Out 

Sl. Out 

Mod. Out 

025 

026 

027 

Forest Park 9 

 5 

13 

 5 

 2 

 4 

0 

0 

0 

10.2 

10.1 

12.1 

10 Urban park Solid 4 Good Med. 

Large 

Small 

Low 

Low 

Low 

In 

In 

In 

028 

029 

030 

Honeymoon 

Island 

 1 

 2 

 8 

 1 

 3 

 4 

0 

0 

0 

10.7 

11.4 

11.5 

12 Suburban 

beach 

No 0 

and 

 5 

Unlimited Large 

Med. 

Large 

High 

Low 

No 

In 

In 

Sl. Out 

031 

032 

033 

Minuteman 3 

 7 

16 

 3 

 3 

 0 

0 

0 

0 

 9.6 

12.6 

12.8 

12 Suburban 

wooded 

Dashed 3 Fair Med. 

Med. 

Med. 

Med. 

High 

Med. 

In 

In 

High Out. 

034 

035 

036 

Paul Dudley 2 

 5 

17 

 1 

 2 

 0 

0 

0 

0 

11.5 

12.3 

10.9 

 8 Urban 

harbor 

Dashed 1 Poor Med. 

Small 

Med. 

Low 

Low 

Med. 

In 

In 

In 

1 ft = 0.305 m, 1 mi/h = 1.61 km/h 

a = Active passes 

b = Passive passes 

c = In focus, slightly out of focus, moderately out of focus, or highly out of focus. 

The 36 video sequences were presented in four blocks of nine trials each. The order in which the 

blocks were shown to different groups was varied randomly to avoid learning or fatigue biases. 

A trial consisted of a 60-s video presentation, followed by 30 s during which the screen just 

displayed the trial number and the participants recorded their responses on the printed response 

sheet that was provided. Overall testing time, with a short break between blocks two and three, 

was approximately 80 min. 

Format of Participants’ Responses 

Participants were required to rate their perceptions of selected facility conditions on a 5-point 

scale, an example of which is shown in figure 20. Explanations of “lateral separation,” 
92 

“longitudinal separation,” “ability to pass,” and “overall” were provided to subjects at the outset 

in the introductory fact sheet (see appendix A). 

Figure 20. Representative response format. 

These four response parameters were selected in order to: (1) solicit user perceptions along 

dimensions pertinent to the design and operation of the type of facility of interest, and (2) to 

obtain user perception data consistent with the variables in the equations that we developed in 

chapter 3. In particular, we were interested in user perceptions about the interaction between 

structural and operational facility characteristics and the ability to pass, since the latter is 

probably a key construct in estimating the perceived LOS of a facility. 

In general, the respondents were able to follow the directions that we provided and to complete 

the rating of all 36 video sequences. Only one respondent left early, and no respondents reported 

motion sickness from viewing the images. Some respondents indicated that the video image 

quality was a problem (see chapter 7 for an analysis of that factor); however, most indicated that 

they were able to fairly rate the path and its operation from the images displayed. Appendix C 

shows a typical frame from the video clips that we used for each of the 10 paths. 

Condition Number XX

BAD POOR FAIR GOOD EXCELLENT

LATERAL SEPARATION

LONGITUDINAL SEPARATION

ABILITY TO PASS

OVERALL
93

7. ANALYSIS OF PERCEPTION SURVEY RESPONSES

INTRODUCTION 

The objective of this portion of the study was to use the perceptions of trail adequacy provided 

by the respondents during the perception survey (described in chapter 6) to produce a predictive 

model that could be applied to all shared paths. We wanted to develop a model of the form: 

Trail adequacy rating = f (operational and geometric characteristics) 

We could use such a model to establish which variables contributed to user perceptions of trail 

adequacy. We could also use such a model to help set the LOS criteria. 

Once the surveys were completed, they were collected and the participants’ answers, as well as 

their respective demographic data, were entered into Microsoft® Excel and into our statistical 

software (SAS), in order to facilitate data analysis. Qualitative answers were assigned a 

particular code. The ratings were given a quantitative score: 

1 = Bad 

2 = Poor 

3 = Fair 

4 = Good 

5 = Excellent 

We thus produced a large database, with 105 respondents, each making 4 responses to each of 36 

video clips. There were relatively few missing values within the database, with the percentage of 

those ranging from 3.2 percent for latitudinal separation to 3.4 percent for longitudinal 

separation. Most of the missing values stemmed from the first group for which data were 

collected in Raleigh, when 5 subjects left after observing half (18) of the video clips because of a 

lengthy technical malfunction. Since the testing conditions were the same for these 5 respondents 

as for the other 100 respondents, we left those data in the database. 

DATA OVERVIEW 

Table 28 shows average rankings (averaged across all respondents) for each video clip for each 

of the four response categories. From table 28, it is apparent that the respondents were fairly 

positive about the paths and scenes that they were viewing. The average “overall” score for all 

respondents and all paths was 3.45, well above midpoint on the 1 to 5 scale. Furthermore, it is 

apparent that the respondents, in general, did not draw huge distinctions between most of the 

clips. Again using the “overall” category, the highest rated clip had an average score of 4.27, 

while the lowest rated clip had an average score of 2.27. Most of the average “overall” scores for 

a clip were bunched in the 3 to 4 range. 

The respondents judged longitudinal separation most leniently and lateral separation most 

harshly among the four response categories. The average longitudinal separation score was 3.93, 

and many clips had an average score above 4. On the other hand, the average lateral spacing 
94

score was 3.24, with only a couple of video clips rating an average score of over 4, and one clip 

having an average score below 2. The “ability to pass category” generally had a distribution of 

average scores very similar to the “overall” category. 

The respondents were clear about the paths that they liked better and the paths that they did not 

like as much. The White Rock Lake (Dallas) and South Bay (Santa Monica) paths scored the 

highest. These were wider trails with low to moderate volumes and interesting surroundings. The 

Lakefront (Chicago), Dr. Paul Dudley (Boston), and Forest Park (Saint Louis) paths scored 

lowest, probably for different reasons. The Lakefront path was very crowded; the Dr. Paul 

Dudley path was very narrow and constrained; and the Forest Park path was busy, relatively 

narrow, and had generally lower quality video. 
95

Table 28. Average ratings of each video clip. 

Rank 

(based on 

average 

overall 

rating) 

Video 

clip 

number 

Location 

Average 

lateral 

separation 

rating 

Average 

longitudinal 

separation 

rating 

Ability to 

pass 

rating 

Average 

overall 

rating 

 1 013 White Rock L. 4.12 4.41 4.32 4.27 

 2 015 White Rock L. 3.90 4.50 4.04 4.12 

 3 023 South Bay 4.05 4.45 4.11 4.07 

 4 014 White Rock L. 3.77 4.36 3.77 4.02 

 5 022 South Bay 3.94 4.52 4.24 3.97 

 6 033 Minuteman 3.62 4.32 3.75 3.91 

 7 010 Mill Valley 3.72 4.38 3.94 3.88 

 8 008 Sammamish R. 3.50 4.38 3.85 3.86 

 9 028 Honeymoon Is. 3.73 4.54 4.15 3.85 

10 009 Sammamish R. 3.50 4.38 3.85 3.86 

11 007 Sammamish R. 3.64 4.31 3.76 3.82 

12 005 Sammamish R. 3.47 4.37 3.87 3.80 

13 031 Minuteman 3.25 4.34 3.72 3.78 

14 004 Sammamish R. 3.45 4.41 3.88 3.72 

15 006 Sammamish R. 3.21 4.23 3.53 3.62 

16 016 Lakefront 3.62 3.81 3.47 3.61 

17 032 Minuteman 3.33 4.02 3.30 3.57 

18 011 Mill Valley 3.18 4.27 3.63 3.56 

19 035 Paul Dudley 3.08 4.37 3.50 3.55 

20 030 Honeymoon Is. 3.40 3.95 3.63 3.54 

21 029 Honeymoon Is. 3.47 4.20 3.70 3.51 

22 012 Mill Valley 3.23 3.62 3.34 3.37 

23 026 Forest Park 3.02 4.04 3.28 3.32 

24 003 Lake Johnson 2.92 3.70 3.15 3.31 

25 002 Lake Johnson 2.87 3.88 3.22 3.30 

26 001 Lake Johnson 2.91 3.85 3.19 3.11 

27 019 Lakefront 3.15 3.09 2.90 3.09 

28 027 Forest Park 2.63 3.49 2.88 3.04 

29 024 South Bay 2.81 3.28 2.88 2.99 

30 018 Lakefront 2.93 2.94 2.63 2.83 

31 034 Paul Dudley 2.40 3.89 2.90 2.82 

32 036 Paul Dudley 2.37 3.70 2.75 2.76 

33 021 Lakefront 2.78 2.90 2.52 2.73 

34 020 Lakefront 2.80 2.81 2.51 2.72 

35 017 Lakefront 2.83 2.66 2.44 2.71 

36 025 Forest Park 1.96 3.06 2.14 2.27 

Average 3.24 3.93 3.41 3.45 
96

Demographic Variables 

An exploration of the demographic variables that we collected on the respondents showed that 

these variables generally had little effect on the score that the respondents provided. The next 

few paragraphs illustrate the general point. 

Figure 21 shows how respondent age affected the “overall” score they provided. The video clip 

rank corresponds to that provided in table 30 (i.e., clip 1 was from the White Rock Lake path and 

had the highest average “overall” score of the 36 clips). The plotted lines correspond to four age 

groups, and there were at least 20 respondents in each group. Obviously, the lines track each 

other very closely. The 32- to 45-year-old age group generally provided the highest scores, the 

46- to 58–year-old age group generally provided the lowest scores, and the other age groups fell 

between those two. However, except for that shift in average score, respondents of different ages 

generally perceived the differences between two video clips to be about the same. 

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1 6 11 16 21 26 31 36

Video clip by overall rank

Mean overall score

Age 18-31 years Age 32-45 years

Age 46-58 years Age 59 years and older

Figure 21. Effects of respondent age on overall rating. 

Figures 22 through 25 show lines similar to figure 21, except that the variables graphed are 

respondent gender, mode of travel (bicyclist versus pedestrian), health status, and trail use. 

Again, the pattern for all of these cases is that there were shifts from one group of respondents to 

another; however, respondents of different groups generally perceived the differences between 

two video clips to be about the same. In figure 22, we see that men generally provided higher 

scores than women. In figure 23, it appears that respondents from a pedestrian point of view 

provided generally higher scores than respondents from a bicyclist point of view. From figure 24, 

we note that those reporting themselves to be in fair health provided generally lower scores than 

those reporting themselves to be in good or excellent health. Finally, figure 25 shows no clear 
97

trend in ratings by the reported amount of trail use. Overall, these respondent demographic 

variables seem to matter regarding the score magnitude, but they do not seem to interact with the 

information on the video image of the path. 

2.0

2.5

3.0

3.5

4.0

4.5

1 6 11 16 21 26 31 36

Video clip by overall rank

Mean overall score

Male Female

Figure 22. Effects of respondent gender on overall rating. 
98

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1 6 11 16 21 26 31 36

Video clip by overall rank

Mean overall score

Bicyclist Pedestrian

Figure 23. Effects of path user type on overall rating. 

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1 6 11 16 21 26 31 36

Video clip by overall rank

Mean overall score

Excellent Good Fair

Figure 24. Effects of respondent health status on overall rating. 
99

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1 6 11 16 21 26 31 36

Video clip by overall rank

Mean overall score

A few times a year More than once a month

More than twice a week Almost daily

Figure 25. Effects of respondent path use on overall rating. 

Path Design Variables 

A look at the relationship between path width and respondent rating shows that path width 

should probably be an important variable in an LOS model. Figure 26 plots path width against 

mean overall rating for each of the 36 video clips. The ratings appear to be generally heading 

upward as the path width rises from 2.4 to 4.2 m (8 to 14 ft). For the 6.1-m- (20-ft-) wide trail 

video clips (from the Lakefront Trail in Chicago), the average ratings fall back down; however, 

that may be a result of the very heavy volumes and numbers of events shown during those clips 

rather than the path width. A similar relationship is seen between the path width and the average 

rating of the lateral separation perceived by the respondents. 

Table 29 summarizes the responses for other key path design variables, including the presence of 

a centerline, the width of clear zone extending laterally from the edge of the path, and the 

forward sight distance along the path. The table shows average overall ratings for all of the video 

clips that have a particular level of a variable and average ratings for either lateral separation or 

the ability to pass, depending on which of those was more relevant for that variable. 
100

2.0

2.5

3.0

3.5

4.0

4.5

5.0

8 10 12 14 16 18 20

Path width, feet

Mean overall rating

1 ft = 0.305 m 

Figure 26. Effects of path width on overall rating. 

The presence of a centerline seems to be strongly related to the overall rating—paths with no 

centerline rated, on average, about 0.1 points better than did paths with dashed centerlines, and 

about 0.4 points better than did paths with solid centerlines. This may be a result of the perceived 

restrictions in freedom to maneuver the bicycle imposed by a centerline. Clear-zone width does 

not seem to have a strong or consistent relationship with average overall rating, or with the 

average rating of lateral separation. Finally, while paths with poor sight distances were generally 

rated lowest overall and for the ability to pass, improvements in sight distance above poor did 

not produce consistently improving ratings. 

Events on the Path 

Respondents were not in a very good position to judge the volume of traffic on the paths in the 

video clips since they only viewed 1-min time slices from a moving-bicyclist perspective. 

However, the advantage of the moving-bicyclist perspective was that we were able to convey the 

number of events during that minute—the meetings and passings—quite realistically. Figure 27 

shows the relationship between meetings during the 1-min clip, and average overall rating, while 

figure 28 relates the number of active passings and the average overall rating. Both figures 

suggest that average ratings decline as the number of meetings and active passings rise, with the 

decline for active passings being a bit more pronounced. The project team had concerns for both 

of these graphs in the sense that the Chicago path, with its relatively large number of meetings, 

average overall rating, and active passings, was dominating the results from the other paths. This 

concern will be addressed in the next section, which describes the detailed statistical modeling of 

these results. 
101

Table 29. Effects of other path design variables on average ratings. 

Variable Level Average overall 

rank 

Average lateral 

separation rank 

Average ability 

to pass rank 

No 3.61 3.35 – 

Dashed 3.49 3.21 – 

Presence of 

centerline 

Solid 3.23 3.13 – 

0 and 5* 3.63 3.53 – 

 1 3.05 2.62 – 

 2 3.24 2.90 – 

 3 3.76 3.40 – 

 4 2.88 2.53 – 

 6 3.60 3.38 – 

 8 3.78 3.46 – 

15 4.13 3.93 – 

Clear zone, feet 

20 3.19 3.21 – 

Poor 3.05 – 2.93 

Fair 3.76 – 3.59 

Good 3.48 – 3.45 

Sight distance 

Unlimited 3.76 – 3.81 

*The path shown in these video clips had, on average, no clear zone on one side and 1.5 m (5 ft) on the other side. 
102

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0 10 20 30 40 50 60 70 80

Number of meetings per minute

Mean overall rating

Figure 27. Effects of the number of meetings on overall rating. 

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0 2 4 6 8 10 12 14 16 18 20

Number of active passings per minute

Mean overall rating

Figure 28. Effects of the number of active passings on overall rating.
103

Video Quality 

The final category of perception data variables that the project team analyzed was the quality of 

the video. As stated earlier, we chose to show the respondents videos shot from the movingbicyclist perspective during the operational data collection in order to portray real paths with real 

traffic loads. However, the downside of this approach was that the quality of the video varied 

from quite good to quite poor. Three important qualities that captured this variability were 

amount of glare, quality of the focus, and amount of vertical tilt since all of these were judged by 

the project team. Table 30 shows how these varied in the average overall rating provided by the 

respondents. None of the three quality variables had a strong relationship with average overall 

rating. Only the glare variable appeared as if it might matter during detailed modeling efforts 

since the average overall rating for the video clips with no glare was higher than for video clips 

with some glare; however, as the amount of glare increased from low to medium to high, the 

average overall ratings did not continue to decline. It appears from table 30 that the respondents 

were able to rate the paths according to other criteria aside from video quality. 

Table 30. Effects of video quality on average ratings. 

Variable Level Number of video 

clips 

Average overall 

rating 

No 11 3.73 

Low 12 3.39 

Medium 5 3.18 

Glare 

High 8 3.33 

In focus 21 3.32 

Slightly out of focus 11 3.68 

Moderately out of 

focus 3 3.39 

Focus 

Highly out of focus 1 3.91 

None 16 3.39 

Small tilt 6 3.68 

Moderate tilt 10 3.41 

Vertical tilt 

Large tilt 4 3.45 
104

MODEL CREATION 

Interactions 

The basic statistical model was based on a two-way layout that considered both subject 

characteristics and trail characteristics without interaction between the two. Below is the basic 

model: 

(57) 

where: 

 

ij y = Service rating for a trail i by a subject j

μ = Intercept 

αi = Effect of trail characteristics 

β j = Effect of subject characteristics 

ij e = Error 

The interaction of subject characteristics and trail characteristics could be a potentially 

complicating factor in this experiment setup. The simplification brought on by excluding such an 

interaction has great value; moreover, the validity of the analysis would be suspect if strong 

interactions were present. The experiment setup precluded any replication—no subject had 

multiple ratings of the same video or trail under the same conditions. While the full subject-bytrail interaction could not be addressed in this model, we did look for interactions between 

subject characteristics and trails, and trail characteristics and subjects. 

In the case of subject characteristics, the appropriate model was as shown in equation 58: 

(58) 

where yij, μ, αi, and eij are as defined above; k represents the response variables; and zjk

represents the subject characteristics. Testing for interactions meant creating models and then 

examining them for the presence of (αη)k. We examined the following subject characteristics—

age, gender, pedestrian or bicycle point of view, and fitness level. As we suspected from the data 

we examined earlier in this chapter, none of these interactions proved to be sizeable. 

In the case of trail characteristics, the appropriate model was as shown in equation 59: 

(59) 

ij i j ij y e = μ ++ + α β

= + + ∑ + ∑ +

k

k jk ij

k

y ij μ α i η k zjk (αη ) z e

= + ∑ + ∑ + + ∑ +

k

j k ik ij

k

k ik

k

yij μ θkwik δ x β (βδ ) x e
105

where yij, μ, βj, k, and eij are as defined above; wik represents traffic-load variables such as 

meetings and passings; and xik represents the trail and location characteristics. Testing for 

interactions meant constructing a model and then looking for the presence of (βδ)k. We tested for 

trail pavement material, shoulder presence, shoulder width, shadow in the video, glare in the 

video, horizontal curvature, vertical curvature, sight distance, urban versus other environment, 

presence of a centerline, and average clear-zone width. The greatest interactions were found with 

the pavement material, presence of a shoulder, and urban versus other environment, with Fstatistics in these cases as large as 4.87, 3.93, and 3.54, respectively. However, practically 

speaking, these were not very important interactions in a database of more than 3,500 

observations. The modest size of these interaction effects, coupled with the ineffectiveness of 

these three variables as explanatory variables in subsequent modeling (see below), gave 

sufficient support for excluding these interactions from the model. 

While the respondents were sampled from a relevant population, this sample may not be 

representative of some shared paths at some times, and adjustments to the model may be needed 

in those cases. If the characteristics of the users of a path of interest were different from those of 

our respondents in terms of age, gender, etc., the lack of interaction seen with these variables in 

trial models dictates that the effect of these changes in respondent characteristics would only 

make an additive shift in the ratings, and will not change the relationship with the trail 

characteristics. Averaging over subjects leads to the following refinement from equation 59: 

(60) 

where all variables are as previously defined, and the overbars and dots are reminders that we are 

averaging over the second subscript (respondents). The mean of the random subject effect, β , 

will be subsumed by the intercept, μ, as we fit the model to the data. It is important to note with 

this model that the variance of the error, var( ei. ), is now substantially reduced because it is 

divided by the number of respondents, i.e., σ

2

/N. Inclusion of this random respondent effect only 

affects forecasting for a new sample of respondents, and only through its mean, β , with a 

substantially smaller variance than it would have otherwise. 

Choice of Overall Response 

Since we asked for responses for four different ratings of each video clip, we had to choose 

which to use in our model of quality of service of the path. We chose to create a model using the 

overall rating for two primary reasons. First, table 31, which provides the correlations among all 

the responses fit from a preliminary modeling effort using equation 48 above, shows that the 

responses were highly correlated among the four perception measures or responses. In fact, the 

overall rating was the most highly correlated with the other perception measures. Furthermore, 

the overall rating was designed to capture the respondents’ feelings with regard to all aspects of 

the path scene that they were viewing; however, the other perception measures honed in on more 

specific aspects. For example, lateral separation will probably capture the respondents’ feelings 

about path width, but not about the sight distance ahead on the path. For these reasons, the model 

development that follows is concentrated on the overall rating provided by the respondents. 

= + ∑ + ∑ + + ∑ +

k

k ik i

k

k ik

k

y i . μ θ kw ik δ x β ( βδ ) x e .
106

Table 31. Correlation between the four perception measures. 

Response Longitudinal separation Ability to pass Overall 

Lateral separation 0.375 0.569 0.614 

Longitudinal separation – 0.469 0.481 

Ability to pass – – 0.654 

Fitting the Model 

A preliminary analysis indicated that some variables appeared to be highly significant toward 

explaining variation in the overall rating. Careful examination, however, suggested that some 

variables were mere surrogates for one of the more influential, but very distinctive, trails 

(Lakefront in Chicago). This trail was different from the others in that it is situated in a highly 

urbanized environment and it was extremely crowded when we recorded the video that we 

showed to the respondents. Considering that most applications of this model would be outside of 

such an urbanized environment, the pursuit for the best model first excluded this location. 

Variable selection was performed with only the remaining 30 observations (instead of 36) from 9 

locations. After the variables were selected, the model was then refit using all 36 observations. 

Thus, the responses to the Chicago path helped fit the model, but it did not have undue influence. 

The following trail variables were included in the variable selection process: 

• Reciprocal of path width. 

• Square of the reciprocal of path width. 

• Reciprocal of average clearance. 

• Presence of a centerline. 

• Surrounding environment (urban or not). 

• Horizontal curvature. 

• Sight distance. 

The variables representing trail pavement material, presence of a shoulder, and presence of a 

vertical curve were not considered because, for each of these variables, eight locations took one 

value and two locations took the other. These variables would be acting more like “dummy 

variables” for a single location, which would have no value in predicting the service rating for a 

new location. The video quality variables included in the selection process were: 

• Glare. 

• Shadow. 

• Focus. 

The operational characteristics included in variable selection were: 

• Meetings. 

• Active passings. 

• Events (meetings + active passings). 

• Weighted events (meetings + 10 * active passings). 
107

• Events per foot width of trail. 

• Weighted events per foot width of trail. 

Note that the factor of 10 weighting the number of active passings was constructed by fitting 

equation 60, not including the effect of the location characteristics. Other weightings were 

considered, but a weight of 10 fit best for all four response variables. A different weighting for 

the heavily traveled Lakefront Trail was considered and abandoned for the sake of simplicity. 

Because the number of potential explanatory variables exceeded the number of locations by a 

factor of nearly two, forward selection was employed in the model selection process. The 

following variables were consistently useful as explanatory variables, leading to models with 

high R2

-values: 

• Reciprocal of path width (in feet). 

• Weighted events. 

• Weighted events per foot width of trail. 

• Presence of a centerline. 

• Glare. 

• Focus. 

The investigation using 9 of the 10 locations (excluding Lakefront) led to a model that used the 

first 4 explanatory variables (the glare and focus variables were dropped out of the running). 

Including the responses to the six Lakefront video clips led to nearly the same quality of fit, and 

most of the coefficients were not substantially different. However, the variable for the weighted 

events per foot width of trail did not contribute. Dropping that variable led to a simpler model 

with approximately the same fit. Therefore, our recommendation as the model that best predicted 

the overall rating (on our 1 to 5 scale) was: 

(61) 

 

where: 

E = Weighted events per min (meetings + 10 * active passings) 

RW = 1 / path width (in feet) 

CL = Presence of a centerline (0 if no, 1 if yes) 

Tables 32 and 33 show the analysis of variance and parameter estimate tables for this model 

from our statistical software (SAS). The output shows that the model fit the data well, with a 

high F-value that was highly significant. The R2

-value for this model was a healthy 0.64, and the 

adjusted R2

-value was also good at 0.61. The model should be easy to employ by path designers 

and analysts, with just three variables, two of which are under the designer’s direct control and a 

third which is available from the methods described in chapter 3. The negative signs of the 

weighted events and width variables are as expected. The sign of the centerline variable is 

probably a result of feeling restricted by bicyclists on a path with a centerline, particularly under 

the relatively low-volume conditions depicted on most of the video clips. The standard error and 

t-values for the intercept and each of the variables showed that they were all significantly 

Overall Rating = 5.446 –0.00809(E) –15.86(RW) – 0.287(CL) 
108

different from zero at the 95-percent level, with the weighted events and width variables being 

greatly different from zero. In sum, based on goodness of fit to all of the locations tested, ease of 

use, and logic in the relationship, equation 61 for overall rating should serve well as a quality-ofservice predictor. 

Table 32. Analysis of variance table for the final model. 

Source Degrees of 

Freedom 

Sum of 

Squares 

Mean 

Square 

F-value Pr > F 

Model 3 5.45675 1.81892 19.31 <0.0001 

Error 32 3.01489 0.09422 

Corrected Total 35 8.47165 

Table 33. Parameter estimate table for the final model. 

Variable Degrees of 

Freedom 

Parameter 

Estimate 

Standard 

Error 

t-Value Pr > |t| 

Intercept 1 5.44572 0.33921 16.05 <0.0001 

E 1 − 0.00809 0.00115 −7.05 <0.0001 

RW 1 −15.86222 3.03043 −5.23 <0.0001 

CL 1 − 0.28730 0.11940 −2.41 0.0221 
109

8. LOS PROCEDURE 

INTRODUCTION 

The previous chapters have shown how the study team gathered the raw materials needed to 

fulfill the objective of the research—a new LOS procedure for shared-use paths. The necessary 

literature is gathered in chapter 2, the theory is developed in chapter 3, the operational data is 

collected in chapters 4 and 5, and the perceptions of the path users are in chapters 6 and 7. In this 

chapter, we bring all this together to propose, justify, and demonstrate the use of the new 

method. Note that much of the information in this chapter is also in the User’s Guide that 

accompanies this report. This chapter contains more technical detail on the development of the 

procedure, while the User’s Guide contains more practical information for the user, such as case 

study applications of the procedure; however, much of the information is common to both. 

The new LOS procedure is intended primarily for trail planners, designers, and managers, which 

include professionals from a wide variety of disciplines (e.g., planners, landscape architects, 

transportation engineers, bicycle and pedestrian transportation specialists, and park and 

recreation planners and managers). It may also be useful for trail, bicycle, and pedestrian 

advocates; elected officials; planning and park commissioners; and other members of the public, 

especially those individuals who find themselves involved in trail planning efforts or situations 

involving trail user conflicts that stem from high volumes and diverse mode mixes. These 

conditions are increasingly common on trails located in urban, suburban, and high-use 

recreational areas. 

The LOS procedure can be used for a variety of trail planning tasks where quantitative 

evaluation is needed to assist in solving design or management problems, including: 

• Planning of appropriate widths and cross sections for new trails. 

• Evaluation of the LOS provided on existing trails. 

• Guiding of the design of improvements for existing trails where additional capacity is 

needed. 

• Determination of how many additional users a trail may be able to serve given a 

minimum LOS threshold. 

• Evaluation of the LOS for specific timeframes when particular trip purposes need to be 

served, such as weekday mornings and evenings when commuting trips are heaviest. 

• Determination of the LOS at a particular location on a trail, such as a narrow pinch point, 

in an unusually high-use area, or in an area with many reported user conflicts. 
110

TYPES OF SHARED-USE PATHS TO WHICH THIS STUDY APPLIES 

Readers of this report may understand the term shared-use path (or multi-use trail) to be 

applicable to a very wide range of facility types and settings. It is important to note that the LOS 

procedure introduced in this chapter was not developed to be applicable to every type of shareduse path. The list below describes the limits of the study and the specific applicability of its 

findings: 

• The procedure is applicable only to paved hard surface paths (asphalt or concrete). Paths 

surfaced with gravel, dirt, wood chips, or other materials were not evaluated in the 

research. Surface type and quality is not a component of this LOS evaluation. 

• The procedure evaluates path LOS in terms of bicycle mobility. While the findings and 

recommendations will probably improve a trail’s conditions for all users (pedestrians, 

joggers, inline skaters, etc.), the study was conducted from the point of view of the 

bicyclist. 

• The tool does not accommodate the use of specific mode-split inputs for users outside the 

five user groups identified in chapter 5 (i.e., adult bicyclists, pedestrians, joggers, inline 

skaters, and child bicyclists). Moreover, it is not applicable to situations that seek to 

evaluate the unique impact on LOS that other trail users may have, such as push scooters, 

wheelchair users, equestrians, cross-country skiers, electric vehicles, or others who may 

be a part of the mix on some trails. 

• This tool is not applicable to trail segments that have stop signs, signal controls, or road 

crossings more frequently than every 0.40 km (0.25 mi). 

• The tool is structured to address two-way, shared-use path facilities. It was not created 

with bicycle-only or one-way paths in mind; however, it may be applicable to paths of 

this nature. It does not apply to on-street bicycle facilities. 

LOS DEFINED 

For motor vehicles on roadways, the HCM defines LOS as a quality measure describing 

operational conditions within a traffic stream, generally in terms of such service measures as 

speed and travel time, freedom to maneuver, traffic interruptions, and comfort and 

convenience.(4) The HCM defines six levels of service for any particular facility type and uses 

letters from A through F to represent them (from best to worst). Each LOS represents a range of 

operating conditions. Although there has been some discussion of this in the literature, safety is 

not included in the measures that establish motor vehicle service levels. 

The trail LOS procedure developed through this research is similar to that used for motor vehicle 

LOS. These similarities include: 

• The shared-use path LOS procedure uses six levels of service categories (letters A 

through F, representing best to worst). 
111

• Maintaining an optimum speed (for the bicyclist) is a key criterion. 

• The service measures are primarily related to the concept of freedom to maneuver; 

specifically, they include meetings, active passings, and delayed passings. 

 

• Safety is not included in the set of measurements that establish service levels. 

However, there are some key differences, including that the trail LOS does not factor in travel 

time or traffic interruptions such as signals or stop signs at grade crossings. 

It is important to note that there are a host of other factors that the reader may think are important 

to consider in a trail user’s assessment of comfort and enjoyment of a trail, such as: 

• Pavement/surface condition and materials. 

• Weather. 

• Frequency and design of curves. 

• Presence and degree of grade changes (hills). 

• Proximity to adjacent motor vehicle traffic. 

• Quality of scenery. 

• Physical setting. 

• Quality of bicycling equipment in use. 

• Perceived safety of the surrounding neighborhood. 

Just as motor vehicle LOS measures a limited aspect of the experience of driving (it does not 

take into account the quality of the vehicle in which a person travels, the scenery along the road, 

etc.), the trail LOS model measures a limited aspect of the experience of bicycling on a trail. 

While other factors of trail design are also important to the user’s experience, those factors will 

be left to further research. 

THE PERCEPTION SURVEY RESPONSE SCALE 

The LOS procedure is based primarily on the perception survey responses documented in 

chapter 7. The 105 respondents provided a rating of each video clip on a scale of 1 (poor) to 5 

(excellent). Since this is the best current data set on path user perceptions, the research team 

wanted to ensure that the LOS scale corresponded with this response scale as closely as possible. 

Table 34 shows how we made this correspondence. We placed the midpoint of the LOS scale, at 

the C/D boundary, at the midpoint of the response scale (3.0). The extreme levels of service, A 

and F, accounted for larger parts of the response scale because the respondents tended to cluster 

their responses toward the middle values (i.e., relatively few respondents scored any video clip 

as a 1 or a 5). 
112

Table 34. Correspondence between perception score and LOS. 

Overall Perception Score LOS 

 X > 4.0 A 

3.5 < X < 4.0 B 

3.0 < X < 3.5 C 

2.5 < X < 3.0 D 

2.0 < X < 2.5 E 

 X < 2.0 F 

In general, A through C can be considered above-average levels of service, and D through F as 

below-average levels of service, compared to the opinions of the respondents viewing scenes 

from 10 trails across the United States. The LOS descriptions that follow are based on the 

experiences of the research team, may provide a more refined framework for trail designers and 

planners. 

A benefit of this LOS framework is that it provides a uniform quantitative measurement for use 

throughout the United States and North America. However, there is certainly latitude for each 

political jurisdiction and trail managing agency to adopt differing policies about which scores 

and grades will be regarded as acceptable levels of service for trails within their own 

communities, as is the case with roadway levels of service. To some degree, determining what 

scores and grades are acceptable can vary in each different application of the model. For 

example, a jurisdiction may elect to establish a policy to ensure that new trails meet a higher 

performance standard than that which is considered acceptable for existing trails. 

In general, A through C can be considered above-average levels of service, and D through F as 

below-average levels of service, compared to the opinions of the respondents viewing scenes 

from 10 trails across the United States. The LOS descriptions below, based on the experiences of 

the research team, may provide a more refined framework for trail designers and planners. 

A: Excellent. Trail has optimum conditions for individual bicyclists and retains ample 

space to absorb more users of all modes while providing a high-quality user experience. 

Some newly built trails will provide A-level service until they have “been discovered,” or 

until their ridership builds up to projected levels. 

B: Good. Trail has good bicycling conditions and retains significant room to absorb more 

users while maintaining an ability to provide a high-quality user experience. 

C: Fair. Trail has minimum width to meet current demand and to provide basic service to 

bicyclists. A modest level of additional capacity is available for bicyclists and skaters; 

however, more pedestrians, joggers, or other slow-moving users will begin to diminish 

the LOS for bicyclists. 

D: Poor. Trail is nearing its functional capacity given its width, volume, and mode split. 

Peak-period travel speeds will probably be reduced by levels of crowding. The addition 

of more users of any mode will result in significant service degradation. Some bicyclists 
113

and skaters will probably be adjusting their experience expectations or avoiding peakperiod use. 

E: Very Poor. Given trail width, volume, and user mix, the trail has reached its 

functional capacity. Peak-period travel speeds will probably be reduced by levels of 

crowding. The trail may enjoy strong community support because of its high usage rate; 

however, many bicyclists and skaters will probably be adjusting their experience 

expectations or avoiding peak-period use. 

F: Failing. Trail is popular to the point of significantly diminishing the experience for at 

least one, and probably all, user groups. It does not effectively serve most bicyclists; 

significant user conflicts should be expected. 

A benefit of this LOS framework is that it provides a uniform quantitative measurement for use 

throughout the United States and North America. However, there is certainly latitude for each 

political jurisdiction and trail managing agency to adopt differing policies about which scores 

and grades will be regarded as acceptable levels of service for trails within their own 

communities, as is the case with roadway levels of service. To some degree, determining what 

scores and grades are acceptable can vary in each different application of the model. For 

example, a jurisdiction may elect to establish a policy to ensure that new trails meet a higher 

performance standard than that which is considered acceptable for existing trails. 

DEVELOPING THE LOS PROCEDURE 

The research team embarked on the task of assembling an LOS procedure with several key 

objectives in mind. The procedure should: 

• Be based on the operational and perception data from the previous chapters as much as 

possible (meaning that it had been based on recent field data from U.S. paths and users). 

• Be in conformance with the scale presented in table 34 above. 

• Use inputs that are easy to assemble for the typical trail planner or designer. 

• Have a logical calculation that could be explained to the public and to decisionmakers. 

• Have a calculation that could be completed quickly in a spreadsheet or similar format. 

• Have clear and unambiguous output. 

• Include all possible grades (A through F), depending on the user volume (i.e., 

continuous). 

The next few sections describe the procedure we developed to meet these objectives. 
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Start With the Model from Chapter 7 

Our LOS procedure starts with the model of perception survey responses that emerged from 

chapter 7. That model was: 

(61[repeated]) 

where: 

E = Weighted events per min = meetings per min + 10 * active passings per minute 

RW = Reciprocal of path width (i.e., 1 / path width (in feet)) 

CL = 1 if the trail has a centerline, 0 if the trail has no centerline 

The researchers recommend using the spreadsheets described in chapter 3 to compute the 

number of meetings and passings per minute needed for equation 61. The spreadsheets from 

chapter 3 were based on the best current traffic-flow theory, and were validated reasonably well 

against field data in chapter 5. The number of meetings and passings is difficult to compute by 

hand, since the calculation must be repeated many times over small slices of the path; however, it 

lends itself to an efficient spreadsheet computation. The inputs into the meetings and passings 

calculation include one-way volumes of each user group, mean and standard deviation of the 

speed of each user group, and the “propensity to pass” factor discussed in chapter 3. Planners and 

designers should have at least some notion of the volumes they expect on a path to be analyzed; 

and most planners and designers will be content using default values (based on solid research) 

for the other inputs under most circumstances. 

Model Does Not Cover All Combinations 

Applying equation 61 directly with the LOS scale in table 34 proved to be troublesome, 

however, because the model was not sensitive enough under low-volume or high-volume 

conditions. Table 35 illustrates the difficulty. Table 35 basically covers the range of volumes 

shown to the perception survey respondents. Average values of mode split, PHF, and directional 

split were applied, and the computation assumed no centerline on the paths. The levels of service 

in table 35 are based on the scale in table 34 being applied to the score computed from 

equation 61. For 2.44-m (8-ft) paths, an LOS of A or B is impossible, and for 3.05-m (10-ft) 

paths, an LOS of A is impossible. At the other extreme, for a 4.88-m (16-ft) path, one-way flow 

rates above 1,600 users per hour is needed to get an LOS of F; even on a 3.66-m (12-ft) path, a 

one-way flow rate of 1,400 users per hour leads only to an LOS of E. 

The reason for the lack of sensitivity in the result from equation 61 is probably because 

respondents did not see enough video clips at the extreme combinations of volume and path 

width. For example, we did not show any clips from 2.44-m (8-ft) paths with very high volumes, 

or wide paths with very low volumes. We would have liked to have shown such clips to the 

respondents; however, we did not record any of these in the field. 

LOS Score = 5.446 – 0.00809(E) – 15.86(RW) – 0.287(CL) 
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Table 35. Scores and levels of service based only on equation 61. 

8-ft-wide 

path 

10-ft-wide 

path 

12-ft-wide 

path 

16-ft-wide 

path 

Oneway 

flow 

rate, 

users/h 

Meetings 

per hour 

Passings 

per hour Model 

score 

LOS Model 

score 

LOS Model 

score 

LOS Model 

score 

LOS 

 0 0 0 3.47 C 3.86 B 4.13 A 4.46 A 

 10 27 8 3.45 C 3.85 B 4.11 A 4.44 A 

 20 54 16 3.44 C 3.84 B 4.10 A 4.43 A 

 30 81 24 3.42 C 3.82 B 4.09 A 4.42 A 

 40 107 32 3.41 C 3.81 B 4.07 A 4.40 A 

 50 134 40 3.40 C 3.79 B 4.06 A 4.39 A 

 60 161 48 3.38 C 3.78 B 4.04 A 4.37 A 

 70 188 56 3.37 C 3.76 B 4.03 A 4.36 A 

 80 215 64 3.35 C 3.75 B 4.01 A 4.34 A 

 90 242 72 3.34 C 3.73 B 4.00 B 4.33 A 

 100 268 80 3.32 C 3.72 B 3.98 B 4.31 A 

 200 537 160 3.18 C 3.58 B 3.84 B 4.17 A 

 300 805 240 3.04 C 3.43 C 3.70 B 4.03 A 

 400 1074 320 2.89 D 3.29 C 3.55 B 3.88 B 

 500 1342 400 2.75 D 3.14 C 3.41 C 3.74 B 

 600 1611 480 2.60 D 3.00 C 3.26 C 3.60 B 

 700 1879 560 2.46 E 2.86 D 3.12 C 3.45 C 

 800 2148 640 2.32 E 2.71 D 2.98 D 3.31 C 

 900 2416 720 2.17 E 2.57 D 2.83 D 3.16 C 

1000 2685 800 2.03 E 2.43 E 2.69 D 3.02 C 

1100 2953 880 1.88 F 2.28 E 2.55 D 2.88 D 

1200 3222 960 1.74 F 2.14 E 2.40 E 2.73 D 

1300 3490 1040 1.60 F 1.99 F 2.26 E 2.59 D 

1400 3759 1120 1.45 F 1.85 F 2.11 E 2.44 E 

1500 4027 1200 1.31 F 1.71 F 1.97 F 2.30 E 

1600 4296 1280 1.17 F 1.56 F 1.83 F 2.16 E 

1 ft = 0.305 m 

DELAYED PASSING ADJUSTMENT 

One way in which to respond to the difficulty illustrated in table 35 would be to adjust the LOS 

scale to something other than that shown in table 34. However, the research team was very 

reluctant to change the scale because the respondents to the perception survey had provided 

absolute responses. They told us what the average path looked like by scoring it as a 3.0, and, by 

definition, that should correspond to the LOS C/D boundary. Also, the researchers wanted to 

keep the scale symmetrical in the sense that LOS C covered the same range of scores as LOS D, 

B covered the same as E, and A covered the same as F. In addition, shifting the scale one way to 

allow, for instance, LOS A to cover lower scores, would have helped for narrow paths, but would 

have made the situation untenable for wider paths, where LOS A would have then applied to 
116

paths that were quite busy. The research team thought about establishing different scales for 

different path widths; however, that would be awkward to implement and would again violate 

the spirit of the respondent survey in that the respondents had supplied answers regarding paths 

of widths from 2.44 to 6.1 m (8 to 20 ft) on one scale. 

Rather than adjust the scale, the researchers looked for an important factor that was not included 

in the perception survey and, therefore, had no chance to be reflected in equation 53. This factor 

was the number of delayed passings—the times in which the test bicyclist had to slow down 

before passing a slower path user traveling in the same direction. Delayed passings were not 

included in the perception survey (there was virtually no opportunity for respondents to view one 

of those cases). Yet, the researchers had good reason to include these delayed passing cases in 

the process, including the fact that bicyclists despised delayed passings because of the wasted 

time and the energy needed to regain the desired speed. In addition, there are similar factors 

defining LOS in other facets of highway capacity, including the two-lane highway chapter. The 

assumption that no passings are delayed is one of the key criticisms of the shared-path LOS 

methodology in the 2000 HCM.(4)

Chapter 3 showed how delayed passings may be calculated and provided a spreadsheet for the 

calculation. Inputs for the calculation include user volumes by mode, PHF, directional split, 

mean speeds, the number of lanes, the proportion of users occupying two lanes, and the distance 

needed for the test unit to pass one of the five other modes. Most of these inputs are the same as 

for equation 61. The number of lanes did not figure into the perception survey result in 

equation 61; however, some assumptions will be needed for this calculation. For this procedure, 

the researchers recommend that: 

• Paths from 2.44 to 3.20 m (8 to 10.5 ft) wide operate as two-lane paths. 

• Paths from 3.35 to 4.42 m (11 to 14.5 ft) wide operate as three-lane paths. 

• Paths from 4.57 to 6.1 m (15 to 20 ft) wide operate as four-lane paths. 

These widths roughly correlate with the AASHTO Guide for the Development of Bicycle 

Facilities’ recommended 1.2-m (4-ft) minimum allocation of space for safe bicycle operation.(1)

These widths also matched what the research team generally observed during its operational data 

collection effort on 15 trails across the United States. The proportion of users occupying two 

lanes, and the distance needed for the test unit to pass one of the five other modes, will be based 

on the data provided in chapter 5. 

A major question was how to integrate delayed passing into the perception survey model 

(equation 61) in order to eliminate the issues noted above in table 35, without creating new 

problems. The researchers investigated basing LOS on both the score from equation 61 and on an 

estimate of the number or percentage of delayed passings—in a manner similar to that in the 

two-lane highway chapter (chapter 20) in the 2000 HCM.(4) However, basing an LOS on two 

criteria introduces the possibility of discontinuities (cases when the LOS might skip from A to C, 

for instance, with the addition of just one user). 

To account for delayed passings without introducing potential discontinuities, the researchers 

introduced another factor to equation 61: 
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 Overall Rating = 5.446 – 0.00809(E) –15.86(RW) – 0.287(CL) − (DP) (62) 

where E, RW, and CL are as defined above; and DP is the delayed passing adjustment. After 

examining a number of possibilities, the researchers decided on a maximum DP of 1.5, which 

corresponds to, at most, three levels of service. Since the range of delayed passings per hour was 

from about 1 to 180 per hour (using the spreadsheets from chapter 3 and the average values of 

needed inputs), the researchers recommended that the delayed passing adjustment be a linear 

function covering this range or: 

(63) 

where DP is as defined above, and DPPH is the number of delayed passings per hour from the 

spreadsheet produced in chapter 3. 

The application of the delayed passing adjustment, as described above, makes the LOS 

procedure much more sensitive to higher volumes, while avoiding discontinuities. The inputs are 

reasonable and the calculation is easy using the spreadsheet that we developed. Tables provided 

later will show the effect that the delayed passing adjustment had on the LOS for various paths. 

Adjustment for Low Number of Events 

To address the difficulty noted above, that for narrow paths, levels of service of A or B are not 

possible, the research team introduced a low-volume adjustment as follows: 

• All paths with a number of weighted events per minute of five or below are scored as 

LOS A. 

• All paths with a number of weighted events per minute from just over 5 to 10 are scored 

as LOS B, unless they were otherwise going to earn an LOS A grade based on 

equation 62. 

The researchers chose those levels to ensure that every LOS was possible at every trail width 

studied, from 2.44 to 6.1 m (8 to 20 ft). The levels of these adjustments are rather modest. Five 

weighted events per minute, with average mode splits and other assumptions, means a flow rate 

of about 30 one-way users per hour, while 10 weighted events per minute mean a flow rate of 

about 60 one-way users per hour. 

Putting It All Together 

With the delayed passing adjustment in place to remove the difficulties at high volumes, and the 

low-volume adjustment in place removing those difficulties, the LOS procedure is complete. In 

step-by-step form, the recommended LOS procedure is: 

1. Compute the number of meetings per minute using the spreadsheet from chapter 3. 

2. Compute the number of active passings per minute using the spreadsheet from chapter 3. 

DP = DPPH * 1.5 / 180 
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3. Combine the meetings and active passings from steps 1 and 2 into an estimate of 

weighted events per minute by multiplying the active passings by 10 and adding that 

result to the meetings. 

4. Compute the number of delayed passings per hour using the spreadsheet from chapter 3. 

5. Convert the estimate of delayed passings per hour into the delayed passing adjustment 

factor using equation 63. 

6. Using the path width, the presence of a centerline, and the results from steps 3 and 5, 

compute the LOS score from equation 62. 

7. Use table 34 to convert the LOS score from step 6 into a tentative LOS grade. 

8. Apply one of the low-volume adjustments given above if the number of weighted events 

per minute is 10 or lower to find the final LOS grade. 

The recommended procedure meets all of the objectives laid out at the beginning of the chapter: 

• It is largely based on the operational and perception data from the previous chapters, 

including the perception survey responses at the heart of the procedure. 

• It conforms to the scale presented in table 34 above. 

• The inputs are easy to assemble for the typical trail planner or designer. 

• The calculation is logical and could be explained to the public and to decisionmakers. 

• The calculation can be completed quickly in a spreadsheet. 

• The procedure produces a single LOS grade (as clear and unambiguous an output as 

possible). 

• All LOS grades are possible at each path width studied. 

Table 36 shows the effects of the delayed passing adjustment and the low-volume adjustment on 

the same combinations of volume and path width as examined in table 35 for the unadjusted 

model. The low-volume adjustment changed the LOS grade for the first six rows in the 2.44-m 

(8-ft) column and the first three rows in the 3.05-m (10-ft) column from C to A or B. The 

weighted passing adjustment tended to change the LOS above about 100 one-way users per hour 

by one or two levels. 
119

Table 36. Scores and grades based on complete LOS procedure. 

8-ft-wide 

path 

10-ft-wide 

path 

12-ft-wide 

path 

16-ft-wide 

path 

Oneway 

flow 

rate, 

users/h 

Meetings 

per hour 

Passings 

per 

hour 

Model 

score*

LOS Model 

score*

LOS Model 

score*

LOS Model 

score*

LOS

 0 0 0 3.47 A 3.86 A 4.13 A 4.46 A 

 10 27 8 3.45 A 3.84 A 4.11 A 4.44 A 

 20 54 16 3.42 A 3.81 A 4.10 A 4.43 A 

 30 81 24 3.38 B 3.78 B 4.08 A 4.41 A 

 40 107 32 3.33 B 3.73 B 4.06 A 4.39 A 

 50 134 40 3.28 B 3.68 B 4.03 A 4.36 A 

 60 161 48 3.23 C 3.62 B 4.01 A 4.34 A 

 70 188 56 3.17 C 3.56 B 3.98 B 4.32 A 

 80 215 64 3.10 C 3.50 C 3.95 B 4.29 A 

 90 242 72 3.04 C 3.43 C 3.92 B 4.26 A 

 100 268 80 2.97 D 3.36 C 3.88 B 4.23 A 

 200 537 160 2.21 E 2.61 D 3.44 C 3.86 B 

 300 805 240 1.54 F 1.93 F 2.82 D 3.40 C 

 400 1074 320 1.39 F 1.79 F 2.09 E 2.85 D 

 500 1342 400 1.25 F 1.64 F 1.91 F 2.26 E 

 600 1611 480 1.10 F 1.50 F 1.76 F 2.10 E 

 700 1879 560 0.96 F 1.36 F 1.62 F 1.95 F 

 800 2148 640 0.82 F 1.21 F 1.48 F 1.81 F 

 900 2416 720 0.67 F 1.07 F 1.33 F 1.66 F 

1000 2685 800 0.53 F 0.93 F 1.19 F 1.52 F 

1100 2953 880 0.38 F 0.78 F 1.05 F 1.38 F 

1200 3222 960 0.24 F 0.64 F 0.90 F 1.23 F 

1300 3490 1040 0.10 F 0.49 F 0.76 F 1.09 F 

1400 3759 1120 0.00 F 0.35 F 0.61 F 0.94 F 

1500 4027 1200 0.00 F 0.21 F 0.47 F 0.80 F 

1600 4296 1280 0.00 F 0.06 F 0.33 F 0.66 F 

* Before low-volume adjustment. 

1 ft = 0.305 m 

Comparison to HCM Method 

The recommended new procedure will return different LOS values than the LOS method in the 

2000 HCM(4) for a given path. (4) Figure 29 shows a comparison of the recommended new 

method to the LOS method in the 2000 HCM for a 2.44-m- (8-ft-) wide (two-lane) path with no 

centerline, while figure 30 shows a comparison for a 3.66-m- (12-ft-) wide (three-lane) path with 

no centerline. In both comparisons, all other variables are at their average or default values. Both 

figures show LOS values for the 2000 HCM method in two different ways: (1) considering 

meetings and passings of bicycles and pedestrians only, and (2) considering meetings and 

passings of all path users by the test bicyclist. 
120

0 100 200 300 400 500

One-Way Volume, Users per Hour

Level of Service

New Procedure HCM Bike and Ped. Only

HCM All Users

A

D

B

C

E

F

Figure 29. Comparison of recommended new LOS procedure to 2000 HCM procedure

for a 2.44-m- (8-ft-) wide (two-lane) path with no centerline. 

Figures 29 and 30 both show that the new LOS procedure is more optimistic than the 2000 HCM

method. The difference is typically one LOS, until the new procedure predicts LOS F; however, 

it was as high as three levels of service for 3.66-m- (12-ft-) wide paths at 200 one-way users per 

hour. The differences in LOS predictions probably result from the generally optimistic ratings of 

the video clips provided by the perception survey respondents. 
121

0 100 200 300 400 500

One-Way Volume, Users per Hour

Level of Service

New Procedure HCM Bike and Ped. Only

HCM All Users

A

D

B

C

E

F

Figure 30. Comparison of recommended new LOS procedure to 2000 HCM procedure 

for a 3.66-m- (12-ft-) wide (three-lane) path with no centerline. 

APPLYING THE MODEL 

The LOS procedure is applicable to a variety of trail planning and design problems related to 

crowding and accommodating diverse user groups. It is especially useful for trail planning and 

design tasks that need to augment qualitative criteria with quantitative measures in order to 

strengthen the basis for making trail design decisions. 

To enable the model to be easily used by practitioners, it has been programmed into a 

spreadsheet tool. This tool is called the Shared-Use Path Level of Service (SUPLOS) Calculator 

and it accompanies this report. This section helps the reader understand how to apply this tool 

effectively and to generate results that are appropriate for the particular problem at hand. To 

make the best use of the Calculator, it is important to understand the limits of its application, and 

how contextual factors should be considered when collecting or structuring the input data. 

Link Analysis 

The LOS procedure is a link analysis tool. It is designed to provide an LOS evaluation for a 

particular link or segment of a linear trail. It is not designed to evaluate trail/roadway 

intersections, rest stops, or trailheads. 

In general, segment length is not a limiting factor in selecting a link for analysis. 
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The key to determining how much trail can be evaluated with one calculation is whether or not 

the trail conditions and use characteristics remain the same over the entire length that has been 

selected. However, because of the distance assumptions used in the model, trail segments under 

0.4 km (0.25 mi) are not recommended for analysis. Moreover, because typical trip distances for 

some trail users are limited, and user turnback rates will begin to undermine the accuracy of the 

volume and mode-split data on longer segments, 3.2 or 4.8 km (2 or 3 mi) is a recommended 

maximum segment length. 

Each practitioner needs to exercise professional judgment in making these decisions. To assist in 

that effort, the following list of conditions and characteristics should remain roughly the same 

over the entire distance of trail that is being considered as one link (segment): 

• Trail width. 

• Trail user volume. 

• Trail user mix (mode split). 

• Presence of a centerline stripe. 

• Absence of significant flow interruptions such as stop signs, signalized road crossings, or 

other grade crossings. 

• Absence of spur trails, trailheads, or other access points that may significantly affect user 

volumes or mix. 

If any of these characteristics change significantly over the length of a trail segment, it is 

recommended that the segment be divided into one or more links using road crossings, access 

points, or other locations where characteristics change as endpoints for the smaller segments. 

Moreover, when considering establishing a trail volume and user mix profile for a single 

segment that is longer than 1.6 km (1 mi), based on counts taken in only one location, it is 

important to determine if turnback rates for pedestrians or for other users might be significant 

enough to affect the accuracy of these data for the sections of the segment that are farthest from 

the data collection point. In other words, if users often turn around partway through a segment, a 

single count may not represent the whole segment very well. 

As with any model, the quality and accuracy of the output can be no better than that of the 

inputs. It is understood that the quality and accuracy of input data will vary for each user of the 

tool. Moreover, each user and/or situation does not demand a uniform level of accuracy to 

produce a useful result. For these and other reasons, professional judgment is critical in 

determining what level of accuracy is required for the data to be used in any particular 

application. In most situations, slight variations in data may not affect LOS scores significantly, 

and the tool itself can be used to test variations in data and to determine what impact they do 

have on LOS results. 

The LOS procedure does not factor in the potential delay and other impacts of stop signs, 

signalized road crossings, or other grade crossings that interrupt the flow of trail traffic. The 

model is designed to generate LOS scores for trail segments of 0.40 km (0.25 mi) or longer, with 

no flow interruptions. If LOS is desired for a length of trail that includes these types of 

interruptions, it should be segmented at these locations and, if possible, separate volume and 

mode-split data should be developed for each segment. Segments shorter than 0.40 km (0.25 mi) 
123

are generally dominated by the stop signs or signals at the ends; consequently, a segment LOS 

would not normally be appropriate for those places. 

Data Requirements 

Only four data inputs are needed to generate an LOS estimate: (1) trail width, (2) presence of a 

centerline, (3) one-way user volume, and (4) mode split. The following discussion of data 

requirements is provided to help readers apply the tool correctly and effectively to their unique 

situations. 

Trail Width 

Trail width should be measured in feet. Widths may be entered in half-foot increments (i.e., 8.0, 

8.5, 9.0, etc.) (1 ft = 0.305 m). The procedure was calibrated on data from trails that had widths 

between 2.4 and 6.1 m (8 and 20 ft). Widths greater or less than these amounts will produce LOS 

outputs; however, the model is not designed to address widths outside of the 2.4- to 6.1-m (8- to 

20-ft) range. 

Centerline 

Centerline is a “yes/no” input to be based on the existing striping pattern of each segment or the 

proposed striping pattern for an unbuilt trail. 

Trail User Volume 

The volume data needed for the procedure can be provided in one of three ways, depending on 

how the tool is being applied: 1)By using actual volume counts collected on an existing trail, 2) 

by using estimated volume counts developed by extrapolating from actual volume data gathered 

on another trail that is determined to be similar to the test trail (this can be one of the trails 

addressed in this study), or 3) the “average trail” described in the User’s Guide. 

or 

By developing projected user volumes, such as for an unbuilt trail, where LOS calculations will 

be used to aid in the trail planning and design process. 

Whether volume data are developed from estimates or actual counts, they should be structured, 

or restructured, in the following ways: 

• A one-way trail volume should be calculated for each separate segment (link) of trail for 

which an LOS score is desired. If counts are structured as total two-way volumes, an 

assumed 50/50 directional split is recommended for conversion to one-way volumes. 

Volume data should be in, or converted to, users per hour. 

• Volume data should include a total count of all user types (modes) that use the treadway 

being evaluated. If there is a separate parallel treadway in the same trail corridor, such as 

a jogging track or equestrian trail, users on this treadway should not be included in the 

volume or in the user mix data used in the model. 
124

• If new user counts are collected for use in this tool, it is recommended that a minimum of 

three hourly, two-way counts be taken for each trail segment for which an LOS score is 

desired. For each test trail segment, an average, per hour, one-way volume can be created 

from the three hourly, two-way counts. 

Mode Split 

Mode split is expressed as a percentage of one-way trail users per hour. The model provides the 

opportunity to input a mode-split percentage for up to five different modes: adult bicyclists, 

pedestrians, joggers, inline skaters, and child bicyclists. In the calculation tool, mode-split inputs 

can be round numbers or precise numbers using one decimal place, and they need to add up to 

100 percent exactly. Zero is an acceptable entry for any mode. Given the entire set of user types 

that are found on shared-use paths, these five categories were developed based on the actual 

users that were observed on the 15 study trails. 

Other users, such as push scooters, electric scooters (used by disabled persons), wheelchairs, etc., 

may be present or expected on trails where this tool is applied. If the mode-split data being used 

for the test trail segment include a breakout of user percentages in categories other than the five 

used by the Calculator, add that percentage to whichever one of the five modes has the closest 

corresponding travel speed. Average travel speeds by mode for each of the five modes listed 

above were presented in chapter 5, and are also in the User’s Guide. 

If actual mode splits are not known and estimates need to be developed, users can: 

 

• Use mode splits from data gathered on another trail in the community or region that is 

sufficiently similar to the trail to be analyzed. 

• Use the mode split for the average trail (i.e., the average mode split for the 15 trails for 

which operational data were collected during this research, presented in chapter 5). This 

can be accomplished by clicking a button in the Calculator spreadsheet labeled default 

mode split. 

• Review the mode splits of the 15 study trails from this research (see chapter 5 or the 

User’s Guide) and use a mode split from one of the study trails judged to be similar to the 

trail to be analyzed (i.e., the trail is located in a similar community, has a similar setting, 

has a similar width, etc.). 

On many trails, user volumes and mixes will vary considerably along different segments of the 

facility. When more accurate LOS scores are desired, trail segmenting should take these 

variations into account. The following is a list of spatial and behavioral factors that can generate 

significant volume and mode-split fluctuations, and which can be used to guide segmentation: 

• Locations of trail access points, including junctions with other trails, spur trails, 

trailheads, park nodes along a trail, and access points between the trail and adjacent trip 

generators and destinations. 
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• Trip generators and destinations associated with points of access, such as housing 

developments, employment centers, schools, parks, university campuses, entertainment 

attractions, or other institutions or public properties. 

• Typical trip lengths and turnback rates. These will vary by user type and from trail to trail 

because they are influenced by a number of trail-specific factors, such as trail layout, trip 

purposes, landscape character, and the personal habits and needs of local trail users. 

Additionally, when new user counts are planned, these factors may be used to inform the 

location, frequency, and timing of the counts. When volume and mode-split estimates are being 

used for model inputs, these factors may be used to make adjustments to the estimates to increase 

the accuracy. 

Effects of Temporal Factors 

Temporal factors (e.g., season of the year, day of the week, and time of day) may be the most 

significant factors affecting trail user volumes. Therefore, any LOS estimate applies only to the 

timeframes in which the volume data counts were taken. 

In most cases, trail analysts seek to understand trail operations under fully loaded conditions. 

When this is the case, the volume data to be used in the Calculator should reflect those 

conditions. The volume data should be gathered during (or adjusted to reflect) the typical highest 

use times. The exact number and duration of user counts that are needed to fully describe fully 

loaded conditions may vary from case to case, depending on the level of detail desired for the 

volume profile and on how the resulting LOS score is to be used. 

To be consistent with the data collection methods used on the 15 trails studied in this project and 

to ensure some statistical stability in the results, at least three 1-h counts are recommended for 

each trail segment evaluated. Assuming that the purpose of the LOS score is to determine 

whether, and how, to improve service during high-use periods, counts should be taken during the 

high-use season, on high-use days, at high-use times of the day. 

In some cases, the crux of a trail manager’s problem may center as much around determining the 

duration or extent of high-use periods as it does around determining how bad levels of service 

get during high-use times. In other words, if poor levels of service are only experienced a few 

weekends a year, or for an hour or two on a weekend day, it may be more tolerable than if a trail 

is crowded all day long throughout the spring, summer, and fall. In this way, the duration of time 

over which a certain LOS applies may be as important to know as the LOS score itself. In these 

cases, the volume data that are used in the Calculator will need to be more extensive and reflect 

greater temporal diversity. 

Some users of this tool may seek an LOS evaluation for a more specific purpose, such as to 

determine what the LOS is for bicycle commuters during the afternoon peak. In such a case, the 

data would need to be gathered on weekdays, during the season(s) that generate the highest 

commuting rates, and would focus on the particular afternoon hours when bicyclists are present 

on the trail segments in question. 
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Assumptions and Default Values 

The following are the key assumptions and default values built into the Shared-Use Path LOS 

Calculator. These are generally based on average values from the data we collected during this 

project on 15 representative trails scattered across the United States. Users with significant 

departures from these values can change them in the Calculator. 

Directional Split 

The Calculator assumes a 50/50 directional split. 

User Speed 

The Calculator uses the average speeds and standard deviations for each user group, as shown in 

chapter 5. The default speed for the test bicyclist is 20.61 km/h (12.8 mi/h), the same speed as 

that of the average bicyclist. 

Peak-hour Factor 

The Calculator uses a default PHF of 0.85. This factor was calculated using the data collected on 

the study trails and was based on the peak 15 min, as is the custom in the HCM. The model 

applies the PHF of 0.85 to the one-way, per hour, user volume, which results in a volume boost 

of 17.6 percent. This factor ensures that the model results are responsive to the typical peak flow 

conditions that are found on trails. 

Number of Lanes 

This and other trail research has found that bicyclists on trails tend to operate in distinct lanes, 

whether or not the lanes are indicated on the trail surface with striping. Typical patterns include 

two-lane, three-lane, and four-lane operations: 

• On two-lane operations, passing maneuvers are made in the opposing lane. 

• On three-lane operations, each direction of travel shares use of a “middle” lane for 

passing maneuvers. 

• On four-lane operations, each direction of travel has its own passing lane. 

Because there are no existing universal standards that correlate trail width with lane operations, 

this study assumed the following for the LOS procedure: 

• Paths from 2.44 to 3.20 m (8 to 10.5 ft) wide operate as two-lane paths. 

• Paths from 3.35 to 4.42 m (11 to 14.5 ft) wide operate as three-lane paths. 

• Paths from 4.57 to 6.1 m (15 to 20 ft) wide operate as four-lane paths. 

These widths roughly correlate with the AASHTO Bicycle Facility Design Guide’s 

recommended 1.22-m (4-ft) minimum allocation of space for safe bicycle operation.(1) These 
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widths also match what the research team generally observed during its operational data 

collection effort on 15 trails across the United States. 

Lane configuration matters only in the calculation of the delayed passing factor. The Calculator 

automatically determines the correct lane configuration to be used based on trail width. The 

delayed passing factor is computed differently for each of the three possible lane configurations, 

using the overall trail volume, mode split, and average travel speeds, to calculate the probability 

of encountering delay in a passing maneuver: 

• Two-lane operations have the greatest potential for creating delayed passings because the 

bicyclist must use the opposing lane to pass a slower user, and that lane may be occupied 

by users traveling in the opposite direction. 

• Three-lane operations provide the bicyclist with better conditions for passing slower 

users because of the presence of a center, shared passing lane. Delay is determined 

primarily by the likelihood that a trail user traveling in the opposite direction is using the 

center lane to make a passing maneuver; thus, there is less likelihood of delay. 

• Four-lane operations provide even better passing conditions because the probability of a 

delayed passing maneuver is greatly reduced, unless overall user volume is extremely 

high. 

LOS LOOKUP TABLES 

Appendix C of the User’s Guide includes a series of lookup tables that provide LOS estimates 

for a variety of combinations of volume, mode split, and trail width, based on default values for 

other variables. 

Instructions for Using the LOS Calculator 

The Shared-Use Path Level of Service Calculator is provided in the form of a spreadsheet. It has 

the complete LOS model programmed into it, and provides a one-page, user-friendly interface 

(worksheet) that allows the user to analyze up to five data sets at a time. The Calculator requires 

only four inputs to generate an LOS estimate from the bicyclist perspective—trail width, 

presence of a centerline, trail user volume, and mode split—for up to five user types (adult 

bicyclists, pedestrians, joggers, inline skaters, and child bicyclists). 

The Shared-Use Path Level of Service Calculator should be opened in Excel. “Enable Macros”

should be selected in the first dialogue box. If it is not already selected, the SUP_LOS_ 

Calculator tab at the bottom of the window should be selected. This will open the Calculator 

worksheet. 

Before beginning the data entry process, review the previous discussion about data requirements. 

Based on that discussion, assemble the data necessary to conduct the analysis. 

• The first column provides a cell to enter the trail or segment name. Type in a name or 

segment identifier. 
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• The second column provides a cell for the trail width. Enter a number representing the 

desired width in feet. Widths may be entered in half-foot increments (i.e., 8.0, 8.5, 9.0, 

etc.) (1 ft = 0.305 m). 

• The third column asks if the trail has (or will have) a centerline. Type in a “1” for yes or a 

“0” for no. 

• The fourth column provides a cell for one-way trail volume per hour. Enter a number. 

• Columns five through nine provide cells for mode split. Entries may or may not use 

decimal increments in tenths. The sum of the five mode splits must total exactly 100, or 

an error message will appear above the data entry row. 

• Data entries cannot be made in any of the spreadsheet cells, other than those described 

above. 

Once all of the numeric inputs are provided, columns 11 and 12 will calculate the level of service 

score and will provide the level of service grade, automatically. 

In the upper right-hand corner of the spreadsheet, a scale is provided that correlates scores with 

the grade. In general, grades A through C can be considered above-average levels of service, and 

grades D through F as below-average levels of service. 

Once the LOS has been computed, the whole sheet may be copied or printed to a word or 

spreadsheet file to create a permanent record of these cases and results. Once the information has 

been pasted into a new file, revision of model calculations will not be possible in that new 

document. The results may be printed using the regular print commands to print directly from 

Excel, or by printing the file to which copies have been saved. By copying and saving the results 

to another file, the Calculator can be used over and over again without losing the results of 

previous scenarios. 

For convenience, the Calculator worksheet has been designed with a separate one-click button to 

reset the default mode split for each row. 
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Implications for Trail Design 

The central findings of this study have important implications for trail design. The following is a 

list of key findings that can be used to inform design choices: 

• Width is the key factor in determining LOS, and every additional foot of trail width has a 

positive impact on LOS, according to the model. 

• Bicyclists’ LOS on pathways is very sensitive to user mix; when the amount of foot 

traffic (joggers and pedestrians) surpasses the 15 percent trail level, the level of service is 

significantly impacted. 

• Bicyclists are responsive to the use of a centerline stripe to divide directional flows. 

Trail Width 

The LOS procedure provides strong support for the standard trail-width guidance provided in the 

AASHTO Guide for the Development of Bicycle Facilities.

(1) Trails that are 2.4 m (8 ft), which 

AASHTO recommends only in “rare instances,” were found to have poor levels of service, 

except at very low volumes, or with user mixes that included few pedestrians and joggers. The 

findings of this research supports AASHTO’s(1) minimum “recommended paved width for a twodirectional shared-use path of 10 feet [3 m].” 

The procedure shows that widths of 3.35 to 4.57 m (11 to 15 ft) provide improved levels of 

service for higher volumes and more balanced user mixes. This is also consistent with AASHTO 

recommendations that, “under certain conditions, it may be necessary or desirable to increase the 

width of a shared-use path to 12 ft [3.6 m], or even 14 ft [4.2 m], due to substantial use by 

bicycles, joggers, skaters, and pedestrians.”(1) Trails of 3.35 to 4.57 m (11 to 15 ft) are wide 

enough to operate as three-lane paths. The increased passing capacity provided by a trail that 

operates as three lanes improves LOS and increases the trail’s ability to absorb higher volumes 

and more diverse mode splits without severely degrading service. 

The implications for designers of these results regarding trail width include: 

• During design of new trails and widening of existing trails, designers may want to 

consider varying the trail width to achieve LOS goals in key locations while not 

“overbuilding” in other locations. Adding width to improve LOS is valuable to trail users, 

even if it is provided only on selected segments. 

• When considering wider trails, designers and decisionmakers may want to think in 0.305-

m (1-ft), rather than in 0.61-m (2-ft), increments. Typical practice has been to consider 

trail widths in 0.61-m (2-ft) increments (i.e., 3.05, 3.66, 4.27 m (10, 12, 14 ft), etc.). This 

approach may lead designers to miss opportunities to provide measurable increases in 

LOS, while at the same time, containing costs and minimizing environmental impacts. 
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Centerline Striping 

A striped centerline was found to have a strong impact on the bicyclist’s perception of freedom 

to maneuver. This finding appears to support the intent of trail designers in providing a 

centerline, which is to clearly delineate two opposing travel lanes. A centerline reinforces the 

idea that to pass a slower moving user, the bicyclist may need to use the travel lane of opposing 

trail users, and should pass only when the opposing lane is open. 

This research found that the presence of a centerline stripe results in a significant reduction in the 

LOS score. It appears that bicyclists felt less comfortable making a same-direction passing 

movement when a centerline stripe was present. While this finding might appear initially to 

mean that a centerline should not be used, it is important to note that there may be other valid 

safety reasons for providing a centerline stripe, particularly on crowded trails, on curves with 

limited sight distance, and in other appropriate circumstances.

Only two trails in this study were striped with more than two travel lanes. The Pinellas Trail near 

Saint Petersburg, FL, was striped as a three-lane trail, one in each direction for bicycles and 

skaters, and one lane for pedestrians. The Lakefront Trail in Chicago, IL, was striped as four 

lanes, two in each direction. These two examples did not represent a sufficient number of study 

trails to fully assess the impact of multi-lane striping patterns on LOS. However, the LOS 

procedure is based on the idea that having sufficient trail width for a four-lane operation (a 

minimum of 4.57 m (15 ft)) increases the ability of bicyclists to pass slower moving users 

without encountering blockage from trail users in the opposing lanes. 

Multiple Treadways 

A number of shared-use trails have been designed with two treadways in the same trail corridor. 

Often, one is paved and the other is a soft surface. Frequently, one of the treadways is provided 

for exclusive use by one or two trail user groups, or user restrictions are imposed on both paths 

in an effort to segregate users. 

Given the impact of user mix on bicyclist LOS, a multiple treadway design that effectively 

reduces the number of pedestrians and joggers mixing with bicyclists will have significant LOS 

benefits for the treadway used by bicyclists and skaters. This study did not address compliance 

with use restrictions—an issue that is often raised by trail managers as a problem when separate 

treadways are provided. 

Trail Operations and Management 

While this study did not examine issues related to trail operations and management, the 

framework of the tool may lend itself to applications in this area. Ideas include using LOS grades 

in warrants for warning or trail etiquette signs. These trail etiquette signs address sharing of the 

treadway or use of designated passing protocols. The LOS procedure may also be useful in 

setting trail speed limits or in establishing other advisory or regulatory protocols that will 

increase user safety and will moderate user conflicts. 
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CASE STUDIES 

The User’s Guide contains several case studies posing typical problems facing designers and 

showing how the LOS procedure can help provide solutions to those problems. 

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9. CONCLUSIONS AND RECOMMENDATIONS 

INTRODUCTION 

Shared-use paths are paved, off-street travel ways designed to serve nonmotorized travelers. 

Shared-use paths are gaining popularity in two different ways in recent years in the United 

States. First, there are many more paths and many more miles of paths being created. Second, 

shared-use paths are attracting a increasingly greater amount of use. Some urban trails attract 

thousands of users per hour during peak periods and some are experiencing rush hours and traffic 

jams. Trail managers are becoming increasingly concerned about user conflicts and injuries, and 

some are also concerned that potential users are deciding not to come out and use a trail because 

of crowding. 

The design of a new path or a path to be rebuilt is thus an increasingly important activity. During 

the design of every shared-use path, someone eventually asks, “How wide should this pathway 

be?” That question nearly always raises even more questions: What types of users can we 

reasonably expect? When will we need to widen the path? Do we need to separate different types 

of users from each other? These are very difficult questions for designers who face that classic 

design dilemma of overbuilding versus obsolescence. If the designer specifies a trail to be wider 

than future use justifies, there is a waste of money that could otherwise have been used to 

construct more miles of trail elsewhere. If the designer specifies a trail that proves to be too 

narrow for the future volume and mix of users, there will be more user conflicts and collisions, 

greater unhappiness among users, and the need to consider expensive trail widening. 

At the present time, conventional design manuals do little to help designers resolve their 

dilemmas. The classic procedure first specified by Hein Botma in The Netherlands, which bases 

LOS on the estimated number of meetings and passings for bicyclists, is an attractive framework 

that could help designers. There can be little debate that, in general, paths where bicyclists must 

make more meetings and passings should be less desirable than trails with fewer meetings and 

passings. However, the LOS procedure in the 2000 HCM(4), adapted from Botma’s work, has a 

number of serious limitations that make it difficult for designers to use in order to resolve their 

path design dilemmas. 

The overall project objective was the production of a tool that professionals can use to evaluate 

the operational effectiveness of a shared-use path, given a traffic forecast or observation at an 

existing path along with some geometric parameters. The project adopted Botma’s method as the 

basic framework for the LOS procedure. In particular, the objective was to produce a tool that 

overcomes the major limitations in the current LOS procedure noted above. The desired 

procedure emerging from this project would: 

• Be calibrated and validated. 

• Be based on U.S. data. 

• Have LOS criteria based on user input. 

• Include more modes. 

• Include the ability to change key parameters such as mean speeds. 
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• Account for delayed passing. 

• Analyze the full range of existing and possible path widths. 

• Be in a form ready for use by path designers. 

CONCLUSIONS 

Our four major achievements during the project included: 

• Development of the additional theoretical framework necessary to overcome the 

limitations to the existing procedure noted above. 

• Collection of field data on path operations to calibrate and validate the theoretical 

equations for U.S. conditions. 

• Collection of path user perception data to establish LOS criteria. 

• Development of an LOS estimation tool that professionals working with shared-use paths 

can use and a plan to provide them with that tool. 

During the project, we made two significant advances in the theory of traffic flow on shared-use 

paths. First, we extended the previous method to other modes, other speed distributions, and 

passive passings. As noted, a passive passing is an occasion when the test bicyclist is passed by a 

faster path user. Second, the team developed a way to calculate the number of delayed passings. 

These are times when the test bicyclist would arrive behind a slower path user and not be able to 

pass because of the lack of an adequate gap in the next lane to the left (oncoming or same 

direction). Obviously, delayed passings are undesirable for bicyclists since they then have to 

slow down and then probably expend energy accelerating when an adequate gap does appear. 

Delayed passings are also critical because they are so highly related to path width. Prior to this 

project, there were some delayed passing calculations in the literature related to two-lane 

highway operation, etc.; however, there was nothing in the literature related to shared-path 

operation. Our new theory will estimate delayed passings of various modes for two-lane, threelane, and four-lane paths. 

The objective of the operational data collection portion of this project was to collect the field 

data needed to calibrate and validate the LOS model for shared-use paths. To calibrate and 

validate an LOS model, the main variables that we needed to collect were meetings and desired 

and actual passings by path users, and the speeds and volumes of the path user groups. The team 

chose to use the moving-bicycle method to collect these variables. We collected a database of 

more than 700 runs of (mostly) 0.80 km (0.5 mi) each by our test bicyclist on 15 shared-use 

paths in 10 cities across the United States. The database included a wide array of volumes and 

speeds for five different major user groups. The major analysis of the database was to use the 

theory described above to predict of the number of meetings and passings for each run, and to 

compare that prediction to the number of meetings and passings counted in the field. Our results 

showed that the prediction matched the field count fairly well at most sites. We also were able to 

use the operational database to find the default values that we needed for the LOS procedure, and 

to use the videotapes for our perception data collection. 
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The third major part of this effort was to collect data on user perceptions of multi-use trail 

designs and operations to help set the LOS criteria. We did this by showing more than 100 

volunteer respondents thirty-six 1-min videotapes of paths recorded using the helmet camera 

during the operational data collection effort. We selected the videotapes to represent a wide 

range of path designs and operations. The respondents were asked to state, on a five-point scale, 

how much they would enjoy using the path in four different ways: longitudinal separation, 

latitudinal separation, ability to pass, and overall. An analysis of the responses showed that there 

was a strong relationship between the number of meetings and passings and enjoyment, and that 

path width was the primary geometric variable that affected user perception. The user perception 

model that we developed from the data was the backbone of our LOS procedure. 

The most important accomplishment of the project team was the production of a new method to 

estimate the LOS for a shared-use path, based on the results from the other parts of the project. 

The procedure uses simple inputs that should be readily available to path designers, including 

one-way user volume, mode split, path width, and the presence or absence of a centerline. The 

procedure uses the theory that we developed and validated to predict the number of meetings and 

passings that will occur; it uses a number of default values based on data we collected if local 

values were not available. The LOS is based primarily on the model of user satisfaction that 

emerged from our perception study. Our model of delayed passing also plays a role in some LOS 

estimations. The output from the LOS procedure is a traditional A through F grade with which 

users can judge the performance of an existing path or a design alternative. The project team has 

also produced software to calculate the LOS automatically, greatly easing the burden on future 

users. 

RECOMMENDATIONS 

The new LOS estimation method produced during this project should be widely adopted by path 

designers because it provides many advantages over current methods, including: 

• The new method is based on sound theory. The theory (development of which is shown 

in chapter 3) began with the work of Hein Botma in The Netherlands, and was extended 

here to include more modes, more speed distributions, passive passing, and delayed 

passing. 

• The procedure to estimate meetings and passings has been validated for the first time in 

the United States on a wide variety of trails. 

• The LOS method and scale has now been calibrated based on the perceptions of a sample 

of U.S. users viewing videotapes of actual operations on actual U.S. paths. 

• The method only provides the LOS for bicycles, but does so while accounting for four 

other modes, including child bicyclists, inline skaters, pedestrians, and joggers. 

• The method works for and was based on path widths from 2.44 to 6.1 m (8 to 20 ft). 
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• The method works for and was based on a full range of volumes, from virtually zero 

users in an hour to several thousand users per hour. 

• The inputs to the procedure are still easy for a user to assemble (as were the inputs to the 

previous procedures), and sound default values are provided for most variables. 

• The research team has provided easy-to-use software to make the LOS calculation. 

Marketing the New LOS Procedure 

Unlike the roadway environment, which is almost exclusively the domain of civil engineers, 

shared-use paths are designed by a wide variety of practitioners. Some of the most creative and 

unique trails in the country are the direct result of the diverse skills that these designers bring to 

the table. We identified three main target audiences for the marketing of our shared-use path 

LOS model: transportation professionals; trail designers/coordinators; and walking, bicycle, and 

trail advocates and organizations. 

Reaching each of these three groups with the results from this research will be challenging. To 

reach transportation professionals, the Highway Capacity and Quality of Service Committee 

should consider the new LOS method for inclusion in the next edition of the HCM, or as an 

interim release until the next edition is ready. Presentations and papers at conferences and in 

journals of the Transportation Research Board and the Institute of Transportation Engineers will 

also help. To reach trail designers and trail advocates, the LOS method and User’s Guide should 

be posted on FHWA’s pedestrian and bicycle Web site and on the sites of other pedestrian and 

bicycle information clearinghouses. The new LOS method should also be presented to the 

AASHTO Task Force on Geometric Design for their consideration. If they were to include it in 

an update of the Guide for the Development of Bicycle Facilities,

(1) there would be a great 

likelihood that it would be widely used and accepted. In addition, presentations and papers at 

conferences and in the journals of the major parks and recreation societies will also be helpful. 

Future Research Needs 

The scope of the project, and therefore of the products emerging from the project, was limited in 

several important ways, as explained below. We recommend that future research on shared-use 

paths focus on erasing those limitations and otherwise extending the work conducted here. 

First, we recommend research to extend this work to estimate LOS from other points of view 

besides bicyclists. While one can easily assume that wider paths with fewer events lead to better 

levels of service for pedestrians and for other path users, as they do for bicyclists, the precise 

nature of that relationship is in doubt. We collected some user perceptions from the pedestrian 

point of view; however, the sample was not large and the videos that the respondents were rating, 

were not from the pedestrian point of view. It would be quite feasible to conduct similar user 

perception surveys from groups of pedestrians, inline skaters, joggers, and others by showing 

each group video clips and learning their reactions. It would also be possible to extend the LOS 

procedure to consider other modes emerging as important users on shared-use paths, such as 
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tandem bicyclists, Segways, and wheelchairs. The data on emerging path users compiled in a 

recent FHWA project(44) provide a good start. 

Second, we recommend collecting more data on high-volume and four-lane paths to put the LOS 

method on a more firm footing in these situations. The database collected in this project had one 

such path—the Lakefront Trail in Chicago, IL—and we recorded extremely heavy volumes on 

that path. However, there are other paths of that width in the United States, and there will be 

more in the future. In addition, if the current growth in path volumes continues across the United 

States in the next few years, there will be many more paths with Chicago-like volumes. We 

noted in chapter 7 that the perception data from Chicago was almost an outlier compared to the 

rest of the data set; we need more data from high-volume paths to fully calibrate the LOS method 

under those conditions. 

Third, we recommend a research effort to validate the delayed passing theory that we developed 

during this project. The number of delayed passing attempts would seem to be a natural factor in 

an LOS procedure, and our theory appears sound. However, we developed the theory relatively 

late in the project, and did not have the opportunity to show that the theory correctly estimates 

the number of delayed passings versus field data. In addition, none of our perception video clips 

showed bicyclists that had delayed passings. We need more direct evidence of how bicyclists 

weigh delayed passings against undelayed active passings, meetings, and other factors critical to 

the LOS. 

Fourth, we recommend a project to validate the video method of obtaining user perceptions. This 

could be done by comparing a sample of perceptions from viewers of videos to a sample of 

perceptions from people actually using the trails. 

Next, we heartily recommend a project to find a better LOS procedure for intersections on shared 

paths. The current intersection analysis procedure in the HCM(4) is like the current LOS 

procedure for shared-use path segments in that it is based on good logic, but is not validated 

against field data from the United States. This project, of course, only provided data and analysis 

for segments between intersections. Given the difficulty we had during this project in finding 

high-volume path segments of at least 0.80 km (0.5 mi) long, intersections are probably a major 

concern for path users across the United States. 

Finally, the theory estimating the number of delayed passings we developed in this project could 

be applied to improve the LOS procedure for two-lane highways. The current procedure in the 

HCM(4) is based on a series of microscopic simulation runs. It would require some field data 

collection; however, with some effort, our theory could be applied to two-lane highways, and a 

comparison to the simulation could be revealing. 

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APPENDIX A. PERCEPTION SURVEY FACT SHEET 

AND INFORMED CONSENT FORM 

Figure 31. Fact sheet for conformed consent. 

FACT SHEET

You are being asked to voluntarily participate in a research study being conducted by NC State 

University for the Federal Highway Administration (FHWA). The study is addressing the operational 

characteristics of recreational paths and trails that are used by a variety of different users (for example, 

pedestrians, bicyclists, runners, in-line skaters, etc.). An important part of the study involves 

determining how someone like yourself perceives different trail conditions. For example, we want to 

know how the following factors affect your perception of the trail. . . factors like the degree of ‘lateral’ 

separation between users (that is, the distance between you and those on your right or left); the 

degree of ‘longitudinal’ separation (that is, the distance between you and those in front of or behind 

you); and users’ ability to pass other users when they want or need to. 

As a volunteer in the study, you will be asked to 

view thirty-six, 1-minute video segments showing a 

range of trail conditions and users. You will 

experience these conditions as if you were riding a 

bicycle along the trail. After viewing each 1-minute 

segment, you will be asked to provide your 

perception of trail conditions in terms of (1) lateral 

separation, (2) longitudinal separation; and (3) 

ability to pass. You will also asked to give an 

‘overall’ rating of the trail. You will provide these 

ratings using the 5-point scale shown below. When 

you have finished with your ratings for a particular 

trail condition, we will move to the next condition. 

There will be 36 sets of conditions in all. We’ll 

pause briefly after each 9 conditions . 

When you and the rest of the group have finished viewing all 36 segments, we would like for you to 

stay for a few minutes to collect any other thoughts or perceptions you may have that you feel were not 

addressed by the conditions that you viewed or the scale that you used to provide your perceptions of 

these conditions. 

Your responses will remain anonymous and will not used in any way outside the context of the present 

study. Your signature below indicates your concurrence with the conditions of the study. At any point in 

time that you wish more information about the study, you can contact either Dr. Joe Hummer (NCSU) at 

919-515-7733 or Ms. An Do (FHWA) at 202-493-3319. 

__________________________ (last name) ________ (date) last 4 digits SSN: ___ ___ ___ ___ 

I am participating as a: Bicyclist Pedestrian 

LATERAL

SEPARATION

LONGITUDINAL 

SEPARATION 

PASSING

Condition Number XX

BAD POOR FAIR GOOD EXCELLENT

LATERAL SEPARATION

LONGITUDINAL SEPARATION

ABILITY TO PASS

OVERALL

141

APPENDIX B. PERCEPTION SURVEY BACKGROUND 

INFORMATION FORM 

Shared Use Paths and Trails 

Background Information 

1. GENDER: MALE FEMALE 

2. AGE RANGE: 

 18-24 25-31 32-38 39-45 46-51 52-58 59-65 over 65 

3. WALKING (OR RIDING YOUR BIKE) FOR RECREATION OR 

FITNESS: 

 NEVER 

 A FEW TIMES A YEAR 

 MORE THAN ONCE A MONTH 

 MORE THAN TWICE A WEEK 

 ALMOST DAILY 

4. HOW OFTEN DO YOU WALK (OR RIDE YOUR BIKE) ON SHARED 

USE PATHS/TRAILS? 

 NEVER 

 RARELY 

 OCCASIONALLY 

 REGULARLY 

 1-3 TIMES A WEEK 

 ALMOST DAILY 

 
142

 Bike or Ped Last 4 of SSN: ________ Chapel Hill, Raleigh, TRB 

5. WHAT ARE YOUR MOST FREQUENT WALKING (OR BIKE RIDING) 

PURPOSES WHEN USING A SHARED USE PATH/TRAIL? 

COMMUTING TO WORK 

COMMUTING TO SCHOOL 

 UTILITARIAN TRIPS 

TO SOCIALIZE WITH A FRIEND(S) 

RECREATION 

PHYSICAL FITNESS 

6. WHAT OTHER ACTIVITIES DO YOU PARTICIPATE IN ON SHARED 

USE PATHS/TRAILS? 

WALK RUN 

HIKE CROSS-COUNTRY SKI 

IN-LINE SKATE PUSH A CHILD IN A STROLLER 

JOG TRAVEL WITH A DISABLED PERSON 

WALK A DOG OTHER (DESCRIBE) 

7. IF RESPONDING AS A BICYCLIST, WHAT OTHER TYPES OF 

BICYCLE EQUIPMENT DO YOU USE ON A SHARED USE 

PATH/TRAIL? 

A RECUMBANT BIKE 

A TANDEM BICYCLE 

A BICYCLE TRAILER 

A TRAIL-A-BIKE WITH A CHILD RIDER 

A FOLDING BICYCLE 

OTHER (PLEASE DESCRIBE) 

8. YOUR ESTIMATE OF YOUR OVERALL HEALTH STATUS: 

 POOR FAIR GOOD EXCELLENT 
143

APPENDIX C. SCREEN SHOTS FROM PERCEPTION STUDY VIDEO 

Figure 32. Lake Johnson Trail. 

Figure 33. Sammamish River Trail. 
144

Figure 34. Mill Valley-Sausalito Pathway. 

Figure 35 White Rock Lake Trail. 
145

Figure 36. Lakefront Trail. 

Figure 37. South Bay Trail. 
146

Figure 38. Forest Park Trail. 

Figure 39. Honeymoon Island Trail. 
147

Figure 40. Minuteman Bikeway. 

Figure 41. Dr. Paul Dudley Bicycle Path. 

149

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