Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-23-078 SEPTEMBER 2023 

Developing Crash Modification Factors 

for Separated Bicycle Lanes
FOREWORD

In 2020, 938 bicycle roadway fatalities occurred in the United States. Transportation agencies 

are actively exploring alternative bicycle lane configurations that may further reduce the number 

of bicycle-related crashes on the road. The project titled Developing Crash Modification Factors 

for Separated Bicycle Lanes considered the safety effectiveness of separated bicycle lanes 

(SBLs), sometimes known as protected bicycle lanes, whereby the bicycle lane is separated from 

motor vehicle traffic with a spatial buffer or with a vertical barrier such as a flexible post 

(flexi-post). 

The findings from this study supported the development of a crash modification factor (CMF) for 

converting a traditional bicycle lane to an SBL with flexible posts. The analysis also resulted in a 

CMF for SBLs that have a blended vertical element located in the buffer area. This report may be 

of interest to transportation practitioners, transportation safety researchers, industry 

representatives, and engineers and others working to improve transportation safety. 

Brian P. Cronin, P.E. 

Director, Office of Safety and Operations

Research and Development 

Notice

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

(USDOT) in the interest of information exchange. The U.S. Government assumes no liability for 

the use of the information contained in this document.

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

The Federal Highway Administration (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-23-078

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

4. Title and Subtitle

Developing Crash Modification Factors for Separated Bicycle Lanes

5. Report Date

September 2023

6. Performing Organization Code

7. Author(s)

Karen Dixon (ORCID: 0000-0002-8431-9304), Raul Avelar-Moran 

(ORCID: 0000-0002-3962-1758), and Maryam Mousavi Seyedeh (ORCID: 

0000-0001-8188-4466)

8. Performing Organization 

Report No.

Contract HRDS30180007PRPRDTFH6116D00039.

9. Performing Organization Name and Address

Texas A&M Transportation Institute

The Texas A&M University System

College Station, Texas 77843-3135

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-16-D-00039

12. Sponsoring Agency Name and Address

Office of Safety Research and Development

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101

13. Type of Report and Period 

Covered

Final Report; August 2017—

November 2022

14. Sponsoring Agency Code

HRSO-30

15. Supplementary Notes

The contracting officer’s representative was Ann Do (HRSO-30; ORCID: 0000-0001-9932-7422).

16. Abstract

This project investigated how conversions of a traditional bicycle lane to a separated bicycle lane (SBL) 

influence the safety performance of the roadway. The study further assessed the role of the type of vertical 

element used in the SBL and its influence on safety performance based on a reduction in bicycle-related crashes. 

The study models included data from Cambridge, MA; San Francisco, CA; and Seattle, WA. Data from Austin, 

TX, and Denver, CO, were then used for model validation. The study produced exposure models and crash 

modification factors (CMFs) for Cambridge, San Francisco, and Seattle. The research summarizes the 

development of individual city models and a three-city composite model for CMFs. Assuming a baseline 

condition that uses a traditional bicycle lane and a countermeasure that upgrades the bicycle lane to an SBL, the 

resulting CMF values varied based on the type of vertical element. The resulting CMFs ranged from 0.28 to 

0.60; however, the condition of converting a traditional bicycle lane to an SBL that uses flexible posts (known as 

flexible posts) had a rounded CMF value of 0.50. This outcome suggests that this SBL treatment can reduce 

crashes by 50 percent. The report provides additional CMFs for variations of the bicycle lane and the vertical 

elements. These CMFs had similar results. The findings from this study can be useful to an agency interested in

reducing bicycle-related crashes.

17. Key Words

Crash modification factor, CMF, separated bicycle 

lane, SBL, protected bicycle lane

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Alexandria, Virginia 22312.

http://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

117

22. Price

N/A

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

 

 

 

 

 
iii

TABLE OF CONTENTS

EXECUTIVE SUMMARY .......................................................................................................... 1

Database Development ........................................................................................................... 1

Study Sites .......................................................................................................................... 1

Exposure and CMF Models ................................................................................................ 1

Resulting CMFs ....................................................................................................................... 2

CHAPTER 1. INTRODUCTION ................................................................................................ 3

Introduction ............................................................................................................................. 3

Project Objective ................................................................................................................. 3

Study Approach .................................................................................................................. 3

CHAPTER 2. LITERATURE REVIEW .................................................................................... 5

Definitions and Common Terms............................................................................................ 6

Bicycle Lane ....................................................................................................................... 6

Bikeway .............................................................................................................................. 6

Separated Bicycle Lane ....................................................................................................... 6

SBL Characteristics to Consider ........................................................................................... 7

Unique Geometric Configurations ...................................................................................... 8

Directional Considerations and Associated Dimensions .................................................... 8

Vertical Elements ................................................................................................................ 9

Contextual Applications—Transit and Pedestrian Considerations ................................... 10

Intersection-Specific Issues .............................................................................................. 10

Segment (Nonintersection)-Specific Issues ...................................................................... 11

Summary of Key Variables to Consider ........................................................................... 12

Known Safety Effects of SBLs ............................................................................................. 13

Safety Benefits .................................................................................................................. 13

Related Safety Research ................................................................................................... 14

Safety Assessment Methods ............................................................................................. 17

Literature Review Closing Comments ................................................................................ 19

CHAPTER 3. DATA COLLECTION AND DATABASE DEVELOPMENT ...................... 21

Site Identification .................................................................................................................. 21

Data Collection ...................................................................................................................... 21

Database Development ......................................................................................................... 24

CHAPTER 4. ESTIMATING BICYCLE EXPOSURE: CAMBRIDGE .............................. 25

Introduction ........................................................................................................................... 25

Data Collection: Available Bicycle and SITE Data ........................................................... 25

Data Manipulation and Preparation ................................................................................... 26

Statistical Modeling .............................................................................................................. 33

AM and PM Models (15 Min) .......................................................................................... 34

Peak-Hour Models ............................................................................................................ 45

Aggregated AM and PM Peak-Hour Models .................................................................... 49

Estimating AADBs from the Short-Term Bicycle Counts ................................................ 53

CHAPTER 5. ESTIMATING BICYCLE EXPOSURE: SAN FRANCISCO ....................... 55

Introduction ........................................................................................................................... 55
iv

Data Collection: Bicycle Count and Site Feature Variables ............................................. 55

Preliminary Statistical Assessment: Correlation Test ....................................................... 58

Modeling Results for San Francisco .................................................................................... 60

CHAPTER 6. ESTIMATING BICYCLE EXPOSURE: SEATTLE ..................................... 63

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

Data Collection: Bicycle Count ............................................................................................ 63

Data Collection: Site Features ............................................................................................. 64

Preliminary Statistical Assessment: Correlation Test ....................................................... 65

Modeling Results ................................................................................................................... 67

CHAPTER 7. DEVELOPMENT AND TESTING OF SBL CMFS ....................................... 73

Introduction ........................................................................................................................... 73

Study Design .......................................................................................................................... 73

Cross-Sectional Analysis .................................................................................................. 73

Balancing Covariates with Propensity Score (PS) Methods ............................................. 74

Data Analysis Methods ......................................................................................................... 75

CMF Estimation ................................................................................................................ 76

Contrasts of Interest for CMF Development .................................................................... 77

Segment Analysis Results ..................................................................................................... 78

San Francisco Segment Analysis ...................................................................................... 78

Cambridge Segment Analysis ........................................................................................... 83

San Francisco and Cambridge Combined Segment Analysis ........................................... 85

Summary of SBL CMF Findings ...................................................................................... 87

Seattle Segment Analysis .................................................................................................. 89

San Francisco, Seattle, and Cambridge Combined Segment Analysis ............................. 92

Validation Effort with Additional Data ............................................................................ 97

CHAPTER 8. CONCLUSIONS ............................................................................................... 103

Safety Effects ....................................................................................................................... 103

Recommended CMFs .......................................................................................................... 103

Future Research .................................................................................................................. 104

ACKNOWLEDGMENTS ........................................................................................................ 105

REFERENCES .......................................................................................................................... 106
v 

LIST OF FIGURES

Figure 1. Photo. SBLs for both directions of travel. ....................................................................... 7

Figure 2. Map. Anderson Bridge and JFK Street at Memorial Drive in Cambridge.(24) .............. 27

Figure 3. Map. Bicycle movements at the intersection of Anderson Bridge and JFK Street 

and Memorial Drive in Cambridge.(23,24) ................................................................................ 29

Figure 4. Map. Cambridge data points with labels.(23,24) .............................................................. 30

Figure 5. Chart. Distribution of AM 15-min bicycle count data for Cambridge bicycle 

volume..................................................................................................................................... 34

Figure 6. Chart. Average AM 15-min bicycle count data for the Cambridge study years 

(2016–2019). ........................................................................................................................... 35

Figure 7. Chart. Distribution for Cambridge PM 15-min bicycle count data. .............................. 38

Figure 8. Chart. Average PM 15-min bicycle count data for the Cambridge study years. ........... 39

Figure 9. Chart. Distribution of the aggregated 15-min AM and PM bicycle count data for 

Cambridge. .............................................................................................................................. 41

Figure 10. Chart. Distribution for Cambridge aggregated 15-min AM and PM bicycle 

count data for the study years. ................................................................................................ 42

Figure 11. Chart. Distribution for Cambridge peak-hour bicycle count data by hourly 

volume..................................................................................................................................... 45

Figure 12. Chart. Distribution of Cambridge peak-hour bicycle count data for the study 

years (2016–2019). ................................................................................................................. 46

Figure 13. Chart. Distribution of Cambridge aggregated AM and PM peak-hour count 

data. ......................................................................................................................................... 49

Figure 14. Chart. Distribution of Cambridge aggregated peak-hour bicycle count data for 

the PM periods. ....................................................................................................................... 50

Figure 15. Equation. Calculation based on K-factor and AADB. ................................................. 53

Figure 16. Graph. Average AADB for San Francisco (2016–2021). ........................................... 56

Figure 17. Chart. AADB distribution for San Francisco (2016–2019). ....................................... 56

Figure 18. Graph. Scatterplot for San Francisco model. ............................................................... 61

Figure 19. Graph. Change in the Seattle coefficients of variable Year for consecutive 

years. ....................................................................................................................................... 68

Figure 20. Graph. Seattle residual plot for model 1 where the dashed line represents the 

mean of observations and the dots represent the individual observations. ............................. 69

Figure 21. Graph. Residual plot for Seattle model 2 where the dashed line represents the 

mean of observations and the dots represent the individual observations for the New 

Year variable (Seattle model 2). .............................................................................................. 70

Figure 22. Equation. Seattle bicycle exposure prediction. ............................................................ 71

Figure 23. Equation. Probability equation. ................................................................................... 74

Figure 24. Equation. Conditional probability of Y value, given explanatory variables and 

site characteristics. .................................................................................................................. 75

Figure 25. Equation. Binomial-lognormal mixed-model parameterization. ................................. 76

Figure 26. Equation. Parameterization of explanatory variables in regression model. ................ 76

Figure 27. Equation. Standard error for contrast in log scale. ...................................................... 77

Figure 28. Graph. Model San Francisco relationship between length and bicycle crashes. ......... 80

Figure 29. Graph. Model San Francisco relationships between AADB and bicycle 

crashes. .................................................................................................................................... 81
vi 

Figure 30. Graph. Seattle model relationships between AADB and bicycle crashes. .................. 90

Figure 31. Graph. Model relationships and the influence of length on bicycle crashes for 

Cambridge. .............................................................................................................................. 94

Figure 32. Graph. Model relationships between AADB and bicycle crashes for 

Cambridge. .............................................................................................................................. 95

Figure 33. Graph. Model relationships between AADB and bicycle crashes for 

San Francisco. ......................................................................................................................... 96

Figure 34. Graph. Model relationships between AADB and bicycle crashes for Seattle. ............ 96

Figure 35. Equation. Estimator of the linear predictor. ................................................................ 98

Figure 36. Equation. Response variable in new dataset. ............................................................... 98

Figure 37. Equation. Linear predictor for the new combined dataset. .......................................... 99
vii 

LIST OF TABLES

Table 1. Recommended SBL directions and dimensions.(2,5) ......................................................... 9

Table 2. Example SBL vertical elements to consider. .................................................................. 10

Table 3. SBL variables to consider. .............................................................................................. 12

Table 4. SBL detailed site characteristics and data limitations. ................................................... 18

Table 5. Site information in database. .......................................................................................... 22

Table 6. Data table for Anderson Bridge, JFK Street, and Memorial Drive in Cambridge. ........ 28

Table 7. Updated data table for Anderson Bridge, JFK Street, and Memorial Drive in 

Cambridge, including origin and destination. ......................................................................... 31

Table 8. Descriptive statistics of the study variable for the 15-min AM data for 

Cambridge. .............................................................................................................................. 35

Table 9. Frequency of various bicycle facility types for the 15-min AM data for 

Cambridge. .............................................................................................................................. 36

Table 10. Frequency of various land uses for the 15-min AM data for Cambridge. .................... 36

Table 11. Correlation test for the 15-min AM data for Cambridge. ............................................. 37

Table 12. Descriptive statistics of the study variable for the 15-min PM data for 

Cambridge. .............................................................................................................................. 38

Table 13. Frequency of various bicycle facility types for the Cambridge 15-min PM data. ........ 39

Table 14. Frequency of various land uses for the 15-min PM data for Cambridge. ..................... 39

Table 15. Correlation test for the 15-min PM data. ...................................................................... 40

Table 16. Descriptive statistics of the study variable for the aggregated 15-min AM and 

PM bicycle count data for Cambridge. ................................................................................... 41

Table 17. Frequency of various bicycle facility types for the aggregated 15-min AM and 

PM bicycle count data for Cambridge. ................................................................................... 42

Table 18. Frequency of various land uses for the aggregated 15-min AM and PM bicycle 

count data for Cambridge. ....................................................................................................... 42

Table 19. Correlation test for the aggregated 15-min AM and PM bicycle count data for 

Cambridge. .............................................................................................................................. 44

Table 20. Descriptive statistics of the study variable for the peak hour. ...................................... 45

Table 21. Frequency of various bicycle facility types for the peak hour. ..................................... 46

Table 22. Frequency of various land uses for the peak-hour data. ............................................... 47

Table 23. Correlation test for Cambridge peak-hour data. ........................................................... 48

Table 24. Descriptive statistics of the study variable for the aggregated AM and PM peak 

hour. ........................................................................................................................................ 50

Table 25. Frequency of various bicycle facility types for the aggregated AM and PM 

peak-hour data. ........................................................................................................................ 51

Table 26. Frequency of various land uses for the aggregated AM and PM peak-hour data. ........ 51

Table 27. Correlation test for the aggregated AM and PM peak-hour data. ................................. 52

Table 28. Permanent Count Station K-factors. ............................................................................. 54

Table 29. Distribution of bicycle count data over years for San Francisco. ................................. 55

Table 30. Frequency of various types of bicycle facilities for San Francisco. ............................. 57

Table 31. Study variables for San Francisco. ............................................................................... 57

Table 32. Descriptive statistics for the study variables for San Francisco. .................................. 57

Table 33. Correlation test for San Francisco. ............................................................................... 59

Table 34. Final bicycle surrogate model for San Francisco.......................................................... 60
viii

Table 35. Bicycle counters and number of available years of data. ............................................. 63

Table 36. Frequency of different bicycle facilities. ...................................................................... 65

Table 37. Frequency of different land-use categories. .................................................................. 65

Table 38. Descriptive statistics of the numerical study variables. ................................................ 65

Table 39. Correlation test for Seattle. ........................................................................................... 66

Table 40. Mixed-effect Gaussian model, including 2020 data. .................................................... 67

Table 41. Mixed-effect Gaussian model excluding 2020 data. .................................................... 68

Table 42. Mixed-effect Gaussian model excluding 2020 data and using New Year. ................... 70

Table 43. Number of segments by bicycle lane type per direction in San Francisco 

(N = 384). ................................................................................................................................ 78

Table 44. Initial model coefficient estimates for potential bicycle crashes in San 

Francisco (N = 384 segments). ............................................................................................... 79

Table 45. Basic CMFs for bicycle crashes in San Francisco. ....................................................... 81

Table 46. Coefficient estimates for bicycle crash risk in San Francisco (N = 384 

segments). ............................................................................................................................... 82

Table 47. Detailed CMFs for bicycle crashes in San Francisco. .................................................. 83

Table 48. Segments by bicycle lane condition per direction in Cambridge (N = 179). ................ 84

Table 49. Coefficient estimates for bicycle crashes in Cambridge (N = 179 segments). ............. 84

Table 50. CMFs for bicycle crashes in Cambridge. ...................................................................... 85

Table 51. Coefficient estimates for bicycle crash risk in San Francisco and Cambridge 

(N = 563 segments). ................................................................................................................ 85

Table 52. CMFs for bicycle crashes in San Francisco and Cambridge. ....................................... 87

Table 53. Summary of SBL CMF values for roadway segments. ................................................ 88

Table 54. Number of segments by bicycle lane condition per direction in Seattle 

(N = 660). ................................................................................................................................ 89

Table 55. Coefficient estimates for bicycle crash risk in Seattle (N = 660 segments). ................ 89

Table 56. CMFs for bicycle crashes (Seattle). .............................................................................. 90

Table 57. Coefficient estimates for bicycle crash risk in Seattle (N = 660 segments). ................ 91

Table 58. Reestimation of CMFs for bicycle crashes in Seattle. .................................................. 92

Table 59. Coefficient estimates for bicycle crash risk in San Francisco, Seattle, and 

Cambridge (N = 1,223 segments). .......................................................................................... 93

Table 60. CMFs for bicycle crashes in San Francisco, Seattle, and Cambridge. ......................... 97

Table 61. Number of segments by bicycle lane condition per direction in Austin (N = 68). ....... 99

Table 62. Estimates for validation analysis on Austin segments (N = 68). .................................. 99

Table 63. Number of segments by bicycle lane condition per direction in Denver 

(N = 384). .............................................................................................................................. 100

Table 64. Estimates for analysis on Denver segments (N = 384). .............................................. 100

Table 65. Hypothesis test result for vertical element CMF on Denver and Austin 

segments. ............................................................................................................................... 100

Table 66. CMFs for converting to an SBL. ................................................................................ 104
ix

LIST OF ABBREVIATIONS AND ACRONYMS

AADB annual average daily bicycle (units: bicycles per day) 

AADT annual average daily traffic (units: vehicles per day) 

AASHTO American Association of State Highway and Transportation Officials

ADBT average daily bicycle traffic

ADT average daily traffic

AIC Akaike information criterion

Bpd bicycles per day

CMF crash modification factor

CV coefficient of variation

df degrees of freedom

FHWA Federal Highway Administration

flexi-post flexible post

GLM generalized linear model

Log logarithm with a base of 10 

LU land use 

MV motor vehicle

NACTO National Association of City Transportation Officials

NB northbound 

REML restricted maximum likelihood

Pr probability 

PS propensity score 

PSW propensity score weighting 

SBL separated bicycle lane

vpd vehicles per day

WB westbound 

1 

EXECUTIVE SUMMARY

During the first phase of this project, the research team evaluated whether enough constructed 

separated bicycle lanes (SBLs) existed to support a statistical safety-based analysis. Following 

this assessment and confirmation of the number of candidate sites, the research team scheduled 

data collection for spring 2020. Due to the pandemic, the data collection effort was limited to 

identification of preexisting databases and physical site assessments using aerial photos and an 

interactive panoramic street viewing application. 

The objective of this study was to determine a crash modification factor (CMF) that estimates the 

safety effect of converting a traditional bicycle lane to an SBL. The SBL is characterized by a 

buffer with a vertical element located between the motor vehicle (MV) and bicycle lanes. The 

safety analysis is based on bicycle-related crashes.

DATABASE DEVELOPMENT

Due to travel restrictions in 2020, the research team developed a database that included 

information that could be compiled from existing bicycle count data, road characteristic 

information, and crash data available from the target jurisdictions. Locations that did not have 

these three critical features could not be included; however, the team allowed some variability 

for how much bicycle count data could be used and potentially extrapolated to surrounding 

facilities.

Study Sites

The team developed a second matching database that included street-level information, such as

traffic operations (one-way versus two-way roads for bicycles and MVs), varying traffic control 

at bounding intersections, parking presence and type, and roadway cross section. 

Based on the identified site requirements, the following candidate locations were identified:

• Austin, TX. 

• Cambridge, MA.

• Denver, CO.

• San Francisco, CA. 

• Seattle, WA.

Data for Austin and Denver were reserved for validation purposes. The analysis focused on 

Cambridge, San Francisco, and Seattle. 

Exposure and CMF Models

Due to the limited number of bicycle counts, the team developed statistical models to estimate 

bicycle exposure at the sites included in the database that did not have available bicycle volumes. 

These exposure models were developed so that each of the three target cities could be 

independently assessed.
2 

RESULTING CMFs 

Following the exposure model development, the research team then developed CMFs that 

estimated the following scenarios:

• Converting traditional bicycle lanes to SBLs with flexible posts (flexible posts) 

(CMF = 0.50). 

• Converting flush buffered bicycle lanes to SBLs with flexible posts (CMF = 0.44). 

• Converting traditional or flush buffered bicycle lanes to SBLs with flexible posts 

(CMF = 0.47). 

• Converting traditional bicycle lanes to SBLs with a blend of flexible posts and other 

vertical elements (CMF = 0.64).

• Converting flush buffered bicycle lanes to SBLs with a blend of flexible posts and other 

vertical elements (CMF = 0.57).

• Converting traditional or flush buffered bicycle lanes to SBLs with a blend of flexible 

posts and other vertical elements (CMF = 0.60). 

Possibly, other bicycle crashes may have occurred and were not reported. The research team was 

unable to incorporate these unreported crashes into the scope of this study.
3 

CHAPTER 1. INTRODUCTION

INTRODUCTION

In 2020, the United States experienced 938 bicycle fatalities due to roadway-related crashes.(1)

This high number of bicycle-involved collisions emphasizes the need to place priority on 

analyzing and enhancing the safety of bicyclists. In recent years, transportation agencies have 

constructed a variety of bicycle lane configurations, including separated bicycle lanes (SBLs). 

An SBL is also sometimes referred to as a “protected bicycle lane.” 

SBLs provide a bicycle lane that is separated from the adjacent motor vehicle (MV) lanes by 

both a buffer and a vertical element between the MV lanes and the bicycle lane. The SBL is a 

newer treatment, and the safety and operational effects of the SBLs are not fully known.

Project Objective

This report summarizes the development of crash modification factors (CMFs) based on SBLs 

for facilities in Cambridge, MA; San Francisco, CA; and Seattle, WA, and then tested for 

facilities in Austin, TX, and Denver, CO. Including bicycle exposure in safety assessments is 

necessary because the number of bicycles that use the roadway can be expected to directly 

contribute to the number of crashes that occur on that facility.

Study Approach

The annual average daily bicycle volume (AADB) is one of the most common terms used to 

represent bicycle exposure. Unfortunately, AADB is not a common variable that is widely 

collected, and locations where transportation agencies do acquire the AADB information may be 

limited to seasonal or short-term count values. Because the purpose of this study was to evaluate 

the safety performance of SBLs, the researchers needed to assess known bicycle count data and 

contrast those data with other site and road characteristics, with a goal of identifying surrogate 

variables that may be used to collectively estimate the bicycle count information for roadway 

facilities with similar characteristics. These models are known as exposure models (see chapters 

4, 5, and 6 for additional information on the exposure models). 

An initial stage of this project included data collection and analysis that would enable the 

research team to estimate bicycle exposure that could then be used in the development of CMFs. 

The way people traveled, worked, and used their bicycles for leisure and commuting purposes 

dramatically shifted in 2020 due to the COVID-19 pandemic. Initially, the data collection for this 

SBL project was to be based on field data to account for exposure; however, the research team 

had to modify the data sources for the project and target cities that did have some available 

bicycle counts. Instead of field visits, the research team used online mapping visual tools to 

document the road conditions for the database on this project. (See chapter 3 for additional 

information regarding the data collection and database development.) Data from 2020 or later 

were excluded from this study because of the altered usage pattern that occurred due to the 

COVID-19 outbreak.
4 

This report begins with chapter 1 (this chapter), which introduces the project purpose. Chapter 2 

then summarizes published literature regarding known safety performance of bicycle facilities 

with a focus on SBLs. Chapter 3 provides a brief overview of the data collection process used for 

this analysis. Chapters 4, 5, and 6 review exposure model estimation for Cambridge, 

San Francisco, and Seattle, respectively. Chapter 7 then summarizes the estimation efforts for 

developing CMFs for SBLs. The body of the report concludes with conclusions in chapter 8, 

appendixes, and references.
5 

CHAPTER 2. LITERATURE REVIEW

The construction of various lane configurations that accommodate bicycles along an urban 

collector or arterial can help to remove cyclists from sidewalks or from travel lanes where the 

bicycles must share the lane with MVs. Bicycle lanes, as an example, can reduce conflicts 

between pedestrians and bicyclists (for sidewalk locations) and between MVs and bicycles (for 

shared travel lane scenarios at nonintersection locations). The bicycles and MVs, however, must 

still interact at intersection locations.

Intuitively, providing a dedicated bicycle lane should result in fewer and less severe bicyclerelated crashes, yet the research related to bicycle lanes provides mixed results.(1,2) The inclusion 

of a buffer between the MV lane and the bicycle lane provides additional lateral separation 

between the bicycle and adjacent MVs. Enhancing this design by introducing a vertical element 

that physically separates the bicycles and the MVs and converts the bicycle facility to an SBL 

further improves cyclist comfort.

Recently, two studies specifically focused on bicycle lane performance. The report, 

Recommended Bicycle Lane Widths for Various Roadway Characteristics (NCHRP Report 766),

provides guidance on recommended bicycle lane widths and associated roadway 

characteristics.(1) The authors of that report noted that a buffered lane provides advantages over a 

standard or a wide bicycle lane. The Federal Highway Administration (FHWA) further evaluated 

the safety performance of existing SBLs in appendix C of its Separated Bicycle Lane Planning 

and Design Guide.

(3)

 The authors noted, however, that the inconsistent nature of the available 

data—in particular, bicycle volume data—created difficult analysis issues for SBL facilities

where exposure is a key element related to these facilities’ overall safety.

Many additional factors can contribute to the safety effects of bicycle lanes, including the 

following examples:

• The corridor speed limit. 

• The frequency of driveways. 

• The lateral placement of roadside objects.

• The buffer separation between vehicle lanes and the bicycle lane. 

• The number and width of neighboring lanes. 

• The width of the actual bicycle lane. 

• The number and type of intersections. 

• The volume of MVs (passenger cars and trucks), bicycles, and pedestrians.

• The fact that the construction of a bicycle facility is likely to attract more cyclists to the 

corridor location.

The role of the corridor as part of a larger street network must also be considered to capture the 

context of a facility, including items such as bicycle route alternatives, facility connectivity, and 

operational consistency.

The following section provides brief definitions of bicycle lanes and SBLs and then further 

defines the objective that was the focus of this study. 
6 

DEFINITIONS AND COMMON TERMS

The distinction between a bicycle lane, a bikeway, and an SBL facility merits clarification. This 

section defines these facilities and, in doing so, highlights why the SBL is uniquely different than 

other bicycle facilities.

Bicycle Lane

The use of bicycle lanes in the United States dates back several decades. The 2012 American 

Association of State Highway and Transportation Officials (AASHTO) Guide for the 

Development of Bicycle Facilities, fourth edition, defines a bicycle lane as:

A portion of roadway that has been designated for preferential or exclusive use by 

bicyclists by pavement markings and, if used, signs. The roadway portion is intended for 

one-way travel, usually in the same direction as the adjacent traffic lane, unless designed 

as a contraflow lane.(4)

Bikeway 

AASHTO’s 2012 guide further defines a bikeway as:

A generic term for any road, street, path, or way which in some manner is specifically 

designated for bicycle travel, regardless of whether such facilities are designated for the 

exclusive use of bicycles or are to be shared with other transportation modes.(4)

Separated Bicycle Lane

The SBL is also often referred to as a “cycle track” or a “protected bikeway” or “bicycle lane.” 

A simple description by Bicycle East Bay in Oakland, CA, refers to SBL facilities as sidewalks 

for bicycles.(5) The unique characteristics of this facility include lateral physical separation or 

protection of the bicycle lane from the adjacent MV lanes. The physical separation of an SBL is 

accomplished with a vertical element that may include a variety of applications such as 

consistently spaced flexible posts (flexible posts) that do not fully limit access. The physical 

separation could also restrict adjacent MV access with continuous longitudinal barriers. 

The SBL may be one-way or two-way and can be positioned on the left or right side of roadways 

that are also one-way or two-way; however, the most common applications tend to locate the 

SBL on the right side of the road in the direction of travel (figure 1).
7 

Source: FHWA. 

Figure 1. Photo. SBLs for both directions of travel.

AASHTO’s 2012 guide does not include an SBL as a recommended bicycle facility, but this type 

of design option is addressed in recent documents by FHWA and the National Association of 

City Transportation Officials (NACTO).(3,6)

The 2015 FHWA Separated Bicycle Lane Planning and Design Guide defines an SBL as:

An exclusive facility for bicyclists that is located within or directly adjacent to the 

roadway and that is physically separated from motor vehicle traffic with a vertical 

element. Separated bicycle lanes are differentiated from standard and buffered bicycle

lanes by the vertical element.

(3)

The 2012 NACTO Urban Bikeway Design Guide uses the term cycle track and defines it as: 

A cycle track is an exclusive bicycle facility that combines the user experience of a 

separated path with the on-street infrastructure of a conventional bicycle lane. A cycle 

track is physically separated from motor traffic and distinct from the sidewalk.(6)

A key component that distinguishes the SBL from other bicycle facilities is the use of a physical 

separation that includes some type of vertical element. This vertical element is one of several 

factors that should be considered when assessing the safety performance of an SBL facility.

SBL CHARACTERISTICS TO CONSIDER

This literature review focused on the safety aspects of SBLs located in North America. The 

section that reviews SBL characteristics to consider includes a summary of the varying SBL 

features. This review also includes a review of the known safety effects of the SBLs.
8 

The implementation of an SBL can include several elements that collectively contribute to the 

safety performance of a facility. The following factors should be deliberated when considering 

deployment of SBLs to a specific facility:

• Unique geometric configurations. 

• Directional considerations and associated dimensions.

• Vertical elements.

• Contextual applications.

• Intersection-specific issues.

• Segment (nonintersection)-specific issues.

Unique Geometric Configurations

The unique design of urban arterial and collector corridors, where SBLs are most often located, 

must be carefully balanced with the adjacent land use. Many of these corridors include attractors 

that increase the likelihood of commuting and recreational cyclists. These locations require 

specific assessments on how to accommodate vulnerable road users while maintaining the 

appropriate level of corridor operations.

Directional Considerations and Associated Dimensions

For SBL facilities, the bicycle lane may be one-way or two-way, depending on the demands of 

the facility, though the use of contraflow bicycle lanes is generally discouraged for two-way 

streets. The use of contraflow bicycle lanes at streets with one-way MV activity may be 

acceptable in the following situations: 

• The contraflow bicycle lane helps substantially reduce out-of-direction travel.

• Wrong-way bicycle activity exists at the facility, and the contraflow bicycle lane will 

accommodate these bicycles.

• The contraflow lane provides access to a high-use destination property. 

• The number of intersecting streets, alleys, or driveways is limited.

• The contraflow lane bicycle entrance and exit are safe and convenient.(7)

In addition to the one-way or two-way SBL facilities or MV lanes, the design must also consider 

the lateral placement of the SBLs. The width of the road cross section and available space for the 

SBL buffer are also critical elements of the design.

In addition, any characteristics that are critical for traditional bicycle lanes will generally be of 

concern for SBL applications. For example, extreme vertical grades will often require bicycle 

lane width adjustments.(1,3) If a continuous facility that does not permit same-direction passing is 

constructed, the managing transportation agency can expect to observe reduced compliance that 

will likely affect safety.
9 

Table 1 summarizes common SBL directional configurations, companion bicycle lane and buffer 

width recommendations, and additional considerations that should be included when assessing 

the selection and placement of SBL configurations. As noted in this table, many of the options 

will further complicate bicycle, pedestrian, and MV interactions at intersection locations. The 

values shown in table 1 are as cited in the noted references; however, each agency may use 

widths that vary from those shown. 

Table 1. Recommended SBL directions and dimensions.(2,5)

SBL Type

MV 

Lanes

Lateral 

Placement

Bicycle Lane 

Width (ft)

Buffer 

Width (ft)

Additional 

Considerations

One-way One-way • Generally rightside conditions 

(preferred)*

• Left side (under 

unique 

circumstances)

7 preferred

5 minimum

3 minimum Avoid using minimum 

bicycle lane width where 

same-direction bicycle

passing is expected

One-way Two-way Both sides (left 

and right)

7 preferred

5 minimum

3 minimum Avoid using minimum 

bicycle lane width where 

same-direction bicycle

passing is expected

One-way Two-way Central median Varies based 

on median 

configuration

Varies based 

on median 

configuration

Fewer turning conflicts 

but creates challenges at 

intersections

Two-way One-way Right side 

typical*

12 Varies Creates challenges at 

intersections

Two-way Two-way Right side 

typical*

12 Varies Fewer turning conflicts 

but creates challenges at 

intersections; used if 

space cannot be 

achieved to 

accommodate separate 

SBLs on each side

*Left-side SBL applications are considered when:(3,6)

• Route has high transit demand.

• Left side has fewer access points than right side. 

• Land use on left side is more likely cyclist destination.

• Right side of road has on-street parking.

Vertical Elements

A wide variety of potential vertical elements may be used to separate the bicycle lane from 

adjacent MV lanes and sidewalks. Table 2 summarizes several of the vertical elements observed 

in the published literature.(2,5,7) Many of the potential vertical elements can be generally separated 

into a continuous treatment category, whereby the bicycle traffic is distinctly separated from the 

adjacent MV or pedestrian traffic for the length of the nonintersection SBL section. As an 

alternative, several less rigid or flexible vertical elements enable periodic gaps in the physical 

separation. Though the continuous vertical element may generally be preferred, there may also 
10 

be a need to blend these two treatment types at locations where some access must be maintained 

for other road users.

Table 2. Example SBL vertical elements to consider.

Vertical Element Source

On-street parking (24-h posted) Continuous at nonintersection locations(6,8)

Raised curb or median Continuous at nonintersection locations(6,8)

Concrete barriers Continuous at nonintersection locations(3)

Raised lane Continuous at nonintersection locations(3)

Landscaped buffers Continuous at nonintersection locations(8)

Tubular markers (bollards) or flexible 

posts

Flexible with limited gaps in vertical elements at 

nonintersection locations(6)

Planters Flexible with limited gaps in vertical elements at 

nonintersection locations(3,6)

Parking stops Flexible with limited gaps in vertical elements at 

nonintersection locations(3)

Combination of treatments Blended vertical treatments(3)

Contextual Applications—Transit and Pedestrian Considerations

As indicated in table 1, the placement and orientation of SBL facilities are often influenced by 

corridor user demands. Transit operations and supporting facilities and pedestrian demand are 

two contextually specific characteristics that are likely to occur at urban roadways where SBL 

facilities are also considered.

In addition to increased bus volumes along an urban corridor, the location of transit lanes, bus 

stops, or transit stations can directly influence the recommended placement of SBL facilities. As 

noted in table 1, left-side placement of an SBL may be appropriate if the corridor has high transit 

demand and uses the right side of the road to conduct transit operations.(6) In many locations, the 

SBL is located to the right of active bus lanes or parking lanes. This type of configuration 

requires a pedestrian to cross the SBL to access the bus or parked vehicles.

Some agencies prohibit the placement of bicycle lanes between the curb and a parking lane. For 

example, the Wisconsin Department of Transportation does not allow this configuration and 

notes that these bicycle lane configurations restrict visibility for both the drivers of MVs and 

bicyclists at intersection or driveway locations.(7)

Intersection-Specific Issues

The transition from a nonintersection SBL location to an intersection configuration presents 

many challenges. This location requires some level of interaction between all road users and can 

introduce confusing configurations that road users do not always interpret correctly.
11 

Monsere et al. defined SBL intersection configurations as three categories.

1 

• Mixing zones where bicycles and turning vehicles are required to share the same space.

• Turning zones where the through bicycle maneuver is shifted away from the curb and 

located to the left or right of turning MVs.

• Signalization where bicycles and turning MVs receive separate signalized movements.

Following execution of a series of surveys for study sites at five locations, Monsere et al. 

(

determined that the mixing zones are more readily understood by cyclists and drivers.(9)

Researchers at Portland State University in Oregon extended their research by evaluating user 

“comfort” curves to assess suitable intersection configurations.(9)

 That study focused on the 

mixing and turning zone configurations. The goal of the Portland State University research effort 

was to determine design recommendations based on the comfort levels of the bicyclists and their 

interaction with other road users.(10) Rather than assessing crash data, the study used a 

combination of video, surveys, and microsimulation activities. Due to the available study sites, 

the research primarily focused on one-way configurations and right-turn maneuver interactions.

Segment (Nonintersection)-Specific Issues

In addition to the transit issues previously noted, SBL nonintersection operations are more 

straightforward than their intersection counterparts. Locations where midblock access points are 

permitted, however, can compromise SBL operations, introduce conflicts, and create visibility 

issues. At locations where on-street parking is permitted, the parking should be limited to 

immediately upstream and downstream of the access point to enhance sight distance. In addition, 

signage should clearly indicate to road users that the SBL operations have priority over driveway 

operations.(3)

 

1

Monsere, C. 2017. “Contextual Guidance at Intersections for Protected Bicycle Lanes.” NACTO C4C Conference 

Call presentation, unpublished.
12 

Summary of Key Variables to Consider

A variety of issues need to be considered when assessing the safety of an SBL facility. Table 3

summarizes these potential elements.

Table 3. SBL variables to consider.

Data Need Potential Elements

Crash data • Total number of crashes.

• Number of fatal and injury crashes (specific consideration to who is 

injured: MV drivers or passengers, bicyclists, or pedestrians). 

Exposure • ADT or peak-hour volumes for MVs (passenger cars and trucks), 

bicycles, and pedestrians. 

• Land use and driveway types (as potential volume surrogates). 

• The increase in bicyclists due to the placement of SBL facilities is 

also an important exposure consideration.

Year of 

construction

SBLs constructed earlier than 2010 may not be suitable if exposure 

information and crash data are not available.

Width of 

buffer

Locations with on-street parking should be further assessed to determine 

actual and effective buffer widths.

Vertical 

element

• Flexible posts, bollards, and light poles.

• Curb or raised median. 

• Landscaping and planters. 

• Concrete (zebra/armadillo) bumps, buttons, and parking stops. 

• Parked cars. 

• Grade. 

• Concrete barrier, guardrail, and fence. 

Length (miles) Primarily for nonintersection SBL assessment.

Intersection 

channelization

and traffic 

control 

• Primarily for intersection SBL assessment.

• Islands (traditional, bend-in, bend-out, turn lanes, etc.). 

• MV and bicycle signalization. 

• Signage and pavement markings. 

Lighting Street light presence, placement, and type

Corridor 

information

• Speed limit.

• Driveway density. 

• Number, type, and width of lanes. 

• Number and type of intersections. 

• Street network configuration. 

• Vehicle parking restriction hours. 
13 

Data Need Potential Elements

Roadway 

facility type

• Urban arterial (two-way and one-way).

• Urban collector (two-way and one-way). 

• Urban local street (two-way and one-way). 

SBL facility 

configuration 

• Right side, one-way or two-way.

• Left side, one-way or two-way. 

• Middle, two-way. 

Unique SBL 

features

• Truck aprons.

• Mixing zones or elevated SBLs at driveways/crossings. 

• Green markings. 

• Bicycle facility continuity and operational consistency. 

ADT = average daily traffic. 

KNOWN SAFETY EFFECTS OF SBLS

Within the last decade, many U.S. transportation agencies have implemented SBLs in urban 

regions to help promote the bicycle as a viable transportation option. Though empirical safety 

data analysis related to SBLs in the United States continues to evolve, the widespread European 

application and safety evaluation of these facilities have suggested several potential safety 

benefits that are identified in guidance documents by FHWA and NACTO. This section 

summarizes these expected safety benefits, describes the North American safety studies that have 

occurred in recent years in greater detail, reviews the study design and analysis methods used for 

these studies, and then summarizes these findings.

Safety Benefits

In 2015, FHWA published a guidance document entitled Separated Bicycle Lane Planning and 

Design Guide.(3) This document specifically notes the following four expected safety benefits of 

implementing an SBL:

• Physical separation will reduce crash frequency and severity while also increasing cyclist 

comfort.

• Additional protection helps to promote use by providing peace of mind to novice cyclists. 

• Strategic applications of SBLs can be integrated into the bicycle network at locations 

with the greatest need.

• Designs can help enhance pedestrian safety by shortening crossing distances and 

accommodating additional pedestrian refuge at crossing locations. 
14 

NACTO’s Urban Bikeway Design Guide refers to SBLs as “cycle tracks” and further identifies 

the following safety benefits:

• Provides protected, dedicated space for cyclists.

• Reduces cyclist fear of overtaking vehicles.

• When contrasted to traditional bicycle lanes, minimizes safety concerns related to 

“dooring” where cyclists are struck by the opening doors of parked cars.(6)

These identified safety benefits are intuitive, but confirming that these benefits apply to SBL 

applications in the United States is necessary. The North American safety research is 

summarized in the following section.

Related Safety Research

Because the number of SBL locations in the United States has been limited, much of the 

published literature related to the safety effect of these facilities has focused on northern Europe, 

where bicycle facilities of this type have existed for many years. Many of the European facilities 

are not located on multilane roadways and, therefore, have different contextual configurations 

than the U.S. and Canadian SBL applications. This difference suggests that the use of crash 

information from other countries may be informative but not directly applicable to the United 

States. A 2013 paper by Thomas and DeRobertis reviews the literature associated with urban 

SBLs.(11)

 The authors identified 23 papers that extended as far back as 1987 and noted that 22 of 

the papers were based on European facilities. Thomas and DeRobertis did compile the following 

general findings that may help transportation professionals in the United States determine key 

safety-related issues to consider:

• One-way SBLs tend to be safer than two-way SBLs.

• Constructing SBLs reduces the number of collisions and injuries. 

A 2011 study by Lusk et al. examined six SBL locations in Montreal, QB, Canada, and 

contrasted each location with up to two reference streets where bicycle facilities were not 

present.(12)

 The original intent of the study was to focus on one-way and two-way SBL facilities, 

but the researchers determined that all the Montreal locations available to study were two-way, 

and, therefore, they ultimately focused on this configuration. Lusk et al. determined the crash 

rate for the SBLs to be 10.5 crashes per million bicycle-km. They also found the associated 

injury crash rate to be 8.5 injuries per million bicycle-km. The authors cautioned that the small 

sample size of six locations limited opportunities to determine unique factors that contributed to 

safety performance. The study corridors included entire SBL segments and did not separate 

intersection versus nonintersection crashes. Lusk et al. concluded that two-way SBL facilities 

appear to reduce or maintain crash and injury rates compared to streets without similar facilities.

In 2013 study, Lusk et al. evaluated 19 SBL facilities in the United States.(13) The researchers 

assessed crash data that extended from 0.3 yr to 8.6 yr. At some locations, exposure information 

was not available, so the researchers estimated the average daily bicycle count. The 19 study 

locations included sites in California, Colorado, Florida, Massachusetts, Minnesota, New York, 
15 

Oregon, and Vermont. The physical characteristics—such as, one-way versus two-way, one side 

versus two sides, and type of physical separation—varied. Study corridor lengths varied from 

0.16 km to 4.83 km. The researchers evaluated 55 crashes between MVs and bicycles. The study 

resulted in a crash rate of 2.3 crashes per million bicycle-km. The authors noted that some 

potential limitations of the study included the limited amount of available crash data associated 

with SBL facilities and a need to estimate or adjust bicycle counts for consistent periods.

Another 2013 study evaluated bicycling injury patterns in the Canadian cities of Vancouver, BC, 

and Toronto, ON.

(14)

 For this study, the researchers used a case crossover design whereby the 

experimental design used injured cyclists as their own controls. The researchers recruited 

690 study participants from local hospitals where they reported bicycle-related crash injuries. Of 

the associated crashes, 201 occurred at intersection locations, and 478 occurred at 

nonintersection locations. Though the research team evaluated a wide variety of road 

configurations and associated elements, they concluded that features that separate cyclists from 

MVs and pedestrians are associated with substantially lower-risk bicycle crashes.

In a 2013 study, Goodno et al. evaluated innovative bicycle facilities in Washington, DC.(15) As 

part of this study, the researchers evaluated an SBL facility on 15th Street by examining 

before–after crash data and conducting 6-h conflict studies at intersection locations. At the time 

of the study, only 10–14 mo of after crash data were available, so the authors could not draw 

definitive conclusions but did provide some observations. The number of crashes that involved 

bicycles increased after the SBL configuration was implemented. The authors noted, however, 

that approximately 40 percent of the cyclists were observed to disobey traffic signals, with many 

of them running the red lights. The researchers further noted that the local jurisdiction continues 

to monitor the crash trends at these locations and recommended adding bicycle traffic signal 

heads to help clarify cyclist expectations at these locations.

Three studies evaluated perceived and observed SBL safety.(15–16) The researchers did not have 

sufficient crash data to assess before and after conditions, so they used user survey data from 

144 h of video (consisting of 12,900 bicycles) to assess cyclists’ behavior at the SBL intersection 

study locations in Austin; Chicago, IL; Portland; San Francisco; and Washington, DC.(15,16) Most 

of cyclists indicated that the SBL installation resulted in a facility that felt safer. The MV drivers 

in the study areas provided mixed feedback, with only 37 percent indicating a perception of 

increased safety and 30 percent suggesting there was no perceived change to safety along the 

corridors. Pedestrians similarly provided mixed responses, with 33 percent suggesting an 

increase in safety and 48 percent suggesting no safety change. For the observed safety 

assessment, the researchers did not observe any collisions or near collisions during the period for 

which they acquired video data. The researchers did observe six minor conflicts, five of which 

occurred at the turning and mixing zones of the intersections. 

In 2014, the New York City Department of Transportation evaluated SBLs constructed on 

Manhattan streets.(17) Although the before conditions varied across the sites, the New York City 

transportation officials concluded that the SBLs improved safety based on comparisons before 

and after at each site. General findings from this study relative to safety are as follows:

• A reduction in injury crashes (by 17 percent). 

• A decrease in pedestrian injuries (by 22 percent).
16 

• A decrease in cyclist injuries that occurred while bicycle volumes increased.

• A reduction in cyclists’ risk for serious injury (by 75 percent) from 2001 to 2013. 

In 2015, FHWA developed a planning and design guide for SBL facilities.(3) As part of this 

effort, the study noted limitations in existing U.S. SBL research and provided recommendations 

for future research efforts. For safety activities, the report recommended that as the number of 

SBL facilities increases, researchers should develop CMFs that consider the varying SBL 

configurations. The report further notes a need to improve crash-reporting practices, incorporate 

conflict analysis into the overall assessment, and expand the safety analyses based on design 

elements and intersection characteristics. Based on an assessment of data for several study 

locations, the report further notes a need to collect or improve the estimation of bicycle volume 

at study locations so that exposure can be directly considered in the safety assessment process.

Rothenberg, Goodman, and Sundstrom documented the crash analysis from the 2015 FHWA 

study.(3,18) They emphasized that safety assessments for SBL facilities should consider changes 

in total crashes (all crashes, including bicycle crashes) as well as changes due to the addition of 

these facilities. The researchers also noted that the following treatments appear to influence 

overall corridor safety:(18)

• The use of parking lanes in combination with other treatments resulted in fewer total 

crashes.

• A concrete curb (by itself and combined with other treatments) reduced the total number 

of crashes. However, the authors cautioned that only 14 of their 19 study sites included 

this treatment.

• Plastic bollards combined with other treatments reduced total crashes; however, plastic 

bollards as the only vertical element were associated with an increase in total crashes.

• Mixing zones combined with other treatments reduced total crashes (observed at five of 

six sites).

• Lateral shift at intersections contributed to fewer crashes, except when combined with 

other treatments (when total crashes appear to increase).

In a 2016 study, Zangenehpour et al. evaluated the safety of signalized intersections for locations 

in Montreal, PQ, Canada.(19) In this study, the research team used video data based on post 

encroachment time to evaluate cyclist behavior at 23 intersections (eight locations without an 

SBL, eight sites with the SBL on the right side, and seven locations with the SBL on the left 

side). This surrogate post encroachment value represents the time between when a cyclist departs 

a location and the potential time of collision with an MV. This metric is represented by the 

intersection of the two trajectories. Based on video reduction and analysis followed by a 

statistical assessment, the researchers provided the following observations:

• Intersections with SBLs also experienced a higher bicycle volume than locations where 

SBLs were not present (possibly suggesting cyclists prefer these facilities).
17 

• The average bicycle speed appeared to be similar between all facilities.

• The number of interactions was greater at intersections with SBLs, but when exposure 

was considered, the rate of these interactions appeared to be lower for intersections with 

SBLs.

• Intersection approaches with SBLs on the right or left were safer than intersections that 

did not have SBLs. 

• The likelihood of a dangerous interaction increased as the MV turning volume increased 

and decreased as the approaching bicycle volume increased. 

Safety Assessment Methods

The existing SBL research has generally used the following three analytical approaches:

• Crash analysis.

• Video analysis (for conflicts and user compliance assessment).

• User surveys.

For each of these safety assessment methods, the researchers applied robust statistical 

techniques, where possible. The following summaries briefly describe how the researchers 

approached these analysis techniques.

Crash Analysis

Where practical, most of the published North American research has explored the use of crash 

analysis to assess, where feasible, before and after crash conditions. (See references 3, 9, 12, 13, 

15, and 18.) This before–after approach included simple comparisons that do not directly 

consider changes in exposure up to and including robust assessments that incorporated 

comparison sites. In all cases, the greatest limitation to this approach appeared to be the limited 

availability of after crash data. This constraint resulted in small sample sizes or evaluations of 

very short periods after SBL implementation.

In general, a significant demand for determining the safety of SBL facilities in the United States

exists. To date, traditional crash analysis research related to SBL configurations has been limited 

because after crash data are not always available, and optimal sample sizes that enable direct 

evaluations of the various SBL characteristics are therefore hard to obtain. The North American 

SBL literature draws attention to the need to better understand the influence of some unique SBL 

characteristics and to identify and populate missing data that, if available, could help enhance the 

transportation profession’s understanding of SBL applications. Table 4 identifies the issues 

highlighted in the published literature and summarizes key elements that need to be determined 

as part of a comprehensive SBL safety assessment.
18 

Table 4. SBL detailed site characteristics and data limitations.

Type of Issues Issues

Detailed site 

characteristics

noted in safety 

literature. 

• Intersection versus nonintersection locations/crashes.

• Type of vertical element separating traffic. 

• One-way versus two-way SBL facilities. 

• One-way versus two-way MV lanes. 

• SBL position (right side, left side, or other). 

• Site geometry (vertical grade, etc.). 

• Placement of bicycle traffic control devices. 

• Intersection transition types (turning zones, mixing zones, or 

separated). 

Data limitations 

that contribute to 

expected safety 

performance. 

• Law enforcement and user compliance.

• Changes in cyclist route choice after SBL implementation. 

• Limited before or after site and crash data. 

• Unavailable bicycle volume information. 

• MV traffic volume, including turning volumes. 

Video Analysis 

Researchers used video data to evaluate actual user performance and conducted conflict studies 

to assess expected safety performance at each SBL location. (See references 9, 10, and 19.) This 

approach generally focused on two methods: 

• Conflict analysis.

• Cyclist compliance and perceived understanding and adherence to traffic control and 

geometric configurations.

A secondary benefit of the video analysis was the ability to confirm bicycle volumes at each 

facility.

User Surveys

The strategic application of user surveys served as an additional safety assessment method. 

Researchers deployed surveys through a variety of techniques. One Canadian study used hospital 

records as a recruitment strategy.(14)

 Two studies deployed surveys by distributing them at the 

study sites and mailing them to residences near the SBL applications.(10,15,16)
19 

LITERATURE REVIEW CLOSING COMMENTS

The published literature and evolving use of SBL treatments clearly indicate that this unique 

bicycle facility holds promise for enhanced safety along a corridor. Because the deployment of 

SBL facilities has been limited, finding sufficient after data at a site to conduct a meaningful 

evaluation can be challenging. Over time, as these SBL configurations are refined and 

implemented in the United States and Canada, this limitation will subside. 

21 

CHAPTER 3. DATA COLLECTION AND DATABASE DEVELOPMENT

This chapter reviews the data collection and database development. Initially, the team proposed 

to select one or two cities and conduct physical bicycle counts that could be used for exposure 

estimates. The data collection effort was scheduled for spring 2020, but the research team’s

efforts were curtailed because the pandemic began at the same time. Therefore, the researchers

developed an alternative data collection approach that culminated with the development of a 

five-jurisdiction dataset.

SITE IDENTIFICATION

The literature review included an extended list of sites that might have SBL configurations. The 

team also used the Green Lane Project database as an additional source of information for 

identifying these sites (see reference 20 for more information on this study). 

During earlier SBL studies, the researchers were challenged to identify enough locations with 

SBL implementation that would support a statistical assessment of study sites. Between 2010 and 

2020, several transportation agencies implemented numerous additional SBL implementations. 

As a result, the research team for this project determined that the prospective sample size for an 

SBL study was sufficient to conduct a statistical assessment of the safety performance at SBL 

sites.

Site information included in the Green Lanes database was input by cyclists on a voluntary basis. 

Though there appeared to be some confusion about bicycle facility names (i.e., often a buffer 

bicycle lane was coded as an SBL), the Green Lanes database provided a rich resource for all 

types of bicycle facilities.(20) Most importantly, the street name, city, and State for this study’s 

data were accessed from the Green Lanes database. The researchers used the city and State 

information in the Green Lanes database as well as supplemental data, such as crash data from 

local agencies, to select the following cities to include in this study:

• Cambridge. 

• San Francisco.

• Seattle.

In addition, the research team selected two additional cities to test the validity of the CMFs 

ultimately developed for this study: 

• Austin. 

• Denver. 

Within each city, several potential bicycle facilities may be present. Information about the data 

collection variables is addressed in the following data collection section. 

DATA COLLECTION

The team compiled a list of data-related issues to consider when assessing the safety of an SBL 

facility. Table 5 identifies and defines these data requirements. To develop a CMF, there must be 
22 

a base condition (e.g., no bicycle lane or standard bicycle lane) and a target CMF (e.g., buffered 

bicycle lane or SBL). For this reason, site selection included all bicycle facilities within the study 

region. The project team also included variables that could potentially influence the safety 

performance of SBL facilities as well as the bicycle exposure estimate. This supplemental 

information provided valuable insight into the operational characteristics of the facilities.

Table 5. Site information in database.

Data Need Type of Data Collected Site Data Elements

Location 

description

Location • Street name

• City

• State

• Beginning and ending cross streets 

Starting 

latitude/longitude

Location Measured to extended curb if at intersection

Ending 

latitude/longitude

Location Measured to extended curb if at intersection

Length Location Distance in miles

Cross streets Location Between beginning and ending points

Number of MV

lanes

Site 

characteristics

Total for both directions of travel

Roadway facility 

type 

Site 

characteristics

• Urban arterial (two-way and one-way)

• Urban collector (two-way and one-way)

• Urban local street (two-way and one-way)

Intersection 

channelization and 

traffic control

Site 

characteristics

• Islands (traditional, bend-in, bend-out, turn 

lanes, etc.)

• MV and bicycle signalization 

• Signage and pavement markings 

Lighting Site 

characteristics

Street light presence, placement, and type

Corridor 

information

Site 

characteristics

• Speed limit

• Driveway density 

• Number, type, and width of lanes 

• Number and type of intersections 

• Street network configuration

• Vehicle parking restriction hours 

• Presence and location of sidewalks

Direction of 

bicycle traffic flow

Site 

characteristics

Approaching or departing field of view

Traffic operations 

for MV lanes

Site 

characteristics

One-way or two-way
23 

Data Need Type of Data Collected Site Data Elements

Median Site 

characteristics

Physical separation of opposing MV maneuvers

Parking lane 

location

Site 

characteristics

Presence and location of parking lane located left 

and/or right side of road if applicable.

Type of bicycle 

facility

Bicycle facility 

characteristics

• No dedicated lane.

• Traditional bicycle lane. 

• Buffered bicycle lane. 

• SBL.

Year of 

construction

Bicycle facility 

characteristics

SBLs constructed earlier than 2010 may not be suitable 

if exposure information and crash data are not available 

Width of buffer Bicycle facility 

characteristics

Locations with on-street parking should be further 

assessed to determine actual and effective buffer 

widths.

Vertical element Bicycle facility 

characteristics

• Flexible posts, bollards, or light poles.

• Curb or raised median. 

• Landscaping and planters. 

• Concrete (zebra/armadillo) bumps, buttons, and 

parking stops. 

• Parked cars. 

• Grade. 

• Concrete barrier. 

• Guardrail. 

• Fence. 

SBL facility 

configuration 

Bicycle facility 

characteristics

• Right side, one-way or two-way.

• Left side, one-way or two-way. 

• Middle, two-way. 

Unique SBL 

features

Bicycle facility 

characteristics

• Truck aprons.

• Mixing zones. 

• Elevated SBLs at driveways/crossings. 

• Green markings. 

• Bicycle facility continuity and operational 

consistency. 

Crash data Supplemental 

data from 

agency where 

sites are 

located

Total number of crashes and number of fatal and injury 

crashes (specific consideration to who is injured: MV

drivers or passengers, bicyclists, and pedestrians). 
24 

Data Need Type of Data Collected Site Data Elements

Exposure Supplemental 

data from 

agency where 

sites are 

located

• ADT or peak-hour volumes for MVs (passenger 

cars and trucks), bicycles, and pedestrians).

• Land use and driveway types (as potential 

volume surrogates). 

• Increase in bicyclists due to the SBL placement.

DATABASE DEVELOPMENT

Team members acquired crash data and road characteristic information from local agencies for 

each study site. After inspecting this information, however, the researchers determined that more 

site-specific data would be required. To develop a robust dataset, the team used a combination of 

aerial photographs, primarily available at an online mapping website, and then catalogued the 

site features for each region of interest. The primary tool for this effort was the interactive 

panoramic street viewing feature of the online mapping site. A team member virtually navigated 

each road in the study region and documented the site features. This virtual navigation enabled 

the researcher to document sites with no bicycle lanes, sites with traditional bicycle lanes, SBLs, 

and other types of sites. Ultimately, locations with a shared-use path or a sharrow were excluded 

from additional analyses. At the completion of this database development effort, the team had 

compiled data for five jurisdictions where bicycle facilities were common. These data were then 

used for the subsequent analysis phases of this project. Chapters 4, 5, and 6 provide additional 

database information about select study sites.
25 

CHAPTER 4. ESTIMATING BICYCLE EXPOSURE: CAMBRIDGE

INTRODUCTION

This chapter reviews the estimation of bicycle exposure for Cambridge, first by assessing 

statistical relationships and models and, ultimately, by developing adjustment factors for the 

Cambridge study area. The City of Cambridge collects short-term traffic counts that can be used 

for this type of analysis. Cambridge has installed one permanent bicycle counter and conducts 

short-term counts at multiple locations regularly. The research team used these count data and 

blended these data with additional site features collected by the research team. The goal of this 

data-merging effort was to develop regression models that could reliably predict bicycle count 

data for typical urban roadway facilities. This chapter reviews this effort, first by identifying the 

data collection and database development effort and then by reviewing the subsequent modeling 

process.

DATA COLLECTION: AVAILABLE BICYCLE AND SITE DATA

The City of Cambridge has been consistently collecting bicycle count data for 16 intersections 

since 2003.(21) This effort includes bicycle count data for every year from 2003 to 2006 and for 

every other year since 2008. Due to the adverse weather in 2018, the city collected data in 2019 

as well. The counts are typically collected for morning (AM) (7:30–9:15) and evening (PM) 

(4:30–6:45) peaks during weekdays in September. Additionally, in 2015, the city installed a 

permanent bicycle counter on Broadway in Kendall Square.(22)

The research team retrieved the original data from online sources and then used the Cambridge 

data to develop bicycle surrogate models. The data cover AM and PM peaks and provide the 

following information:

• Count location: the intersection at which the count was conducted. 

• Year: the year of the count.

• Date: the date of the count.

• Time period: AM or PM count data. 

• Time: the 15-min time interval of the data collection.

• Street: the name of particular streets for the counts at each intersection.

• Traffic direction: the direction of traffic.

• Cyclist location: where the cyclists were riding, on a street, sidewalk, or bicycle path. All 

the bicycle path data were removed from further analysis since the purpose of this study 

was evaluating the safety of various bicycle paths adjacent to MV roadways. 

• Cyclist direction: the direction of bicyclists, with or against traffic or on a bicycle path. 
26 

• Movement type: how the bicyclists were moving through the intersection.

• Count: the counts of bicyclists. 

• Temperature: the temperature when collecting data.

• Weather: the weather condition while collecting data.

• Latitude: the latitude of the conducted count. 

• Longitude: the longitude of the conducted count. 

For this study, the research team extracted the 2016 (September 13, 2016) and 2019 

(September 17, 2019) data for further analysis. In addition, the team obtained the permanent 

bicycle count data from its website for similar dates and times as in the temporary bicycle count 

data.(22)

The permanent bicycle counter device is assumed to slightly undercount the number of bicyclists

because some of the bicyclists probably do not use the designated bicycle path to cross the road. 

To address this issue, the city conducted tube and manual counts to determine the missing 

number of bicyclists. The results indicated that an adjustment factor of 1.167 could be used for 

considering the missing bicyclists from the permanent bicycle counter.(22) Therefore, all the 

retrieved values from the permanent count station were multiplied by 1.167 to reflect the actual 

number of bicyclists. 

The research team also acquired land use data for the study area.(1)

 

DATA MANIPULATION AND PREPARATION

The research team then developed the estimated exposure thresholds for each site in the study 

area. To illustrate this development, this section explains the data preparation process for one 

example (the intersection of Anderson Bridge and John F. Kennedy (JFK) Street at Memorial 

Drive) (figure 2). The average daily traffic count data used for this demonstration was acquired 

from the Cambridge open data website.(23)

 
27 

Original map © 2019 Google® EarthTM (see Acknowledgments).

Figure 2. Map. Anderson Bridge and JFK Street at Memorial Drive in Cambridge.

(24)

Table 6 provides a part of the original dataset from the intersection of Anderson Bridge and JFK 

Street and Memorial Drive collected by Cambridge. Specifically, the table shows data were 

collected on bicyclists who arrived to or departed from the intersection and made either right, 

left, or through movements between 7:15 a.m. and 7:30 a.m. on September 17, 2019. In addition, 

the data include whether bicyclists rode with or against the traffic and on either street or 

sidewalk.
28 

Table 6. Data table for Anderson Bridge, JFK Street, and Memorial Drive in Cambridge. 

Count Location Year Date

Time 

Period Time Street

Traffic 

Direction

Cyclist 

Location

Cyclist 

Direction Movement Count

Temp

(°F) Weather

Anderson Bridge 

and JFK St. and

Memorial Dr.

2019 9/17/2019 AM 7:30 JFK St. Northbound Street With traffic Left 0 52 Clear

Anderson Bridge 

and JFK St. and

Memorial Dr

2019 9/17/2019 AM 7:30 JFK St. Northbound Street Against 

traffic

Left 0 52 Clear

Anderson Bridge 

and JFK St. and

Memorial Dr.

2019 9/17/2019 AM 7:30 JFK St. Northbound Sidewalk With traffic Left 0 52 Clear

Anderson Bridge 

and JFK St. and

Memorial Dr.

2019 9/17/2019 AM 7:30 JFK St. Northbound Sidewalk Against 

traffic

Left 0 52 Clear

Anderson Bridge 

and JFK St. and

Memorial Dr.

2019 9/17/2019 AM 7:30 JFK St. Northbound Street With traffic Right 2 52 Clear

Anderson Bridge 

and JFK St. and

Memorial Dr.

2019 9/17/2019 AM 7:30 JFK St. Northbound Street Against 

traffic

Right 0 52 Clear

Anderson Bridge 

and JFK St. and

Memorial Dr.

2019 9/17/2019 AM 7:30 JFK St. Northbound Sidewalk With traffic Right 0 52 Clear

Anderson Bridge 

and JFK St. and

Memorial Dr.

2019 9/17/2019 AM 7:30 JFK St. Northbound Sidewalk Against 

traffic

Right 0 52 Clear

Anderson Bridge 

and JFK St. and

Memorial Dr.

2019 9/17/2019 AM 7:30 JFK St. Northbound Street With traffic Through 10 52 Clear

Anderson Bridge 

and JFK St. and

Memorial Dr.

2019 9/17/2019 AM 7:30 JFK St. Northbound Street Against 

traffic

Through 0 52 Clear

Anderson Bridge 

and JFK St. and

Memorial Dr.

2019 9/17/2019 AM 7:30 JFK St. Northbound Sidewalk With traffic Through 0 52 Clear
29 

Figure 3 depicts all the movements of the bicyclists at the intersection, regardless of whether 

they are riding with or against the traffic. A bicycle path is located parallel to Memorial Drive, 

but the movements are not indicated in the figure because bicycle paths are not within the scope 

of this research.

Original map © 2019 Google® EarthTM (see Acknowledgments). 

Figure 3. Map. Bicycle movements at the intersection of Anderson Bridge and JFK Street 

and Memorial Drive in Cambridge.

(23,24)

To better analyze the data, the research team labeled the data to indicate from which street the 

bicyclists are coming and to which street they are going. The team manually determined and 

labeled the origin and destination of the bicyclists by using the variables Street, Traffic 

Direction, and Movement Type for each leg of all the study intersections. The new variables were 

called From and To, respectively. For instance, the southbound road was labeled as “John F. 

Kennedy St.–SB” and the eastbound as “Memorial Dr.–EB.” Hence, the origin for the bicyclists 

that were going southbound and turning right was “John F. Kennedy St.–SB,” and the destination 

was “Memorial Dr.–EB.” Moreover, the direction of bicyclists was included in the variables 

because direction is an important factor for aggregating the count data in the next steps. Taking 

this example into consideration, the origin of the bicyclists would be “John F. Kennedy St.–SB–

From,” and the destination would be “Memorial Dr.–EB–To.” 
30 

Two additional variables included the Year and Time Period. These variables were also 

considered in the development of the final From and To variables. Table 7 represents the data 

table, including the new variables. Figure 4 graphically depicts a few data points and their 

associated names after manipulating and labeling the original dataset.

Original map © 2019 Google® MapsTM (see Acknowledgments). 

NB = northbound; WB = westbound.

Figure 4. Map. Cambridge data points with labels.

(23,24)
31 

Table 7. Updated data table for Anderson Bridge, JFK Street, and Memorial Drive in Cambridge, including origin and 

destination.

Count Location Street From To Year Date

Time 

Period

Traffic 

Direction

Movement 

Type

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–SB

John F. Kennedy St.–

SB_From_2016_AM

John F. Kennedy St.–

NB_To_2016_AM

2016 9/13/2016 AM Southbound Through

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–SB

John F. Kennedy St.–

SB_From_2019_AM

John F. Kennedy St.–

NB_To_2019_AM

2019 9/17/2019 AM Southbound Through

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F 

Kennedy 

St.–SB

John F. Kennedy St.–

SB_From_2016_PM

John F. Kennedy St.–

NB_To_2016_PM

2016 9/13/2016 PM Southbound Through

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–SB

John F. Kennedy St–

SB_From_2019_PM

John F. Kennedy St.–

NB_To_2019_PM

2019 9/17/2019 PM Southbound Through

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–NB

John F. Kennedy St–

NB_From_2016_AM

John F. Kennedy St.–

SB_To_2016_AM

2016 9/13/2016 AM Northbound Through

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–NB

John F. Kennedy St–

NB_From_2019_AM

John F. Kennedy St.–

SB_To_2019_AM

2019 9/17/2019 AM Northbound Through

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–NB

John F. Kennedy St–

NB_From_2016_PM

John F. Kennedy St.–

SB_To_2016_PM

2016 9/13/2016 PM Northbound Through

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–NB

John F. Kennedy St–

NB_From_2019_PM

John F. Kennedy St.–

SB_To_2019_PM

2019 9/17/2019 PM Northbound Through

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F,

Kennedy 

St.–NB

John F. Kennedy St–

NB_From_2016_AM

Memorial Drive–

EB_To_2016_AM

2016 9/13/2016 AM Northbound Through

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–NB

John F. Kennedy St–

NB_From_2019_AM

Memorial Drive–

EB_To_2019_AM

2019 9/17/2019 AM Northbound Left
32 

Count Location Street From To Year Date

Time 

Period

Traffic 

Direction

Movement 

Type

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–NB

John F. Kennedy St.–

NB_From_2016_PM

Memorial Drive–

EB_To_2016_PM

2016 9/13/2016 PM Northbound Left

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–NB

John F. Kennedy St.–

NB_From_2019_PM

Memorial Drive–

EB_To_2019_PM

2019 9/17/2019 PM Northbound Left

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–SB

John F. Kennedy St.–

SB_From_2016_AM

Memorial Drive–

EB_To_2016_AM

2016 9/13/2016 AM Southbound Right

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–SB

John F. Kennedy St.–

SB_From_2019_AM

Memorial Drive–

EB_To_2019_AM

2019 9/17/2019 AM Southbound Right

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–SB

John F. Kennedy St.–

SB_From_2016_PM

Memorial Drive–

EB_To_2016_PM

2016 9/13/2016 PM Southbound Right

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–SB

John F. Kennedy St.–

SB_From_2019_PM

Memorial Drive–

EB_To_2019_PM

2019 9/17/2019 PM Southbound Right

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–NB

John F. Kennedy St.–

NB_From_2016_AM

Paul Dudley White 

Path–

WB_To_2016_AM

2016 9/13/2016 AM Northbound Right

Anderson Bridge 

and JFK St. and

Memorial Dr.

John F.

Kennedy 

St.–NB

John F. Kennedy St.–

NB_From_2019_AM

Paul Dudley White 

Path–

WB_To_2019_AM

2019 9/17/2019 AM Northbound Right
33 

STATISTICAL MODELING

The main purpose of preparing the dataset was to develop bicycle surrogate exposure models for 

Cambridge, MA. Therefore, in addition to the bicycle count data, the research team collected and 

developed new variables. These variables, as defined by the team, included the following factors: 

• Average daily traffic (ADT): Researchers obtained the ADT data from the City of 

Cambridge Open Data Portal.(23)

• Land use: Researchers downloaded the land-use data from the geographic information 

system of the City of Cambridge.(1)

• Residential binary: Researchers defined this new variable as an extension of the variable 

land use. Residential binary indicates whether the dominant land use near a bicycle 

counter was residential.

• Population density: Researchers downloaded the population and area (m2

) information at 

the census tract level from the American Community Survey data.(25) This variable 

includes the population density (people/mi2) for each census tract.

• Bicycle facility type: Researchers collected this information from aerial views to 

represent the type of bicycle facility, such as SBL, painted bicycle lane (with and without 

a buffer), and no bicycle lane.

• Length of bicycle lanes within 1 mi of the bicycle counters: Researchers determined the 

bicycle facilities within Cambridge using a Web mapping platform.(24) The data were 

imported to a geospatial mapping software system for additional analysis. Team members 

drew buffers with 1-mi radii around each bicycle counter. Eventually, the team measured 

the length of bicycle miles within each buffer. The measurements considered each 

direction of travel separately. 

• Bicycle lane continuity: Researchers developed this variable from an aerial view to 

indicate whether a counter is located on a roadway with a continuous bicycle facility 

(i.e., the bicycle lane does not stop and start as a result of adjacent development).

• One-way roadway: Researchers used aerial views on a Web mapping platform to 

determine whether the roadway of interest operated as a one-way facility.

• Presence of bicycle lane: Researchers collected this variable from aerial views. The 

variable is a binary value and indicates whether a bicycle lane exists, regardless of the 

facility type.
34 

Because the data are short-term counts and the AADB data are not available, two different 

approaches were considered for developing bicycle surrogate models: 

• Conducting two separate models for AM and PM peaks. 

• Developing peak-hour data models. 

In other words, the team developed two different sets of models for AM and PM data. Moreover, 

the research team calculated hourly data using the given 15-min data throughout to estimate the 

peak-hour volume. The peak-hour data for each leg of each intersection were used to developed 

peak-hour surrogate models. The following sections cover each separate analysis approach. 

AM and PM Models (15 Min) 

The original dataset provided 15-min bicycle count data for the AM and PM periods. In this 

section, the team directly used the given data to develop AM and PM bicycle surrogate models.

AM Models (15 Min) 

Figure 5 shows the distribution of the 2,340 available bicycle count data points observed for the 

AM period, and table 8 provides the descriptive statistics of the study variables.

Source: FHWA. 

Figure 5. Chart. Distribution of AM 15-min bicycle count data for Cambridge bicycle 

volume.
35 

Table 8. Descriptive statistics of the study variable for the 15-min AM data for Cambridge. 

Variable Mean Standard Deviation Minimum Maximum

Count 14.22 20.42 0 160

ADT (vpd) 11,073.09 7,136.59 1,032 24,264

Length (mi) of bicycle lanes 

within 1 mi

11.98 3.12 4.96 15.67

Presence of one-way roadway

(1 = yes)

0.25 0.43 0 1

Presence of continuous 

bicycle lane (1 = yes)

0.51 0.50 0 1

Presence of separated bicycle

lane (1 = yes)

0.41 0.52 0 2

Population density (pop/mi

2

) 20,428.16 10,367.45 5,692 49,022

Presence of bicycle lane

(1 = yes)

0.49 0.50 0 1

Presence of residential 

development (1 = yes)

0.15 0.36 0 1

pop = population; vpd = vehicles per day.

Figure 6 represents the average bicycle count data over all the sites for each year separately. No

noticeable increase/decrease in the average count occurred from 2016 to 2019. Cambridge 

provides bicycle count data every other year, but due to severe winter weather followed by 

unusually high heat trends in 2018, the city collected bicycle count data in 2019 as well. Table 9

and table 10 summarize the average number of bicycles for each bicycle facility and land use, 

respectively.

Source: FHWA.

Figure 6. Chart. Average AM 15-min bicycle count data for the Cambridge study years 

(2016–2019). 
36 

Table 9. Frequency of various bicycle facility types for the 15-min AM data for Cambridge. 

Bicycle Facility Type Frequency

(No./15 min.)

No bicycle lane 1,200

Painted bicycle lanes/no buffer 884

Painted bicycle lanes with buffer adjacent to active travel lane 80

Separated bicycle lane with buffer and vertical element 176

Total 2,340

Table 10. Frequency of various land uses for the 15-min AM data for Cambridge. 

Land Use Frequency

(No./15 min.)

Commercial 560

Mixed use 160

None 64

Open space 128

Other 1,072

Residential 356

Total 2,340

Next, the research team conducted a correlation test to determine any correlations between the 

independent variables. Table 11 provides the results of the correlation test.
37 

Table 11. Correlation test for the 15-min AM data for Cambridge. 

Variable

Population 

Density/

1,000

Persons

Continuous 

Bicycle

Lane

One-Way 

Roadway

Presence 

of Bicycle

Lane

Separated 

Bicycle

Lane

Length (mi) 

of Bicycle

Lanes 

Within 1 Mi

Residential 

Binary

Log 

(ADT)

ADT/

1,000

Per 

Day

Population 

density/1,000

persons

1 ― ― ― ― ― ― ― ―

Continuous bicycle

lane (1 = yes)

−0.06 1 ― ― ― ― ― ― ―

One-way roadway

(1 = yes)

0.04 −0.06 1 ― ― ― ― ― ―

Presence of bicycle

lane (1 = yes)

−0.11 0.83 −0.19 1 ― ― ― ― ―

Separated bicycle

lane (1 = yes)

−0.10 0.74 −0.26 0.77 1 ― ― ― ―

Length (mi) of 

bicycle lanes 

within 1 mi

−0.07 0.02 0.10 0.06 0.02 1 ― ― ―

Residential 

(1 = yes)

−0.12 −0.11 0.13 −0.10 −0.04 0.20 1 ― ―

Log(ADT) 0.06 0.26 −0.42 0.25 0.19 −0.05 −0.17 1 ―

ADT/1,000 0.13 0.20 −0.43 0.20 0.13 −0.22 −0.24 0.92 1

Log = logarithm. 

Note: Values shown as bold indicate variable correlation of 0.7 or higher.
38 

The research team considered using nested mixed-effect regression models because each single 

study intersection has multiple legs, and each leg has multiple data points to represent 

15-min intervals. In the models, two variables of intersection (represented as CountLoc) and 

study location (defined as Group) were considered as random-effect variables where CountLoc 

represents the physical location of each count. The following sections provide the results of 

nested mixed-effect Poisson and nested mixed-effect negative binomial models, respectively.

PM Models (15-Min) 

The PM period included 2,920 data points, and figure 7 shows the distribution of PM bicycle 

count data. Table 12 provides the descriptive statistics of the study variables.

Source: FHWA. 

Figure 7. Chart. Distribution for Cambridge PM 15-min bicycle count data.

Table 12. Descriptive statistics of the study variable for the 15-min PM data for 

Cambridge. 

Variable Mean Standard Deviation Minimum Maximum

Count 14.67 19.94 0 170

ADT (vpd) 11,085.18 7,136.41 1,032 24,264

Length (mi) of bicycle lanes 

within 1 mi

11.97 3.12 4.96 15.67

One-way roadway (1 = yes) 0.25 0.43 0 1

Continuous bicycle lane 

(1 = yes)

0.51 0.50 0 1

Separated bicycle lane (1 = yes) 0.41 0.52 0 2

Population density (pop/mi2) 20,420.47 10,374.21 5,692 49,022

Presence of bicycle lane 

(1= yes)

0.49 0.50 0 1

Residential (1 = yes) 0.15 0.36 0 1
39 

Figure 8 shows the average PM bicycle counts for the study years, and table 13 and table 14

provide the frequency of each bicycle facility and land use types for the PM data, respectively.

Source: FHWA. 

Figure 8. Chart. Average PM 15-min bicycle count data for the Cambridge study years.

Table 13. Frequency of various bicycle facility types for the Cambridge 15-min PM data.

Bicycle Facility Type

Frequency

(No.)

No bicycle lane 1,500

Painted bicycle lanes/no buffer 1,100

Painted bicycle lanes with buffer adjacent to active travel 

lane

100

Separated bicycle lane with buffer and vertical element 220

Total 2,920

Table 14. Frequency of various land uses for the 15-min PM data for Cambridge. 

Land Use Frequency (No.)

Commercial 700

Mixed use 200

None 80

Open space 160

Other 1,340

Residential 440

Total 2,920

In a manner similar to the AM data modeling effort, the research team conducted a correlation 

test to determine any possible correlation between the study variables before developing the 

surrogate models. Table 15 represents the correlation test results.
40 

Table 15. Correlation test for the 15-min PM data.

Variable ADT

Population 

Density/1,000

Continuous 

Bicycle 

Lane

One-Way 

Roadway

Presence 

of 

Bicycle 

Lane

Separated 

Bicycle 

Lane

Length 

(mi) of 

Bicycle 

Lanes 

Within 

1 Mi

Residential 

Binary

Log 

(ADT)

ADT (vpd) 1 ― ― ― ― ― ― ― ―

Population density/

1,000 persons

0.15 1 ― ― ― ― ― ― ―

Continuous bicycle 

lane (1 = yes)

0.25 −0.07 1 ― ― ― ― ― ―

One-way roadway

(1 = yes)

−0.42 0.02 −0.07 1 ― ― ― ― ―

Presence of bicycle 

lane (1 = yes)

0.25 −0.12 0.82 −0.21 1 ― ― ― ―

Separated bicycle 

lane (1 = yes)

0.17 −0.11 0.73 −0.27 0.76 1 ― ― ―

Length (mi) of 

bicycle lanes within 

1 mi

−0.19 −0.09 0.00 0.08 0.05 0.01 1 ― ―

Residential (1 = yes) −0.24 −0.13 −0.12 0.11 −0.10 −0.04 0.19 1 ―

Log(ADT) 0.92 0.07 0.30 −0.42 0.29 0.22 −0.02 −0.15 1

Note: Values shown as bold indicate variable correlation of 0.7 or higher.
41 

Next, the team developed nested mixed-effect negative binomial models and nested mixed-effect 

Poisson models, which were developed by using the variables CountLoc and Group as the 

random-effect variables where CountLoc refers to the physical location of the bicycles. 

Aggregated 15-Min AM and PM Models

The dataset included 5,260 data points when aggregating AM and PM 15-min data for the study 

sites. Figure 9 represents the distribution of the bicycle count data, and table 16 provides 

descriptive statistics of the study variables.

Source: FHWA. 

Figure 9. Chart. Distribution of the aggregated 15-min AM and PM bicycle count data for 

Cambridge. 

Table 16. Descriptive statistics of the study variable for the aggregated 15-min AM and PM 

bicycle count data for Cambridge. 

Variable Mean Standard Deviation Minimum Maximum

Bicycle count 14.47 20.15 0 170

ADT (vpd) 11,079.80 7,135.81 1,032 24,264

Length (mi) of bicycle lanes 

within 1 mi

11.97 3.12 4.96 15.67

One-way roadway (1 yes) 0.25 0.43 0 1

Continuous bicycle lane

(1 = yes)

0.51 0.50 0 1

Separated bicycle lane

(1 = yes)

0.41 0.52 0 2

Population density (pop/mi

2

) 20,423.89 10,370.22 5,692 49,022

Presence of bicycle lane

(1 = yes)

0.49 0.50 0 1
42 

Additionally, figure 10, table 17, and table 18 provide information about the average bicycle

count data for each year as well as the frequency of various bicycle facilities and land uses.

Source: FHWA. 

Figure 10. Chart. Distribution for Cambridge aggregated 15-min AM and PM bicycle

count data for the study years. 

Table 17. Frequency of various bicycle facility types for the aggregated 15-min AM and 

PM bicycle count data for Cambridge. 

Bicycle Facility Type

Frequency

(No.)

No bicycle lane 2,700

Painted bicycle lanes/no buffer 1,984

Painted bicycle lanes with buffer adjacent to active travel lane 180

Separated bicycle lane with buffer and vertical element 396

Total 5,260

Table 18. Frequency of various land uses for the aggregated 15-min AM and PM bicycle

count data for Cambridge. 

Land Use

Frequency

(No.)

Commercial 1,260

Mixed use 360

None 144

Open space 288

Other 2,412

Residential 796

Total 5,260
43 

The research team conducted another Pearson’s correlation test before the development of 

regression models (see table 19).
44 

Table 19. Correlation test for the aggregated 15-min AM and PM bicycle count data for Cambridge. 

Variable

Population 

Density/

1,000

Continuous 

Bicycle 

Lane

One-Way 

Roadway

Presence 

of 

Bicycle 

Lane

Separated 

Bicycle 

Lane

Length (mi) 

of Bicycle 

Lanes 

within 1 Mi

Residential 

Binary

Log

(ADT)

ADT/

1,000

Population 

density/1,000

1 ― ― ― ― ― ― ― ―

Continuous 

bicycle lane

−0.07 1 ― ― ― ― ― ― ―

One-way 

roadway

0.03 −0.07 1 ― ― ― ― ― ―

Presence of 

bicycle lane

−0.12 0.82 −0.21 1 ― ― ― ― ―

Separated 

bicycle lane

−0.11 0.73 −0.27 0.76 1 ― ― ― ―

Length (mi) of 

bicycle lanes 

within 1 mi

−0.09 0.00 0.09 0.05 0.01 1 ― ― ―

Residential 

binary

−0.13 −0.12 0.12 −0.10 −0.04 0.19 1 ― ―

Log(ADT) 0.07 0.30 −0.42 0.29 0.22 −0.02 −0.16 1 ―

ADT/1,000 0.15 0.25 −0.42 0.25 0.17 −0.19 −0.24 0.92 1

Note: Values shown as bold indicate variable correlation of 0.7 or higher.
45 

Peak-Hour Models

The peak-hour data contained 292 observations. Figure 11 depicts the distribution of the 

peak-hour bicycle count data for the study sites, and table 20 provides descriptive statistics of the 

peak-hour data. 

Source: FHWA.

Figure 11. Chart. Distribution for Cambridge peak-hour bicycle count data by hourly 

volume. 

Table 20. Descriptive statistics of the study variable for the peak hour.

Variable Mean Standard Deviation Minimum Maximum

Peak-hour bicycle volume 99.05 113.63 0 582

ADT (vpd) 11,085.18 7,147.44 1,032 24,264 

Length (mi) of bicycle lanes 

within 1 mi

11.97 3.13 4.96 15.67

One-way roadway (1 = yes) 0.25 0.43 0 1

Continuous bicycle lane

(1 = yes)

0.51 0.50 0 1

Separated bicycle lane

(1 = yes)

0.41 0.52 0 2

Population density 

(pop/mi

2

) (1 = yes)

0.49 0.50 0 1

Presence of bicycle lane

(1 = yes)

0.15 0.36 0 1

Residential (1 = yes) 0.15 0.36 0 1
46 

Figure 12 shows the average peak-hour bicycle counts over all the study sites for each year 

separately. As depicted, in 2019, the number of bicyclists increased over that observed for 2016. 

Table 21 and table 22 show the distribution of each bicycle facility and land use type, 

respectively.

Source: FHWA. 

Figure 12. Chart. Distribution of Cambridge peak-hour bicycle count data for the study 

years (2016–2019). 

Table 21. Frequency of various bicycle facility types for the peak hour.

Bicycle Facility Type

Frequency

(No.)

No bicycle lane 150

Painted bicycle lanes/no buffer 110

Painted bicycle lanes with buffer adjacent to active travel 

lane

10

Separated bicycle lane with buffer and vertical element 22

Total 292
47 

Table 22. Frequency of various land uses for the peak-hour data.

Land Use Frequency

(No.)

Commercial 70

Mixed use 20

None 8

Open space 16

Other 134

Residential 44

Total 292

The research team considered the results of the correlations tests, represented in table 23, to 

guide variable selections when developing the bicycle surrogate models. 
48 

Table 23. Correlation test for Cambridge peak-hour data.

Variable

Population 

Density/

1,000

Continuous 

Bicycle 

Lane

One-Way 

Roadway

Presence 

of 

Bicycle 

Lane

Separated 

Bicycle 

Lane

Length (mi) 

of Bicycle 

Lanes 

within 1 Mi

Residential 

Binary

Log

(ADT)

ADT/

1,000

Population 

density/1,000

1 ― ― ― ― ― ― ― ―

Continuous 

bicycle lane

(1 =1 yes)

−0.07 1 ― ― ― ― ― ― ―

One-way 

roadway 

(1 = yes)

0.02 −0.07 1 ― ― ― ― ― ―

Presence of 

bicycle lane

(1 = yes)

−0.12 0.82 −0.21 1 ― ― ― ― ―

Separated 

bicycle lane

(1 = yes)

−0.11 0.73 −0.27 0.76 1 ― ― ― ―

Length (mi) of 

bicycle lanes 

within 1 mi

−0.09 0.00 0.08 0.05 0.01 1 ― ― ―

Residential 

(1 = yes)

−0.13 −0.12 0.11 −0.10 −0.04 0.19 1 ― ―

Log(ADT) 0.07 0.30 −0.42 0.29 0.22 −0.02 −0.15 1 ―

ADT/1,000 0.15 0.25 −0.42 0.25 0.17 −0.19 −0.24 0.92 1

Note: Values shown as bold indicate variable correlation of 0.7 or higher.
49 

Nested Mixed-Effect Negative Binomial

Although the research team made several attempts to statistically develop a nested mixed-effect 

negative binomial model, they were unable to identify any models that converged. 

Aggregated AM and PM Peak-Hour Models

In this section, the research team considered both the AM and PM peak-hour data points for each 

location in the analysis. The dataset included 585 observations. Figure 13 depicts the distribution 

of the peak-hour bicycle count data for the study sites. The trends observed for the AM and PM 

as well as average AADB, as shown in the AM and PM summaries, are consistent with the 

aggregated summary. 

Source: FHWA. 

Figure 13. Chart. Distribution of Cambridge aggregated AM and PM peak-hour count 

data. 
50 

Figure 14 indicates the average peak-hour bicycle count over all the study sites for each year 

separately. As depicted, the number of bicyclists was higher in 2019 than in 2016. Table 24

provides descriptive statistics of the peak-hour data. Table 25 and table 26 show the distribution 

of each bicycle facility and land-use type, respectively.

Source: FHWA.

Figure 14. Chart. Distribution of Cambridge aggregated peak-hour bicycle count data for 

the PM periods. 

Table 24. Descriptive statistics of the study variable for the aggregated AM and PM peak 

hour.

Variable Mean

Standard 

Deviation Minimum Maximum

Peak-hour bicycle volume

(count)

70.36 92.71 0 582

ADT (vpd) 11,073.09 7,141.17 1,032 24,264

Length (mi) of bicycle lanes 

within 1 mi

11.98 3.13 4.96 15.67

One-way roadway (1 = yes) 0.25 0.43 0 1

Continuous bicycle lane

(1 = yes)

0.51 0.50 0 1

Separated bicycle lane

(1 = yes)

0.41 0.52 0 2

Population density (pop/mi2) 20,428.16 10,374.11 5,692 49,022

Presence of bicycle lane

(1 = yes)

0.49 0.50 0 1
51 

Table 25. Frequency of various bicycle facility types for the aggregated AM and PM 

peak-hour data.

Bicycle Facility Type

Frequency

(No.)

No bicycle lane 300

Painted bicycle lanes/no buffer 221

Painted bicycle lanes with buffer adjacent to active travel lane 20

Separated bicycle lane with buffer and vertical element 44

Total 585

Table 26. Frequency of various land uses for the aggregated AM and PM peak-hour data.

Land Use

Frequency

(No.)

Commercial 140

Mixed use 40

None 16

Open space 32

Other 268

Residential 89

Total 585
52 

The research team considered the results of the correlations tests, represented in table 27, for conducting the bicycle surrogate models.

Table 27. Correlation test for the aggregated AM and PM peak-hour data.

Variable

Population 

Density/

1,000

Continuous 

Bicycle 

Lane

One-Way 

Roadway

Presence 

of Bicycle 

Lane

Separated 

Bicycle 

Lane

Length (mi) 

of Bicycle 

Lanes 

within 1 Mi

Residential 

Binary

Log

(ADT)

ADT/

1,000

Population 

density/1,000

1 ― ― ― ― ― ― ― ―

Continuous 

bicycle lane 

(1 = yes)

−0.07 1 ― ― ― ― ― ― ―

One-way 

roadway 

(1 = yes)

0.03 −0.07 1 ― ― ― ― ― ―

Presence of 

bicycle lane 

(1 = yes)

−0.13 0.86 −0.24 1 ― ― ― ― ―

Separated 

bicycle lane 

(1 = yes)

−0.11 0.73 −0.27 0.82 1 ― ― ― ―

Length (mi) 

of bicycle 

lanes within 1 

mi

−0.09 0.00 0.09 0.06 0.01 1 ― ― ―

Residential 

(1 = yes)

−0.12 −0.12 0.12 −0.10 −0.04 0.19 1 ― ―

Log(ADT) 0.07 0.30 −0.42 0.30 0.22 −0.02 −0.16 1 ―

ADT/1,000 0.15 0.25 −0.43 0.26 0.18 −0.20 −0.24 0.92 1

Note: Values shown as bold indicate variable correlation of 0.7 or higher.
53 

Nested Mixed-Effect Negative Binomial

No nested mixed-effect negative binomial models converged for this dataset. Models included 

Year as a potential variable, but the results indicated this variable was insignificant in all the 

models. Consequently, the team next explored ways to potentially estimate the AADBs from the 

available short-term counts.

ESTIMATING AADBs FROM THE SHORT-TERM BICYCLE COUNTS

The City of Cambridge does not provide yearly bicycle count data to enable calculating AADBs 

for the given sites and using them for estimating AADBs for other locations. However, the city 

does have one permanent bicycle count station that can be used to extrapolate short-term bicycle

counts to AADBs. Therefore, the research team calculated K-factors for the permanent bicycle 

count station and applied that to the other study sites. 

A K-factor is the proportion of design hourly volume to average annual daily traffic (AADT), as 

defined in figure 15. In this case, the team considered the peak-hour bicycle count data. 

Figure 15. Equation. Calculation based on K-factor and AADB.

Where: 

K = the factor used for traffic volume adjustment for volume distribution in a 24-h period. 

DHV = the design hourly bicycle volume for the 30th highest hourly volume for both 

directions.

The team considered the following steps to calculate K-factors for permanent bicycle counts 

given its raw data:

• Extracting the 2016 and 2019 permanent bicycle count data according to the dates of the 

study intersection. 

• Adjusting the values given the adjustment factor of 1.167.(22)

• Calculating AADB by considering the number of days within each year and the number 

of missing days. 

• Calculating two sets of K-factors for both dominant and nondominant directions, with

2016 and 2019 data to be further applied to the other study sites.

The research team considered each direction of travel as an independent site and calculated 

AADB values separately. Table 28 shows the calculated and adjusted AADBs for each direction 

of each year and the K-factors for the permanent count station data. The year 2016 was a leap 

year, and the analysis included this additional adjustment factor when calculating the AADB.
54 

Table 28. Permanent Count Station K-factors.

Date Direction Time

Peak-Hour 

Volume AADB K-Factor

9/13/2016 Eastbound Peak hour 79 678 0.11652

9/13/2016 Westbound 

(dominant)

Peak hour 224 547 0.40951

9/17/2019 Eastbound Peak hour 77 717 0.10739

9/17/2019 Westbound 

(dominant)

Peak hour 207 661 0.31316

Ultimately, the team applied the calculated K-factors represented in table 28 to the other study 

sites considering the dominant direction and year. For areas with only one direction of travel for 

bicyclists, the team implemented the K-factor of the dominant direction. If both directions had 

the same bicycle volume, the team applied a value of 0.12367, which equals 

(79+224)∕(716+661)∕2, for 2016 and a value of 0.10305, which equals (77+207)∕(716+661)∕2) for 

2019 for both directions. 

The research team then mapped the adjusted exposure estimates to site and crash data for an 

assessment of safety for bicycle facilities in Cambridge.
55 

CHAPTER 5. ESTIMATING BICYCLE EXPOSURE: SAN FRANCISCO

INTRODUCTION

For this chapter, the research team selected the San Francisco as a case study to determine the 

feasibility of estimating AADB values based on known counts. San Francisco has installed both 

permanent and temporary bicycle counters at various locations within the city. Each bicycle 

counter provides multiyear cyclist counts for the bicycle counter location. The research team 

used these count data and blended these data with additional site features collected by the 

research team (refer to chapter 3 for more information on data collection). 

The goal of this data-merging effort was to develop a database that could be used to develop 

regression models that could reliably predict AADB for typical urban roadway facilities. This 

chapter reviews this effort, first by identifying the data collection and database development 

effort and then by reviewing the subsequent modeling process. 

DATA COLLECTION: BICYCLE COUNT AND SITE FEATURE VARIABLES

The team obtained San Francisco bicycle count data from the San Francisco Municipal 

Transportation Agency website.(25) The data collection covered the period from 2016 to 2021 and 

included data from 1 to 5 yr, depending on the availability at each site. Table 29 provides the 

distribution of the bicycle count data for each year. 

Table 29. Distribution of bicycle count data over years for San Francisco.

Year Frequency

2016 32 (leap year)

2017 32

2018 26

2019 24

2020 20 (leap year)

2021 11

Total 145

The bicycle count data represent the total number of bicyclists over each year for each site. To 

calculate the AADB for each individual site and year, the number of days per year should be 

considered (because years divisible by four are leap years). Figure 16 depicts the AADB over the 

sites for each year. The drop in the AADB starting in 2020 might be associated with the 

pandemic. Therefore, to avoid any bias in the results due to the pandemic, the team removed the 

31 data points for 2020 and 2021. Figure 17 represents a graphical distribution of the AADB 

values for the study sites after removing the 2020 and 2021 data. 
56 

Source: FHWA. 

Figure 16. Graph. Average AADB for San Francisco (2016–2021). 

Source: FHWA. 

Figure 17. Chart. AADB distribution for San Francisco (2016–2019).

The sites located in the study region represent a variety of bicycle facilities. Table 30

summarizes the bicycle facilities as identified by the data collection efforts. 
57 

Table 30. Frequency of various types of bicycle facilities for San Francisco.

Bicycle Facility Type

Frequency

(No.)

Nothing 34

Painted bicycle lane: no buffer 36

Painted bicycle lane: with buffer 6

SBL: with buffer 38

Total 145

For further statistical analysis and modeling, the research team assembled various roadway and 

site characteristic variables to evaluate as surrogates for AADB. Table 31 identifies and defines 

these study variables, and table 32 provides the descriptive statistics for the variables.

Table 31. Study variables for San Francisco. 

Variable Definition

Length (mi) of bicycle

lanes within 1 mi 

Length (mi) of bicycle lanes within 1 mi radius of a bicycle

counter

ADT ADT (vehicle/day)

Population density Population density (people/mi2)

One-way roadway Whether a roadway is one-way

Presence of bicycle lane Whether a bicycle lane exists at the count location

Residential binary Whether the adjacent land use is residential

Continuous bicycle lane Whether the adjacent bicycle lane is continuous

Table 32. Descriptive statistics for the study variables for San Francisco.

Variable Mean

Standard 

Deviation Minimum Maximum

AADB 570.76 546.79 39 2,137

ADT 12,512.84 6,142.11 0 24,110

Population density 24,010.02 10,247.75 175 46,570

Continuous bicycle lane 0.70 0.46 0 1

One-way roadway 0.21 0.41 0 1

Residential binary 0.33 0.47 0 1

Length (mi) of bicycle lanes within 1 mi 8.26 5.21 0.15 16.55

Presence of bicycle lane 0.72 0.45 0 1

The team considered Land Use as another variable in the AADB analysis. Most of the adjacent 

land use is residential, whereas commercial, industrial, public, and mixed use are sparsely 

distributed within the study region of the city. 
58 

PRELIMINARY STATISTICAL ASSESSMENT: CORRELATION TEST

As an initial step, the research team conducted a correlation test to assess correlation between the 

individual variables. Table 33 presents the results of the Pearson’s correlation test. None of the 

variables are highly correlated. Therefore, there was no need to consider including any 

interaction terms in the prediction models.
59 

Table 33. Correlation test for San Francisco.

Variable ADT Log(ADT)

Population 

Density

Residential 

Binary

One-Way 

Roadway

Length (mi)

of Bicycle

Lanes Within 

1 Mi 

Presence 

of 

Bicycle

Lane

ADT 1 ― ― ― ― ― ―

Log(ADT) 0.66 1 ― ― ― ― ―

Population density –0.11 –0.11 1 ― ― ― ―

Residential binary –0.14 –0.25 0.16 1 ― ― ―

One-way roadway 0.04 0.11 0.20 –0.09 1 ― ―

Length (mi) of bicycle lanes within 

1 mi

0.10 0.30 0.38 –0.31 0.26 1 ―

Presence of bicycle lane 0.21 0.33 0.06 –0.06 0.13 0.21 1
60 

MODELING RESULTS FOR SAN FRANCISCO

The research team examined various statistical techniques and explored a combination of the 

study variables to develop the AADB prediction models. Ultimately, among the developed 

models, the research team determined that the Poisson mixed–effect model represented in 

table 34 and figure 18 provided the best prediction. In this model, AADB is the dependent 

variable, site number is the random-effect variable, and log(ADT), miles of bicycle lanes within 

1 mi, population density, one-way roadway, and land use are considered independent variables. 

Table 34 depicts the cumulative residual plot based on miles of bicycle lane with 1 mi. similarly 

depicts the cumulative residual plot based on ADT. 

Table 34 further demonstrates that an increasing log(ADT), population density, miles of bicycle 

lanes within 1 mi, and consideration of providing one-way roadways were associated with an 

increase in the expected AADB). Moreover, providing mixed-use, public, and residential land 

uses appeared to be associated with a decrease in the number of bicyclists compared to the 

commercial and industrial land uses.

Table 34. Final bicycle surrogate model for San Francisco.

Variable Estimate Standard Error z-Value Pr(>|z|)

(Intercept) 4.668 0.481 9.699 <0.0001

Length (mi) of bicycle lanes within 1 0.094 0.020 4.672 <0.0001

Factor(one-way roadway) 0.813 0.206 3.951 <0.0001

Log(ADT) 0.089 0.047 1.899 0.0576

Population density/1,000 0.017 0.009 1.933 0.0533

factor(NewLU) mixed use* −1.104 0.222 −4.965 <0.0001

factor(NewLU)public −1.555 0.326 −4.765 <0.0001

factor(NewLU)residential −1.354 0.232 −5.835 <0.0001

Functional form: AADB ~ log(ADT) + Length (mi) of Bicycle Lanes Within 1 Mi + Population Density/1,000 + 

factor(One-Way Roadway) + factor(NewLU) + (1 | Location).

*Base scenario: commercial and industrial. 

LU = land use; Pr = probability.

Note: Akaike information criterion (AIC) = 1,934.2; deviance = 1,916.2; random-effect variance = 0.3491; average 

prediction error = 284.77; coefficient of variation (CV) = 0.499.
61 

Source: FHWA.

bpd = bicycles per day.

Figure 18. Graph. Scatterplot for San Francisco model. 

63 

CHAPTER 6. ESTIMATING BICYCLE EXPOSURE: SEATTLE

INTRODUCTION

This chapter summarizes the bicycle exposure modeling process for Seattle. The research team 

selected Seattle as a case study to determine the feasibility of estimating AADB values based on 

known counts. Seattle has installed both permanent and temporary bicycle counters at various 

locations within the city. Each bicycle counter provides multiyear cyclist counts for the bicycle 

counter location. The research team blended these count data with additional site features 

collected by the research team. The goal of this data-merging effort was to develop regression 

models that could reliably predict AADB for typical urban roadway facilities. This chapter 

reviews this effort, first by identifying the data collection and database development effort and 

then by reviewing the subsequent modeling process. 

DATA COLLECTION: BICYCLE COUNT

The research team assembled a variety of roadway and site features to determine reasonable site 

features that might serve as surrogates for AADB. Because the goal of this effort was to base the 

predictive method on actual bicycle volume information, where possible, the team acquired 

bicycle count data that were previously collected from Seattle.(27)

 The team then used these

bicycle count data to calculate the respective AADB values for each count station location. The 

City of Seattle installed 12 bicycle counters within the city, and the data from 10 of the counters 

are publicly available. 

Among the 10 bicycle counters for which data were readily available, 3 were permanent counters 

that monitored cyclists 24 h/d, 7 d/w, and 365 d/yr. The City of Seattle uploads the data from 

these three counters once a day. The city uploads the data for the remaining seven counters once 

a month. The data acquired from these bicycle counters date back to 2014. Based on the 

reporting frequency of the data and the available number of months and days for each counter 

(and adjusting for some missing days or months), the research team calculated the AADB for 

each year at each site. Table 35 shows the sites and the number of available years of data.

Table 35. Bicycle counters and number of available years of data.

Bicycle Counter Name Count at Site

2nd Ave Bicycle Counter 5

39th Ave NE Greenway at NE 62nd St 4

Broadway Cycle Track North of E Union St 6

Burke Gilman Trail North of NE 70th St 6

Chief Sealth Trail North of Thistle 2

Elliott Bay Trail in Myrtle Edwards Park 7

Fremont Bicycle Counters 7

MTS Trail West of I-90 Bridge 7

NW 58th St Greenway at 22nd Ave NW 7

Spokane Bicycle Counters 7

Total 58

Note: The italicized cells indicate the permanent counters.
64 

The team members eliminated nine data points that collectively represented two bicycle trails

from further analysis because of the different characteristics of bicycle trails compared to onstreet bicycle facilities. Ultimately, the bicycle count database included 49 observations available 

for further analysis. 

DATA COLLECTION: SITE FEATURES

In addition to the acquisition of actual bicycle count data, the research team also compiled a 

database that included on-road bicycle facilities (primarily traditional bicycle lanes, bicycle 

buffers, and SBLs). For each of these facilities, team members documented a variety of site 

characteristics that could potentially influence the route choice and resulting roadway facility 

bicycle volume. The following eight variables introduce features assembled in the site feature 

database for which the variables appeared to have some potential correlation to the AADT (or 

ADT), as observed during the subsequent statistical modeling efforts: 

• ADT: The research team obtained the annual ADT data from the Washington Geospatial 

Open Data Portal.(28)

 The available data date back to 2014. 

• Presence of bicycle lane: Team members collected this variable from aerial views. The 

variable is a binary value and indicates whether a bicycle lane exists, regardless of the 

facility type.

• New land use: The researchers combined the single-family and multifamily residentials

to represent “Residential” as a single category. This information was acquired by 

examining the land use through a street view perspective on a Web mapping platform. 

• Residential binary: Team members downloaded land-use data from the Washington 

Geospatial Open Data Portal and converted this information to a binary variable to 

indicate whether the dominant land use near a bicycle counter was residential.(27)

• Population density: The team acquired population and area (m2) data from a free on-line 

data source. These data provided information at the census tract level.(25) This 

information includes the population density (population/mi2

) for each census tract.

• Length of bicycle lanes within 1 mi of the bicycle counters: The research team 

determined the bicycle facilities within Seattle using an online mapping platform. The 

team then imported the data to a geospatial mapping system drew buffers with 1-mi radii 

around each bicycle counter. Eventually, the team measured the length of bicycle miles 

within each buffer. Each direction of travel was considered separately in the 

measurements.

• Bicycle lane continuity: Team members developed this variable from an aerial view to 

indicate whether a counter is located on a roadway with a continuous bicycle facility 

(i.e., the bicycle lane does not stop and start as a result of adjacent development).

• One-way roadway: Team members used Google Earth aerial views to determine whether 

the roadway of interest operated as a one-way facility.
65 

Table 36 through table 38 summarize the study variables.

Table 36. Frequency of different bicycle facilities.

Bicycle Facility Count

None 38

SBL 11

Total 49

Table 37. Frequency of different land-use categories.

Land Use Count

Commercial/mixed use 6

Downtown 5

Manufacturing/industrial 14

Multifamily 13

Single family 11

Total 49

Table 38. Descriptive statistics of the numerical study variables.

Variable Mean Standard Deviation Minimum Maximum

AADB 872.31 836.50 94 3,259

Population density 12,111.94 9,323.02 2,439 32,933

Presence of bicycle lane 

(1 = yes)

0.22 0.42 0 1

One-way roadway 0.10 0.31 0 1

ADT 12,510.73 8,703.73 6,300 31,855

Continuous bicycle lane 0.22 0.42 0 1

Residential (1 = yes) 0.49 0.51 0 1

Length (mi) of bicycle lanes 

within 1 mi of the bicycle 

counters

6.13 5.30 0.28 15.232

The 2020 AADB decreased consistently for all study sites with available data. Most likely, this 

observed change in bicycle operations was associated with changes in traffic patterns resulting 

from the 2020 pandemic. Therefore, for the subsequent statistical analysis, the variable Year has 

been considered in the modeling process to evaluate the models for any annual significance.

PRELIMINARY STATISTICAL ASSESSMENT: CORRELATION TEST

An initial way to assess the potential influence of roadway features and their relationship to the 

AADB is to determine how the individual variables may be correlated. Table 39 shows the 

Pearson’s correlation coefficient test between the numerical variables. The italicized cells in 

table 39 indicate variables that are highly correlated (with values approaching 1.0). For these 

variables, an inclusion term to capture the interaction between variables should be considered if 

both variables are retained in a model.
66 

Table 39. Correlation test for Seattle. 

Variable ADT

Populati

on 

Density

Presence 

of 

Bicycle 

Lane

Residential 

Binary

Continuous 

Bicycle 

Lane

One-Way 

Road

Length (mi) of 

Bicycle Lanes 

Within 1 Mi 

of the Counter

ADT 1 ― ― ― ― ― ―

Population density 0.06 1 ― ― ― ― ―

Presence of bicycle 

lane

–0.09 0.76 1 ― ― ― ―

Residential binary –0.50 –0.48 –0.54 1 ― ― ―

Continuous bicycle 

lane

–0.09 0.76 1.00 –0.54 1 ― ―

One-way road 0.20 0.08 0.58 –0.32 0.58 1 ―

Length (mi) of bicycle 

lanes within 1 mi of 

the counter

–0.04 0.82 0.91 –0.48 0.91 0.55 1

Note: The italicized cells indicate variables that are highly correlated (with values greater than 0.7).
67 

MODELING RESULTS

The research team explored a variety of statistical techniques for the development of AADB 

predictive models. Ultimately, the research team determined that a mixed-effect Gaussian model 

that used the log(AADB) as the dependent variable and the site number as a random-effect 

variable provided the best predictive fit for the model. The following summary provides an 

overview of this mixed-effect Gaussian process model.

As an initial step, the research team evaluated a combination of several study variables to 

achieve a reasonable model to predict AADB. The initial mixed-effect Gaussian model included 

all the years of data, including the pandemic year of 2020 (table 40).

Table 40. Mixed-effect Gaussian model, including 2020 data. 

Parameter Estimate Standard Error df t-Value Pr(>|t|)

(Intercept) −6.898 2.644 6.173 −2.609 0.03915

Log(ADT) 1.461 0.286 6.173 5.114 0.002

Factor(Year)2015 −0.192 0.147 35.046 −1.311 0.198

Factor(Year)2016 −0.357 0.143 35.107 −2.501 0.017

Factor(Year)2017 −0.403 0.143 35.107 −2.823 0.008

Factor(Year)2018 −0.371 0.149 35.211 −2.489 0.018

Factor(Year)2019 −0.171 0.149 35.211 −1.151 0.257

Factor(Year)2020 −0.617 0.166 35.449 −3.715 0.0007

Functional form: log(AADB) ~ log(ADT) + factor(Year) + (1 | Site).

df = degrees of freedom. 

Note: Random effect variance = 0.188. 

Based on these initial modeling results, the value of log(AADB) increases by increasing the 

log(ADT). The model used 2014 as a base year. The number of bicycles decreased for all the 

years compared to the 2014 base year scenario. In addition, a substantial drop in the coefficient 

for the Year variable for the pandemic year of 2020 was also notable. This observation is 

graphically depicted in figure 19. Based on these observations, the research team then removed 

the 2020 data points from further analysis. This observation is also important because it indicates

that an analysis based solely on traffic counts from 2020 cannot be expected to provide reliable 

results when contrasted with bicycle volume information from the most recent years before the 

pandemic. 
68 

Source: FHWA.

Figure 19. Graph. Change in the Seattle coefficients of variable Year for consecutive years.

After removing five observations that represented the 2020 data, the team members developed 

another regression model. The results are shown in table 41. As shown in figure 19, a negative 

coefficient suggests a decrease in the volume and an increase in coefficient suggests an increase 

in volume. During 2020, the bicycle usage trends changed significantly. This observation 

suggests that a model without 2020 data may be needed. Additionally, figure 20 depicts the 

residual plot of the model. As provided, an increase in the log(ADT) is similarly associated with 

an increase in the log(AADB). The data showed significantly fewer bicycles for 2016–2018 than 

in 2014, whereas 2015 and 2019 data did not show a significant relationship when compared to 

the AADB.

Table 41. Mixed-effect Gaussian model excluding 2020 data. 

Parameter Estimate Standard Error df t-Value Pr(>|t|)

(Intercept) −6.797 2.513 6.016 −2.705 0.035

Log(ADT) 1.452 0.272 6.020 5.344 0.002

Factor(Year)2015 −0.192 0.151 31.035 −1.274 0.212

Factor(Year)2016 −0.367 0.147 31.129 −2.491 0.018

Factor(Year)2017 −0.413 0.147 31.129 −2.804 0.009

Factor(Year)2018 −0.380 0.153 31.238 −2.476 0.019

Factor(Year)2019 −0.181 0.153 31.238 −1.179 0.247

Functional form: log(AADB) ~ log(ADT) + factor(Year) + (1 | Site).

Note: Random effect variance = 0.1643; residual variance = 0.07965; restricted maximum likelihood 

(REML) = 40.8. 
69 

Source: FHWA.

Figure 20. Graph. Seattle residual plot for model 1 where the dashed line represents the 

mean of observations and the dots represent the individual observations.

The research team developed a new variable referred to as New Year because the data from years 

2015 and 2019 were not significant in the model compared to the 2014 base year condition. If the 

year of interest was either 2014, 2015, or 2019, then the value of New Year was equal to one. 

Values for New Year for other years had a value of zero. This difference in past years will 

introduce a challenge when trying to use the model to predict future years. However, this model 

does represent the best performing model, but future model evaluations should assess the 

sensitivity of this year-based variable and how it influences predictive capabilities beyond the 

2014–2020 study period. For the current model, the analysts replaced the variable Year with 

New Year and added new variables. This change resulted in the final model that is presented in 

table 42. 

As indicated, an increase in the log(ADT) is associated with a significant increase in the number 

of bicycles. Moreover, the variable New Year shows that the AADB significantly decreased 

during 2016–2018 compared to 2014, 2015, and 2019. Additionally, providing a bicycle lane and 

increasing the population density corresponded to a significant reduction in the AADB value; 

however, the positive sign of the interaction term between the presence of a bicycle lane and 

population density should not be neglected. 
70 

Table 42. Mixed-effect Gaussian model excluding 2020 data and using New Year. 

Variable Estimate Standard Error df t-Value Pr(>|t|)

(Intercept) −10.021 2.221 3.051 −4.513 0.020

Log(ADT) 1.827 0.250 3.044 7.321 0.005

Presence of bicycle lane −1.896 0.812 3.206 −2.337 0.096

Population density/1,000 −0.069 0.028 3.002 −2.474 0.090

Factor(New Year)1 0.257 0.084 35.068 3.075 0.004

Presence of bicycle lane:

population density/1,000

0.115 0.044 3.076 2.632 0.076

Functional form: log(AADB) ~ log(ADT) + Presence of Bicycle Lane + Population Density/1,000 + Presence of 

Bicycle Lane × Population Density/1,000 + factor(New Year) + (1 | Site).

Note: Random effect variance = 0.08396; residual variance = 0.07534; REML = 39.3. 

Figure 21 depicts the residual plot model 1 where the dashed line represents the mean of 

observations and the dots represent the individual observations. 

Source: FHWA. 

Figure 21. Graph. Residual plot for Seattle model 2 where the dashed line represents the 

mean of observations and the dots represent the individual observations for the New Year

variable (Seattle model 2). 
71 

Based on this analysis, the best predictive model is shown in table 42 and represented by 

figure 22. The research identified this model as the most reliable model that could be 

implemented to estimate the AADB based on ADT, New Year variables, presence of bicycle 

lane, and population density. 

Figure 22. Equation. Seattle bicycle exposure prediction.

Where: 

New Year = 1 if predicting for 2014, 2015, or 2019 (otherwise zero). 

Presence of bicycle lane = 1 if a bicycle lane is present (otherwise zero).

Population density = census tract population density (population/mi2

).

73 

CHAPTER 7. DEVELOPMENT AND TESTING OF SBL CMFs 

INTRODUCTION

The goal of this research effort was to determine whether a reliable CMF could be developed for 

SBL facilities. As documented in the previous chapters of this report, the research team 

developed a data collection procedure and then conducted data collection using aerial 

photography, the Street View features of Google Maps, and database information that could be 

acquired for the study sites. Because the team could not physically collect the data (due to the 

pandemic restricting travel), the research team developed bicycle exposure models to represent 

bicycle volumes for the study sites. This CMF development included data from Cambridge; San 

Francisco; and Seattle. The project team also acquired data for Austin, and Denver, for ultimate 

transferability testing of the CMFs. This chapter reviews the CMF development for SBLs.

STUDY DESIGN

Cross-Sectional Analysis 

The following two basic designs for observational studies are frequently used in safety 

evaluations: 

• Cross sectional.

• Before and after (longitudinal).

The research team pursued a cross-sectional design after identifying the potential data sources, 

their strengths and limitations, and the challenges to collect key variables for a before–after 

evaluation. Particularly, a longitudinal (before–after) design requires knowledge of the date of 

conversion for the studied facilities extending from any prior condition to when the SBL or other 

conditions were implemented. These detailed longitudinal data were not available for this study. 

Additionally, a longitudinal study is generally characterized by the risk of a smaller dataset, 

considering that no guaranties of finding sufficient locations with the exact prior condition that 

were converted to SBL with the same characteristics existed. Finally, a before–after longitudinal 

analysis can suffer from site selection bias because treatments are typically implemented at the 

locations with the greatest need. Therefore, the team selected a cross-sectional analysis that 

allowed the team to develop larger datasets for the cities under study.

Good observational studies rely on data from sites with and without the study treatment, and the 

data should be assembled in a manner consistent with control-group experiments. To obtain 

sufficient data to develop CMFs for SBL facilities, the research team obtained data for locations 

with SBL facilities and roadways that featured bicycle lanes without SBLs. This approach 

strengthens the analysis by enabling base condition evaluations (bicycle lane with no offset) as 

well as SBLs with additional enhancements. For this study, the best base condition should most 

likely be sites with traditional bicycle lanes, because the vast majority of on-street bicycle 

facilities are traditional configurations. The target configuration would then be SBLs with 

varying vertical elements. 
74 

Balancing Covariates with Propensity Score (PS) Methods

Data-matching and data-balancing methods are used to assist causal inference that quantifies the 

impact of a treatment on a response variable (crashes in this case). The principle behind these 

methods is to ensure that untreated locations (i.e., no SBL present) are similar in their 

safety-relevant covariates (e.g., AADT, average daily bicycle traffic (ADBT), number of lanes) 

at the treated locations (i.e., SBL present) in a way that the contrast between the two groups of 

sites is balanced. In that case, the comparison between the two groups of sites should reflect 

safety differences due to the treatment of interest. 

The PS is a metric of similarity between covariates from the cases and can be estimated using 

parametric or nonparametric tools, such as logistic regression or random forest analysis.

In the case of binary logistic regression, used as a basis for PS estimation, figure 23 shows the 

definition of the conditional probability of a site receiving treatment T. 

Figure 23. Equation. Probability equation.

Where: 

P(Ti|Xi) = the PS denoting the probability of the site i receiving the treatment T. 

Ti = the treatment status of the site i which takes binary values {0, 1}. 

Xi = a vector of covariates that covary with the treatment presence.

αi = the vector of coefficients through the binary logistic regression. 

In a balanced sample, the distribution of PSs is expected to be similar for treated sites P(TA|XA) 

and comparison sites P(TB|XB). PS matching consists of using some form of PS to inform the 

selection of the data for a study. Alternatively, PS weighting (PSW) consists of using weights in 

the analysis to balance two or more partitions of the data by the variable of interest 

(i.e., treatment or comparison). Balance is achieved by defining appropriate weights for each unit 

of analysis so that they represent an underlying target population of sites. The data are weighted 

based on the probabilities of being in either the comparison or treatment group.(29) The definition 

of the weights selected implies a target population of sites. If all weights are equal, then the 

dataset is implied to be a simple random sample from an actual population of sites. However, 

through the use of appropriate weights, more flexible definitions of the target population are 

possible, as can be found in the statistical literature.(24,30) 
75 

The research team set the target population in this study as the overlap between the treated and 

comparison populations as proposed by Li, Morgan, and Zaslavsky.(31) For this scheme, the 

target population is the set of all sites that have comparable chances to be either in the treatment 

group or in the comparison group. This approach effectively curbs the undue influence of two 

subsets of sites when estimating the average treatment effect of the countermeasure:

• Comparison sites with characteristics that make the sites unlikely to be candidates for the 

treatment.

• Treated sites with unusual characteristics for which no comparable comparison sites are 

represented in the data. 

An additional advantage of this choice of target population is a desirable small-sample exact 

balance property. Additionally, the corresponding weights are known to minimize the asymptotic 

variance of the weighted average treatment effect within their class of weights.(31)

DATA ANALYSIS METHODS

The research team explored the use of various appropriate generalized linear model (GLM) 

specifications (negative binomial, binomial, or binomial mixtures) as needed by each dataset. 

PSW was implemented using the PSs obtained from the final datasets.

GLM methods define an appropriate link function to articulate the relationship between the 

response variable distribution (crashes in this case), such as Poisson or negative binomial, and 

the linear function of explanatory variables in the analysis.

The research team found the number of bicycle crashes to be very small, so they applied 

binomial GLMs to the data after aggregating the crash data to a total count for the complete 

period of analysis. In this instance, the distribution of a response variable Y, indicating the 

number of success observations from a binomial trial (regardless of whether a crash of interest 

has occurred), can be modeled conditional to a set of independent variables X, as shown in 

figure 24. 

Figure 24. Equation. Conditional probability of Y value, given explanatory variables and 

site characteristics.

Where:

P = the probability of Y taking value yi, given the ith realization of a set of explanatory 

variables X.

Y = the count of observed crashes of interest, given n trials.

yi = a particular value in the domain of random count variable Y. 

ni = the reference number of trials for which Y is observed.

Pi = the probability of a crash in the ith location of interest.
76 

For a crash study site i, the logit of pi can be expressed as in figure 25. 

Figure 25. Equation. Binomial-lognormal mixed-model parameterization.

Where: 

ɡ(pi) = the logit function of pi. 

pi = the probability of ith segment. 

X = the vector of independent variables (including key variable in evaluation and other 

safety-influential covariates).

β = the vector of regression coefficients.

All model variables were included in the model, either in the exponential form or directly, 

depending on the best fit to each dataset. For clarity, the last term in figure 25 is implicit of 

multiple variables and can be expanded as in figure 26. 

Figure 26. Equation. Parameterization of explanatory variables in regression model. 

Where: 

Xp = an independent variable in the model, such as AADT, ADBT, the number of lanes, etc. 

βp = the corresponding estimated coefficient.

CMF Estimation

In most cases, the use of regression models to estimate the influence of a dependent variable 

consists of extracting a single parameter estimate and its standard error from the analysis after 

accounting for additional variability in the data due to covariates and an appropriately modeled 

error distribution. However, single-parameter estimation is not possible in every case and, in 

particular for this study, where the estimation often involved contrasting more than one 

coefficient with each other. To estimate the uncertainty of a CMF derived from multiple 

coefficient contrasts, the research team followed CMF development procedures of which the 

basis of these methods is the asymptotically multivariate normal distribution expected from 

maximum-likelihood estimation of multiple variable regression models.(32–34)

For this procedure, the analyst defines appropriate linear combinations of the coefficient 

estimates from each of the best models to statistically assess their resulting value for select 

scenarios. These linear combinations imply predicted crashes for both the before condition 

(e.g., no SBL and some base condition) and the after condition (e.g., SBL present or type of 

SBL). The contrast between these conditions then yields an estimate of the CMF of interest. In 

general, producing the CMF estimate for the scenario is straightforward. To produce the 

corresponding standard error for a given scenario with covariate vectors XB and XA representing 

the before and after conditions, respectively, the contrast of covariate vectors and the maximum 
77 

likelihood model-inversed-information matrix ∑ define the CMF standard error, as indicated in 

figure 27. 

Figure 27. Equation. Standard error for contrast in log scale. 

Where: 

SE = represents the calculated model standard error.

CMF = the crash modification factor to be developed. 

XA and X’A are the values after an intervention, such as crash results after constructing 

intervention such as an SBL. 

XB and X’B are the values before an intervention, such as crash results before constructing 

intervention such as an SBL 

Xp = an independent variable in the model, such as AADT, ADBT, the number of lanes, etc. 

∑ = summation of the model effects.

Contrasts of Interest for CMF Development

For this research, the team members constructed contrasts representing a progression of 

information to be gathered in the form of CMFs. The analysis included the following steps: 

1. In the first hierarchical level, the team developed models that allowed them to test 

research questions involving sites with any type of SBL and other sites without SBLs. 

Depending on the data available, multiple contrasts were constructed at this hierarchical 

level:

a. SBL versus traditional bicycle lane.

b. SBL versus buffered bicycle lane.

c. Buffered versus traditional bicycle lane.

2. In the second hierarchical level, the team expanded the models to allow testing 

differences between sites with specific types of SBLs and other sites without SBLs. 

Again, data availability determined which of the following contrasts were available for 

estimation in each case:

a. Flexi-post SBL versus traditional bicycle lane.

b. Flexi-post SBL versus buffered bicycle lane. 

c. Flexi-post SBL versus traditional or buffered bicycle lane.

d. Blended type SBL versus traditional bicycle lane. 

e. Blended type SBL versus buffered bicycle lane.
78 

f. Blended type SBL versus traditional or buffered bicycle lane. 

g. Flexi-post SBL versus blended type SBL. 

h. Flexi-post SBL or blended type SBL versus traditional bicycle lane. 

i. Flexi-post SBL or blended type SBL versus buffered bicycle lane.

j. Flexi-post SBL or blended type SBL versus traditional or buffered bicycle lane. 

All analyses were conducted using open-source programming software for statistical computing 

and graphics. 

SEGMENT ANALYSIS RESULTS

The analyses used bicycle crashes as the response variable. As previously noted, the data from 

three cities, listed in the order in which the analysis was conducted, were used in this effort: 

San Francisco, Cambridge, and Seattle. For each city, the data were separated in two subsets: 

segments and intersections. The following sections document analyses and results, first by the 

different city datasets developed for this research and then as a combined dataset. The research 

team did not successfully develop CMFs for intersections or corridors with SBLs.

San Francisco Segment Analysis

Table 43 shows the number of segments with bicycle lane characteristics per direction of travel.

Table 43. Number of segments by bicycle lane type per direction in San Francisco 

(N = 384). 

Variable

Bicycle Lane 

(Direction 2)

Buffered 

(Direction 2)

Vertical Element 

(Direction 2)

Bicycle lane 

(direction 1)

266 5 9

Buffered (direction 1) 4 45 10

Vertical element 

(direction 1)

7 10 28
79 

Table 44 shows the results for the most parsimonious model of potential bicycle crashes, when 

considering only contrasts that involve any type of SBL. The research team adjusted up 

(i.e., increased) the estimated standard errors via the quasi-likelihood estimation method to 

capture the increased uncertainty implied by overdispersion because they found binomial 

overdispersion in the residuals of this model. 

Table 44. Initial model coefficient estimates for potential bicycle crashes in San Francisco 

(N = 384 segments). 

Parameter Estimate Standard Error t-value Pr(>|t|) Significance

(Intercept) 2.761 2.6572 1.039 0.299 —

Industrial land use −0.605 0.3817 −1.586 0.114 —

Mixed land use 1.781 0.5794 3.075 0.002 **

Public land −0.824 0.8977 −0.917 0.360 —

Residential 0.870 0.6626 1.314 0.190 —

Log(Length) −1.089 0.4930 −2.208 0.028 *

Length 0.002 0.0010 2.202 0.028 *

One MV lane in each 

direction

−0.027 0.3046 −0.088 0.930 —

Two MV lanes in each 

direction

0.530 0.2686 1.974 0.049 *

No parking in one or both 

directions 

−0.666 0.2808 −2.370 0.018 *

Area bicycle = 1 0.108 0.1964 0.550 0.583 —

Area bicycle = 2 1.125 0.3457 3.255 0.001 **

log (AADB+0.5) −0.559 0.1767 −3.167 0.002 **

AADB 0.011 0.0027 4.130 <0.001 ***

Traditional bicycle lane 

(both directions)

0.254 0.4317 0.589 0.556 —

Buffered bicycle lane 

(both directions)

0.197 0.5127 0.383 0.702 —

SBL (either direction) −0.479 0.4951 −0.968 0.334 —

—Not statistically significant.

*Statistically significant at the 0.05 level.

**Statistically significant at the 0.01 level.

***Statistically significant at the 0.001 level.

In general, the results in table 44 indicate the following factors: 

• Bicycle crash risk increases at mixed land-use locations and potentially decreases at 

industrial land use, compared to other location types. 

• Bicycle crash risk increases at locations with two MV lanes per direction, compared to 

locations with more MV lanes per direction. 
80 

• Bicycle crashes decrease where no parking is permitted in one or both directions of 

travel. This analysis estimated that bicycle crash risk at segments with no parking on 

either side is about half as large as for segments allowing parking, other things being 

equal (0.514 odds ratio estimated as 0.514 = exp(–0.666)).

• Bicycle crash risk increases at locations where Area Bicycle has a 2.0 value (meaning two 

directions where bicycle lanes are parallel and adjacent to the segment of analysis). This 

value could be a surrogate indicator of bicycle travel exposure, in addition to the AADB 

estimated values.

• In the cases of segment length and AADB, the direction of the relationship is not 

immediately apparent because two coefficients are involved with these variables.

Figure 28 shows these relationships. 

• Figure 29 depicts the relationships between AADB and bicycle crashes. Both 

relationships are increasing for most of the range of the two variables, except for the 

lower extreme of the two curves (i.e., segments shorter than 500 ft and AADBs 

approaching zero). 

Source: FHWA.

Figure 28. Graph. Model San Francisco relationship between length and bicycle crashes.
81 

Source: FHWA.

Figure 29. Graph. Model San Francisco relationships between AADB and bicycle crashes.

Next, the research team estimated CMFs defining appropriate contrasts involving the last three 

coefficients in table 44, as shown in table 45. 

Table 45. Basic CMFs for bicycle crashes in San Francisco.

Condition CMF Estimate Standard Error p-Value Significance

Flush buffera 0.944 −0.0577 0.2664 0.8284 —

Vertical elementa 0.509 −0.6758 0.3399 0.0468 *

Vertical elementb 0.480 −0.7336 0.2417 0.0024 **

Vertical elementc 0.494 −0.7047 0.2631 0.0074 **

—Not statistically significant.

*Statistically significant at the 0.05 level.

**Statistically significant at the 0.01 level. a

Base condition: traditional bicycle lane. b

Base condition: flush buffered bicycle lane. c

Base condition: traditional or flush buffered bicycle lane. 

All results in table 45 indicate statistically significant bicycle crash reductions for the presence of 

vertical elements as a separation of bicycle lanes from traffic. Compared to traditional bicycle 

lanes, SBLs are estimated to experience 49.1 percent fewer bicycle crashes (0.509 CMF 

statistically significant at the 0.05 significance level). Similarly, SBLs are estimated to 

experience 52.0 percent fewer bicycle crashes compared to bicycle lanes with flush buffers, 

(0.480 CMF statistically significant at the 0.01 significance level). When compared with 

locations with either traditional bicycle lanes or with flush buffers, this analysis also found that 

SBLs experience 50.6 percent fewer bicycle crashes (0.494 CMF statistically significant at the 

0.01 significance level). Finally, although the analysis estimated bicycle lanes with flush buffers 

to perform better than traditional bicycle lanes, the estimate of that comparison was statistically 

insignificant (0.828 p-value for 0.944 CMF estimate). 
82 

Next, the research team proceeded to reestimate the model in table 44 to include differentiation 

between SBL types. Flexible posts and blended treatments were the two types of vertical element 

with enough representation in the San Francisco dataset to consider for CMF development. 

Accordingly, the refitted model included appropriate terms for these considerations. Table 46

shows the results of the model with these additions. 

Table 46. Coefficient estimates for bicycle crash risk in San Francisco (N = 384 segments). 

Parameter Estimate Standard Error t-value Pr(>|t|) Significance

(Intercept) 3.306 2.6537 1.246 0.2137 —

Industrial land use −0.655 0.3798 −1.725 0.0854 ~

Mixed land use 1.831 0.5791 3.161 0.0017 **

Public land −0.820 0.9087 −0.902 0.3675 —

Residential 0.952 0.6638 1.434 0.1525 —

Log(Length) −1.194 0.4878 −2.447 0.0149 *

Length 0.002 0.0010 2.369 0.0183 *

One MV lane in each 

direction

0.457 0.2721 1.681 0.0937 ~

Two MV lanes in 

each direction

−0.091 0.3043 −0.299 0.7655 —

No parking in one or 

both directions 

−0.567 0.2865 −1.979 0.0486 *

Area bicycle = 1 0.155 0.1976 0.786 0.4324 —

Area bicycle = 2 1.135 0.3413 3.326 0.0010 ***

Log(AADB+0.5) −0.595 0.1787 −3.33 0.0010 ***

AADB 0.012 0.0028 4.348 <0.0001 ***

Traditional bicycle 

lane (both directions)

0.338 0.4318 0.782 0.4348 —

Buffered bicycle lane 

(both directions)

0.254 0.5102 0.497 0.6195 —

SBL (either 

direction)

0.536 0.9615 0.557 0.5776 —

Blended vertical 

elements (either 

direction)

−0.618 0.8132 −0.759 0.4482 —

Flexi-post vertical 

elements (either 

direction)

−1.325 0.8192 −1.618 0.1066 —

—Not statistically significant.

~Statistically significant at the 0.1 level.

*Statistically significant at the 0.05 level.

**Statistically significant at the 0.01 level.

***Statistically significant at the 0.001 level.

Table 46 also shows that the addition of specific coefficients for the two types of vertical 

elements had a minimal effect on the rest of the coefficients in the initial model. Additionally, 

the two new coefficients clearly indicate reductions in bicycle crash risk (i.e., negative 
83 

estimates), and safety performance seems to improve approximately twofold for flexible posts

compared to blended vertical elements. Like the initial model, table 47 shows the corresponding 

CMF contrasts that can be constructed from the updated model. 

Table 47. Detailed CMFs for bicycle crashes in San Francisco.

Condition CMF Estimate

Standard 

Error p-Value Significance

Flush buffera 0.919 −0.0841 0.2675 0.7533 —

Flexible postsa 0.324 −1.1269 0.3519 0.0014 **

Flexible postsb 0.352 −1.0428 0.4141 0.0118 *

Flexible postsc 0.338 −1.0849 0.3602 0.0026 **

Blendeda 0.658 −0.4192 0.2771 0.1304 —

Blendedb 0.715 −0.3351 0.3732 0.3692 —

Blendedc 0.686 −0.3772 0.3003 0.2091 —

Flexible postsd 0.493 −0.7077 0.3805 0.0629 ~

Flexible posts or 

blendeda

0.462 −0.7730 0.2532 0.0023 **

Flexible posts or 

blendedb

0.502 −0.6890 0.3453 0.0460 *

Flexible posts or 

blendedc

0.481 −0.7310 0.2716 0.0071 **

—Not statistically significant.

~Statistically significant at the 0.1 level.

*Statistically significant at the 0.05 level.

**Statistically significant at the 0.01 level. a

Base condition: traditional bicycle lane. b

Base condition: flush buffered bicycle lane. c

Base condition: traditional or flush buffered bicycle lane. d

Base condition: blended vertical element. 

As table 47 shows, the analysis determined that only some of the tested CMFs were statistically 

significant. The first and last three CMFs in table 47 should roughly correspond to the treatments 

and base conditions for the CMFs in table 45. Indeed, the corresponding estimates and standard 

errors in both tables are similar in magnitude. In addition to those comparable CMFs, the results 

in table 47 indicate statistically significant reductions in bicycle lane crash risks for flexi-post 

SBLs and either flexi-post or blended SBLs. For blended SBLs alone, the estimates suggest 

bicycle crash reductions, but those three CMFs were found to be statistically insignificant. 

Finally, table 47 also shows that the estimated CMF for flexi-post SBL versus blended SBL 

indicates a statistically significant reduction in bicycle crash risk (0.629 p-value for 0.493 CMF, 

statistically significant at the 0.10 level).

Cambridge Segment Analysis

Table 48 shows the observed conditions in the database, which includes 132 traditional bicycle

lane sites, 23 buffered sites, and 24 vertical element sites (with buffer). Table 49 shows the 

results for the most parsimonious model of bicycle crash risk that does not include differentiation 

by type of SBL. In the case of Cambridge, the research team found no binomial overdispersion in 
84 

the crash data, so they derived the estimates and standard errors from a maximum likelihood 

estimation model with binomially distributed response. 

Table 48. Segments by bicycle lane condition per direction in Cambridge (N = 179). 

Variable

Bicycle Lane 

(Direction 2)

Buffered 

(Direction 2)

Vertical Element 

(Direction 2)

Bicycle lane (direction 1) 132 0 0

Buffered (direction 1) 0 23 0

Vertical element 

(direction 1)

0 0 24

Table 49. Coefficient estimates for bicycle crashes in Cambridge (N = 179 segments). 

Parameter Estimate Standard Error z-Value Pr(>|t|) Significance

(Intercept) −3.405 3.0172 −1.129 0.2591 —

Open space land use −1.678 0.6973 −2.4060 0.0161 *

Log(AADT+05) 0.034 0.0341 1.0020 0.3165 —

Length 0.001 0.0004 2.2870 0.0222 *

Log(AADB+0.5) −0.053 0.5653 −0.0940 0.9251 —

No parking in one or 

both directions

0.268 0.2184 1.2290 0.2191 —

Traditional bicycle lane 

(both directions)

−0.207 0.6279 −0.3300 0.7413 —

Buffered bicycle lane 

(both directions)

−0.814 0.8093 −1.0050 0.3147 —

SBL (either direction) −1.035 0.9119 −1.1350 0.2564 —

—Not statistically significant.

*Statistically significant at the 0.05 level.

The results in table 49 differ from the results in San Francisco in various ways. In general, the 

Cambridge results indicate the following factors: 

• Bicycle crashes significantly decrease at locations where the land use is open space, 

compared to other location types. 

• The impact of AADB in bicycle crashes was found null in contrast with San Francisco, 

where crashes increased with increasing AADB. 

• Bicycle crash risk increases with increasing segment length. The researchers found 

similar findings with the San Francisco model. 

Next, the research team estimated CMFs defining appropriate contrasts from the Cambridge 

model, as shown in table 50. 
85 

Table 50. CMFs for bicycle crashes in Cambridge.

Condition CMF Estimate Standard Error p-Value Significance

Flush buffera 0.545 −0.6065 0.5022 0.2272 —

Vertical elementa 0.437 −0.8276 0.7115 0.2447 —

Vertical elementb 0.802 −0.2212 0.8606 0.7972 —

Vertical elementc 0.592 −0.5244 0.7486 0.4836 —

—Not statistically significant. a

Base condition: traditional bicycle lane. b

Base condition: flush buffered bicycle lane. c

Base condition: traditional or flush buffered bicycle lane. 

All results in table 50 indicate statistically insignificant bicycle crash reductions for conditions 

other than a traditional bicycle lane. This finding can be contrasted with the San Francisco results 

whereby CMFs indicated the presence of vertical element was statistically significant. The 

sample size for the Cambridge data is smaller, which could explain the lack of statistical 

significance. The results, however, are in the direction of, and with similar magnitudes to, the 

San Francisco CMFs.

The research team attempted fitting an expanded model, including differentiation by SBL type, 

similar to San Francisco; however, due to the smaller sample size for Cambridge and as shown 

when contrasting the relative size of the standard errors in table 45 and table 50, the variance of 

the estimates at least doubles for the Cambridge dataset. For that reason, the research team did 

not compute more specific CMFs for this dataset.

San Francisco and Cambridge Combined Segment Analysis

The researchers anticipated that the combined dataset between San Francisco and Cambridge 

was large enough to support a model with differentiation between SBL by vertical element type, 

so they directly developed a model that included San Francisco and Cambridge in addition to 

individual State models. Table 51 shows the coefficient estimates for the most parsimonious 

model of bicycle crash risk. The research team did not find binomial overdispersion in the 

combined dataset, so no adjustments were warranted for the estimates and standard errors in this 

case.

Table 51. Coefficient estimates for bicycle crash risk in San Francisco and Cambridge 

(N = 563 segments). 

Parameter Estimate Standard Error z-Value Pr(>|t|) Significance

(Intercept) 0.576 1.5527 0.370 0.7106 —

Industrial land use −0.848 0.2650 −3.1990 0.0014 **

Mixed land use 0.956 0.2256 4.2370 <0.0001 ***

Public land −1.872 0.5807 −3.2230 0.0013 **

Residential −0.021 0.2533 −0.0830 0.9342 —

Log(Length) −0.669 0.2827 −2.3660 0.0180 *

Length 0.002 0.0006 2.5630 0.0104 *

One MV lane in each 

direction

0.469 0.1864 2.5160 0.0119 *
86 

Parameter Estimate Standard Error z-Value Pr(>|t|) Significance

Two MV lanes in 

each direction

0.119 0.2072 0.5730 0.5664 —

No parking in one or 

both directions

−0.264 0.1639 −1.6120 0.1069 —

Area bicycle = 1 0.148 0.1322 1.1210 0.2622 —

Area bicycle = 2 0.905 0.2497 3.6230 0.0003 ***

Log(AADB+0.5) −0.497 0.1412 −3.5180 0.0004 ***

AADB 0.008 0.0015 5.5120 <0.0001 ***

Traditional bicycle 

lane (both directions)

0.177 0.3227 0.5490 0.5832 —

Buffered bicycle lane 

(both directions)

−0.097 0.3797 −0.2540 0.7992 —

SBL (either 

direction)

−0.167 0.6628 −0.2520 0.8014 —

Blended vertical 

elements (either 

direction)

−0.119 0.5778 −0.2060 0.8366 —

Flexi-post vertical 

elements (either 

direction)

−0.944 0.5840 −1.6160 0.1061 —

City = San Francisco 0.891 0.1891 4.711 <0.0001 ***

—Not statistically significant.

***Statistically significant at the 0.001 level.

The resulting coefficients are comparable to the ones obtained for San Francisco only. 

Additionally, when the site characteristics are comparable, this analysis found that the bicycle 

crash risk for San Francisco is roughly twice that for Cambridge (0.0004 p-value for a 0.668 

estimate corresponding to an odds ratio of exp(0.668) = 1.950). Table 52 shows the estimated 

CMFs for various bicycle lane configurations derived from the model in table 51. 
87 

Table 52. CMFs for bicycle crashes in San Francisco and Cambridge.

Condition CMF Estimate Standard Error p-Value Significance

Flush buffera 0.761 −0.2737 0.2218 0.2172 —

Flexible postsa 0.276 −1.2875 0.3272 0.0001 ***

Flexible postsb 0.363 −1.0139 0.3796 0.0076 **

Flexible postsc 0.316 −1.1507 0.3366 0.0006 ***

Blendeda 0.629 −0.4629 0.2760 0.0935 ~

Blendedb 0.828 −0.1893 0.3458 0.5842 —

Blendedc 0.722 −0.3261 0.2925 0.2650 —

Flexible postsd 0.438 −0.8246 0.3808 0.0303 *

Flexible posts or 

blendeda

0.417 −0.8752 0.2353 0.0002 ***

Flexible posts or 

blendedb

0.548 −0.6016 0.3092 0.0517 ~

Flexible posts or 

blendedc

0.478 −0.7384 0.2514 0.0033 **

—Not statistically significant. a

Base condition: traditional bicycle lane. b

Base condition: flush buffered bicycle lane. c

Base condition: traditional or flush buffered bicycle lane. d

Base condition: blended vertical element. 

~Statistically significant at the 0.1 level.

*Statistically significant at the 0.05 level.

**Statistically significant at the 0.01 level.

***Statistically significant at the 0.001 level.

The results are comparable to the results found in the San Francisco analysis albeit with a bigger 

combined sample size. This analysis found the CMFs for blended SBLs compared to traditional 

bicycle lanes was statistically significant, in addition to the same CMFs that were found 

significant in table 47. 

Summary of SBL CMF Findings

The CMFs for SBLs show a clear trend in that with their implementation, a transportation agency 

can expect to see a reduction in bicycle crashes. The individual State models suffer from smaller 

sample sizes; however, they continue to result in estimated crash reductions consistent with those 

found with larger sample sizes. The combination of data from different States also results in 

similar trends and, for the most part, greater statistical significance. For the baseline conditions, 

the use of traditional bicycle lanes, bicycle lanes with buffers but no vertical elements, or bicycle 

lanes with a combination of traditional and buffers resulted in generally similar trends. The SBL 

treatments that were the most effective included flexi-post treatments and blended treatments 

(most often flexible posts and other vertical elements). Table 53 summarizes how the CMF 

values changed as the models were merged. This summary begins with San Francisco models 

only and then transitions to the merged model of San Francisco and Cambridge. The final 

merged State model included data from San Francisco, Cambridge, and Seattle. Generally, the 

resulting CMFs had a value of approximately 0.48. This value is a multiplicative factor that 
88 

indicates a decrease in bicycle crashes of approximately 52 percent should be expected when 

bicycle lanes that are traditional or buffered are converted to SBL facilities.

This study included an analysis that focused on roadway segments. The research team assessed 

the prospect of using intersection-only and corridor-type models but found that for the available 

dataset, these two options did not yield statistically viable results.

Table 53. Summary of SBL CMF values for roadway segments. 

Baseline

Condition

Model CMF

Treatment

San Francisco 

Only

San Francisco 

+ Cambridge

San Francisco 

+ Cambridge 

+ Seattle

Suggested 

CMF

Traditional 

bicycle lane

Vertical 

element 

(varies)

0.509 — — 0.51

Traditional 

bicycle lane

Flexible 

posts

0.324 0.276 0.498 0.28–0.50

Traditional 

bicycle lane

Flexible 

posts or 

blended

0.462 0.417 0.640 0.42–0.64

Flush 

buffered 

bicycle lane

Vertical 

element 

(varies)

0.480 — — 0.48

Flush 

buffered 

bicycle lane

Flexible 

posts 

0.352 0.363 0.441 0.35–0.44

Flush 

buffered 

bicycle lane

Flexible 

posts or 

blended

0.502 0.548 0.567 0.50–0.57

Traditional 

bicycle lane 

or flush 

buffered 

bicycle lane

Vertical 

element 

(varies)

0.494 — — 0.49

Traditional 

bicycle lane 

or flush 

buffered 

bicycle lane

Flexible 

posts 

0.338 0.316 0.468 0.32–0.47

Traditional 

bicycle lane 

or flush 

buffered 

bicycle lane

Flexible 

posts or 

blended 

0.481 0.478 0.602 0.48–0.60
89 

Seattle Segment Analysis

Table 54 shows the number of segments with bicycle lane characteristics per direction of travel.

Table 54. Number of segments by bicycle lane condition per direction in Seattle (N = 660). 

Variable

Bicycle Lane 

(Direction 2)

Buffered 

(Direction 2)

Vertical Element 

(Direction 2)

Bicycle lane (direction 1) 484 4 10

Buffered (direction 1) 7 46 8

Vertical element (direction 1) 19 11 71

Table 55 shows the results for the most parsimonious model in Seattle of bicycle crash risk that 

does not include differentiation by SBL type. Similar to their findings with the Cambridge data, 

the research team found no binomial overdispersion in the Seattle crash data, so the model results 

reflect estimates and standard errors derived from a maximum likelihood estimation model with 

a binomially distributed response.

Table 55. Coefficient estimates for bicycle crash risk in Seattle (N = 660 segments). 

Parameter Estimate Standard Error z-Value Pr(>|t|) Significance

(Intercept) −141.086 57.2177 −2.466 0.0137 *

Multi- or single-family 

land use

−1.0370 0.2186 −4.7440 <0.0001 ***

Area bicycle = 1 1.0985 0.3247 3.3840 0.0007 ***

Area bicycle = 2 0.1711 0.3493 0.4900 0.6242 —

log(ADT_1 + 0.5) 0.9394 0.2912 3.2270 0.0013 **

log(Length) 0.8965 0.1932 4.6410 <0.0001 ***

log(AADB + 0.5) 26.1612 11.3722 2.3000 0.0214 *

AADB −0.0799 0.0286 −2.7970 0.0052 **

Traditional bicycle lane 

(both directions)

−0.1207 0.4166 −0.2900 0.7719 —

Buffered bicycle lane 

(both directions)

−0.5266 0.4926 −1.0690 0.2850 —

SBL (both directions) −0.4120 0.8554 −0.4820 0.6301 —

—Not statistically significant.

*Statistically significant at the 0.05 level.

**Statistically significant at the 0.01 level.

***Statistically significant at the 0.001 level.

ADT_1 = Vehicles per day (exclusive only to the San Francisco traffic exposure).

The results in table 55 in general indicate the following factors: 

• Bicycle crashes significantly decrease at locations where the land use is multi- or single 

family, compared to other land use types. 

• Bicycle crash risk increases with increasing segment length, similar to the other two 

cities.
90 

• Bicycle crash risk increases with increasing ADT_1.

The impact of AADB in bicycle crashes is strong, depending on the bicycle crash risk and 

AADB values. Figure 30 depicts the multiplicative effect of AADB on bicycle crash risk for 

Seattle.

Source: FHWA.

Figure 30. Graph. Seattle model relationships between AADB and bicycle crashes.

The model attributes a complex relationship with bicycle crash risk to AADB: Null from 100 to 

200 bicycles/day, increasing from 200 to 330 bicycles/day, and decreasing from 330 to 500 

bicycles/day. Next, the research team estimated CMFs defining appropriate contrasts from the 

Seattle model, as shown in table 56. 

Table 56. CMFs for bicycle crashes (Seattle).

Condition CMF Estimate Standard Error p-Value Significance

Flush buffera 0.666 −0.406 0.354 0.251 —

Vertical elementa 1.121 0.115 0.813 0.888 —

Vertical elementb 0.747 −0.291 0.765 0.703 —

Vertical elementc 0.915 −0.088 0.769 0.909 —

—Not statistically significant. a

Base condition: traditional bicycle lane. b

Base condition: flush buffered bicycle lane. c

Base condition: traditional or flush buffered bicycle lane. 

All results in table 56 indicate statistically insignificant bicycle crash reductions compared to 

traditional bicycle lanes, similar to Cambridge and in contrast to San Francisco.
91 

Next, the research team proceeded to reestimate the model shown in table 56, which includes 

differentiation between SBL types. Flexible posts and blended treatments were the two types of 

vertical elements with enough representation in the Seattle dataset to consider for CMF 

development. Accordingly, the refitted model included appropriate terms for these 

considerations. Table 57 shows the results of the model with these additions.

Table 57. Coefficient estimates for bicycle crash risk in Seattle (N = 660 segments). 

Parameter Estimate Standard Error t-value Pr(>|t|) Significance

(Intercept) −136.820 61.309 −2.232 0.0256 *

Multi- or single-family 

land use

−1.039 0.222 −4.679 <0.0001 ***

Area_bicycle=1 1.093 0.334 3.271 0.001 **

Area_bicycle=2 0.166 0.351 0.472 0.637 —

log(ADT_1+0.5) 0.928 0.296 3.139 0.002 **

log(Length) 0.895 0.193 4.627 <0.0001 ***

log(AADB + 0.5) 25.333 12.180 2.080 0.038 *

AADB −0.078 0.031 −2.514 0.012 *

Traditional bicycle lane 

(both directions)

−0.192 0.599 −0.321 0.748 —

Buffered bicycle lane 

(both directions)

−0.591 0.623 −0.948 0.343 —

SBL (both directions) −0.351 0.945 −0.372 0.710 —

Blended vertical elements 

(either direction)

−0.274 1.476 −0.186 0.853 —

Flexi-post vertical 

elements (either direction)

−0.076 0.796 −0.095 0.924 —

No parking in one or both 

directions

−0.038 0.241 −0.156 0.876 —

—Not statistically significant.

*Statistically significant at the 0.05 level.

**Statistically significant at the 0.01 level.

***Statistically significant at the 0.001 level.

The addition of specific coefficients for the two types of vertical elements had little impact on 

the rest of the coefficients in the initial model. The two new coefficients indicate reductions in 

bicycle crash risk (i.e., negative estimates), though these reductions are not statistically 

significant. Additionally, based on statistical analysis, the safety performance of flexible posts is 

slightly worse when compared to blended vertical elements. Table 58 shows the corresponding 

CMF contrasts that can be constructed from the updated model. This observation may be an 

artifact of the higher number of blended treatments than solitary SBL treatments.

All CMFs in table 58 are statistically insignificant. The following section uses a merged database 

using data from the three cities to produce updated CMFs. 
92 

Table 58. Reestimation of CMFs for bicycle crashes in Seattle.

Condition CMF Estimate Standard Error p-Value Significance

Flush buffera 0.671 −0.399 0.358 0.265 —

Flexible postsa 0.791 −0.235 0.836 0.779 —

Flexible postsb 1.178 0.164 0.883 0.853 —

Flexible postsc 0.965 −0.035 0.841 0.966 —

Blendeda 0.649 −0.433 1.327 0.744 —

Blendedb 0.966 −0.034 1.358 0.980 —

Blendedc 0.792 −0.234 1.330 0.861 —

Flexible postsd 1.219 0.198 1.404 0.888 —

Flexible posts or 

blendeda

0.716 −0.334 0.859 0.698 —

Flexible posts or 

blendedb

1.067 0.065 0.905 0.943 —

Flexible posts or 

blendedc

0.874 −0.134 0.864 0.876 —

—Not statistically significant. a

Base condition: traditional bicycle lane. b

Base condition: flush buffered bicycle lane. c

Base condition: traditional or flush buffered bicycle lane. d

Base condition: blended vertical element. 

San Francisco, Seattle, and Cambridge Combined Segment Analysis

The researchers anticipated that the combined dataset between the three cities was large enough 

to support a model with differentiation between SBL by vertical element type, so they developed 

that model directly and did not depend on the models for the individual State. A visual inspection 

of the data in the combined dataset indicated that some fields were incomplete. In particular, 

several sites did not have an ADT value greater than zero. However, some of the AADB values 

were equal to zero because the bicycle exposure models use ADT as an input. Without additional 

information about the ADT, the team decided to remove all segments with either AADB or ADT 

having values of zero. Table 59 shows the coefficient estimates for the most parsimonious 

binomial model of bicycle crash risk using the combined data from the three cities.

Multiple factors were found to be statistically significant for bicycle crash risk, as table 59

shows. Additionally, after considering the impacts of other key variables, this analysis found 

remaining differences in bicycle crash risk for the three cities and in their relationship with 

AADB. In general, the analysis indicates the following factors: 

• Bicycle crash risk is higher at locations with mixed land use. 

• Bicycle crash risk is lower at locations with industrial or public land use. 

• Bicycle crash risk is lower at locations with more MV lanes. The research team estimated 

that for each additional MV lane, bicycle crash risk decreases by a factor of 0.745 

(0.745 = exp(–0.295)) when compared to roads with similar physical characteristic. 
93 

• Bicycle crash risk is lower at locations where parking is not permitted in at least one 

direction. The researchers estimated that bicycle crash risk at those locations is smaller by 

a factor of 0.718 (0.718 = exp(–0.331)), compared with locations without parking 

restrictions, other things unchanged.

Table 59. Coefficient estimates for bicycle crash risk in San Francisco, Seattle, and 

Cambridge (N = 1,223 segments). 

Parameter Estimate

Standard 

Error t-value Pr(>|t|) Significance

(Intercept) −2.826 3.0524 −0.926 0.3545 —

Mixed land use 0.985 0.1362 7.2340 <0.0001 ***

Industrial land use −0.708 0.2173 −3.2560 0.0011 **

Public land use −1.826 0.5016 −3.6410 0.0003 ***

log(Length) −0.378 0.2274 −1.6640 0.0962 ~

Length 0.001 0.0005 3.0320 0.0024 **

log(ADT_1 + 0.5) 0.093 0.0303 3.0570 0.0022 **

Area bicycle=1 0.271 0.1170 2.3160 0.0206 *

Area bicycle=2 0.655 0.1992 3.2890 0.0010 **

log(AADB + 0.5) −0.028 0.5565 −0.0500 0.9602 —

log(AADB + 

0.5):CitySEA

−6.958 1.0838 −6.4200 <0.0001 ***

log(AADB + 

0.5):CitySFO

−0.580 0.5449 −1.0640 0.2874 —

AADB 0.008 0.0012 6.6830 <0.0001 ***

No parking in one or 

both directions

−0.331 0.1285 −2.5760 0.0100 *

Number of total MV 

lanes

−0.295 0.0602 −4.9000 <0.0001 ***

CitySEA 40.215 6.2917 6.2917 <0.0001 ***

CitySFO 3.692 2.8003 1.318 0.1874 —

Traditional bicycle lane 

(both directions)

−0.167 0.2866 −0.5840 0.5591 —

Buffered bicycle lane 

(both directions)

−0.047 0.3202 −0.1460 0.8841 —

SBL (either direction) 0.252 0.4879 0.5160 0.6058 —

Blended vertical 

elements (either 

direction)

−0.615 0.4227 −1.4550 0.1457 —

Flexi-post vertical 

elements (either 

direction)

−1.117 0.4241 −2.6340 0.0084 **

—Not statistically significant. 

~Statistically significant at the 0.1 level.

*Statistically significant at the 0.05 level.

**Statistically significant at the 0.01 level.

***Statistically significant at the 0.001 level.

CitySEA = City of Seattle; CitySFO = City of San Francisco.
94 

The relationships between bicycle crash risk, segment length, and AADB were captured by 

multiple coefficients, so the trends are not immediately apparent. The plots in figure 31 and 

figure 32 demonstrate the trend lines for these two variables for Cambridge, whereas figure 33

and figure 34 represent the San Francisco and Seattle trends for AADB contrasted to bicycle 

crash risk. 

Source: FHWA.

Figure 31. Graph. Model relationships and the influence of length on bicycle crashes for 

Cambridge. 
95 

Source: FHWA.

Figure 32. Graph. Model relationships between AADB and bicycle crashes for Cambridge.

The line on the graph for figure 33 shows that the relationship between AADB and bicycle 

crashes is exponential for San Francisco. 
96 

Source: FHWA.

Figure 33. Graph. Model relationships between AADB and bicycle crashes for 

San Francisco.

Source: FHWA.

Figure 34. Graph. Model relationships between AADB and bicycle crashes for Seattle.
97 

In general, the bicycle crash risk increased with the increasing segment length, except for very 

short segments (lengths shorter than 100 ft), where the trend was reversed. Table 60 shows the 

estimated CMFs for various bicycle lane configurations derived from the model in table 59. 

Table 60. CMFs for bicycle crashes in San Francisco, Seattle, and Cambridge.

Condition CMF Estimate Standard Error p-Value Significance

Flush buffera 1.128 0.121 0.173 0.484 —

Flexible postsa 0.498 −0.698 0.264 0.008 **

Flexible postsb 0.441 −0.819 0.297 0.006 **

Flexible postsc 0.468 −0.758 0.267 0.005 **

Blendeda 0.822 −0.196 0.252 0.437 —

Blendedb 0.729 −0.316 0.300 0.292 —

Blendedc 0.774 −0.256 0.263 0.331 —

Flexible postsd 0.605 −0.502 0.318 0.114 —

Flexible posts or blendeda 0.640 −0.447 0.203 0.028 *

Flexible posts or blendedb 0.567 −0.568 0.253 0.025 *

Flexible posts or blendedc 0.602 −0.507 0.212 0.017 *

—Not statistically significant.

*Statistically significant at the 0.05 level.

**Statistically significant at the 0.01 level.

aBase condition: traditional bicycle lane.

bBase condition: flush buffered bicycle lane.

cBase condition: traditional or flush buffered bicycle lane.

dBase condition: blended vertical element.

The results indicate that flexible posts separation is linked with a lower risk of bicycle crashes 

compared to the three base conditions without SBLs. Similarly, either flexible posts or blended 

separation is linked with a lower risk for bicycle crashes compared to the three base conditions 

without SBL. Although all the CMFs for blended separation imply bicycle crash reductions, 

none of these CMFs were statistically significant. The comparison between flexible posts and 

blended separation indicated better safety performance for flexible posts, although the statistical 

evidence behind this finding was weak (0.114 p-value for a 0.774 CMF estimate), indicating that 

although these analyses point in the direction that flexi-post could be a better option, both 

treatments might be similarly effective in reducing bicycle crash risk.

Validation Effort with Additional Data

As noted previously, the research team assessed the potential use of the two additional datasets 

(Austin and Denver) in this research. The research team used these additional data in 

combination with the multicity models developed earlier to assess the transferability of the 

analysis to other jurisdictions because the sample sizes were limited in these datasets, rendering 

full statistical analysis of each city impractical.

Analytical Approach

This section summarizes the research team’s efforts to perform a statistical evaluation of the 

hypothesis that the multi-State model is transferable in general, and that the account for the 

bicycle vertical element is consistent with the data of each additional city.
98 

Given the multicity model accounting for vehicle and bicycle exposure, geometry, land use, and 

presence of vertical elements, the product of the regression estimates and the set of variables of 

interest and their covariates define a linear predictor in the link scale. Then, for the new dataset, 

figure 35 provides an estimator of the linear predictor of the multicity model.

Figure 35. Equation. Estimator of the linear predictor.

Where: 

β = the coefficient of the explanatory variable

X’ = an individual observation 

= estimate of the linear predictor determined based on distributional parameters of scale 

(i.e., mean) and dispersion calculated using the original multicity model coefficient 

estimates.

If the data represent a behavior consistent with the multi-State analysis, this estimator should be 

such that, for the new dataset, the response variable for the new dataset is depicted as shown in 

figure 36. 

Figure 36. Equation. Response variable in new dataset. 

Where: 

Y = the response variable in the new dataset.

D = the corresponding conditional distribution of that variable. 

= the estimates of the distributional parameters of scale (i.e., mean) and dispersion 

calculated using the original multicity model coefficient estimates.

A test of this condition can be developed by relying on a regression estimation that allows 

additional multiplicative parameters in conjunction with the multicity model estimates under the 

hypothesis that the regression estimates values are statistically equal to 1.0 in the scale of the 

response—or equivalently, statistically equal to 0.0 in the link scale. In this case, this estimation 

would be the natural logarithm function. 

To allow the estimation to better match the data at hand, the dispersion parameter can be allowed 

to be reestimated for each hypothesis test, given that the main interest is the linear predictor from 

the original multicity analysis (i.e., a hypothesis test only on the scale parameter). Therefore, the 

new model specification is such that figure 37 represents the linear predictor for the new dataset. 
99 

Figure 37. Equation. Linear predictor for the new combined dataset.

Where: 

= the reestimated linear predictor on the new dataset.

Xbl = the indicator variable for bicycle lane present.

Xbb = the indicator variable for bicycle buffer present.

Xve = the indicator variable for vertical element present.

Υ0 = the overall correction for the overall correction for . 

Υ1, Υ2, and Υ3 = the regression coefficients for corrections based on each of the three 

variables coding bicycle lane separation, Xbl, Xbb, and Xve. 

Although it might be interesting to draw conclusions from the individual regression estimates, 

this evaluation focused primarily on the specific linear contrast of Υ1 and Υ3 because that 

contrast represents the CMF estimate provided in the prior analysis.

Analysis and Results

For the exploratory analysis of the Austin data, the research team did not observe any concerning 

issues regarding the range and trends in the data, compared to the other cities. Table 61 shows 

descriptive statistics of the segments in the dataset. Table 62 summarizes the estimation of the 

validation parameters.

Table 61. Number of segments by bicycle lane condition per direction in Austin (N = 68). 

Variable

Bicycle Lane 

(Direction 2)

Buffered 

(Direction 2)

Vertical 

Element 

(Direction 2)

Raised Lane 

(Direction 2)

Bicycle lane (direction 1) 13 1 0 0

Buffered (direction 1) 3 13 10 0

Vertical element 

(direction 1)

0 1 22 0

Raised lane (direction 1) 2 0 0 2

Landscaped buffer 

(direction 1)

0 1 0 0

Table 62. Estimates for validation analysis on Austin segments (N = 68). 

Parameter Estimate Standard Error t-value Pr(>|t|) Significance

Υ0 1.276 2.35 0.543 0.589 —

Υ1 2.286 2.695 0.848 0.4 —

Υ2 −2.55 4.32 −0.59 0.557 —

Υ3 2.738 2.414 1.134 0.261 —

—Not statistically significant.
100 

Table 62 shows that all parameter estimates are statistically equivalent to 1.0. Therefore, the 

analysis fails to reject the null hypothesis, providing no statistical evidence of a different pattern 

in these data regarding the bicycle lane configuration variables compared to the multicity model.

For the Denver dataset, the research team observed very different trends than for other cities in 

the exploratory phase, namely, significantly higher counts of crashes in general and broader 

dispersion in the data. The researchers decided to focus on locations that had bicycle lanes only. 

Additionally, the team verified that only 13 locations had vertical elements on both directions, 

whereas 44 additional locations with the presence of vertical elements only had a single direction 

of travel. To still be able to leverage these additional data despite the significant differences, the 

research team modified the estimation to account for locations with traditional bicycle lanes, 

buffered lanes, or configurations with the buffer and vertical elements in either of the two 

directions of travel. Therefore, this assessment produced a new estimate for vertical element. 

Table 63 shows the descriptive statistics for the Denver dataset.

Table 63. Number of segments by bicycle lane condition per direction in Denver (N = 384). 

Type of Bicycle Lane

Bicycle Lane 

(Direction 2)

Buffered 

(Direction 2)

Vertical Element 

(Direction 2)

Bicycle lane (direction 1) 231 4 2

Buffered (direction 1) 0 133 1

Vertical element (direction 1) 0 0 13

Next, table 64 shows the parameter estimates and standard errors for this analysis.

Table 64. Estimates for analysis on Denver segments (N = 384). 

Parameter Estimate Standard Error t-value Pr(>|t|) Significance

Υ0 2.5972 1.414 1.837 0.067 ~

Υ1 −0.1118 1.4092 −0.079 0.937 —

Υ2 −1.7785 1.4019 −1.269 0.205 —

Υ3 −0.9674 1.5065 −0.642 0.521 —

Table 64 shows that except for the first coefficient, the estimation does not provide evidence of a 

deviation from the multicity model results.

Table 65 shows the contrast factor estimates for each of these analyses. The contrast factor is not 

statistically different from 1.0 in both cases.

Table 65. Hypothesis test result for vertical element CMF on Denver and Austin segments.

City Condition

Contrast 

Factor Estimate

Standard 

Error p-Value Significance

Austin Vertical element 1.572 0.452 0.865 0.601 —

Denver Vertical element 0.425 −0.856 0.880 0.331 —

—Not statistically significant.
101 

The following two observations about these findings are relevant and merit consideration:

• Table 64 shows different specifications of bicycle lane variables for the case of Denver, 

which makes the comparison less direct in that case.

• The standard errors in both contrasts are comparable, indicating similarly low statistical 

power for the contrast for both datasets. The approximate range contrast factors that 

would produce similarly insignificant results for a standard error of that size is from 

0.20 to 5.70. This range means that the validation effort would only be able to detect a 

significant difference in the contrast for contrast factors outside that range.

From this exercise, the research team concluded that regarding the safety of bicycle lane vertical 

separation elements, no significant differences existed between the multicity model and any of 

the two cities reviewed for the validation.

103 

CHAPTER 8. CONCLUSIONS

The placement of effective design features, such as bicycle lanes, offers a safer and more 

efficient transportation system for all users. Therefore, the goal of this research effort was to 

determine whether constructing SBL facilities would provide additional safety opportunities by 

further reducing crashes beyond those reductions expected to occur along a corridor with basic 

design features, such as a traditional bicycle lane configuration.

SAFETY EFFECTS

To assess the safety effects of SBL facilities, the research team reviewed published SBL safety 

literature and found that the use of these facilities has largely occurred in European cities where 

SBL treatments have been applied largely to roads with differing functional purposes. As a 

result, this research explores the safety effects of SBL facilities in the United States. 

Ideally, this assessment should have included known bicycle exposure volumes, just as ADT is 

typically incorporated in MV assessments. However, the implementation of SBLs in the United 

States is in its infancy, and multiyear bicycle counts are limited. In addition, the recent 

COVID-19 pandemic created challenges for research team members to travel to locations and 

conduct additional counts. Ultimately, the bicycle exposure variable, known as the AADB, was 

estimated based on a variety of bicycle count types that included short-term bicycle counts, 

periodic counts that occurred regularly (usually every 2 yr), and a few permanent bicycle count 

stations. The research team selected three of these cities for analysis and developed exposure 

estimates for the three cities (Cambridge; San Francisco; and Seattle). The analysis included sites 

with traditional bicycle lanes, buffered bicycle lanes, and SBLs. The analysis did not include 

shared-use paths or sharrow locations. After the SBL development, the team tested the 

equivalency of the resulting CMFs for two additional cities (Austin and Denver). The Austin 

findings were similar to those from the three study cities, but the Denver findings had some 

differences that could be attributable to seasonal or weather-related conditions. 

RECOMMENDED CMFs 

Ultimately, this research resulted in a series of statistically significant CMF values. The use of a 

CMF requires knowledge of the before treatment (e.g., a traditional bicycle lane) and the after 

treatment (e.g., an SBL). The team found no statistically significant influence on safety for sites 

with a CMF equal to 1.0. A CMF greater than 1.0 indicates that more crashes can be expected, 

whereas a CMF value less than 1.0 indicates a reduction in crashes. Based on the findings 

depicted in table 60, the CMF values shown in table 66 can be used when assessing the potential 

safety influence of adding an SBL to an existing facility. Clearly, these findings indicate that the 

implementation of SBL facilities will help reduce crashes. In addition, a more consistent 

application of flexible posts will provide an additional measure of safety compared to a blend of 

vertical elements along a corridor.
104 

Table 66. CMFs for converting to an SBL. 

Significance 

Level Before Condition After Condition CMF

Standard 

Error

0.01 Traditional bicycle 

lane

SBL with flexible posts 0.498 0.173

0.01 Flush buffered 

bicycle lane

SBL with flexible posts 0.441 0.297

0.01 Traditional or flush 

buffered bicycle lane

SBL with flexible posts 0.468 0.267

0.05 Traditional bicycle 

lane 

SBL with blend of flexible 

posts and other vertical 

elements

0.640 0.203

0.05 Flush buffered 

bicycle lane

SBL with blend of flexible 

posts and other vertical 

elements

0.567 0.253

0.05 Traditional or flush 

buffered bicycle lane 

SBL with blend of flexible 

posts and other vertical 

elements

0.602 0.212

FUTURE RESEARCH

The research team encountered two significant challenges for this effort. First, the limited access 

to bicycle count data and the impact of the historic pandemic with a typical bicycle usage 

rendered acquiring comprehensive bicycle count information infeasible. Fortunately, this bicycle 

issue is a challenge that should resolve itself over time. Currently, several initiatives are 

underway to bolster bicycle count information for many cities and States. Consequently, as the 

SBL facilities continue to be implemented and the bicycle exposure data collection improves, the 

development of consistent and reliable bicycle exposure numbers can be expected to improve. 

The second challenge is the inconsistent nature of SBL applications, particularly at approaches to 

intersections. Initially, the research team hoped to develop CMFs for segments and intersections, 

but their attempts to model intersections and/or entire corridors were unsuccessful. 

Consequently, the team focused on developing robust CMFs for segments. Future work may be 

to conduct research that estimates the safety effect of the various types of SBL-to-intersection 

transitions.

This report does demonstrate that as bicycle facilities continue to evolve in the United States, a 

better understanding of how expanded configuration types will influence crashes will be needed. 

However, the team did determine from this research that SBLs do provide a clear safety benefit 

and reduce crashes by as much as 50 percent or more at segment locations.
105 

ACKNOWLEDGMENTS

The maps in figure 2 through figure 4 were modified by the authors of this report. The original 

maps are the copyright property of Google® EarthTM and can be accessed from 

https://www.google.com/earth.

(24) Figure 2 was modified by the addition of a red dot to indicate 

the location at Anderson Bridge, JFK Street, and Memorial Drive in the City of Cambridge. In 

figure 3, arrows were added to show the movements of the bicyclists at the intersection. In figure 

4, dots were added to show data points indicating bicyclists. 

107 

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Recyclable

Recommended citation: Federal Highway Administration,

Developing Crash Modification Factors for Separated Bicycle Lanes 

(Washington, DC: 2023) https://doi.org/10.21949/1521874