Traffic Analysis Toolbox Volume VI:

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PUBLICATION NO. FHWA-HOP-08-054 January 2007

Office of Operations

1200 New Jersey Avenue, SE

Washington, DC 20590
Foreword 

Traffic analysis tools are used pervasively by transportation professionals to analyze 

transportation systems. While traffic analysis tools have the capability to provide meaningful 

insights into transportation analyses, far too often the definition and interpretation of the 

output measures of effectiveness vary between the analysis tools. 

The purpose of the Definition, Interpretation, and Calculations of Traffic Analysis Tools 

Measures of Effectiveness document is to provide transportation professionals with an 

understanding of some of the most commonly used measures of effectiveness (MOEs) 

generated by traffic simulation and analytical tools, and to provide guidance on the calculation 

of the MOEs in some of the common analysis tools. It is hoped that this document will assist 

the transportation community in creating a more consistent process in the interpretation of 

MOEs from various analysis tools. 

The intended audience for this report is the transportation professional or manager who needs 

an understanding of the MOEs generated by traffic simulation and analysis tools. 

Notice 

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

Transportation in the interest of information exchange. The U.S. Government assumes no 

liability for the use of the information contained in this document. This report does not 

constitute a standard, specification, or regulation. 

The U.S. Government does not endorse products of 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-HOP--

2. Government Accession No. 

 

3. Recipient’s Catalog No. 

 

5. Report Date 

January 2007 

4. Title and Subtitle 

9ROXPH9,Definition, Interpretation, And CalculationOf

Traffic Analysis Tools Measures of Effectiveness 6. Performing Organization Code 

 

7. Author(s) 

Richard Dowling 

8. Performing Organization Report No. 

10. Work Unit No. 

 

9. Performing Organization Name and Address 

CAMBRIDGE SYSTEMATIC, INC. 

150 CAMBRIDGE PARK DRIVE, SUITE 4000 

CAMBRIDGE, MA 02140 

11. Contract or Grant No. 

DTFH61-02-C-00036 

13. Type of Report and Period Covered 

Final Report 

October 2005 – October 2006 

12. Sponsoring Agency Name and Address 

Department of Transportation 

Federal Highway Administration 

Office of 2SHUDWLRQV+270

1HZ-HUVH\$YHQXH6(

Washington, DC 20590 

14. Sponsoring Agency Code 

 

15. Supplementary Notes 

FHWA COTR: John Halkias, Office of Operations, and Grant Zammit, Office of Operations R&D 

16. Abstract 

This report presents the results of an investigation into the appropriate definition, interpretation, and 

computation of measures of effectiveness (MOEs) for traffic operations and capacity improvements. 

Information and guidance on which MOEs should be produced, how they should be interpreted, and 

how they are be defined and calculated in traffic analysis tools are detailed in this report. The document 

recommends a basic set of MOEs that can help rapidly assess the current problems, and benefits of 

alternative improvements at the system level, in a form readily understandable by the decision-maker. 

These basic set of MOEs for decision-making consists of five basic measures: 1) throughput, 2) mean 

delay, 3) travel time index, 4) Freeway segments at breakdown, and 5) surface street intersections with 

long queues, turn bay overflows and exit blockages. The report recommends that vehicle trajectories 

should be used as the common denominator for comparison of results between tools and methods 

between field data collection and analytical tools (HCM, Microsimulation, etc.). At this microscopic 

level, the analyst can compare field data to analysis tool outputs, whether the tool is microscopic or 

macroscopic. 

Finally, this report concludes by illustrating the computation and interpretation of the recommended 

systemwide MOEs for a freeway and an urban arterial street. 

17. Key Words 

Next Generation Simulation, NGSIM, traffic 

simulation, vehicle trajectory, detection, tracking, 

image preprocessing. 

18. Distribution Statement 

No restrictions. 





19. Security Classif. (of this report) 

Unclassified 

20. Security Classif. (of this page)

Unclassified 

21. No of Pages 

128 

22. Price 

 

Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized 
SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol 

LENGTH 

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

AREA 

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha 

mi2 square miles 2.59 square kilometers km2

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3 

yd3 cubic yards 0.765 cubic meters m3 

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

MASS 

oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

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

TEMPERATURE (exact degrees) o

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

or (F-32)/1.8 

ILLUMINATION 

fc foot-candles 10.76 lux lx 

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

APPROXIMATE CONVERSIONS FROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH

mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

AREA mm2

 square millimeters 0.0016 square inches in2 

m2 square meters 10.764 square feet ft2 

m2 square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac 

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3 

m3 cubic meters 1.307 cubic yards yd3 

MASS 

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

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

TEMPERATURE (exact degrees) o

C Celsius 1.8C+32 Fahrenheit oF 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2 candela/m2 0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for th International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E e 380. 

(Revised March 2003)
i 

TABLE OF CONTENTS 

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

Study Objectives ............................................................................................................ 1 

Study Approach............................................................................................................. 1 

This Report ..................................................................................................................... 1 

Measures of Effectiveness (MOEs).............................................................................. 2 

Field Measurements of MOEs...................................................................................... 3 

Simulation of MOEs ...................................................................................................... 3 

Limitations in How MOEs Are Currently Computed.............................................. 6 

Inherent Limitations of Current MOEs ...................................................................... 8 

Recommended Set of Key System MOEs................................................................... 9 

Additional Key System MOEs for Future Research ................................................. 13 

Comparison of MOEs between HCM and Other Analysis Tools........................... 14 

Practical Application of Recommended MOE System............................................. 15 

1.0 INTRODUCTION.......................................................................................................... 16 

1.1 Problem Statement................................................................................................ 16 

1.2 Project Objectives .................................................................................................. 16 

1.3 Report Overview ................................................................................................... 17 

1.4 Report Review Process ......................................................................................... 17 

1.5 Project Objectives Accomplished........................................................................ 18 

2.0 CURRENT PRACTICE ................................................................................................. 20 

2.1 MOEs in Practice ................................................................................................... 20 

2.2 Interpretation of MOEs by Agencies.................................................................. 25 

2.3 Selection of MOEs for Further Evaluation......................................................... 26 

2.4 Commonly Used Traffic Analysis Tools............................................................ 28 

2.5 Selection of Traffic Analysis Tools for Further Evaluation ............................. 30 

3.0 FIELD MEASUREMENT OF MOES........................................................................... 31 

3.1 Measuring HCM LOS in the Field ...................................................................... 31 

3.2 Measuring V/C ..................................................................................................... 31 

3.3 Measuring Travel Time and Speed..................................................................... 31 

3.4 Measuring Delay ................................................................................................... 35 

3.5 Measuring Queues ................................................................................................ 36 

3.6 Measuring Stops.................................................................................................... 37 

3.7 Measuring Density................................................................................................ 37 

3.8 Measuring Variance of Travel Time ................................................................... 38 

4.0 CALCULATION OF MOES BY TRAFFIC ANALYTICAL TOOLS....................... 39 

4.1 Description of MOE Computation Methodologies .......................................... 39 

4.2 Test Bed Results..................................................................................................... 82 

4.3 Conclusions............................................................................................................ 92 
ii 

TABLE OF CONTENTS 

(continued) 

5.0 CRITIQUE OF CURRENT MOE ESTIMATION METHODS.................................. 97 

5.1 Limitations in How MOEs are Currently Computed ...................................... 97 

5.2 Inherent Limitations of Current MOEs.............................................................. 97 

6.0 RECOMMENDED MOES ............................................................................................ 101 

6.1 Recommended System of Key Systemwide MOEs .......................................... 101 

6.2 Comparison of MOEs between Analysis Tools ................................................ 113 

6.3 MOEs for Future Research................................................................................... 114 

7.0 PRACTICAL APPLICATION RESULTS ................................................................... 116 

7.1 Selection of Case Studies...................................................................................... 116 

7.2 The I-80 Freeway Case Study .............................................................................. 116 

7.3 The Lankershim Case Study Data Set ................................................................ 122 

7.4 Conclusions from Case Studies........................................................................... 127 

REFERENCES......................................................................................................................... 128 
iii 

List of Figures 

Figure 1. Test Freeway Facility ......................................................................................... 83 

Figure 2. Test Arterial Street.............................................................................................. 83

Figure 3. Signal Timing for Example Signal on Test Arterial ....................................... 84 

Figure 4. Counting Vehicles on a Link............................................................................. 94

Figure 5. Illustration of Different Components of Throughput ................................... 107 

List of Tables 

Table 1. Common Usage of MOEs in Practice................................................................ 4 

Table 2 Field Measurement of MOEs ............................................................................. 5 

Table 3. Common Limitations of MOEs in Practice ...................................................... 7 

Table 4. Key Measures of Effectiveness for Decision-Making ..................................... 10 

Table 5. Accomplishment of Project Objectives............................................................. 19 

Table 6. Performance Measures Reported in Survey of State DOTs and MPOs ....... 21 

Table 7. FDOT Mobility Performance Measures for Highways .................................. 24 

Table 8 FDOT Mobility Performance Measures for Transit Systems......................... 25 

Table 9. Recommended Focus MOEs .............................................................................. 28 

Table 10. Initial List of Commonly Used Tools................................................................ 29 

Table 11. Recommended Tools for Evaluation ................................................................ 30 

Table 12. Acceleration-Deceleration Delay Correction Factor, CF(s)............................ 36 

Table 13. ICU LOS Thresholds ........................................................................................... 48 

Table 14. Conversion of Delay to Stops............................................................................. 52 

Table 15. Basic Freeway Section Results – Free-Flow ..................................................... 85 

Table 16. Freeway Facility Results – Free-Flow ............................................................... 85 

Table 17. Signalized Intersection Results – Free-Flow .................................................... 86 

Table 18. Arterial Street Results – Free-Flow ................................................................... 86 

Table 19. Basic Freeway Section Results – Calibrated Free-Flow.................................. 88 

Table 20. Freeway Facility Results – Calibrated Free-Flow............................................ 88 

Table 21. Signalized Intersection Results – Calibrated Free-Flow................................. 89 

Table 22. Arterial Street Results – Calibrated Free-Flow................................................ 89 

Table 23. Basic Freeway Section Results – At Theoretical HCM Capacity................... 90 

Table 24. Freeway Facility Results – At theoretical HCM Capacity.............................. 91 

Table 25. Signalized Intersection Results – At Theoretical HCM Capacity ................. 91 

Table 26. Arterial Street Results – At Theoretical HCM Capacity................................. 92 

Table 27. Strengths and Weaknesses of Common MOEs ............................................... 99 

Table 28. Common Limitations of MOEs in Practice ...................................................... 99 

Table 29. Key Measures of Effectiveness for Decision-Making ..................................... 102 

Table 30. TTIs per HCM – Freeways and Highways ...................................................... 110 

Table 31. TTIs per HCM – Arterials – Uncoordinated Signals....................................... 110 

Table 32. TTIs per HCM – Arterials – Coordinated Signals........................................... 111 

Table 33. I-80 Data Dictionary ............................................................................................ 119 

Table 34. I-80 Decision-Maker MOEs ................................................................................ 120 

Table 35. Adjustment to TTI Computation for Delayed Entry to System (I-80).......... 121 

Table 36. Lankershim Data Dictionary.............................................................................. 123 

Table 37. Lankershim Decision-Maker MOEs.................................................................. 124 
1 

EXECUTIVE SUMMARY 

This report presents the results of an investigation into the appropriate definition, 

interpretation, and computation of measures of effectiveness (MOEs) for traffic operations 

and capacity improvements. 

STUDY OBJECTIVES 

The goal of this study was to develop information and guidance on which MOEs should 

be produced, how they should be interpreted, and how they are be defined and calculated 

in traffic analysis tools. Specific objectives of the study were to: 

1. Gain an understanding of the current use and interpretation, by transportation 

professionals, of some of the most commonly used MOEs generated by traffic 

simulation and analytical tools, such as HCM procedures; 

2. Identify how field measurements are processed to estimate the MOEs used in 

conducting traffic analysis studies; 

3. Provide guidance on how these MOEs are defined and calculated in the tools; 

4. Develop an innovative approach to interpret these MOEs when conducting traffic 

analysis studies; and 

5. Demonstrate the validity of the approach through a case study of representative tools. 

STUDY APPROACH 

The above objectives were accomplished in the following five tasks: 

1. A review of current practice regarding the use and interpretation of commonly used 

MOEs (Task B); 

2. A review of how MOEs are computed from field data (Task D); 

3. A review of how MOEs are defined and computed within traffic simulation models 

and the Highway Capacity Manual (Task C); 

4. Development of innovative approach to interpret MOEs produced by traffic studies 

(Tasks D and E); and 

5. Demonstration of innovative approach on a case study (Task F). 

THIS REPORT 

The intended audience for this technical report is transportation planners, traffic analysts, 

traffic engineers, and decision-makers. The purpose of the report is to educate 

transportation professionals on how to correctly interpret their findings and present them 
2 

in a manner that clearly supports their recommendation and is easy to comprehend by the 

decision-makers and the public. 

MEASURES OF EFFECTIVENESS (MOES) 

The purpose of computing one or more traffic performance measures of effectiveness is to 

quantify the achievement of a project’s traffic operations objectives. This study identified 

seven basic measures of effectiveness, which are the building blocks of most existing and 

potential future systems for evaluating the traffic operations performance of highway 

facilities. They are: 

• Travel Time; 

• Speed; 

• Delay; 

• Queue; 

• Stops; 

• Density; and 

• Travel-Time Variance. 

Travel time, speed, and delay are closely related measures of the amount of time that the 

general public must expend in order to complete their trips on the system. 

Queues are indicators of operational problem spots within the system where capacity 

inefficiencies and/or safety problems may exist because of intersection blockages or turn 

bay overflows. 

Stops, delay, and speed are inputs into signal timing optimization algorithms and into fuel 

consumption and air pollutant emission computations. 

Density is used in the Highway Capacity Manual (HCM) to compute level of service for 

uninterrupted flow facilities (facilities without stop signs or traffic signals). 

Travel-time variance currently is a little used performance measure that will soon become 

an important component of various travel-time reliability indices being proposed for the 

assessment of the benefits of traffic operations improvements. 

In addition there are two commonly used indicators of performance that are used to 

communicate to decision-makers the quality of the facility performance: 

• Highway Capacity Manual (HCM) Level of Service; and 

• Volume/Capacity. 

Poor letter grade levels of service (e.g., “F”) and volume/capacity ratios greater than 1.00 

are readily recognizable indicators of poor traffic operations. 
3 

Table 1 describes how these MOEs are typically used in practice. More details are 

provided in Chapter 2 of this report. 

FIELD MEASUREMENT OF MOES

Table 2 summarizes the techniques commonly used to measure each MOE in the field. 

These field measurement methods are often mimicked in simulation models to compute 

MOEs. More details are provided in Chapter 3 of this report. 

SIMULATION OF MOES

There is, of course, a significant difference in how macroscopic, analytic tools compute 

MOEs and how microscopic simulation tools compute the same nominal MOEs. 

However, even among microscopic simulation tools, there are a wide variety of methods 

used to compute MOEs. 

For example, some simulation tools compute the vehicle miles traveled only for vehicles 

that enter the link during the analysis period. Others include the vehicles present on the 

link at the start of the period, presuming that they traveled the full length of the link. 

Others include step-wise integrate the distance traveled on a second by second basis. 

Others include only the vehicles able to exit the link during the analysis period. 

Another example is the computation of vehicle hours traveled (VHT). Some simulation 

tools include the delay incurred by vehicles denied entry to the system. Most others do 

not. The analyst must remember to add the delay in, when appropriate. 

Most tools measure delay against the coded free-flow speed of the link. Some take into 

account the effect of maximum safe turning speed and reduce the computed delay 

accordingly. One tool measures delay against each driver’s desired travel speed, not the 

link free-flow speed. 

There is a great deal of variation among the tools in the reported queue lengths for a 

signal, even under low-flow, pretimed signal conditions when only uniform delay occurs. 

The variations are related to the geographic area included in the search for queuing 

vehicles and the definition of what constitutes a queuing vehicle. 

Simulation model MOEs are usually not directly translatable into Highway Capacity 

Manual (HCM) level of service measures, because the HCM bases hourly level of service 

on the performance of the facility during the peak 15-consecutive-minute period within 

the analysis hour. Also, most all simulation tools report the density of vehicles, while the 

density used in HCM level of service (LOS) for uninterrupted flow facilities (no signals or 

stop signs) is the passenger-car equivalent in passenger car units (pcu) of the actual 

density of vehicles on the facility. 

Chapter 4 provides more detail on the variety of methods used to compute the MOEs by 

the Highway Capacity Manual and various traffic analysis tools. 
4 

Table 1. Common Usage of MOES in Practice. 

Building Blocks of Performance Measurement Systems 

MOE Typical Usage 

Travel Time • Used in long-range planning studies at regional or corridor level to evaluate 

traveler benefits of alternative improvements. 

• Used to evaluate traveler benefits of signal timing improvements for individual 

facility. 

Speed • Used to evaluate alternatives in long-range planning studies at regional or 

corridor level. 

• Used to evaluate benefits of signal timing improvements for individual facility. 

• Used to estimate fuel consumption and air quality impacts. 

Delay • Used to evaluate alternatives in long-range planning studies at regional or 

corridor level. 

• Used to evaluate benefits of signal timing improvements for individual 

intersection or facility. 

• Used to compare different degrees of congestion (Level of Service F). 

• Used to estimate fuel consumption and air quality impacts. 

Queue • Used to identify hot spots, operations problems at points of facility (left turn bays, 

blockages, safety). 

Stops • Used to evaluate fuel and air pollution savings of signal timing improvements for 

individual facility. 

Density • An input to the Highway Capacity Manual level of service for uninterrupted flow 

facilities. 

Travel-Time Variance • Rarely used due to difficulty of computation. Potential use for evaluating benefits 

of traffic operations improvements that reduce variability but not mean travel 

time or delay. 

Indicators of Performance 

MOE Typical Usage 

HCM Level of Service • Used to determine if facility design (for a given set of signal control parameters) 

will provide acceptable traffic operations as reported for the peak 15 minutes. 

Volume/Capacity • Used in planning studies to quickly determine if facility has sufficient number of 

lanes (sizing the facility) regardless of signal timing. 
5 

Table 2. Field Measurement of MOES.

Building Blocks of Performance Measurement Systems 

MOE Field Measurement Technique 

Travel Time • Travel time can be measured in the field through a variety of instrumented 

vehicle or vehicle tracing techniques. Mean travel time is the simple average of 

the traced vehicle travel times. 

• Mean travel time can be estimated from point measurements of speed (less 

accurate than tracing techniques), such as at video or loop detectors. The road 

segment length represented by the detector is divided by the harmonic mean 

speed measured at the loop detector to obtain mean travel time. 

Speed • The mean speed for a road segment can be estimated by measuring travel time 

and dividing the segment length by the mean travel time. 

• Instantaneous speed measurements along the length of a road segment can be 

obtained from instrumented vehicles. 

• The mean speed also can be estimated from point measurements of speed (less 

accurate than tracing techniques), such as at video or loop detectors. The 

harmonic mean speed is computed for the speeds measured at the loop detector. 

Delay • Delay over the length of a road segment is measured in the field using travel-time 

measurement techniques described above. Delay is the difference between the 

measured travel time and the free-flow travel time. 

• Delay at an intersection is measured by counting the queued vehicles every 15 

seconds (or so). The total delay is the number of queued vehicles times the 

interval between queue counts (15 seconds). Judgment is required to determine if 

a slow (but not stopped) vehicle is in a queue. This method misses 

deceleration/acceleration delay so the total delay is factored up to account for the 

missed delay. Stopped delay is multiplied by a factor greater than one to obtain 

control delay (stopped delay plus acceleration and deceleration delay). The 

average delay is the total delay divided by the number of vehicles arriving on the 

subject approach during the analysis period. 

Queue • The best way to measure a queue in the field is to count the second by second 

arrivals at an upstream point and the second by second departures at a 

downstream point. The difference between the cumulative arrivals and the 

cumulative departures at time “t” (after shifting the departure pattern for the freeflow travel time between the upstream and downstream points) is the queue at 

time “t.”

• The average number of vehicles queuing at an intersection can be measured in the 

field using the same technique as described above for measuring delay at an 

intersection. Judgment is required to determine if a slow (but not stopped) 

vehicle is in a queue. 

Stops • Stops along a road segment can be measured using the same vehicle tracing 

techniques as described above for travel time. 

• Stops can be measured at an intersection using the intersection delay method 

described above. 

• Both methods require judgment as to when a slow vehicle is considered stop and 

when a vehicle is moving up in a queue (stops within a queue are not counted). 
6 

Table 2. Field Measurement of MOES (continued) 

Building Blocks of Performance Measurement Systems (continued) 

MOE Field Measurement Technique 

Density • The average density over time at a given point in space is computed by dividing 

the measured flow rate per hour by the measured arithmetic mean speed at the 

point. 

• The average density over the length of a segment at a given point in time is 

measured using aerial photography and counting the number of vehicles present 

on the segment. 

Travel-Time Variance • Travel-time variance is computed from the measured travel times (sum of the 

squared differences between the observation and the mean value). 

Indicators of Performance 

MOE Field Measurement Technique 

HCM Level of Service • The measurement of HCM LOS in the field is described above for speed, delay, 

and density for freeways, highways, arterials, and intersections. 

• For 2-lane highways, the percent time spent following is estimated by measuring 

the percent of vehicles in platoons passing a given point for the peak 15-minute 

flow period within the analysis hour. A vehicle is defined to be in a platoon if it is 

following another vehicle by 3 seconds or less. The percent of vehicles in platoon 

is assumed to be roughly equal to the percent time spent following. 

Volume/Capacity • If the demand exceeds capacity for at least 15 consecutive minutes then the 

capacity can be measured in the field. Capacity is the queue discharge flow rate 

observed for at least 15 consecutive minutes. Once the capacity of a facility is 

known, then the v/c ratio is measured by simply counting the hourly volume and 

dividing by the estimated hourly capacity. 

LIMITATIONS IN HOW MOES ARE CURRENTLY COMPUTED 

Our ability to measure the MOEs in the field and the ability of simulation tools to estimate 

the MOEs are both severely strained by extreme congestion (See Table 3. Common 

Limitations of MOES in Practice). 

Under severe congestion conditions it is very difficult to study a large enough geographic 

area over a long enough analysis period to ensure that congestion does not extend back 

beyond the limits of the study area and congestion is not present at the start or the end of 

the analysis period. Thus most simulation modeling of severe congestion conditions 

usually has queues that extend beyond the temporal and physical limits of the model. 

Failure to include in the MOEs the queues that stretch beyond the geographic and the 

temporal bounds of the study area will bias the computation of travel time, speed, and 

delay, making capacity improvements within the study area appear to perform worse 

than no improvements. 
7 

Table 3. Common Limitations of MOES in Practice. 

Building Blocks of Performance Measurement Systems 

MOE Limitations 

Travel Time • Treatment of delays and travel time for incomplete trips or trips that cannot start 

within the analysis period is an issue. 

• Difficult for a decision-maker to interpret (no value of time is obviously too high 

or too low without knowing more about the trip characteristics). 

Speed • Treatment of incomplete or unbegun trips is an issue, as for travel time. 

• Interpretation is facility-specific. Low speed may be quite acceptable on an 

arterial, but not on a freeway. 

• Once all vehicles come to a stop, the mean speed conveys no further information 

on the severity of congestion (speed cannot go below zero). 

• Speed is a poor indicator of how close the facility is to breakdown (capacity). 

Delay • Treatment of incomplete trips or trips that have not started is an issue. 

• Definition of free-flow speed, against which delay is measured, is a problem. 

Some use the posted speed limit for the free-flow speed; others use the mean 

speed under very low-flow conditions (can be higher or lower than the posted 

speed limit). 

Queue • Definition of when vehicle joins a queue and when it leaves a queue is a problem. 

• Interpretation is road segment and facility-specific. Queues are normal for 

signals, but queues that overflow turn bays or block cross streets are not desirable.

• Tallying of queued vehicles unable to enter the road segment is an issue for many 

tools. Many tools cannot report a queue longer than the storage capacity of the 

road segment or turn bay. 

• Tallying of vehicles, which have not been able to enter the road network during 

the analysis period, is an issue. 

Stops • Definition of what minimum speed is a stop is an issue. (One reviewer 

recommended that stop speed be defined as zero so that model results can be 

better compared to two-fluid models of traffic flow.) 

• Since tallying of stops is normally suspended while a vehicle is moving up within 

a queue, the definition of a queue is an issue. 

• Once all vehicles are queued on a road segment, further increases in congestion 

have no effect on stops. 

Density • Once all vehicles are queued, then further increases in congestion have no effect 

on density. 

Travel-Time Variance • Rarely used due to difficulty of computation. Prediction tools are close to 

nonexistent. 

• Once all vehicles are queued on a road segment, the variation in travel time tends 

to drop towards zero. Variance will tend to peak when the volume is around 

capacity. 
8 

Table 3. Common Limitations of MOEs in Practice (continued) 

Indicators of Performance 

MOE Limitations 

HCM Level of Service • HCM LOS is designed to convey how close the facility is to reaching capacity or 

unacceptable LOS. Once LOS “F” reached, HCM LOS letter grades give no further 

information on relative severity of different degrees of congestion. 

• HCM LOS does not give information on the duration of the condition. 

• HCM LOS ratings do not exist for several situations: roundabouts, 

collector/distributor roads, local residential streets. 

Volume/Capacity • Cannot measure in field or simulate volumes passing a given point that are 

greater than capacity (measured v/c is always less than 1.00). In such situations, 

must add to the measured flow rate the buildup in the queue. 

There also are problems with reporting queues that extend beyond the turn bay and/or 

beyond the link. Only one simulation tool will track the buildup of queues beyond the 

subject link, and none of them will track congestion beyond the temporal or geographic 

limits of the model. With one exception, the reported queue is, by definition, never longer 

than the storage capacity of the turn bay or the link. Thus, the analyst cannot rely upon 

the reported queue length to identify queue overflow problems. The analyst must find the 

upstream links and review the reported queues there. 

Chapter 5 of this report provides more details. 

INHERENT LIMITATIONS OF CURRENT MOES

Most MOEs are satisfactory measures of traffic performance for uncongested conditions. 

Travel time, delay, queues, stops, density, and travel-time variance all increase as traffic 

demand increases relative to capacity and traffic operations worsen. 

Among these MOEs, speed is a less satisfactory indicator of how close a facility is to 

breakdown, because speed is comparatively insensitive to changing traffic flow rates until 

capacity is reached. 

Most all of the MOEs tend to break down under extreme congestion conditions. Speed, 

density, stops, and travel-time variance are invariant under “parking lot” conditions 

where no movement is possible (speed equals zero, density equals jam density, and traveltime variance is zero). The only MOEs that continue to function under parking lot 

conditions are travel time and delay, which continue to increase over the length of the 

analysis period (See Table 3. Common Limitations of MOES in Practice). 

Chapter 5 of this report provides more details. 
9 

RECOMMENDED SET OF KEY SYSTEM MOES

Traffic operations analyses can generate a great deal of numerical output. Any one of the 

numerical outputs can be important to the analyst depending upon the purpose and scope 

of the analysis and the alternatives being evaluated. The set of key systemwide MOEs

recommended below is designed to be the “starting set” but not the end all set of MOEs

for any traffic operations analysis. 

This basic set is kind of like the first set of exams given to every patient when they first 

check into the emergency room. It does not matter what the patient’s complaint, the 

health of every emergency room patient is measured by four indicators: temperature, 

pulse, blood pressure, and blood oxygen. These four basic indicators give the doctors a 

basic understanding of the general health of the patient, and some indication of general 

fields to investigate for the source of the problem. But they are not the definitive 

diagnostic tests. They are not the MRIs, X-Rays, or exploratory surgeries that come later. 

Similarly, our basic system MOEs are designed to be that first round of tests every traffic 

engineer and planner should perform to assess the general health of his or her 

transportation system. They give an indication of the overall health of the system, and 

how serious the problems are. But they do not necessarily tell the engineer/planner 

precisely what is wrong. That is the purpose of additional analyses and additional tests. 

This basic set of MOEs is not designed to replace the more detailed intersection and 

segment MOEs engineers and planners already are accustomed to perform to diagnose 

and solve traffic operations problems on the transportation system. This basic set is 

designed to help the engineer/planner and decision-maker rapidly assess the state of the 

system and identify key avenues of additional analysis to better identify needed 

improvements. 

This basic set of MOEs also is good for rapidly assessing the benefits of alternative 

improvements at the system level, in a form readily understandable by the decisionmaker. 

The basic set of MOEs for decision-making consists of five basic measures: 

1. Throughput; 

2. Mean Delay; 

3. Travel Time Index; 

4. Freeway Segments at Breakdown; and 

5. Surface Street Intersections with long queues, Turn Bay Overflows and Exit Blockages. 

Each of these basic MOEs is highlighted in Table 4. Key Measures of Effectiveness for 

Decision-Making. 
10 

Table 4. Key Measures of Effectiveness for Decision-Making. 

Decision MOE Use/Description/Interpretation 

Throughput 

(Vehicles/Hour) 

and 

Percent Incomplete 

Trips 

• Use – Provides a measure of the relative productivity of the system compared to 

an alternative. This MOE also is required to compute mean delay. 

• Definition – The number of vehicles present at the start plus those attempting to 

enter and successfully entering the system during the analysis period. 

• Computation – At start of analysis period tally number of vehicles present within 

system. During analysis period tally number of vehicles attempting to enter the 

system but never successfully enter. Tally number of vehicles that both enter and 

exit the system during the analysis period. Tally number of vehicles entering the 

system during the analysis period that are remaining on system at end of analysis 

period. 

• Reporting – Number of vehicles at start that successfully exit system during 

analysis period (V1). Number of vehicles at start that fail to exit the system 

during the analysis period (V2). Number of vehicles that entered but did not exit 

during the analysis period (V3). Number of vehicle generated but never entered 

the system during the analysis period (V4). Number of vehicles that successfully 

both entered and exited the system during the analysis period (V5). Report the 

percent of incomplete trips as the ratio of (V1+V2+V3+V4)/(V1+V2+V3+V4+V5). 

• Interpretation – The throughput is compared for different alternatives to 

determine the relative productivity of each alternative. Higher values are desired. 

If the ratio of incomplete trips exceeds 5 percent then the decision-maker should 

consider lengthening the analysis period or investigate the MOEs computed for 

solely the through trips (V5) to see if they might better represent system 

performance. 

Delay/Trip • Use – Comparison of traveler perceptions of average traffic performance of 

alternatives. 

• Definition – The travel time (for all vehicles entering and attempting to enter the 

system during the analysis period) minus the theoretical travel time at the freeflow speed. This difference is divided by the number of vehicle trips to obtain 

mean delay per trip. 

 The free-flow speed is defined as the minimum of the maximum safe speed or the 

analyst coded speed limit. Note that turning vehicles at an intersection would 

have a lower maximum safe speed than through vehicles. 

• Computation – Accumulate distance traveled (VMT) and time expended (VHT) 

each second by vehicles on system (or attempting to enter system) during analysis 

period. Simultaneously accumulate theoretical VHT that would have been 

expended if vehicle had been able to travel the same VMT at free-flow speed. At 

the end of the analysis period the free-flow VHT is subtracted from the 

accumulated VHT to obtain Delay VHY. The Delay VHT is converted to seconds 

and then divided by the total number of vehicle trips over which the delay was 

accumulated (V1+V2+V3+V4+V5). If the number of incomplete trips 

(V1+V2+V3+V4) is greater than 5 percent of all trips, then either the analysis 

period should be lengthened, or Delay VHT should be accumulated separately for 

through trips (V5) and averaged over only the through trips (V5). 

• Reporting – Delay is reported in vehicle hours traveled (VHT) and mean seconds 

per trip. If incomplete trips account for over 5 percent of all trips, that should be 

noted. 

• Interpretation – Subjective. If trips are long in system, then high mean delays may 

be acceptable. If trips are short, even low mean delay may be unacceptable. 
11 

Table 4. Key Measures of Effectiveness for Decision-Making (continued) 

Decision MOE Use/Description/Interpretation 

Travel Time Index • Use – To determine if facility operation during peak periods is unacceptably 

worse than for off-peak periods. 

• Definition – Ratio of observed vehicle hours traveled (VHT) to theoretical VHT at 

free-flow speed for same VMT. 

• Computation – See notes above on computation of mean delay. The travel time 

index is the accumulated VHT divided by the theoretical free-flow VHT. 

• Reporting – System travel time index plus a few words qualifying the result, such 

as “Good,” “Bad.”

• Interpretation – 

- The TTI minus 1.00 gives the mean delay as a percentage of the mean trip time. 

- The inverse of the TTI gives the mean speed as a percentage of the free-flow 

speed for the system. 

- For an entire metropolitan area (including local streets, arterials, and freeways) 

a peak-period TTI of 1.4 or better was typical of 90 percent of the metropolitan 

areas of United States in 2005. 

- For more focused systems of mixed freeway and arterial facilities (no local 

streets) a TTI of under 2.5 is roughly indicative of generally uncongested 

conditions and good signal coordination. 

- For a system of solely unsignalized facilities (freeways, highways, 2-lane rural 

roads) a TTI of over 1.4 is indicative of a facility that is overcapacity for the 

entire length of analysis period. 

- For a system of exclusively signalized arterials a TTI of over 2.5, is generally 

indicative of overcapacity conditions and/or poorly coordinated traffic signals. 

- A qualifier of “Good” for TTIs <= 1.5, “Potentially Acceptable” for TTIs 

between 1.5 and 2.5, and “Less Desirable” for TTIs > 2.5 would help the 

decision-makers. 

Freeways: Percent 

Breakdowns 

• Use – To identify geographic and temporal extent of unsatisfactory operations for 

freeways in system. 

• Description – Breakdown is defined as a density of vehicles per lane greater than 

the density at capacity flow rate. The HCM assigns LOS “F” to these densities 

higher than capacity. 

Two percents are computed: 

- Maximum percent of directional freeway miles at LOS F at any onetime during 

the analysis period. 

- Percent of analysis period when any directional freeway segment is at LOS F. 
12 

Table 4. Key Measures of Effectiveness for Decision-Making (continued) 

Decision MOE Use/Description/Interpretation 

Freeways: Percent 

Breakdowns 

(continued) 

• Computation – Determine maximum LOS “E” density for each directional 

freeway segment in system according to the Highway Capacity Manual. 

Determine PCE values for each vehicle type in system for each directional freeway 

segment according to the Highway Capacity Manual. For each second in the 

analysis period tally number of vehicles on directional segment and multiply by 

appropriate PCE value to obtain equivalent PCUs. At end of model run compute 

15-minute running averages of PCUs for each directional segment. Identify start 

and end times when 15-minute average PCU exceeds HCM LOS E maximum 

density. Find earliest start time of LOS F and latest end time of LOS F for system. 

The percent duration of LOS “F” is equal to the latest end time minus the earliest 

start time of LOS F, divided by the length of the analysis period. 

The maximum percent LOS “F” is the maximum number of directional segment 

miles operating at LOS “F” (averaged over 15 minutes) at any one time divided by 

the total directional miles of freeway in the system. 

• Interpretation – The geographic and temporal percents LOS “F” are indicators of 

congested freeway operations. The lower, the better. 

Surface Street: 

Percent of 

Intersections Exits 

Blocked 

• Use – To identify geographic and temporal extent of unsatisfactory operations for 

urban streets in system. Intersection exit blockages due to downstream queues 

extending back to an upstream intersection reduce through capacity and are 

potential safety concerns. 

• Definition – Two percents are computed: 

- Maximum percent of intersection exits that are blocked at any onetime during 

the analysis period. 

- Percent of analysis period when any intersection exit is blocked. 

• Computation – For every directional street link and for each second in the analysis 

period compare the distance of back bumper of last car in any lane of the 

directional street link to the total estimated distance to the nearest upstream 

intersection (If the upstream node has only one link feeding the subject 

downstream link, that node is not an intersection. Abandon the test on the subject 

link and perform the test instead on the upstream link.). Determine if the back of 

queue is within 25 feet of storage length of the directional link. 

- Accumulate number of seconds during analysis period when one or more 

directional links are full. The percent of the analysis period with one or more 

full directional links is the number of seconds when one or more directional 

links are full divided by the number of seconds in the analysis period. 

- Tally number of directional links meeting this “full” criterion at any one time. 

The maximum percent full links is the maximum number of full directional 

links at any one time divided by the total number of directional street links 

(exclude freeway links which have their own LOS F test) in the system. 

• Report – Maximum percent of full directional street links at any one time. Percent 

of analysis period during which one or more directional street links are “full.”

• Interpretation – The geographic and temporal percents of “full” links are 

indicators of potential capacity and safety problems. The lower, the better. 

Since a common goal for designs of new turn bays is to provide storage for the 95 

percentile demand per cycle, a goal of no more than 5 percent of the analysis 

period with full street links would be desirable for a new design. 
13 

Table 4. Key Measures of Effectiveness for Decision-Making (continued) 

Decision MOE Use/Description/Interpretation 

Surface Street: 

Percent Turn Bay 

Overflows 

• Use – To identify geographic and temporal extent of unsatisfactory operations for 

urban streets in system. Turn bay overflows reduce through capacity and are 

potential safety concerns. 

• Definition – Two percents are computed: 

- Maximum percent of full turn bays at any onetime during the analysis period. 

- Percent of analysis period when any turn bay is full. 

• Computation – For each second in the analysis period compare the distance of 

back bumper of last car in turn bay from stop bar to total estimated turn bay 

storage length. Determine if the back of queue is within 25 feet of storage length 

of turn bay. 

- Accumulate number of seconds during analysis period when one or more turn 

bays are full. The percent of the analysis period with one or more full turn bays 

is the number of seconds when one or more turn bays are full divided by the 

number of seconds in the analysis period. 

- Tally number of turn bays meeting this “full” criterion at any one time. The 

maximum percent full turn bays is the maximum number of full turn bays at 

any one time divided by the total number of turn bays in the system. 

• Report – Maximum percent of full turn bays at any one time. Percent of analysis 

period during which one or more turn bays are “full.”

• Interpretation – The geographic and temporal percents of “full” turn bays are 

indicators of potential capacity and safety problems. The lower, the better. 

Since a common goal for designs of new turn bays is to provide storage for the 95 

percentile demand per cycle, a goal of no more than 5 percent of the analysis 

period with full turn bays would be desirable for a new design. 

Their use, definition, computation, reporting, and interpretation are presented in this 

table. 

There are, of course, many other MOEs that every good planning or engineering study 

also will want to consider, such as, user benefits, user costs, fuel consumption, noise, air 

pollutant emissions, and safety. However, the 5 basic system MOEs listed above will give 

the decision-maker and the analyst a good idea of how well the system is performing and 

how one alternative improvement compares to another. Chapter 6 describes them in more 

detail. 

ADDITIONAL KEY SYSTEM MOES FOR FUTURE RESEARCH 

The technical review suggested four additional key MOEs that are likely to be valuable to 

decision-makers but could not be included at this time due to several uncertainties about 

them requiring more research and additional practical experience in order to better define 

them. They are: 
14 

• Buffer Index; 

• Fuel Consumption Ratio; 

• NOx Emissions Ratio; and 

• Safety. 

The buffer index is one of a dozen different measures of travel-time reliability. Future 

research needs to address the selection of the appropriate set of reliability measures for 

decision-making, as well as the extent to which the variability already incorporated into 

microsimulation models can be expanded to include the much larger variability associated 

with demand fluctuations and capacity reducing incidents in the real world. 

The fuel consumption and NOx emissions ratios are an attempt to incorporate energy 

consumption and air pollution considerations in an easily understandable form for 

decision-making. However, there are several issues of how these indices might be 

computed, and consequently there are concerns as to their real meaning. Indeed, the 

United States Environmental Protection Agency may not accept air quality conformity 

analyses results that are produced by any tool except EPA approved tools. 

Safety is one measure of effectiveness that many analysts would like to include in 

operations analysis considerations, however; the tools to do so are still very limited and in 

the development stage, where available. 

COMPARISON OF MOES BETWEEN HCM AND OTHER ANALYSIS TOOLS 

Since each microscopic and macroscopic analysis tool (including the Highway Capacity 

Manual method) has notably different definitions of what constitutes stopped and queued 

vehicles and because the tools also vary significantly in the determination of which 

vehicles to include in the computations (vehicles that both entered and exited during the 

analysis period, vehicles that did neither, or vehicles that either entered or exited but not 

both during the analysis period), it is not feasible for an analyst to take the macroscopic 

output from one tool, apply a conversion factor (or procedure) and compare the results to 

that of another tool. The analyst simply does not have access to sufficient data at the 

macroscopic level to be able to compare MOEs across tools. 

This conclusion means that looking up Highway Capacity Manual levels of service using 

MOEs produced by a different analytical method is not appropriate. It is prone to bias 

and error. 

For similar reasons, the analyst should not be switching tools in the middle of a 

comparison between alternatives. One set of tools should be consistently used to evaluate 

MOEs across all alternatives. One should not evaluate one alternative with one tool, 

another alternative with a second tool, and then use the MOEs produced by both tools to 

select among the alternatives. 

Comparison of results between tools and methods is possible only if the analyst looks at 

the lowest common denominator shared by all field data collection and analytical tools: 
15 

vehicle trajectories.1 At this microscopic level, the analyst can compare field data to 

analysis tool outputs, whether the tool is microscopic or macroscopic. By computing 

aggregate, macroscopic MOEs from the vehicle trajectory data the analyst can compare the 

results of macroscopic and microscopic tools to field data and to each other in a consistent 

manner. This is the only appropriate method for comparing results between tools, 

validating the model results against field data, or using the outputs of other tools to 

compute level of service as defined by the Highway Capacity Manual. 

Consequently, it would be highly desirable if all microscopic analysis tools had the option 

of generating vehicle trajectory data in a universally readable format (for example, a 

generally accepted database format, such as DBASE), so as to make post-processing and 

comparison of results across tools possible. 

PRACTICAL APPLICATION OF RECOMMENDED MOE SYSTEM 

This report concludes with a chapter illustrating the computation and interpretation of the 

recommended systemwide MOEs for a freeway and an urban arterial street. 

 

1

 The author is indebted to Dr. Hani Mahmassani for suggesting this concept. 
16 

1.0 INTRODUCTION 

Traffic analysis tools play a critical role in prioritizing public investment in strategies 

employed by transportation professionals to relieve congestion. Traffic analysis tools help 

practitioners to: 

• Measure the operational performance of transportation facilities; 

• Evaluate various ITS strategies and innovative transportation management concepts; 

• Evaluate alternate designs and operational improvements; 

• Decrease the time and cost for evaluation and design of transportation facilities and 

systems; and 

• Reduce the risk and disruption to traffic caused by field experimentation. 

1.1 PROBLEM STATEMENT 

Use of the Highway Capacity Manual (HCM) and traffic simulation tools has become the 

standard approach for evaluating transportation design alternatives, operational 

performance, Intelligent Transportation Systems (ITS), and traffic operations strategies. 

However, the HCM procedures and traffic simulation tools seldom result in identical 

performance measurements. Moreover, there are no guidelines on interpreting these 

performance measurements. This leaves decision-makers and transportation 

professionals with the dilemma of identifying the true performance of the design 

alternatives and strategies. 

1.2 PROJECT OBJECTIVES 

The goal of this study was to develop information and guidance on which MOEs should 

be produced, how they should be interpreted, and how they should be defined and 

calculated in traffic analysis tools. Specific objectives of the study were to: 

1. Gain an understanding of the current use and interpretation, by transportation 

professionals, of some of the most commonly used MOEs generated by traffic 

simulation and analytical tools, such as HCM procedures; 

2. Identify how field measurements are processed to estimate the MOEs used in 

conducting traffic analysis studies; 

3. Provide guidance on how these MOEs are defined and calculated in the tools; 

4. Develop an innovative approach to interpret these MOEs when conducting traffic 

analysis studies; and 

5. Demonstrate the validity of the approach through a case study of representative tools. 
17 

1.3 REPORT OVERVIEW 

This report presents innovative approaches for interpreting and presenting traffic 

simulation and analytical tool MOEs. This report considers alternative MOEs in addition 

to the Level of Service performance measure prescribed by the HCM. It recommends 

MOEs that decision-makers could use to interpret results from traffic analysis studies 

conducted by transportation professionals. 

The Executive Summary provides more detail on the contents of this report. 

1.4 REPORT REVIEW PROCESS 

A draft of this report was submitted to the FHWA Contracting Officer Technical 

representative (COTR) and to a panel of volunteer experts in the application of traffic 

microsimulation to performance measurement for comment. 

The volunteer experts were: 

Invitee Affiliation Type 

Scott Aitken Quadstone Vendor 

John Albeck Trafficware Vendor 

Jaume Barcelo Aimsun Academic/Vendor 

Loren Bloomberg CH2M-HILL Private 

Ken G. Courage University of Florida Academic 

Mohammed Hadi Florida International University Academic 

Steve Hague California Department of Transportation Government 

David Hale McTrans Academic/Vendor 

Michael Hunter Georgia Tech University Academic 

Liang Hsia Florida Department of Transportation Government 

Hani Mahmassani University of Maryland Academic/Vendor 

Cathy McGhee Virginia DOT Transportation Research Council Academic 

Arash Mirzaei North Central Texas Council of Governments Government 

Kiel Ova PTV-America Vendor 

John Shaw Wisconsin Department of Transportation Government 
18 

In addition, the report was submitted to the following project team members for review: 

John Halkias Federal Highway Administration 

James Colyar Federal Highway Administration 

Grant Zammit Federal Highway Administration 

John Tolle Federal Highway Administration 

James McCarthy Federal Highway Administration 

Vassili Alexiadis Cambridge Systematics 

Vijay Kovvali Cambridge Systematics 

Alexander Skabardonis University of California, Berkeley 

The efforts, advice, and suggestions of the volunteer technical reviewers, the Federal 

Highway Administration Project Team, and the Consultant Project Team are greatly 

appreciated. However, it should be noted that the opinions presented in this report are 

those of the author and not necessarily those of its reviewers. Remaining errors are the 

responsibility of the author. 

1.5 PROJECT OBJECTIVES ACCOMPLISHED 

The goal of this study was to develop information and guidance on which MOEs should 

be produced, how they should be interpreted, and how they should be defined and 

calculated in traffic analysis tools. The table below explains how the specific objectives of 

this study have been accomplished. 
19 

Table 5. Accomplishment of Project Objectives. 

Objective How Accomplished 

1. Gain an understanding of the current use and 

interpretation, by transportation professionals, of 

some of the most commonly used MOEs 

generated by traffic simulation and analytical 

tools, such as HCM procedures. 

Chapter 2 provides an overview of current practice. 

It describes MOEs used in practice and in research. It 

identifies commonly used tools for computing MOEs.

2. Identify how field measurements are processed to 

estimate the MOEs used in conducting traffic 

analysis studies. 

Chapter 3 describes how MOEs are measured in the 

field. 

3. Provide guidance on how these MOEs are 

defined and calculated in the tools. 

Chapter 4 documents how MOEs are computed by a 

range of macroscopic and microscopic tools. 

4. Develop an innovative approach to interpret 

these MOEs when conducting traffic analysis 

studies. 

Chapter 5 presents a recommended approach to 

defining, calculating, and interpreting MOEs, which 

were reviewed by a panel of technical experts in 

Chapter 6, and refined accordingly. The final refined 

set of MOEs and their interpretation are provided. 

5. Demonstrate the validity of the approach through 

a case study of representative tools. 

Chapter 7 illustrates the application of the MOEs to 

two case studies taken from vehicle trajectory data 

developed under the NGSIM program. This chapter 

illustrates the computation and interpretation of the 

MOEs. The final set of recommended MOEs and 

their interpretation based on the results of technical 

review and the case studies are also provided. 
20 

2.0 CURRENT PRACTICE 

This chapter reviews current practice in the use of MOEs in traffic operations analysis and 

recommends a subset of MOEs and traffic analysis tools for further evaluation. 

2.1 MOES IN PRACTICE 

This section identifies the measures of effectiveness most commonly used by 

transportation professionals in evaluating transportation design alternatives, operational 

performance, and ITS/traffic operations strategies. Three primary sources were 

consulted: 

1. NCHRP Synthesis 311, Performance Measures of Operational Effectiveness for 

Highway Segments and Systems; 

2. Interim report for NCHRP 7-15, Cost-Effective Measures and Planning Procedures for 

Travel Time, Delay, and Reliability; and 

3. Interim report for NCHRP 3-68, Guide to Effective Freeway Performance 

Measurement. 

NCHRP Synthesis 311 [Shaw, 2003] examined the use of performance measures for the 

monitoring and operational management of highway segments and systems. The study 

surveyed the current state of practice and assessed the relative strengths and weaknesses 

of the more than 70 measures identified. 

The most successful measures related to conditions directly experienced by the traveler, 

e.g., travel time, speed, and delay. Measures derived from these, such as travel indices, 

were less relevant to the operational environment than to policy planners. 

For purposes of this project, the most relevant finding is on performance measures that are 

used by public agencies. Table 6 below shows the results of a survey of state DOTs and 

MPOs on the performance measures they use. LOS, traffic volume, and VMT were the 

measures cited most often. Travel time, speed, and incidents also were frequently cited. 

Most other measures were used by only one or a few of the agencies interviewed. All of 

the top six most frequently cited measures, except for incidents, can be estimated by 

current traffic analysis tools. 

NCHRP 7-15 [Cambridge Systematics 2005] is intended to develop structured, costeffective methods and procedures that calculate measures of travel time, delay, and 

reliability. These methods are to be used by practitioners to measure, predict, and report 

on transportation system performance from a customer-oriented perspective and make 

decisions about policies, programs, or projects. 

The interim report includes the findings of a review of current research and literature, a 

review of current agency and industry practice, and the results of several practitioner 

workshops. 
21 

Table 6. Performance Measures Reported in Survey of State DOTs and MPOs. 

Performance Measure Typical Definition N* 

Level of Service (LOS) A (best) to F (worst) based on measures of effectiveness 11 

Traffic Volume Annual average daily traffic, peak-hour traffic, or peak-period 11 

Vehicle Miles Traveled Volume times length 10 

Travel Time Distance divided by speed 8 

Speed Distance divided by travel time 7 

Incidents Traffic interruption caused by a crash or other unscheduled event 6 

Duration of Congestion Period of congestion 5 

Percent of System Congested Percent of miles congested (usually defined based on LOS E or F) 5 

Vehicle Occupancy Persons per vehicle 5 

Percent of Travel Congested Percent of vehicle miles or person miles traveled 4 

Delay Caused by Incidents Increase in travel time caused by an incident 3 

Density Vehicles per lane per period 3 

Rail Crossing Incidents Traffic crashes that occur at highway–rail grade crossings 3 

Recurring Delay Travel time increases from congestion; this measure does not 3 

Travel Costs Value of driver’s time during a trip and any expenses incurred 3 

Weather-Related 

Traffic Incidents 

Traffic interruption caused by inclement weather 3 

Response Times to Incidents Period required for an incident to be identified, verified, and for an 

appropriate action to alleviate the interruption to traffic to arrive at 

the scene 

2 

Commercial Vehicle 

Safety Violations 

Number of violations issued by law enforcement based on vehicle 

weight, size, or safety 

1 

Evacuation Clearance Time Reaction and travel time for evacuees to leave an area at risk 1 

Response Time to WeatherRelated Incidents Period required for an incident to be identified, verified, and for an 

appropriate action to alleviate the interruption to traffic to arrive at 

the scene 

1 

Security for Highway 

and Transit 

Number of violations issued by law enforcement for acts of violence 

against travelers 

1 

Toll Revenue Dollars generated from tolls 1 

Travel-Time Reliability Several definitions are used that include: 1) variability of travel 

times; 2) percent of travelers who arrive at their destination within 

an acceptable time; and 3) range of travel times 

1 

Source: Shaw, 2003. 

*N = Number of responses from agencies. 
22 

The key findings on performance measures include the following: 

• Several state DOTs use congestion and reliability measures. 

• MPOs and smaller agencies still measure capacity and level of service. 

• Most agencies surveyed acknowledge the growing importance of congestion and 

reliability performance measures. 

• Some practitioners believe that the general public and high-level transportation 

officials are less interested in reliability measures than in total delay and congestion 

measures. The consensus opinion was that reliability has not yet gained the attention 

of the vast majority of the general public and decision-makers at the current time. 

• Many organizations use economic measures such as benefit/cost ratios in their 

planning processes. 

NCHRP 3-68 will develop a guidebook that codifies best practices in freeway performance 

measurement. The Interim Report [Cambridge Systematics, 2004] covers the results of 

interviews with more progressive agencies on their use of performance measures. The 

interviews covered the motivation for performance measures, the performance measures 

used, and use of the reported performance measures. 

With regard to the types of performance measures used by agencies, the main findings 

were as follows:2

• Both outcome and output measures are used by agencies. It is clear that agencies who 

have undertaken freeway performance measurement have accessed the literature on 

performance measurement because they use the outcome/output terminology. 

• For outcome measures, derivatives of speed and delay are commonly used by both 

operating and planning agencies. The Travel Time Index is a popular metric. Level of 

service as a metric is still in use in both planning and operations agencies, though it is 

not as widespread as it might have been 10 years ago. Reliability metrics have not yet 

found their way into widespread use. (Seattle and Minneapolis are exceptions.) These 

metrics are usually formulated for short segments or at key locations. An exception is 

Seattle where a series of defined “freeway trips” have been defined – these can involve 

travel over multiple freeway routes for extended lengths. 

 

2

 Quoted directly from the NCHRP 3-68 Interim Report by Cambridge Systematics. 
23 

• Some of the more interesting metrics used by agencies include: 

− The number of very slow trips (half of free flow speed) that occurs each year by 

time of day and major trip (Seattle); 

− Percentage of reduction in incident congestion delay; and 

− Percent of freeway lane-miles below congested volumes (based on volume per 

lane). 

• Output measures are used primarily by operating agencies, and then primarily for 

incident management activities and the operation of field equipment (e.g., sensors, 

cameras). 

Many areas are beginning to define more sophisticated measures for measuring 

congestion/mobility performance but have not yet implemented them. Overall, there 

appears to be a trend away from the general categories of performance (LOS) and toward 

continuous measures that are based on delay and travel time. Further, consideration of 

travel-time reliability is growing in acceptance, though its implementation is still 

problematic, primarily due to data requirements. 

Florida Mobility Performance Measures Program 

The Florida Department of Transportation (FDOT) has identified four dimensions for 

measuring the effectiveness of programs in meeting the objectives of FDOT. They are: 

1. Quantity of Travel – The number of vehicles or people using a facility or service; 

2. Quality of Travel – The degree of congestion; 

3. Accessibility – The ease with which people can gain access to the transportation system; 

and 

4. Utilization – The percentage of capacity utilized. 

The first table below shows the measures of effectiveness for highway performance. The 

second table below shows the MOEs selected for transit systems. The performance 

measures are reported for two time periods: Peak Hour and Daily. 

Data on existing performance is obtained from FDOT’s Roadway Characteristics 

Inventory (RCI) and Traffic Characteristics Inventory (TCI). Average vehicle occupancy 

data is obtained from the 1990 National Personal Transportation Survey (NPTS). Transit 

system data is collected from local transit authorities. A land use database (GIS) was used 

to measure accessibility. Special studies were performed to obtain reliability data. Travel 

demand modeling and the Highway Capacity Manual methods are used to forecast the 

MOEs. 
24 

Table 7. FDOT Mobility Performance Measures for Highways. 

Dimension Mobility Performance Measure Definition 

Quantity of Travel Person miles traveled AADT * Length * Vehicle Occupancy 

 Truck miles traveled AADT * Length * Percent Trucks 

 Vehicle miles traveled AADT * Length 

 Person trips Total person trips 

Quality of Travel Average speed Average speed weighted by person miles traveled

 Delay Average delay 

 Average travel time Distance/mean speed 

 Average trip time Door-to-door trip travel time 

 Reliability Percent of travel times that are acceptable 

 Maneuverability Vehicles per hour per lane 

Accessibility Connectivity to intermodal facilities Percent within 5 miles (1 mile for metro area) 

 Dwelling unit proximity Percent within 5 miles (1 mile for metro area) 

 Employment proximity Percent within 5 miles (1 mile for metro area) 

 Industrial/warehouse proximity Percent within 5 miles 

 Percent miles bicycle accommodations Percent miles with bike lane/shoulder 

Percent miles pedestrian 

accommodations 

Percent miles with sidewalk 

Utilization Percent system congested Percent miles at LOS E or F 

 Percent travel congested Percent daily VMT at LOS E or F 

 Vehicles per lane-mile AADT * Length/lane miles 

 Duration of congestion Percent miles at LOS E or F 

Source: FDOT Mobility Performance Measures Brochure (2000) [FDOT 2000]. 

http://www.dot.state.fl.us/planning/statistics/mobilitymeasures/mmbrochure.pdf. 

Notes: AADT = average annual daily traffic. 
25 

Table 8. FDOT Mobility Performance Measures for Transit Systems. 

Dimension Mobility Performance Measure Definition 

Quantity of Travel Ridership Total passenger trips 

Quality of Travel Auto/Transit Travel-Time Ratio Door-to-door trip time 

 Reliability On-time performance 

Accessibility Coverage Percent person minutes served 

 Frequency Buses per hour 

 Span Hours of service per day 

Utilization Load Factor Percent seats occupied 

Source: FDOT Mobility Performance Measures Brochure (2000) [FDOT 2000]. 

http://www.dot.state.fl.us/planning/statistics/mobilitymeasures/mmbrochure.pdf. 

2.2 INTERPRETATION OF MOES BY AGENCIES 

Interpretation of MOEs consists of distinguishing between acceptable and unacceptable 

traffic operations. 

Local and state public agencies primarily use the HCM LOS grades to determine if 

operations are acceptable or unacceptable. A certain letter grade is set as the agency 

standard. Then project designs and project impacts are compared against that letter grade 

standard. A project design is considered unacceptable if the letter grade level of service is 

below the agency standard. An impact is determined to be significant if the project 

changes the letter grade from an acceptable letter to an unacceptable letter. 

The meaning of density on a freeway or highway is interpreted based upon its equivalent 

HCM LOS grade. Similarly the meaning of speed on an arterial street and delay at an 

intersection is determined based upon their equivalent HCM LOS grades. 

Volume/capacity ratios are used by some agencies to determine acceptability of 

operations. A v/c less than one is acceptable. A v/c greater than one is unacceptable. 

There is however no guidance or standard practice on the interpretation of the other 

MOEs that do not have HCM LOS equivalents, such as travel time, reliability, vehicle 

hours traveled, vehicle miles traveled, mean system speed, etc. 

Agencies typically use non-HCM performance measures for comparing alternatives. In an 

alternatives analysis, less of an undesirable feature (such as vehicle hours traveled or 

variability of travel time) is considered better, but there is no threshold of acceptability. 

Interpretation of these non-HCM measures is currently difficult, in the absence of 

information on national averages or national practice that would help an agency 

characterize a particular value of a non-HCM MOE as acceptable. 
26 

2.3 SELECTION OF MOES FOR FURTHER EVALUATION 

Volume 1 of the Traffic Analysis Toolbox [FHWA 2004a] lists numerous performance 

measures that are generated by most analysis tools. The list is a mix of input performance 

measures, basic performance measures, and derived performance measures. Some of the 

measures in this list, such as v/c ratio, are necessary for computing the basic travel time, 

speed, and delay performance measures. Travel time and speed are interchangeable. IF 

one is known the other can be computed. Other performance measures, such as delay or 

fuel consumption can be derived from the basic travel time/speed performance measure. 

LOS, in particular, is a derived performance measure. LOS is not strictly a performance 

measure, but a method of reporting one or more selected numerical performance 

measures in a system of easily understandable letter grades. 

Basic Performance Measures 

Basic performance measures are those measures, which cannot be derived from other 

performance measures. The major task of the analytical tool is to compute these basic 

performance measures. Once these measures have been computed, it is possible (at least 

in theory) to load the output into a spreadsheet and compute the other measures. 

Capacity, although often considered to be an input, is a basic facility characteristic crucial 

to the calculation of many of the other performance measures. For simulation models, 

capacity is a calibratable output. For analytic models it is often a direct input or a 

computed input based on the geometric characteristics of the facility. 

Speed, and its inverse, travel time, are the basic performance measures from which most 

all other performance measures can be computed. 

Number of stops is another basic performance measure that cannot be derived directly 

from the other performance measures. It is crucial for predicting fuel consumption, noise, 

and air pollutant emission performance measures. 

Travel-time reliability is another basic performance measure that cannot be directly 

derived from the other performance measures. The profession currently is exploring how 

“reliability” might be used in decision-making, so it has not seen much use so far. Many 

popular analytical tools do not produce measures of reliability. 

Derived Performance Measures 

Derived performance measures are those that could, at least in theory, be computed from 

the basic performance measures and other inputs. It is often not practical though for the 

user to go through model output to extract the data needed to make the computations. 

Thus it is a practical advantage to the user if the analytical tool computes these derived 

measures automatically. 

Volume or throughput can be computed from demand and capacity, although upstream 

capacity constraints need to be considered in determining downstream volumes. Volume 

then becomes an input to several performance measure computations. 
27 

Delay is the difference between ideal travel time and the actual travel time. 

Density can be computed from volume and speed from the “flow equals density times 

speed” equation. 

The number of vehicles in queue can be derived from the volume arriving on red (for 

signals) and/or the difference between the volume and the capacity. The queue length is 

the number of vehicles in queue divided by the density (for freeways) or the average 

spacing between stopped vehicles (for signals). 

Travel Distance and accumulated Travel Time (VMT/PMT and VHT/PHT) can be 

derived from the volume and the travel time. Distance traveled is the sum of link 

volumes and link distances or the volume divided by speed times the number of hours. 

The volume capacity ratio (V/C) is computed from volume and capacity, which are inputs 

to the other performance measures. 

The duration of congestion is the difference between the demand and the capacity, 

divided by the capacity. 

Mode split is computed from the ridership, AVO, and auto volume performance 

measures. 

Crashes are beyond the ability of commonly used traffic analysis tools to predict. 

Noise, fuel consumption, and pollutant emissions are measures that can be calculated 

from the other measures of volume, speed, delay, stops. 

Methods of Summarizing Performance Results 

The methods for summarizing performance take quantitative physical performance 

results (e.g., stops, delays, etc.) and convert them into qualitative or quantitative results, 

either letter grades or monetary values. The innovative approaches considered in this 

research will look at different methods for summarizing facility and system performance. 

Level of service (LOS) is computed based on delay or density depending on the facility 

type. A look-up table is used to convert quantitative numerical results into qualitative 

letter grades. 

Benefit/Cost is computed from the other performance measures. The other measures are 

converted into monetary equivalents and summed to obtain benefits and costs. 

Whatever method is used to summarize performance results, it should be capable 

comparing alternatives that differ along the following dimensions: 

• Type. Increasingly, managers are faced with deciding between different types of 

alternatives: e.g., operations improvements versus capacity improvements, 

maintenance of existing facilities versus constructing new facilities. 
28 

• Physical scale, e.g., a large single interchange reconstruction on a freeway versus 

signalization improvements to all interchanges along a freeway. 

• Time scale, i.e., alternatives with different lifetimes. 

Recommended MOEs for Focus of Tool Evaluation 

It is recommended that the following eight measures of effectiveness shown in the table 

below become the focus of analytical tool evaluations for this study. They were selected 

because they constitute the basic building blocks of most all performance measures 

currently used by public agencies to monitor agency performance. Thus, if one can 

predict HCM LOS, volume/capacity ratio, travel time, speed, delay, queues, stops, and 

travel-time variance, one can estimate most any performance measure currently used by 

public agencies in the transportation sector. Economic costs, air quality, noise, and energy 

use can be estimated from these basic eight MOEs. 

Table 9. Recommended Focus MOEs.

• HCM Level of Service • Delay 

• Volume/Capacity • Queue 

• Travel Time • Stops 

• Speed • Density 

• Travel-Time Variance 

2.4. COMMONLY USED TRAFFIC ANALYSIS TOOLS 

This section provides an inventory of commonly used traffic simulation and analytical 

tools that are used to evaluate transportation design alternatives, operational 

performance, ITS, and traffic operation strategies. 

Following the typology laid out by Volume 1 of the Traffic Analysis Toolbox, the tools fall 

into the following categories: 

1. Sketch Planning Tools; 

2. Travel Demand Models; 

3. Deterministic Analytical Tools; 

4. Traffic Signal Optimization Tools; 

5. Macroscopic Simulation Models; 
29 

6. Mesoscopic Simulation Models; and 

7. Microscopic Simulation Models. 

Since the focus of this research effort is on tools for design and operation analysis, we will 

focus on the traffic signal optimization tools and simulation models (macro, meso, and 

micro). Within these four categories Volume 1 of the Toolbox identifies dozens of 

software. 

The determination of “commonly used” was based upon the extent to which the tools are 

referenced in the published technical literature for papers accepted for presentation at the 

2004 and 2005 Annual Meeting of the Transportation Research Board. The selection of 

analysis tools to be used in this research was then made from among the most popular 

tools with the intent of obtaining a representative sample of the range of commonly used 

analysis tools, while keeping the total number of tools evaluated to a tractable number. 

Table 10. Initial List of Commonly Used Tools. 

Category of Analysis Tool Representative Software 

Deterministic Analytical Tools 

Signalized Intersections Synchro6 

Urban Streets FDOT ARTPLAN 

Rural Two-Lane Roads HCS 

Rural Multi-Lane Highways HCS 

Freeways HCS 

Traffic Optimization Tools 

Signalized Intersections Synchro6 

Freeway Ramp Metering FREQ 

Macroscopic Simulation Tools 

Urban Streets Transyt-7F 

Rural Highways None 

Freeways TRAF-CORFLO 

Mesoscopic Simulation Tools Dynasmart-P 

Microscopic Simulation Tools CORSIM 

 SimTraffic 

 Vissim 

 Paramics 

 Aimsum 
30 

2.5 SELECTION OF TRAFFIC ANALYSIS TOOLS FOR FURTHER EVALUATION 

It is beyond the budget and timeframe available for this research to investigate all tools 

and MOEs they produce, so the initial list of commonly used tools was narrowed down to 

the following recommended subset of analysis tools for evaluation during the remainder 

of the project. 

Table 11. Recommended Tools for Evaluation. 

Tool Category Representative Tool to be Evaluated 

Deterministic Analytical Tools Synchro6 and HCS 

Traffic Optimization Tools Synchro6 

Macroscopic Simulation Tools Synchro6 

Mesoscopic Simulation Tools Dynasmart-P 

Microscopic Simulation Tools CORSIM, SimTraffic, Vissim, Paramics, Aimsum 

The above list is not intended as an exhaustive list or a list of even the most commonly 

used tools for evaluation of traffic operations in the United States. Indeed, many popular 

tools are absent from this list. It would be beyond the resources available to this project to 

investigate all of the popular tools in the United States. This list is intended to be a 

representative cross-section of typical tools illustrative of the range and breadth of tools 

employed in the United States for traffic operations analysis at the macroscopic, 

mesoscopic, and microscopic levels. 
31 

3.0 FIELD MEASUREMENT OF MOES

This chapter identifies how field measurements are processed to estimate the eight MOEs

selected for further investigation in the previous chapter. 

3.1 MEASURING HCM LOS IN THE FIELD 

The Highway Capacity Manual to date has used single field measurable performance 

measures for level of service. For freeways it is density in terms of equivalent passenger 

cars. For two-lane rural highways it is percent time delay. For arterial streets it is mean 

speed of through traffic. For an intersection it is mean delay. The measurement of these 

performance measures in the field is described below in the following sections. 

3.2 MEASURING V/C 

Volume/capacity (v/c) ratio cannot be measured in the field unless there is a time period 

when the facility is observably at capacity.3

 Then the v/c for any other period can be 

estimated by taking the ratio of the counted demand to the observed capacity. 

3.3 MEASURING TRAVEL TIME AND SPEED 

The FHWA publication #FHWA-PL-98-035, Travel Time Data Collection Handbook, 

dated March 1998, prepared by the Texas Transportation Institute provides a complete 

overview of techniques for gathering travel-time data (see 

http://www.fhwa.dot.gov/ohim/start.pdf). 

There are numerous speed and travel-time measuring techniques, but they can all be 

grouped into three large categories according to their method of sampling the travel-time 

universe. 

1. Spot speed measurement techniques measure vehicle speeds only for a given point of 

geography or a given point of time. 

2. Vehicle tracing techniques measure vehicle travel times only for a select portion of all 

trips. 

3. Trip maker tracking techniques are similar to vehicle tracing techniques but measure 

traveler trip times rather than vehicle trip times. 

The mechanics of employing many of these techniques are described in the Institute of 

Transportation Engineers publication, Manual of Transportation Engineering Studies, Edited 

by H. Douglas Robertson, 2000. The Transportation Research Board Publication, Highway 

 

3

 There is a queue of vehicles waiting to be served by the facility that persists for at least 15 

continuous minutes. 
32 

Capacity Manual, 2000 also provides some limited guidance on travel time, speed, and 

delay data collection in the appendices to Chapters 15 and 16 of the manual. 

Spot Speed Measurement Techniques 

Spot speed measurement techniques use roadside sensors to measure the instantaneous 

speeds of vehicles either at specific spots of the roadway or at specific times of the day. 

These techniques are very cost-effective at gathering large amounts of speed data for 

specific segments of the transportation system but cannot provide door-to-door travel 

times. 

Roadside sensors include in-the-road loop detectors, roadside radar, microwave sensors, 

video sensors, and infrared sensors. They are “location-based sampling” methods which 

suffer from the biases inherent in measuring speeds at a point and assuming the speed is 

applicable to other points on the roadway. Technological variations include: single loop 

detectors, double loop detectors, portable machine double hose counters, radar, lidar, 

microwave, and infrared sensors, and video camera sensors. Recent technological 

advances; such as vehicle signature and platoon signature matching, may allow 

measurements of elapsed time between stations. 

Single loop detector occupancies are converted to speeds based upon an estimated 

average vehicle length during the survey period. This average length varies according to 

the mix of autos and heavy vehicles present on the facility by time of day and direction. 

Radar guns register only the fastest vehicle speed in the platoon, so there may be some 

upward bias. Also, the “angle of incidence” (the angle between the road centerline and 

the hypothetical straight line between the radar gun and the vehicle) of roadside radar 

and lidar measurements affect the precision of the speed results, but usually the effect is 

considered negligible. 

The minimum sample size is determined based upon the desired confidence interval for 

the mean speed. Larger variances in measured speeds require larger sample sizes to 

achieve a desired confidence interval for the mean speed. 

Vehicle Tracing Techniques 

Vehicle tracing techniques involve tracking either test vehicles or randomly selected 

vehicles through to determine the travel times between preselected checkpoints. Vehicle 

tracing techniques are an example of “trip-based” method of sampling travel times. They 

are good techniques for measuring trip segment travel times (a geographic portion of the 

traveler’s total trip). However, they generally are not easily adaptable to measurement of 

door-to-door travel times, because of the expense and difficulty of obtaining a reasonable 

sample of door-to-door locations. 

Vehicle tracing techniques consist of: Test Vehicle, Non-Instrumented Vehicle Tracking, 

and Passive Probes. 
33 

1. Test Vehicle (Floating Car) Technique – The test vehicle technique is the most common 

travel-time collection technique employed to date. This technique consists of hiring a 

driver and vehicle to drive a vehicle along a preselected route and measuring the 

elapsed time and distance traversed. The driver is instructed to “pass as many 

vehicles as pass him or her” so that the vehicle is in effect driving at the median speed 

of traffic. Labor saving variations equip the test vehicles with distance measuring 

instrument (DMI) or global positioning satellite (GPS) to automate measurement and 

recording and to eliminate the need for a second person in the vehicle. 

2. Non-Instrumented Vehicle Tracking Technique – This technique uses any one of 

several technologies for identifying randomly selected vehicles at various checkpoints 

within the study area and measuring the time between appearances at each 

checkpoint. Vehicle tracking is different than using test vehicles, because the drivers 

have not been hired to do the study. The drivers may take different paths and they 

may make stops in between checkpoints, throwing off the travel-time computations. 

Technological variations include: License Plate matching, License Plate matching with 

matching software, and Loop detectors with vehicle signature matching. 

3. Passive Probe Technique – This technique requires some sort of special tracking 

instrumentation on the vehicles as well as the roadside. The vehicle driver is not hired 

to drive a particular route and goes about his or her normal business. Either readers 

are mounted on the road to record the time and identity of all transponder-equipped 

vehicles passing by, or readers are mounted in the vehicle to record the times and 

movements of the vehicle past each transponder location. The drivers may take 

different paths and they may make stops in between checkpoints, throwing off the 

travel-time computations. Technological variations include: Automatic vehicle 

location (AVL), Automatic vehicle identification (AVI), Emergency vehicle tracking, 

Cellular phone geolocation, and Global positioning satellite (GPS). 

4. Transit Vehicle Tracking Techniques – The previous vehicle tracing techniques are 

applicable to all vehicles, including transit vehicles. The discussion under this 

category focuses on the special issues involved in working with public transit agencies 

to monitor public transit vehicles. Most public transit operators already publish route 

schedules and monitor on-time performance. A few operators are able to use 

automated techniques for tracking vehicle movements, but most currently rely upon 

manual checkpoint and ride check techniques. The data is stored in varying formats in 

varying software formats, making electronic transmittal of data difficult. 

5. Truck Tracking Techniques – Trucks also can be tracked using all of the previously 

described vehicle tracking techniques. Tracking trucks though requires the active 

cooperation of the vehicle fleet owner who must consent to the placement of any 

special devices in the vehicle, or must transcribe manual logs and share the 

information with interested public agencies. Public agencies wishing to track 

commercial vehicles must demonstrate to the vehicle fleet owner that the owner will 

receive some direct benefit in return for the expense of transcribing and sharing the 

vehicle tracking information. In most cases, travel-time information is mixed in with 
34 

sensitive proprietary information on customers, and must be manually sorted out by 

the operator before it can be transmitted to a public agency. 

Tripmaker Tracing Techniques 

Trip maker tracing techniques survey travelers either after they have completed their trip 

or recruit volunteers in advance to record and report their travel times as part of their 

daily activities. 

1. Retrospective Surveys – Retrospective surveys quiz the traveler about their trip travel 

times and experiences after the fact. The traveler is not prepped in advance, so 

questions must be limited to what can be reasonably remembered from the previous 

day’s or that morning’s commute. Variations explored here include: household 

telephone surveys, surveys of employees at their work sites, and web site/e-mail 

surveys. 

2. Prospective Surveys – Prospective surveys involve at least two contacts with each 

individual: one contact to recruit the individual, and a second to collect the 

information. A third contact may be required to deliver a trip diary form or a GPS 

unit to the individual to aid in recording information. Travelers can be asked in 

advance to note a great deal of detail about their trips, including travel times for 

specific segments of the trip. Technological variations include: global positioning 

satellite receivers/recorders, e-mail reporting, and cell phone call-in. 

HCM Method for Measuring Arterial Speed 

Appendix B of Chapter 15 of the Highway Capacity Manual describes the following 

method for measuring the mean speed of through traffic on an arterial street. 

“a. Use the appropriate equipment to obtain [cumulative travel time and stopped delay 

time]. The equipment may be computerized or simply a pair of stopwatches. 

b. Travel times between the centers of signalized intersections should be recorded, along 

with the location, cause, and duration of each stop. 

c. Test-car runs should begin at different time points in the signal cycle to avoid all trips 

starting first in the platoon. 

d. Some mid-block speedometer readings also should be recorded to check on unimpeded 

travel speeds and how they relate to FFS. 

e. Data should be summarized for each segment and each time period, the average travel 

time, the average stopped time for the signal, and other stops and events (four-way 

stops, parking disruptions, etc.). 

f. The number of test-car runs will depend on the variance in the data. Six to 12 runs 

may be adequate for each traffic-volume condition.”
35 

3.4 MEASURING DELAY 

The Highway Capacity Manual defines delay as “The additional travel time experienced 

by a driver, passenger, or pedestrian.” Delay is thus the difference between an “ideal”

travel time and the actual travel time. Since the definition of delay depends on a 

hypothetical “ideal travel time,” delay is not always directly measurable in the field. 

If the ideal travel time is defined as the off-peak travel time, then the measured delay is 

the difference between the actual measured travel time during the peak period, and the 

actual measured travel time during the off-peak period. 

If the ideal travel time is defined as travel at the posted speed limit, then the delay cannot 

be directly measured in the field. It is estimated by subtracting the hypothetical travel 

time at the posted speed limit from the measured mean travel time in the field. 

HCM Method for Measuring Free-Flow Speed 

Appendix B of Chapter 15 of the Year 2000 Highway Capacity Manual explains how to 

measure the free-flow speed for an arterial street. 

“This can be determined by making runs with a test car equipped with a calibrated speedometer 

during periods of low volume. An observer should read the speedometer at mid-block locations 

when the vehicle is not impeded by other vehicles and record speed readings for each segment. 

These observations can be supplemented by spot speed studies at typical mid-block locations 

during low-volume conditions.”

HCM Method for Measuring Intersection Control Delay 

The Year 2000 Highway Capacity Manual (see Appendix A, page 16-90 of the HCM) 

describes the following field procedure for measuring and computing intersection control 

delay for a signalized intersection. 

1. The survey should begin at the start of the red phase of the lane group. 

2. At regular intervals of between 10 and 20 seconds (but not an interval length that is 

evenly divisible into the cycle length) count the number of queued vehicles. 

3. A vehicle is considered as queued when it approaches within one car length of a 

stopped vehicle and is itself about to stop. 

4. All vehicles that join a queue are then included in the vehicle-in-queue counts until the 

rear axel of the vehicle crosses the stop line. 

5. Simultaneously count the total number of arriving vehicles (whether they stop or not). 

6. At the end of the survey period, continue counting vehicles in queue for all vehicles 

that arrived during the survey period until all of them have exited the intersection. 

This step requires mentally noting the last stopping vehicle that arrived during the 

survey period in each lane of the lane group and continuing the vehicle-in-queue 
36 

counts until the last stopping vehicle or vehicles, plus all vehicles in front of the last 

stopping vehicles, exit the intersection. Stopping vehicles that arrive after the end of 

the survey period are not included in the final vehicle-in-queue counts. 

The time in-queue per vehicle is equal to 90 percent of the interval between queue counts 

multiplied by the sum of the vehicles in queue each interval divided by the total arriving 

vehicles. The 90 percent factor is intended to correct for the tendency of this method to 

over count delay. 

V

Q i

TQ I ∑ = ( ) 90%* * Equation 1

V

VS D = TQ + CF * Equation 2

Where: 

D = Average control delay per vehicle (secs). 

TQ = Time in-queue per vehicle (secs). 

I = The interval (length of time) between queue counts (secs). 

Q(i) = The number of vehicles in queue at time point “i.”

V = Total arriving volume of vehicles (whether or not queuing). 

CF = Correction Factor to convert stopped delay to control delay. 

VS = Number of arriving vehicles stopping. 

The correction factor is determined from the following look-up table taken from 

Exhibit A16-2 of the HCM. 

Table 12. Acceleration-Deceleration Delay Correction Factor, CF(s). 

Free-Flow Speed <= 7 Vehicles 8-19 Vehicles 20-30 Vehicles 

<=37 mph +5 +2 -1 

>37-45 mph +7 +4 +2 

> 45 mph +9 +7 +5 

Source: Exhibit A16-2, Highway Capacity Manual, Transportation Research Board, 2000. 

3.5 MEASURING QUEUES 

According to the ITE Manual of Transportation Engineering Studies, the macroscopic 

approach to measuring queues is to count the arrival and departure volumes for facility 

aggregated to five-minute intervals. The count should start before any queues are present 

and it should not end until the queues have all cleared. The difference between the 
37 

cumulative five-minute arrivals and the five-minute departures is the number of vehicles 

in queue. The arrival data must be counted just upstream of the end of the longest 

expected queue. 

For a microscopic analysis, the license plate, arrival time, and departure time of each 

vehicle is recorded. The number of vehicles in queue at any point in time is the difference 

between the cumulative number of arrivals up to that point minus the cumulative 

departures up to that point in time. 

The Year 2000 Highway Capacity Manual (HCM) defines a Queue as: “A line of vehicles, 

bicycles, or persons waiting to be served by the system in which the flow rate from the 

front of the queue determines the average speed within the queue. Slowly moving 

vehicles or people joining the rear of the queue are usually considered part of the queue. 

The internal queue dynamics can involve starts and stops. A faster-moving line of 

vehicles is often referred to as a moving queue or a platoon.”

The HCM defines the Back of Queue as: “The distance between the stop line of a 

signalized intersection and the farthest reach of an upstream queue, expressed as a 

number of vehicles. The vehicles previously stopped at the front of the queue are counted 

even if they begin moving.”

The HCM method described above for measuring control delay at a signal also can be 

used to measure queues. 

3.6 MEASURING STOPS 

The number of stops is can be obtained from a floating car survey, where the number of 

stops during each run is recorded. The number of stops obtained in this manner is 

representative only of vehicles driving the same path as the floating cars (usually just 

through traffic on the arterial). 

Similarly, the HCM control delay measurement method described above can be used to 

identify the number of vehicles stopping on the approach to a traffic signal. 

3.7 MEASURING DENSITY 

The Year 2000 Highway Capacity Manual does not describe a method for directly 

measuring density in the field. 

Aerial photography has been used to measure densities on freeways in several cities for 

the purposes of congestion monitoring (see Santa Clara VTA Annual Congestion 

Monitoring Reports for an example). A single photo is shot every half hour during the 

peak period and the number of vehicles counted between interchanges to obtain an 

average density representative of that half-hour period for each segment of the freeway 

facility. Vehicle density is NOT converted to passenger car equivalents. 

Alternatively, density can be computed from loop detector measurements of speed and 

flow using the fundamental relation d=v/s (shown in HCM, equation 23-4, page 23-12), 
38 

where “v” is the flow rate in vehicles per hour, “d” is the density in vehicles per mile, and 

“s” is the speed in mph. 

The volume is counted manually, with temporary machine counters, or with permanent 

loop detectors for the desired analysis period. If an axle counter is used then an 

adjustment may be made to the count based on a separate truck axle count (See ITE 

Manual of Transportation Engineering Studies). 

The spot speed is measured using any one of a variety of devices (see Measuring Speed, 

above). A sample size is selected to reduce the confidence interval for the true mean 

speed to the desired size. 

3.8 MEASURING VARIANCE OF TRAVEL TIME 

The variance of travel time is not usually measured because of the expense involved. An 

agency may take the inverse of speed (travel time per mile) measured at loop detectors 

and compute the variance of the travel time per mile at the detectors. 
39 

4.0 CALCULATION OF MOES BY TRAFFIC ANALYTICAL TOOLS 

This chapter presents the results of a comprehensive investigation into how commonly 

used traffic analytical tools define and calculate commonly used MOEs. First the 

computation methodologies are explained based upon published user guides for each tool 

and informal correspondence with the software developers. Then the tools are tested on a 

simple test bed under both free-flow and congested traffic demand conditions. Variations 

in the reported MOEs are noted. 

4.1 DESCRIPTION OF MOE COMPUTATION METHODOLOGIES 

This section describes the methodologies used by the operations analysis tools to compute 

the MOEs that were selected for further investigation in Chapter 2. 

4.1.1 HCS 

The section describes how the MOEs are computed by the HCS software, Version 5.2, 

developed by the University of Florida, in 2005. 

Computation of HCM Level of Service 

The HCS software is intended to be a faithful implementation of the Highway Capacity 

Manual. As such, excepting for programming errors, which are corrected as soon as they 

are found, the HCS software computes the HCM LOS exactly according to the HCM 

methods. HCS implements the following HCM methods: Signalized Intersections, 

Unsignalized Intersections, Urban Streets, Freeway Basic, Freeway Weave, Freeway 

Ramps, Freeway Facilities, two-lane Rural Roads, Multi-lane Rural Highways, and 

Transit. 

Computation of Volume Capacity Ratio 

The HCS software computes capacity per the HCM method.4

 The HCM defines capacity 

as; “The maximum sustainable flow rate at which vehicles or persons reasonably can be 

expected to traverse a point or uniform segment of a lane or roadway during a specified 

time period under given roadway, geometric, traffic, environmental, and control 

conditions...” The HCM capacity for each facility type is computed according to the 

equations below. 

The following equation is used to compute the capacity of all uninterrupted flow facilities 

(freeways, multi-lane highways, and two-lane rural roads): 

 

4

 Highway Capacity Manual, Transportation Research Board, Washington D.C., 2000. 
40 

Cap = Base Cap * N * fhv * PHF* fp* fg Equation 3

Where: 

Cap = Capacity in terms of vehicles per hour. 

Base Cap = Base capacity in terms of passenger cars per hour per lane (varies by facility type). 

N = Number of through lanes. 

fhv = Heavy vehicle adjustment factor (varies by facility type, vehicle mix, and grade). 

PHF = Peak-hour factor (the ratio of the peak 15-minute flow rate to the average hourly flow rate). 

fp = Driver population adjustment factor (used for freeway and multilane only). 

fg = Adjustment factor for grades (used for two-lane highways only). 

For freeway and highway weaving sections the capacity is a function of weaving type, 

number of lanes, free-flows speed, length and weaving ratio. Due to the discontinuous 

nature of the procedure for weaving sections, no closed form equation for capacity is 

available. 

The following equation is used to compute the lane group capacity for an approach to a 

signalized intersection: 

s = (g/C) so N fw fHV fg fp fbb fa fLU fLT fRT fLpb fRpb Equation 4

Where: 

cap = Capacity for subject lane group, expressed as a total for all lanes in lane group (veh/h). 

g/C = Effective green time per cycle ratio for lane group. 

So = Base saturation flow rate per lane (pc/h/ln). 

N = Number of lanes in lane group. 

fw = Adjustment factor for lane width. 

fHV = Adjustment factor for heavy vehicles in traffic stream. 

fg = Adjustment factor for approach grade. 

fp = Adjustment factor for existence of a parking lane and parking activity adjacent to lane group. 

fbb = Adjustment factor for blocking effect of local buses that stop within intersection area. 

fa = Adjustment factor for area type. 

fLU = Adjustment factor for lane utilization. 

fLT = Adjustment factor for left turns in lane group. 

fRT = Adjustment factor for right turns in lane group. 

fLpb = Pedestrian adjustment factor for left-turn movements. 

fRpb = Pedestrian-bicycle adjustment factor for right-turn movements. 

The following equation is used to compute the lane group capacity for the approach to a 

roundabout and for a stop sign controlled approach to an intersection: 

( )

1 exp( ) / 3600

exp / 3600

, ,

, ,

, ,

c x f x

c x c x

p x c x v t

v t

c v − −

− = Equation 5
41 

Where: 

cp,x = Potential capacity of minor movement x (veh/h). 

vc,x = Conflicting flow rate for movement x (veh/h). 

tc,x = Critical gap (s). 

tf,x = Follow-up time for minor movement x (s). 

For a roundabout x=1, since there is only one movement on the approach. 

The capacity of an all-way stop is a function of the demands on the other approaches and 

is determined through an iterative procedure. 

Capacities and volume/capacity ratios are reported by HCS as follows: 

Facility Type Lane or Movement V/C and Capacity Overall V/C and Capacity

Signals Yes – v/c by lane group Yes – v/c only 

Two-Way Stops Yes – v/c by lane group No 

All-Way Stops Yes – Capacity by lane group No 

Roundabouts Yes – Upper/lower bounds v/c of approaches No 

Arterial Yes – Intersection v/c and through capacity No 

Freeway Basic No No 

Freeway Weave No Yes – Capacity only 

Freeway Ramp Yes – Capacity of ramp and two lanes of freeway No 

Freeway System Yes – Capacity and v/c by freeway segment No 

Two-Lane Highway Yes – Two-way and one-way v/c No facility capacity 

Multi-Lane Highway Not applicable No facility capacity 

Transit No capacity by bus No capacity by route 

Computation of Speed and Travel Time 

HCS computes mean speeds for urban streets, freeway facilities, multilane highways, and 

two-lane highways. The HCM formulae used by HCS are explained below. 

Freeway Facilities 

HCS computes the mean speeds for individual freeway segments and time periods using 

the following procedure from HCM. 

If no queuing is present on the segment during any of the time periods then HCS uses the 

heuristic formula contained in the HCM for basic freeway sections to predict speed given 

the equivalent passenger volume and the free-flow speed. 
42 

If queuing is present on one or more segments during the analysis period then the speed = 

flow/density equation is used to compute speed: 

( , )

( , ) ( ) *

( , )

( , ) ( , ) NV i pSF i p L i

K i p

SF i p U i p = = Equation 6

Where: 

U(i,p) = The average speed on segment “i” during period “p” (mph). 

SF(i,p) = The segment flow rate (vph). 

K(i,p) = The average segment density (veh/mi). 

L(i) = The length of segment (miles). 

NV(i,p) = The number vehicles. 

The segment performance measures are then summed to total and average system 

performance using the following formulae: 

∑∑

∑∑ =

p i

p i

U i p

L i SF i p

SF i p L i

SMS

( , )

( ) ( , ) *

( , ) * ( )

Equation 7

Where: 

K = Mean facility density across all segments (i) and time periods (p) (veh/mi). 

L = Length of segment (miles). 

U = Mean speed on segment (mph). 

Multilane Highways 

For volumes less than capacity a heuristic formula contained in the HCM for multilane 

highways is used to predict speed given the equivalent passenger volume and the freeflow speed. No procedure is provided for volumes greater than capacity. 

Two-Lane Highways 

For volumes less than capacity HCS uses the following HCM formula for estimating speed 

for two-lane highways: 

ATS = FFS – 0.00776vp – fnp Equation 8

Where: 

ATS = Average travel speed for both directions of travel combined (mi/h). 

FFS = Free-flow speed (mi/h). 

fnp = Adjustment for percentage of no-passing zones. 

vp = Passenger-car equivalent flow rate for peak 15-min period (pc/h). 
43 

No procedure is provided for volumes greater than capacity. 

Urban Streets 

HCS uses the following HCM formulae to estimate mean speed (in one direction) for 

urban streets. 

∑ ∑

∑

+

∗

j

j

i

i

i

RT L d

3600 L

SMS = * Equation 9

Where: 

SMS = Space mean speed over the length of the facility (mph or kph). 

Li = Length of segment “i” (mph or kph). 

RT = Running time per mile (sec/mile or sec/km). 

dj = Delay at signal “j” for through traffic in the subject direction. 

The delay for through traffic at each signal is computed according to the following 

formulae. 

d = d1 PF d2+d3 ( ) + Equation 10

[1 -(g/C)* X ]

C [1 -(g/C)] d =

2

2 min(1, ) 1∗

∗

Equation 11

cap T

k I X

d = T (X - 1)+ (X - 1 ) +

2

⎭

⎬

⎫

⎩

⎨

⎧

∗ ∗

*

8* * *

2 900 Equation 12

Where: 

d = Control delay (sec/veh). 

d1 = Uniform delay (sec/veh). 

d2 = Incremental delay (sec/veh). 

d3 = Initial queue delay (sec/veh). 

PF = Progression adjustment factor. 

C = Cycle length (secs). 

g = Effective green time for the lane group (secs). 

X = Volume/capacity ratio for the subject lane group. 

k = Actuated control factor. 

I = Upstream filtering factor. 

v = Volume per hour (veh/hr). 

cap = Capacity (sat * g/c) for the through lane group (veh/hr). 

T = Duration of analysis period (hr). 
44 

Computation of Delay 

HCS computes delay only for intersections. The analyst must use the output speeds for 

freeway facilities, multilane highways, two-lane highways, and urban streets to manually 

compute facility delay. 

Freeway Facilities 

The HCM does not provide a procedure for estimating delay for freeway facilities, so the 

HCS does not output this MOE. However the analyst can manually compute facility 

delay by dividing the predicted speed into the length of the facility to get travel time. 

Subtracting the predicted travel time from the free-flow travel time gives facility delay. 

Multilane Highways 

The HCM does not provide a procedure for estimating delay for multilane highways, so 

the HCS does not output this MOE. It can be estimated manually by the analyst from the 

HCS outputs as described above for freeway facilities. 

Two-Lane Highways 

The HCM does not provide a procedure for estimating delay for two-lane highways, so 

the HCS does not output this MOE. It can be estimated manually by the analyst from the 

HCS outputs as described above for freeway facilities. 

Urban Streets 

Urban street speed is computed by HCS but not delay. Mean and total facility delay can 

be estimated manually by the analyst using the HCS outputs as described above for 

freeway facilities. 

The HCS does directly compute delay for each individual signalized intersection as 

described above under speed estimation for urban streets. 

Unsignalized intersection delay is computed by HCS using an intersection approach delay 

formula similar to the one described above for signalized intersections. 

Computation of Queue 

Freeway Facilities 

HCS computes queue lengths for freeway facilities based on an assumed linear 

relationship between flow and density. The density at capacity is determined based upon 

the capacity flow rate and the mean speed at capacity. The capacity and the speed at 

capacity are taken from the basic freeway section speed-flow curves in the HCM. They 

vary according to the free-flow speed for the freeway. 

Once the density at capacity is known, then the flow rate is assumed to decrease linearly 

until it reaches zero at an assumed jam density of 190 passenger cars per lane-mile. 
45 

The queue density is computed as follows: 

( , )

( ) * ( , 1, ) ( , , ) SC i p

KJ KC SF i t p KQ i t p KJ − − = − Equation 13

Where: 

KQ(i,t,p) = The density of queue on segment “i,” at time step “t” for period “p” (veh/mi). 

KJ = The jam density (veh/lane/mi). 

KC = The density at capacity (veh/lane/mi). 

SF(1,t-1,p) = The segment flow (veh/hr). 

SC(i,p) = The segment capacity (veh/hr). 

The number of vehicles in the queue divided by the queue density and then divided by 

the number of lanes gives the length of the queue. 

Multilane Highways 

The HCM does not provide a procedure for estimating queues for multilane highways, so 

the HCS does not output this MOE. 

Two-Lane Highways 

The HCM does not provide a procedure for estimating queues for two-lane highways, so 

the HCS does not output this MOE. 

Urban Streets 

The HCM does not compute queuing directly for urban streets, so HCS does not do so 

either. The HCM and HCS do compute queues for intersections. 

The mean queue for a lane group approaching a signalized intersection is computed 

according to the following formulae. 

Q = Q1 + Q2 Equation 14

[1 -(g/C)* X ]

v C [1 -(g/C)] Q = PF

3600 min(1, )

*

1

*

∗

∗

Equation 15

Q = cap T {(X - 1)+ (X - 1 ) +Z } 2

2 0.25* ∗ ∗ Equation 16

Where: 

Q = Control delay (sec/veh). 

Q1 = Uniform delay (sec/veh). 

Q2 = Incremental delay (sec/veh). 

PF = Progression adjustment factor. 
46 

C = Cycle length (secs). 

g = Effective green time for the lane group (secs). 

X = Volume/capacity ratio for the subject lane group. 

Z = Composite factor (see page 16-153 of HCM for details). 

v = Volume per hour (veh/hr). 

cap = Capacity (sat * g/c) for the through lane group (veh/hr). 

T = Duration of analysis period (hr). 

The HCM also provides for calculating various percentile probability queues as a function 

of the mean queue. 

Computation of Stops 

The HCM does not provide any procedures for computing number of stops. Thus HCS 

does not provide this MOE. 

Computation of Density 

Freeway Facilities 

If no queuing is present the equivalent passenger car flow rate per lane is divided by the 

mean speed to obtain the density in units of equivalent passenger cars per lane-mile. If 

queuing is present then the following equation is used: 

( )

( , ) ( , ) L iNV i p K i p = Equation 17

Where: 

K(i,p) = The average segment density (veh/mi). 

L(i) = The length of segment (miles). 

NV(i,p) = The number vehicles. 

The segment performance measures are then summed to total and average system 

performance using the following formula: 

∑∑

∑∑

=

p i

p i

L i NV i p

K i p L i

K

( ) * ( , )

( , ) * ( )

Equation 18

Where: 

K = Mean facility density across all segments (i) and time periods (p) (veh/mi). 

L = Length of segment (miles). 

U = Mean speed on segment (mph). 

NV = Number of vehicles on segment. 
47 

Multilane Highways 

HCS computes the mean passenger car density by dividing the mean speed into the 

passenger car equivalent flow rate. 

Two-Lane Highways 

The HCM does not provide a procedure for estimating delay for two-lane highways, so 

the HCS does not output this MOE. However the analyst could divide the mean speed 

into the flow rate to obtain density if desired. 

Urban Streets 

The HCM does not provide a procedure for computing density on urban streets and 

therefore HCS does not output this MOE for urban streets. 

Computation of Travel-Time Variance 

The HCM does not provide any procedures for any facilities for computing travel-time 

variance. Thus HCS does not provide this MOE. 

4.1.2 Synchro 6 

This section describes the formulae used by Synchro 6 (Build 614), released 2005 by 

Trafficware of Sugarland, Texas.5

 Synchro is designed to evaluate the performance of 

urban streets, signalized intersections, and unsignalized intersections (two-way stop, allway stop, and single-lane roundabouts). Synchro does not provide performance data for 

freeways, multilane highways, or two-lane rural roads. 

Computation of HCM Level of Service 

Synchro computes the signalized intersection, unsignalized intersection, and arterial LOS 

according to the HCM. For signalized intersections, Synchro provides the user with two 

additional intersection levels of service (LOS). One is called the Intersection LOS; the 

other is called the ICU LOS. 

The Intersection LOS is based upon a “percentile” control delay computation but uses the 

same numerical delay thresholds as the HCM. The percentile control delay is different 

than the HCM control delay for the intersection. The computation is explained below 

under the delay MOE. 

The ICU LOS is based upon the critical flow ratio (ICU) for the intersection. The critical 

flow ratio is the sum of green +clearance times required to serve the critical movements at 

the intersection divided by a reference cycle length. The ICU LOS is designed to be 

insensitive to the actual signal timing for the intersection. 

 

5

 Source: Synchro6 Help. 
48 

[ ]

CL

Max grn CL v s L

ICU i

∑ i + i

=

(min , *( / )

Equation 19

Where: 

ICU = Intersection capacity utilization ratio. 

Mingrn = Minimum green time for critical movement “i” (secs). 

CL = Reference cycle length (secs). Set to 120 seconds. 

L = Loss time for critical movement “i” (secs). 

The ICU is converted into a letter LOS grade according to the following table. 

Table 13. ICU LOS Thresholds. 

LOS Maximum ICU 

A 55% 

B 64% 

C 73% 

D 82% 

E 91% 

F 100% 

G 109% 

H over 109% 

Computation of Volume Capacity Ratio 

Synchro provides volume/capacity ratios calculated using HCM saturation flow rates for 

signalized intersections, and unsignalized intersections (including two-way stop, all-way 

stop, and roundabouts). 

Synchro provides volume/capacity ratios at intersections for each lane group. Capacity is 

computed using the HCM saturation flow computation method. 

For the intersection as a whole, Synchro reports the maximum lane group v/c ratio for the 

intersection. A special HCM report provides the critical v/c (x) that is computed 

according to the HCM. 

Computation of Speed and Travel Time 

Synchro computes the arterial travel time according to the HCM method. 
49 

Computation of Delay 

Synchro computes control delay according to the HCM method for signalized 

intersections, two-way stops, and all-way stops. Control delay is not computed for 

roundabouts in accordance with the HCM. 

For signalized intersections Synchro also provides an alternative computation of delay 

based upon an assumed distribution of traffic volumes for the intersection. This is called 

the Percentile Delay to distinguish it from the HCM control delay. 

The percentile delay computation uses only the first and third delay terms of the HCM. 

The incremental delay and the progression factor for the uniform delay in the HCM 

method are dropped. The incremental delay term of the HCM is modified for X>1 to use 

the saturation flow rate. The fourth term, queue delay, is added to cover delays due 

queue blockages resulting in unused green time. 

1 3 4 d = d + d + d Equation 20

[1 -(g/C)* X]

C [1 -(g/C)] d =

2

∗

∗

2 1 if X<= 1.00 Equation 21

2 3600

*

1

∗

s C ∗ [1-(g/C)] d =

2

 if X> 1.00 Equation 22

Where: 

D = Delay (sec/veh). 

d1 = Uniform delay (sec/veh). 

d3 = Initial queue delay (sec/veh). 

d4 = Queue interaction term (sec/veh). 

C = Cycle length (secs). 

S = Saturation flow rate (veh/hr-green). 

G = Effective green time for the lane group (secs). 

X = Volume/capacity ratio for the subject lane group. 

Synchro introduces the random component of delay (that, in the HCM, was contained in 

the incremental delay term) by computing the uniform delay term using a series of 5 

volume scenarios. Uniform delay (d1) is computed for the 90th percentile volume, the 70th

percentile volume, the 50th percentile volume, the 30th percentile volume, and the 10th

percentile volume. The 5 delay estimates are averaged to obtain the average delay. 

If the traffic signal is traffic actuated, then the green times also are computed for each 

percentile volume when computing the percentile delay. 

The percentile volumes are computed assuming a Poisson arrival pattern with mean equal 

to the input traffic volume for the lane group divided by the number of cycles per hour. 
50 

[ ] C

v p = v z p v C 3600 ( ) + ( ) * ( * / 3600) * Equation 23

Where: 

v(p) = Percentile volume for percentile “p” (veh/hr). 

V = Lane group volume (veh/hr). 

z(p) = Number of standard deviations for percentile “p” for Poisson Distribution. 

 = -1.28 for 10%. 

 = -0.52 for 30%. 

 = 0 for 50%. 

 = +0.52 for 70%. 

 = +1.28 for 90%. 

C = Cycle length (secs). 

The initial queue delay (d3) is the same as the HCM term. 

The queue interaction term (d4) takes into the account blockages and demand starvation 

resulting from queues that extend longer than the available distance between 

intersections. 

If Min[CapDist, VolDist) > Distance, Equation 24

Then queue interaction is likely to occur. 

Where: 

Distance = Available distance between intersections to store queues (ft). 

CapDist = Distance required to store a flow at capacity. 

 = AVL * [2+g*s/(n*3,600)]. 

VolDist = Distance required to store the 90% volume. 

 = AVL*[2+v90*C/n*3,600)]. 

AVL = Average Vehicle Length (ft). 

G = Green time per cycle (secs). 

S = Saturation flow rate (veh/hr-green). 

N = Number of lanes. 

v90 = 90th percentile volume. 

C = Cycle length (secs). 

If the queue interaction is likely to occur then Synchro calculates the likely reduction in 

capacity flow rates at the downstream intersection for the 50 percentile and 90 percentile 

volumes. The queue interaction delay is then computed using the HCM incremental 

delay formula (d2) and the reduced capacities. 
51 

The storage blocking portion of the queue interaction calculation was recently disabled (as 

of Build 610) due to problems with its representation of short left turn pocket blockages. 

Computation of Queue 

For unsignalized intersections Synchro computes queue lengths according to the HCM 

method. 

For signalized intersections Synchro computes queues for the 50 percentile and 95 

percentile volumes. The 50 percentile queue is computed as follows. 

If the volume is less than capacity then the following formula is used. 

( ) { } N Fu

AVQ

s v p

R v p Q p = **

/ ( ) 1

1 * 6 * 1

3600

( ) ( ) ⎥

⎦

⎤ ⎢

⎣

⎡

− − + Equation 25

If the volume is equal to or greater than capacity then the following formula is used. 

[ ] N

C AVQ Q p = v p v p s g C

3600*

*

( ) ( ) + ( ( ) − * / ) * Equation 26

Where: 

Q(p) = Queue length for percentile “p” volume (ft). 

v(p) = Percentile “p” volume for lane group (veh/hr). 

R = Red time (secs). 

S = Saturation flow rate (veh/hr-green). 

AVQ = Average front-to-front spacing of vehicles in queue (ft). 

N = Number of lanes in lane group. 

Fu = Lane utilization factor. 

C = Cycle length (secs). 

PHF = Peak-hour factor. 

A special value of the 95 percentile volume is used to compute the 95 percentile queue. 

[ ] ⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡

+

v C

v = v Min PHF

(50) *

1.64* 3600 (95) (50) * ,0.90 * 1 Equation 27

If the upstream signal has the same cycle length as the subject intersection, then the 95 

percentile volume on the subject approach is compared to the maximum volume that can 

be delivered by the upstream signal and reduced accordingly for the “metering” effect of 

the upstream signal. 
52 

Computation of Stops 

Synchro computes the number of stops for a signalized intersection approach as equal to 

the number of vehicles queued multiplied by a stopping rate percentage obtained from 

Transyt 7-F (see table below). 

Table 14. Conversion of Delay to Stops. 

Delay (Secs) Percent Stopping 

0 0% 

1 20% 

2 58% 

3 67% 

4 77% 

5 84% 

6 91% 

7 94% 

8 97% 

9 99% 

10 secs or greater 100% 

Computation of Density 

Synchro does not compute density. 

Computation of Travel-Time Variance 

Synchro does not compute travel-time variance. 

4.1.3 Dynasmart-P 

This section discusses Dynasmart-P Version 1.0, August 2004.6

 

6

 Hani S. Mahmassani, Hayssam Sbayti, and Xuesong Zhou, Dynasmart-P Version 1.0 User’s Guide, 

Federal Highway Administration, Office of Operations Research and Development, McLean, 

Virginia, August 2004. 
53 

Overview of MOEs

Dynasmart-P outputs overall vehicle statistics for the network, including trip times, travel 

times, stop times, entry queues, and travel distances. Also, the following link-specific 

statistics are provided averaged over all simulation intervals. 

1. Number of generated vehicles; 

2. Number of vehicles; 

3. Vehicle queue length (number of vehicles in queue); 

4. Link speed (average mph); 

5. Link density (average pc/lane-mile); 

6. Mean Speed of moving vehicles; and 

7. Mean PC Density of moving vehicles. 

Dynasmart also can output individual vehicle trajectories enabling the user to use 

ACCESS or to write a program to compute their own custom measures of effectiveness. 

Computation of HCM Level of Service 

Dynasmart-P does not output HCM level of service. 

Computation of Volume Capacity Ratio 

Dynasmart-P does not output link volume/capacity ratios. However, the analyst can 

compute the v/c ratios by dividing the output link volumes by the input link capacities. 

Computation of Speed and Travel Time 

Dynasmart-P outputs system total trip distance (VMT), trip time (VHT), network entry 

delay (entry queue time), stop time, and average speed. Dynasmart-P also outputs linkspecific mean speeds and mean speeds outside of queues. 

The total trip distance is the sum of the vehicle volume on each link multiplied by the link 

length. Note that Dynasmart requires that all links be longer than the distance that can be 

traveled in one simulation interval (six seconds) at the free-flow speed. The software will 

increase the coded link length as necessary to meet this requirement. Thus some minor 

distortions of the distance traveled may occur for shorter links that fail to meet this 

requirement. 

The total trip time is the sum of the vehicle volume on each link divided by the mean link 

speed plus the entry delay time for vehicles denied entry to the network. 
54 

The system average speed is the system distance traveled divided by the system trip time 

(inclusive of delays to vehicles denied entry to the network). 

The link-specific mean speed is the simple arithmetic average of the spot speeds of all 

vehicles on the link at the end of each six-second simulation interval. The user can 

average the spot speeds over multiple simulation intervals to obtain an hourly average for 

each link. The variance of the mean speeds can be computed manually by reporting the 

mean speed for each simulation interval and porting the file into a spreadsheet. 

The mean speed of vehicles on a link is estimated by Dynasmart-P using a modified 

Greenshields equation. A two-regime equation is used for freeways (shown below). A 

single regime equation is used for arterials. 

( ) ( ) ⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = + − − −

jam

i

i f k

k

v v0 Min FFS v0 , v v0 1 Equation 28

Where: 

Vi = Mean speed (mph). 

V0 = Minimum mean speed (mph). 

FFS = Free-flow speed (mph). 

Vf = Hypothetical speed at y-intercept of Greenshields equation. 

Ki = Mean density (pc/lane-mile). 

Kjam = Theoretical jam density (pc/lane-mile). 

The single regime equation uses the same equation above with the hypothetical 

y-intercept speed (vf) set equal to the actual free-flow speed (FFS). 

Computation of Delay 

Dynasmart-P does not output delay statistics, however; the analyst can compute the delay 

for individual links using the link data output by Dynasmart-P. The individual link 

delays can be summed to system totals in a spreadsheet. 

Computation of Queue 

The method used by Dynasmart-P to identify queued vehicles on the network is not 

described in the Users Guide.7

 

7

 This is not documented in User Guide. Response from the developer regarding their definition of 

queues not received in time for publication. 
55 

Computation of Stops 

Dynasmart-P tallies stop time for vehicles at a complete stop (zero speed). 

Computation of Density 

Dynasmart-P reports the average equivalent passenger car density by link (pc/mile/lane) 

averaged over as many six-second simulation intervals as desired by the analyst. A 

separate density also can be reported for the queue-free portion of each link. 

Trucks are converted to passenger car equivalents using the PCE values for each link 

input by the analyst. 

The extra storage space available on links with left and/or right turn bays is not included 

in the denominator of the density computation. However vehicles in these bays are 

included in the numerator. 

The impacts of partial lane closures on the available storage space due to work zones and 

incidents are not included in the density computation. 

Note that the density computation may be distorted slightly for short links with lengths 

less than FFS*528/60 (where FFS is the free-flow speed). Dynasmart-P will increase the 

lengths of the short links to equal the minimum length. 

Computation of Travel-Time Variance 

Dynasmart-P does not output variance statistics. Vehicle trajectories are output by 

Dynasmart-P which would enable an analyst working with database or similar highcapacity data processing software to compute variances. 

However, the finest level of disaggregation available in Dynasmart is the link and sixsecond-long simulation intervals. Acceleration or deceleration within the link are not 

treated. Variation of vehicle speeds around the link mean speed is not included in the 

model. Thus the variances computed from the vehicle trajectories would not be expected 

to be as large as would be observed in the field. 

4.1.4 SIMTRAFFIC 

This section describes the formulae used by SimTraffic 6 (Build 614), released 2005 by 

Trafficware of Sugarland, Texas.8

 SimTraffic is based on CORSIM. As such, it is designed 

to evaluate the performance of freeways, urban streets, signalized intersections, and 

unsignalized intersections (two-way stop, all-way stop, and single-lane roundabouts). 

SimTraffic does not model passing in opposing flow lanes, and thus is not designed to 

evaluate two-lane rural roads. 

 

8

 Source: SimTraffic 6 Help. 
56 

Computation of HCM Level of Service 

SimTraffic does not output levels of service. 

Computation of Volume Capacity Ratio 

SimTraffic does not compute capacity or volume/capacity ratios. 

Computation of Speed and Travel Time 

SimTraffic outputs travel time and average speed by turn movement, for arterials, and for 

the entire network. 

The Travel Time (VHT) is the total time each vehicle was present on the link. The travel 

time includes time spent by vehicles Denied Entry (waiting to enter the network), but does 

not include the spent by vehicles on the upstream link waiting to enter the subject link. 

= ∑ +∑

i i

VHT t(i) w(i) Equation 29

Where: 

VHT = Total vehicle hours traveled on link or spent in zone waiting to access link (hr). 

t(i) = Time spent by vehicle “i” on link (hr). 

w(i) = Time spent by vehicle “i” in zone waiting to enter link (hr). 

The Average Speed is calculated by dividing Total Distance (VMT) by Total Time spent on 

the network. The time used in the calculation of Average Speed does not include time 

spent by denied entry vehicles while they are waiting to enter the network. Average 

speed may therefore be higher than Total Distance (VMT) divided by Total Time (VHT) 

for the link. 

VHT VHT(w)

VMT AveSpd − = Equation 30

Where: 

AveSpd = Average link speed (mph). 

VMT = Total vehicle miles traveled on link (mi). 

VHT = Total vehicle hours traveled on link or spent in zone waiting to access link. 

VHT(w) = Total vehicle hours spent by vehicles in zone waiting to access link. 

The Total Distance (VMT) is the total distance traveled by all vehicles on the link. Vehicles 

waiting to enter the link do not travel any distance on the link, so they have no impact on 

the VMT computation. 

= ∑

i

VMT d(i) Equation 31
57 

Where: 

VMT = Total vehicle miles traveled on link (mi). 

d(i) = Distance traveled by vehicle “i” on link (mi). 

The Arterial Report is a special report that reports delay, travel time (VHT), distance 

(VMT), and speed for a select subset of vehicles on a select subset of links. It uses origin 

destination data to only count vehicles on the current link that came from the arterial on 

the next upstream link. This is not the same as taking the travel time of only those 

vehicles that travel the entire length of the arterial. 

Computation of Delay 

SimTraffic outputs total delay, delay/vehicle, and stopped delay by turn movement, for 

arterials, and for the entire network. 

Total Delay is defined as the travel time (VHT) minus the time it would take the vehicle if 

traveling at the maximum permitted speed (the speed limit or the maximum safe turning 

speed, whichever is lesser). The delay accrued by vehicles denied entry to the network 

during each 1/10 second time slice is added to this total. 

∑∑ +∑ − = t i i

NDE t

spd l r

spd l r spd i t TD 0.1* ( ) max( , )max( , ) ( , ) 0.1* Equation 32

Where: 

TD = Total vehicle-delay (hr). 

spdmax = Maximum speed on link “l” for turn “r.”

 = Link speed limit except when approaching turn, during turn, and accelerating 

out of turn. 

spd = Actual vehicle speed for vehicle “i” at time slice “t.” Each time slice = 1/10 of 

second. 

NDE = Number of vehicles denied entry during time slice “t.”

Delay per Vehicle is the total delay divided by the Number of Vehicles. 

The Number of Vehicles is defined as the number of vehicles that exited the link during 

time slice “t”: plus one-half of those vehicles remaining on the link at the end of time slice 

“t” minus one-half of those vehicles that were present anywhere on the link at the start of 

time slice “t.” The assumption is that vehicles that were present at the start of the time 

slice and vehicles that were present at the end of the time slice contributed delay for half 

the time as for vehicles both entering and exiting the link during the time slice. 

NV = N(x) − 0.5N(s) + 0.5N(e) Equation 33
58 

Where: 

NV = Number of vehicles. 

N(x) = Number of vehicles exiting the link during time period. 

N(s) = Number of vehicles present on link at start of time period. 

N(e) = Number of vehicles present on link at end of time period. 

Spd = Actual vehicle speed for vehicle “i” at time slice “t.” Each time slice = 1/10 of 

second. 

NDE = Number of vehicles denied entry during time slice “t.”

Note that this formula results in zero vehicles if the link is totally jammed with vehicles 

during the time period with nobody entering or exiting during the time period. 

The Stopped Delay is the sum over all time slices of the number of vehicles traveling at 

less than 10 ft/s (3 m/s) times the length of the time slices (1/10 of one second). The sum 

of the number of denied entry vehicles each time slice times the length of the time slices is 

added to the stopped delay. 

Stop Delay/Vehicles is equal to the Stopped Delay divided by the Number of Vehicles. 

Computation of Queue 

SimTraffic outputs Maximum queue (ft), Average queue (ft), and 95 Percentile queue 

(feet) by lane by turn movement. SimTraffic also outputs the Upstream Block Time, 

Storage Block Time, and the queuing penalty. 

The maximum queue may be less than the 95 percentile queue because the Maximum 

queue is observed during the simulation run and the 95 percentile queue is statistically 

estimated from the mean queue and the standard deviation. 

A vehicle is considered queued when it is traveling at less than 10 ft/s (3 m/s) and either 

at a stop bar or following another queued vehicle. Thus, single vehicle queues are not 

possible except at intersection stop bars. 

All queues in SimTraffic originate at the intersection. Queues cannot originate mid-link. 

For queue length computations SimTraffic uses a front-of-vehicle to front-of-vehicle length 

of 19.5 feet (6 meters). 

The Maximum Queue (feet) is the maximum observed distance from the stop bar to the 

back of queue over the analysis period. Note that Maximum Queue is limited by the 

length of the link. 

Maximum queues are recorded every two minutes during the simulation. 

The Average Queue is the average of the observed maximum queues for each of those 

two-minute periods. 
59 

The standard deviation of the maximum queue is computed for all of the two-minute 

maximum queues recorded during the simulation. 

The 95 Percentile queue is computed as 1.65 standard deviations above the average queue. 

Thus the 95 Percentile Queue can be greater than the Maximum Queue. Also, the 

computed 95 Percentile Queue is NOT limited by the length of the link. 

SimTraffic will exclude from the queuing statistics most vehicles that stop for a lane 

change (depending on the circumstances). 

The Upstream Block Time is the percent of time that the upstream end of the lane in a link 

is blocked. 

The Queuing Penalty is the traffic volume in the lane times the percent of time during the 

simulation period that the lane is blocked. For a storage bay the turning volume in the 

adjacent through lane may be added to the Queuing Penalty volume if the through lane 

back up is preventing vehicles from accessing the turn bay. 

The Storage Bay Block Time is the sum of the percent of time that the storage bay is full 

and the percent of time that queues in the adjacent through lane exceed the length of the 

turn bay. 

Links may have bend nodes in them. The queue for the intersection at the end of the link 

includes the queue for the portion of the link upstream of the bend node. A separate 

column in the report reports the queue upstream of the bend node. 

Computation of Stops 

SimTraffic outputs total stops and stops per vehicle by turn movement. 

The Total Stops is the count of vehicle stops. A stop is added to the tally every time a 

vehicle’s speed drops below 10 ft/s (3 m/s). A stopped vehicle speed must reach 15 ft/s 

(4.5 m/s) again before it can generate any additional stops. That way vehicles moving up 

in a queue do not generate multiple stops. 

Stops/Vehicles is calculated by dividing the number of Stops by the Number of Vehicles 

(See the delay description for the formula used to compute number of vehicles). 

Computation of Density 

SimTraffic does not report vehicle density. 

Computation of Travel Time Variance 

SimTraffic does not report variance of travel time, but it does provide for the execution 

and reporting of multiple runs. The standard deviation of the maximum 2-minute queues 

observed are computed (and used in the 95 percent queue calculation) but the value of the 

standard deviation is not reported. 
60 

4.1.5 CORSIM 

This section presents the CORSIM 5.1 software based on the information contained in the 

CORSIM 5.1 Users Guide by ITT Industries, February 2003. 

For freeways CORSIM 5.1 outputs: Vehicles in, Vehicles out, lane changes, VMT, VHT, 

VHT/veh, Move Time/veh, Delay/veh, Volume, Density, and Speed on a link by link 

basis. 

For arterials streets CORSIM outputs queue time, stop time, percent storage, phase failure, 

average queue by lane, maximum queue by lane, and lane changes on a link-by-link basis. 

VMT, vehicle-trips, speed, percent stops, move time, delay time, and total time (VHT) are 

reported on a turn movement basis. 

Computation of HCM Level of Service 

CORSIM does not output HCM Level of Service. A CORSIM postprocessor within 

another software tool, Transyt-7F can be used to derive HCM level of service. 

Computation of Volume Capacity Ratio 

CORSIM does not compute capacity and therefore does not output v/c ratio. The analyst 

would have to run CORSIM with increasingly higher flows until capacity is reached and 

count the sustained average discharge rate at the bottleneck to obtain capacity. Once the 

capacity is obtained, then the v/c ratio can be computed for the actual volume. 

Computation of Speed and Travel Time 

CORSIM outputs Total Time, Move Time, Delay Time for each link. It outputs total time 

per vehicle. It also outputs various speed statistics by link: Total Time Rate 

(minutes/mile), Delay Time Rate (minutes/mile) and Average Speed. 

Total Time (VHT) is the total time on the link for all vehicles. It therefore does not include 

denied entry vehicle delay. 

VHT(i) N(i,t) *TS / 60 t= ∑ Equation 34

( , )

60* ( ) ( ) N e iVHT i TTPV i = Equation 35

Where: 

VHT = Total vehicle-hours traveled on link “i” (veh-min). 

TTPV = Total time per vehicle (sec/veh). 

TS = Duration of time slice, 1.0 seconds in CORSIM. 

N(i,t) = Number of vehicles present on link “i” at time slice “t.”

N(e,i) = Number of vehicle exiting the link during simulation period. 
61 

The Total Time per Vehicle is computed by dividing the total time by the number of 

vehicles exiting the link during the simulation period. Note that vehicles that fail to exit 

the link during the simulation period will generate total travel time but will not be 

counted in the number of vehicles exiting the link, thus tending to increase the computed 

total time per vehicle. 

The Average Speed is the ratio of VMT over VHT. VMT is computed by multiplying the 

number of vehicles exiting the link during the simulation period by the length of the link. 

Note that it excludes the delay to vehicles denied entry to the network. 

( )

60* ( ) ( ) VHT iVMT i SMS i = Equation 36

VMT(i) = N(e,i) * L(i) Equation 37

Where: 

SMS = Mean speed of vehicles on link “i” (mph). 

VHT = Total vehicle-hours traveled on link “i” (veh-min). 

VMT = Total vehicle-miles traveled on link “i” (veh-mi). 

L = Length of link “i” (mi). 

N(e,i) = Number of vehicles exiting link “i.”

Note that this equation has the effect of shifting to the next simulation period all of the 

VMT generated by any vehicles that fail to leave the link during the current simulation 

period. 

The Total Time Rate (min/mi) is the inverse of the mean speed. 

Computation of Delay 

CORSIM divides Total Travel Time into two additive components: Move Time and Delay 

Time. Move Time is the theoretical time it would take to traverse a link at the free-flow 

speed (even if the vehicle driver doesn’t really want to go at that speed, some may want to 

go slower, some may want to go faster). The Total Travel Time is the amount of time it 

actually takes to traverse a link. The Delay Time is then the difference between the 

observed Total Travel Time and the theoretical Move Time at the free-flow speed. 

CORSIM also outputs the Delay Time Rate in terms of minutes of delay per mile traveled. 

Move Time is the travel time if all travel was done at the link free-flow speed. It is 

computed in CORSIM by multiplying the Total Time measured by the ratio of the freeflow speed to the average speed for the link. 

( )

( ) ( ) ( ) *

FFS i

SMS i MT i = VHT i Equation 38
62 

Where: 

MT = Move Time at free-flow speed on link “i” (veh-min). 

VHT = Total vehicle-hours traveled on link “i” (veh-min). 

SMS = Mean speed of vehicles on link “i” (mph). 

FFS = Free-flow speed on link “i”(mph). 

Delay Time is the difference between the total travel time and the Move Time. It does 

NOT include delay time for vehicles denied entry to the network. 

DT(i) = VHT(i) − MT(i) Equation 39

Where: 

DT = Delay time on link “i” (veh-min). 

VHT = Total vehicle-hours traveled on link “i” (veh-min). 

MT = Move Time at free-flow speed on link “i” (veh-min). 

The Delay Time per Vehicle is the Delay Time divided by the number of vehicles exiting 

the link during the simulation period. 

( , )

( ) ( ) 60*

N e i

DT i DTV i = Equation 40

Where: 

DTV = Delay time per vehicle on link “i” (secs). 

DT = Delay time on link “i” (veh-min). 

N(e,i) = Number of vehicles exiting link “i” during simulation period. 

Note that vehicles denied entry to the link are not included in the delay. Note also that 

vehicles failing to exit the link during the simulation period generate delay that is 

included in the numerator but the number of these vehicles is not included in the 

denominator. 

The Delay Time Rate (min/mi) is the delay time divided by the VMT. 

( )

( ) ( ) VMT iDT i DTR i = Equation 41

Where: 

DTR = Delay time rate on link “i” (min/mi). 

DT = Delay time on link “i” (veh-min). 

VMT = Total vehicle-miles traveled on link “i” (veh-mi). 
63 

Note that the Delay Time Rate includes all vehicles on the link that generate delay during 

the current time period in the numerator but may or may not exclude any vehicles failing 

to exit the link by the end of the current time period from the denominator.9

 Vehicles 

denied entry to the network or the link are NOT included in the delay. 

For non-freeway links CORSIM also identifies two overlapping (and therefore not 

additive) components of Delay Time. They are the Queue Time and the Stop Time. A 

vehicle is in a queue all of the time if it is moving below 3 feet per second (with an 

acceleration rate of less than 2 fpss), and half of the time if it is moving between 3 and 9 

feet per second. The vehicle is “stopped” if its speed is less than 3 feet per second. 

The Queue Time and the Stop Time are not additive since they overlap. Stop time is a 

subset of Queue Time. 

The mean queue time and the mean stop time are computed by dividing the total queue 

time or stop time by the sum of the total number of vehicles exiting the link plus the 

number of vehicles remaining on the link at the end of the simulation period.10

Version 5 of TSIS/CORSIM now computes “control delay” for signalized links. Control 

delay is defined in CORSIM as the difference between the actual travel time through the 

intersection and the theoretical travel time if the vehicle had been able to continue at its 

original “operating” speed. Operating speed is defined as the speed of the vehicle prior to 

the start of deceleration for the signal. Operating speed is lower than the free-flow speed 

for the link, since it takes into account mid-block congestion effects, unrelated to any 

signals. The influence area of the signal is defined to start when the vehicle begins to 

decelerate on a link with a downstream signal and end when the vehicle ceases to 

accelerate on a link with an upstream signal.11

Note that Control Delay will therefore exclude delays that occur on links upstream of the 

link with the traffic signal. 

Computation of Queue 

CORSIM reports density but does not report queues for freeway or ramp links. 

For all other links CORSIM defines a vehicle as being in a queue when it is moving at less 

than 3 fps and is accelerating/decelerating by less than 2 fpss. If a vehicle is moving at a 

 

9

 The Users Guide does not explicitly state how measures of effectiveness for vehicles failing to exit 

the link during the simulation period are accumulated, if at all. 

10 Source for this statement is footnote to CUMULATIVE NETSIM STATISTICS table in CORSIM 

output. 

11 There is some dispute among CORSIM experts as to whether this is indeed an accurate 

description of how CORSIM actually computes control delay. This description is taken from the 

users guide, but has not been independently verified by this author. 
64 

speed of between 3 fps and 9 fps it is considered to be in a queue 50 percent of the time 

(every other second). The “maximum queue” is the largest number of vehicles observed 

to be queued in a lane during the simulation period. The “mean queue” is the mean 

number of queued vehicles over the simulation time period with “zero” queues included 

in the average. 

The percent of link storage use is the mean number of vehicles in queue times the mean 

distance between vehicles divided by the link length times the number of lanes. 

( ) * ( )

( ) *6*

% ( ) NL i L i

AveQ i AVL Storage i = Equation 42

Where: 

%Storage = Percent of available link storage used. 

AveQ = Mean queue length on link “i.”

AVL = Average vehicle length (ft). 

NL = Number of Lanes, including turn bays. 

L = Length of link. 

Phase Failure is the number of cycles during the simulation period that the queue fails to 

clear a traffic signal. Phase Failure is NOT computed for freeway or ramp links. 

Note that the reported queue cannot exceed the length of the link. The percent storage 

CANNOT exceed 100 percent. 

Computation of Stops 

CORSIM does NOT compute stops for freeway and ramp links. For all other links a 

vehicle is recorded as “stopped” if its speed is less than three feet per second. 

Computation of Density 

CORSIM does not provide density for non-freeway links. 

For freeway links density is the average content of the link during the period divided by 

the length of the link times the number of lanes. 
65 

The average content of the link is the cumulative sum of the vehicle-seconds travel time 

accumulated on the link divided by the cumulative number of seconds in the simulation 

period.12

( ) * ( )

( ) * 1

( , ) L i NL i

N t TS

T Density i T t

∑ = Equation 43

Where: 

Density = Mean density of link “i” over time period “T” (veh/lane-mi). 

T = Length of time period (secs). 

N = Number of vehicles on link at time “t.”

TS = Length of time step (= 1.0 secs in CORSIM). 

NL = Number of Lanes, including turn bays. 

L = Length of link. 

Computation of Travel-Time Variance 

CORSIM does not compute the variance in travel time. The model can be run several 

times with different random number seeds to obtain a variance. A TSIS output processor 

can be used to make the computations from the runs. However, the computed variance 

would not include the impacts of variation in demand or capacity reducing incidents on 

the facility. 

4.1.6 Vissim 

This section describes how Vissim computes its measures of effectiveness based upon its 

Users Guide13 and additional information from the vendor. 

MOEs can be collected by Vissim at detector locations or for selected series of links. MOEs 

also can be gathered for paths. 

The link evaluations can produce: 

• Density (veh/km); 

• Speed; 

 

12 It is not clear whether the accumulated VHT at the end of the period is used rather than a running 

total of vehicle-seconds on the link to compute density. We have chosen to show the formula 

assuming that CORSIM uses the sum of the running total of vehicle-seconds. 

13 Vissim 4.1 User Manual, PTV, Planung Transport Verkehr AG, Karlsruhe, Germany, March 2005. 
66 

• Volume; and 

• Lost Time (Delay). 

Node Analysis can produce: 

• Volume; 

• Average Delay; 

• Standing Time; 

• Number of Stops; and 

• Queue Length. 

The Network Performance Evaluation will produce the following systemwide MOEs: 

• Number of Vehicles Left in Network; 

• Number of Vehicles Exiting Network; 

• Total Path Distance (VMT); 

• Total Travel Time (VHT); 

• Average Speed (mph); 

• Total Delay (hrs); 

• Stopped Delay (hrs); 

• Average Stopped Delay; 

• Number of Stops; and 

• Average Number of Stops. 

Computation of HCM Level of Service 

Vissim does not directly output HCM Level of Service. 

Computation of Volume Capacity Ratio 

Vissim does not directly output volume/capacity ratios. 

Computation of Speed and Travel Time 

The computation of speed (in the link segment evaluation) follows the general space-time 

definition of average speed: 

∑

∑

=

i

i

tt i

xx i

v

( )

( )

Equation 44
67 

Where: 

v = Average speed in the link segment during the analysis period. 

xx(i) = Total distance traveled by vehicle i in the link segment during the analysis period. 

tt(i) = Total time spent by vehicle i in the link segment during the analysis period. 

Average speed in the network performance evaluation is defined the same way (total 

miles traveled divided by total hours spent). 

Vissim outputs travel-time results between selected points on the network. The average is 

computed from the actual travel times of all vehicles that have passed the destination 

point after they have passed the start point. Vehicles that have not reached the destination 

point and denied entry vehicles are not included in the travel-time results. 

Computation of Delay 

Vissim defines delay as the difference between the actual vehicle travel time and its 

desired travel time. Note that this is a microscopic definition of delay that is specific to 

each vehicle. This is different than the other software packages that compare actual travel 

times to an average link free-flow travel time. 

The average total delay per vehicle is computed only for the vehicles completing the 

travel-time measurement section. The delay is computed for each vehicle by subtracting 

the ideal travel time from the actual travel time. The ideal travel time is computed 

assuming no other vehicles on the network and no delays at signal controls or stop signs. 

Reduced speeds for turns are taken into account in the ideal travel time. 

Computation of Queue 

Vissim allows the user to define a queue according to the maximum vehicle speed for the 

beginning of the queue (default is 5 km/hr), the minimum vehicle speed at its end (default 

is 10 km/hr), and the maximum spacing between vehicles (default is 20 meters). 

Vissim allows the user to identify a queue counter location. The distance to the farthest 

upstream point of any queue at this location is tallied. If the queue backs up onto multiple 

links, the longest distance is recorded. If the front of the queue begins to discharge Vissim 

keeps tracking to the back of the queue until no queued vehicles remain between the 

queue counter location and the current back of queue. 

I

Q i

AVEQ i

∑

=

( )

Equation 45

Where: 

AVEQ = Average Back Of Queue over analysis period (meters). 

Q(i) = Observed Back of Queue length (meters) at end of time step (i). 

I = Total number of time steps in analysis period. 
68 

Incidentally, if the queue extends from one queue counter location back to another, the 

queue length is truncated to that distance. 

Vissim also will report the number of stops within the queue (which are defined as 

situations when the vehicle enters the queue, i.e., when its speed falls below the speed for 

the beginning of the queue). 

Computation of Stops 

Stops in the delay and node evaluation are defined as situations when the vehicle’s speed 

falls to zero. 

Computation of Density 

The computation of density (in the link segment evaluation) follows the general spacetime definition of traffic density: 

T X

tt i

k i

⋅ =

∑ ( )

Equation 46

Where: 

k = Average density in the link segment during the analysis period. 

tt(i) = Total time spent by vehicle i in the link segment during the analysis period. 

T = Duration of the analysis period. 

X = Length of the link segment. 

Computation of Travel-Time Variance 

Vissim does not directly output travel-time variance information, however; the software 

provides a raw data file with all single travel times of the individual vehicles. These can 

be used to determine the variance. 

4.1.7 Paramics 

This section discusses the MOEs produced by Quadstone Paramics Version 5.0 (2004).14

Paramics produces systemwide and link-specific MOEs. 

 

14 Quadstone Paramics V5.0 Modeler Reference Manual, Quadstone Limited, Edinburgh, Scotland, 

August 2004. 
69 

Systemwide MOEs (Economic Info) that are reported for every minute of the simulation 

include: 

• Number of Vehicles currently on network; 

• Mean speed of vehicles currently on network at specific point in time; 

• Cumulative number of vehicles simulated on network; 

• Accumulated Total Travel Time (VHT) since start of simulation; 

• Accumulated Total Distance Traveled (VMT) since start of simulation; 

• Mean Speed of all vehicles on network since start of simulation; 

• Total number of stops made by public transit vehicles; 

• Total time stopped for public transit; 

• Passengers Boarding; and 

• Passengers Alighting. 

Paramics also outputs performance measures by trip (from origin to destination) and for 

public transit vehicles. 

The Analyzer software of Paramics in addition outputs these link-specific MOEs: 

• Turn counts/flows/proportions; 

• Link counts/flows; 

• Link density; 

• Percentage time delay; 

• Link delay; 

• Link speed; 

• Link stop times; 

• Link LOS; 

• Queues Max/Average; 

• Blocking; 

• OD Data; 

• Journey times; 

• User-defined performance criteria; and 

• LOS, MOEs, intersection LOS. 
70 

Computation of HCM Level of Service 

The Paramics Analyzer does output direct level of service grades for individual links. 

These LOS grades are based upon the threshold numbers given in the HCM.15

Vehicle densities are converted to passenger car units using PCEs provided by the analyst. 

The PCU densities are then used to look up the HCM LOS grade using analyst editable 

look-up tables. 

The Paramics Analyzer can compute Percent Time Delay for use in estimating LOS for 

two-lane rural roads. However, since Paramics does not simulate passing maneuvers 

using opposing lanes, it would be most reliable for 2-lane roads with 100 percent 

no-passing zones. 

The Node LOS is the flow weighted average of the link approach LOSs. The flow weights 

are the number of vehicles exiting each link approach to the node during the analysis 

period. Note that PCUs are vehicles multiplied by their appropriate PCE. The PCEs are 

fixed equivalencies input by the analyst by vehicle-type. 

Computation of Volume Capacity Ratio 

Paramics does not directly output volume/capacity ratios. 

Computation of Speed and Travel Time 

Paramics outputs system-level VMT, VHT, and mean speed accumulated by one-minute 

periods throughout the simulation period. These results exclude delays incurred by 

vehicles denied entry to the network during any part of the simulation period. 

Link speed is equal to the link length divided by the actual travel time = VMT/VHT. The 

delays incurred by vehicles denied entry to the link during any part of the simulation 

period are excluded from this computation. 

Link VMT and VHT are not directly output by Paramics. The link level outputs of flow 

and speed can be combined with the link length inputs to obtain VMT and VHT by link. 

Mean link travel times are not directly output by Paramics but can be computed from the 

link level outputs of flow, speed, and link lengths. 

Computation of Delay 

Paramics computes delay as the difference between the cumulative travel time of all 

vehicles present on a link at some point within each one-minute time period, and the 

 

15 Quadstone Paramics V5.0, Analyser Reference Manual, Quadstone Limited, Version No. 1.0, July 21, 

2004. 
71 

theoretical travel time for those vehicles if they had traveled at the user coded free-flow 

speed for the link. Paramics does not break down the delay into different components. 

Link delay is the difference between the actual travel time and the ideal travel time. 

( ) = ∑ − v Min FFS l MXS v

L l d l i T v l i

( ), ( )

( ) ( , ) ( , , ) Equation 47

Where: 

d(l) = Total Delay for link (l) (veh-hrs) during interval (i). 

T(v,l,i) = Actual travel time for vehicle (v) exiting link (l) during interval (i)(hrs). 

FFS (l) = Free-flow speed for link (l) (mph). 

MXS(v) = Maximum speed for vehicle (v) (mph). 

L(l) = Length of link (l) (miles). 

Average link stop time is the mean time spent stopped for vehicles exiting the link during 

the analysis period. 

Computation of Queue 

Paramics allows a user to select their own definition of a queue according to the maximum 

vehicle speed and a maximum distance between vehicles. The defaults are 2 meters per 

second (for speed), and 10 meters between vehicles. The minimum number of vehicles 

before Paramics records them as a queue also can be set by the analyst (page 14 Analyzer 

Manual). The queue is reported on a second by second basis in terms of number of 

vehicles and in terms of the starting distance and ending distance within the link. 

Paramics Analyzer reports the mean and maximum queue observed over the simulation 

time period (or a user specified time period). The queue must meet the minimum number 

of vehicles thresholds selected by the analyst to be identified as a queue and reported. 

I

Q i

AVEQ i

∑

=

( )

Equation 48

Where: 

AVEQ = Average Queue over analysis period. 

Q(i) = Observed Queue at end of time step (i). 

I = Total number of time steps in analysis period. 

At the end of each time step the vehicles in queue are converted to a queue length by 

multiplying each by its appropriate PCE and summing the lengths of the vehicles. The 

spacing in between vehicles is NOT included in the queue length (see page 21, Analyzer 

Users Manual). 
72 

Note that this mean queue will be different than that for signals, since this mean queue 

includes time steps when the signal is green as well as red, while the signal mean queue is 

limited to time steps when the signal is red. 

Note that the maximum queue cannot be longer than the length of the link. The Average 

Queue will be impacted by this limitation on the maximum queue.16

Computation of Stops 

Paramics does not report the number of stops on the link by non-public transit vehicles. 

Computation of Density 

Paramics computes the mean density in terms of passenger car units per lane-mile over an 

analysis period as the sum of the average density during the simulation period and the 

density of vehicles remaining at the end of the period. 

k = k() ( ) i + k r Equation 49

* ( )

( , ) * ( )

( ) NL L a

NV v i PCE v

k i v

∑

= Equation 50

L(a) = FFS * I Equation 51

Where: 

k = Reported link density for analysis period (pcu/lane-mile). 

k(i) = Computed link density over analysis period (i). 

k(r) = Computed density of vehicles remaining on link at end of analysis period. 

NV(v,i) = Cumulative number of vehicles of type (v) exiting the link during period (i). 

PCE(v) = Passenger car equivalence of vehicle type (v). 

NL = Number of lanes on link. 

L(a) = Available distance on link (mile-hours). 

FFS = Free-flow speed on link (mph). 

I = Duration of analysis period (hrs). 

The density for the remaining vehicles is computed using the same formula as for 

computing the link density during the analysis period, substituting the PCUs remaining 

for the PCUs accumulated during the analysis period in the formula. 

 

16 The SIAS version of Paramics does have a queue tracker that will track a queue back over several 

links. 
73 

The analyst can exclude the “remainder” vehicles from the density calculation, if desired. 

Computation of Travel-Time Variance 

Paramics does not directly output travel-time variance information, however; the software 

can be executed multiple times to determine the variance of the mean travel time for each 

run. An analyzer program provided with Paramics can be used to compute the variance. 

4.1.8 Aimsun 

This discussion is based upon an excerpt of the Aimsun Users Guide currently under 

preparation that was advanced to Dowling Associates on January 13, 2006 by the 

developers of Aimsun (Dr. Jaume Barcelo and Pablo Barcelo). The equations shown 

below are quoted from the draft Aimsun Users Guide. 

Aimsun outputs flow, speed, density, travel time and delay measures of effectiveness for 

several different levels of aggregation: whole system, individual links (called sections in 

Aimsun), series of links, and turning movement. The MOEs can be output by trip origin 

and destination (OD). Public transport performance outputs also are provided. 

Regardless of level of aggregation the following outputs can be generated: 

• Mean Flow; 

• Density; 

• Mean Speed; 

• Harmonic Mean Speed; 

• Total Travel Distance (VMT); 

• Total Travel Time (VHT); 

• Mean Travel Time; 

• Mean Delay Time; 

• Stop Time; 

• Number of Stops; 

• Fuel Consumed; and 

• Pollutants Emitted. 

For individual links or series of links the above MOEs are provided plus the following 

additional outputs: 

• Mean Queue Length (veh/lane); and 

• Maximum Queue Length (veh/lane). 
74 

For OD trip information the general MOEs (listed above for all levels of aggregation) are 

produced plus the following additional outputs: 

• Lost Vehicles – “Total number of vehicles that have been lost while trying to do the 

trip from origin to destination, and which have therefore not reached the correct 

destination.”

The analyst also can customize the calculation of additional MOEs. Detectors also can be 

placed on the network to gather point MOEs. 

Computation of HCM Level of Service 

Aimsun does not directly output HCM level of service. 

Computation of Volume Capacity Ratio 

Aimsun does not directly output volume/capacity ratios. 

Computation of Speed and Travel Time 

For system-level MOEs Aimsun accumulates the following statistics for each vehicle in the 

network. 

The total system vehicle travel time (VHT) is computed as follows. Note that only 

vehicles able to exit the system during the simulation period generate system travel time. 

Vehicles remaining in the system are excluded from the total system travel-time 

computation. 

∑=

= −

Nsys

i

sys TEXi TENi TotalTravTime

1

Equation 52

Where: 

TENi = Entrance time of vehicle (i) in the system (seconds). 

TEXi = Exit time of vehicle (i) from the system (seconds). 

Nsys = Number of vehicles that exit the system during period I. 

The total travel distance (VMT) is computed as follows. Note that only vehicles able to 

exit the system during the simulation period generate system travel distance. Vehicles 

remaining in the system are excluded from the total system travel distance computation. 

∑

=

=

Nsys

i

TotalTravsys Di

1

1000

Equation 53
75 

Where: 

Di = Total Distance traveled by vehicle (i) in the system (meters).17

Nsys = Number of vehicles that exit the system during period I. 

Two averages are computed for mean system speed. One is the arithmetic average. The 

other is the harmonic mean. 

*3.6 1

sys

N

i

i

sys N

S

S

sys

∑= = Equation 54

*3.6

1/

1

∑=

= Nsys

i

i

sys

sys

S

N

HS Equation 55

Where: 

Ssys = Arithmetic Average Speed per vehicle (Km/h). 

HSsys = Harmonic mean Speed per vehicle (Km/h). 

Nsys = Number of vehicles that exit the system during period I. 

Si = Spot speed of vehicle (i) (Km/h). 

For individual links the total distance and time traveled are computed as follows. Note 

that vehicles failing to exit the link during the simulation period are not counted. 

∑=

=

( )

1

( ) ( , ) 1000

NX l

i

VMT l D i l Equation 56

∑=

= −

( )

1

( ) ( , ) ( , )

NX l

i

VHT l TEX i l TEN i l Equation 57

Where: 

VMT(l) = Total distance traveled by all the vehicles (i) exiting the link (l) (Km). 

VHT(l) = Total travel time experienced by all exiting vehicles (i) on link (l) (seconds). 

Di = Distance traveled by exiting vehicle (i) while on link (l). 

NX(l) = Number of vehicles exiting link (l) during simulation period. 

 

17 Although only metric are shown here, Aimsun accumulates all measures in the native unit system 

in which the network is coded, U.S. Customary or Metric units. 
76 

The arithmetic average vehicle speed for the link is computed as follows. 

*3.6

( )

( , ) ( , )

( ) ( )

( )

( )

1

NX l

TEX i l TEN i l

LS l LT l

S l

NX l

i

∑= −

+

=

Equation 58

S(l) = Arithmetic Average Speed per exiting vehicle of a link (Km/h). 

TEN(i,l) = Entrance time of vehicle (i) to link (l) (seconds). 

TEX(i,l) = Exit time of vehicle (i) from link (l) (seconds). 

LS(l) = Length of link (m). 

LT(l) = Length of turn (m). 

NX(l) = Number of vehicles exiting link (l) during simulation period. 

A harmonic mean vehicle speed for vehicles exiting the link also is computed. 

Computation of Delay 

For individual links the delay is computed as follows. 

sec

( )

1

sec N

DT

DT

NX l

i

∑ i

= = Equation 59

( ) ( )⎥

⎦

⎤ ⎢

⎣

⎡ = − +

i t i

t

i s i

s

i i Min SMax S

L

Min SMax S

L

DT TT , *θ , *θ Equation 60

Where 

DTsec = Average Delay Time per vehicle of a section. 

DTi = Average section delay time of vehicle (i) (seconds). 

TT(i) = The difference between the link exit time and the link entrance time for vehicle (i). 

Ss = Speed limit of link (m/s). 

St = Speed limit of turning (m/s). 

θi = Ratio of Speed Limit accepted by vehicle i. 

SMaxi = Maximum desired Speed of vehicle i (m/s). 

Ls = Distance of link (meters). 

Lt = Distance of turning (meters). 

NX(l) = Number of vehicles exiting link (l) during simulation period. 

Stop time also is computed. The Users Guide does not identify whether or not slow 

vehicles are counted as stopped or not. 
77 

Computation of Queue 

For individual links the average and maximum queue lengths in terms of vehicles are 

computed as follows. A similar formula is used for measuring queues for individual 

turning movements. 

sec

sec

) , ,( 1) *( ,

sec

( 1)

NBLanes

l

I

t T

l i t l i t QLl t

AQL i l

∑ ∑ i

∈ ⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎝

⎛

∈

⎥⎦

⎤ ⎢⎣⎡ − −

=

−

Equation 61

sec

sec

sec NBLanes

l

MaxQLl

MaxQL

∑

∈ =

Equation 62

AQL = Average Queue Length (vehicles). 

QLl,t = Queue Length in the lane l at time t (veh). 

MaxQLl = Maximum Queue Length in the lane l (veh). 

I = Interval of statistics (seconds). 

Tl = (0, tl,1,..., t l,m, I): Instants when the queue length in lane l changes. 

NBLanessec = Number of lanes of link (section). 

A vehicle is considered “queued” and therefore “stopped” when its speed below the userspecific queue threshold speed. A vehicle is not considered as having left the queue until 

its speed exceeds the user specified “queue leaving speed.”

Computation of Stops 

For individual links the average number of stops is computed as follows. 

( )

( )

( )

( )

1

NX l

TNS i

NS l

NX l

i

∑= = Equation 63

NS(l) = Average Number of Stops per exiting vehicle on link (l). 

TNS(l) = Total Number of stops accumulated on link by exiting vehicle (i). 

NX(l) = Number of vehicles exiting link (l) during simulation period. 

A vehicle is considered “stopped” when its speed falls below the user-specific queue 

threshold speed. A vehicle is not considered as having left the queue, (and therefore 

cannot generate additional stops) until its speed exceeds the user specified “queue leaving 

speed.”
78 

Computation of Density 

The following equation is used to compute mean density of vehicles on a link. 

[ ]

*1000

* ( )

(sec )

sec

sec

, , ,( 1) ( 1)

∑

∑ ∑

∈

∈ ∈

− ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ −

=

−

l

l

l T t

l t l i l i

L

NVeh t t I

k t i l

i

Equation 64

Where: 

K(sect) = Density for section (link) (veh/lane-m). 

Ll = Length of lane l (m). 

NVehl,t = Number of vehicles in the lane l at time t. 

I = Interval of statistics (seconds). 

Tl = (0, tl,1,..., t l,m, I): Instants when the number of vehicles in lane l changes. 

Computation of Travel Time Variance 

Aimsun does not directly output travel time variance information, however; the software 

can be executed multiple times to determine the variance of the mean travel time for each 

run. 

4.1.9 S-Paramics 

This section discusses the MOEs produced by S-Paramics Version 2005.1. This section has 

been provided to the author by the vendor. The author respects the expertise of the 

people who have provided this description, and the section has been incorporated for that 

reason. However, the author has not independently verified the accuracy of this 

description. 

Paramics produces systemwide, link-specific, path defined, OD and point-specific MOEs. 

Systemwide MOEs (Economic Info) that are reported for every minute of the simulation 

include: 

• Number of Vehicles currently on the network; 

• Mean Speed of vehicles currently on network at specific point in time; 

• Mean Time Traveled reported as mean delay; 

• Accumulated Total Distance Traveled (VMT) since start of simulation; 

• Total number of vehicles passed through the network; 

• Mean Speed of all vehicles on network since start of simulation; and 

• Bus statistics, including bus stopped time and number of passengers on or off. 
79 

For individual links the following MOE outputs are provided: 

• Number of vehicles traveling the link in the period; 

• Flow count normalized to vehicles per hour; 

• Accumulated time on the link in the period; 

• Link length; 

• Number of vehicles at present on the link; 

• Mean speed on the link; 

• Turn counts and flows; 

• Link counts and flows; 

• Density; 

• Emissions; and 

• Queue transitions and queued time. 

For user-defined paths the following queue MOE outputs are available: 

• Minimum, average and maximum vehicles and PCUs queued along the path in a userdefined interval; 

• Minimum, average and maximum length of the queue in meters; 

• Path length; 

• Minimum, average and maximum journey time; and 

• Link by link profile of journey time. 

The following point data is available, for which histogram distributions are produced: 

• Flow; 

• Speed; 

• Headway; and 

• Acceleration. 

Lane use and lane changes also may be measured on a per link basis. 

Computation of HCM Level of Service 

S-Paramics does not directly output level of service. 
80 

Computation of Volume Capacity Ratio 

S-Paramics does not directly output volume/capacity ratios. 

Computation of Speed and Travel Time 

Paramics outputs network level total distance VMH, mean travel time (defined as ‘mean 

delay’ in S-Paramics) and mean speed, all accumulated by one-minute periods throughout 

the simulation period. Total travel time, VHT, is calculated by multiplying the mean 

travel time by the total number of completed trips. 

These results exclude vehicles denied entry to the network during any part of the 

simulation period. However, these vehicles are accounted for separately as off network 

queue time, collected at one-minute periods throughout the simulation period. 

Link VMT and VHT are not directly output by Paramics. The link level outputs of flow 

and speed can be combined with the link length to obtain VMT and VHT by link. The 

delays incurred by vehicles denied entry to the link during any part of the simulation 

period are excluded from this computation. 

Computation of Delay 

S-Paramics does not directly output delay. 

Computation of Queue 

Paramics allows a user to select their own definition of a queue according to the maximum 

vehicle speed and a maximum distance between vehicles. The defaults are 2 meters per 

second (for speed), and 10 meters between vehicles. The minimum number of vehicles 

before Paramics records them as a queue also can be set by the analyst. Additionally, the 

analyst selects the appropriate method to handle multiple queues occurring on the same 

link and lane. 

S-Paramics reports the mean and maximum queue observed over the simulation time 

period. The queue must meet the minimum number of vehicles thresholds selected by the 

analyst to be identified as a queue. 

Once a vehicle has entered a queue by going below a specified head way and/or speed it 

remains in the queued state until the headway and/or speed rise above another set of 

values. Hysteresis is built into the system. 

The average queue is computed by the following equation. The end of the queue is 

defined as the position of the tail of the last vehicle in the queue this may be output by 

lane or over the link. The average queue may be computed for every time spent in the 

reporting period or just by those when there was a queue. Including the times when no 

queue was present will bias the result to a lower value. 
81 

I

Q i

AVEQ i

∑

=

( )

Equation 65 

Where: 

AVEQ = Average Queue over analysis period. 

Q(i) = Observed Queue at end of time step (i). 

I = Total number of time steps in analysis period. 

At the end of each time step the vehicles in queue are converted to a queue length by 

multiplying each by its appropriate PCE and summing the lengths of the vehicles. Note 

that this mean queue will be different than that for signals, since this mean queue includes 

time steps when the signal is green as well as red, while the signal mean queue is limited 

to time steps when the signal is red. 

It also should be noted that the queue is reported based on user-defined paths, which may 

span many links, hence the maximum and average queue is not limited by the length of 

links. 

S-Paramics reports the number of times a vehicle enters a queued state on each link and 

the time spent in this state. 

Computation of Stops 

S-Paramics reports the number of transitions into a queued state and the time spent in that 

state per link. 

Computation of Density 

Paramics computes the mean density in terms of passenger car units per lane-mile over an 

analysis period as the sum of the average density during the simulation period and the 

density of vehicles remaining at the end of the period. 

k = k() ( ) i + k r Equation 66

* ( )

( , ) * ( )

( ) NL L a

NV v i PCE v

k i v

∑

= Equation 67

L(a) = FFS * I Equation 68

Where: 

k = Reported link density for analysis period (pcu/lane-mile). 

k(i) = Computed link density over analysis period (i). 

k(r) = Computed density of vehicles remaining on link at end of analysis period. 
82 

NV(v,i) = Cumulative number of vehicles of type (v) exiting the link during period (i). 

PCE(v) = Passenger car equivalence of vehicle type (v). 

NL = Number of lanes on link. 

L(a) = Available distance on link (mile-hours). 

FFS = Free-flow speed on link (mph). 

I = Duration of analysis period (hrs). 

The density for the remaining vehicles is computed using the same formula as for 

computing the link density during the analysis period, substituting the PCUs remaining 

for the PCUs accumulated during the analysis period in the formula. 

The analyst can exclude the “remainder” vehicles from the density calculation if desired. 

Computation of Travel Time Variance 

S-Paramics reports travel times between OD pairs and also travel times along specified 

paths in the model. As each individual vehicle is logged, variance can be reported within 

each run and across a number of runs. 

4.2 TEST BED RESULTS 

The following tools were tested on a test network to observe how the computations of 

MOEs varies between the tools: HCS, Synchro, SimTraffic, CORSIM, Vissim, Q-Paramics, 

S-Paramics, and Aimsun. 

The Vissim results produced by Dowling Associates were proofed and corrected by PTV 

personnel.18

Q-Paramics is the Quadstone version of Paramics. S-Paramics is the SIAS version of 

Paramics. The Q-Paramics results were produced by Dowling Associates. The S-Paramics 

results were provided by SIAS consultancy. Dowling Associates has not proofed or 

verified these results, but respects the expertise of the people that produced these results, 

and has included these results on that basis. 

The Aimsun results were provided by Dr. Jaume Barcelo, since Dowling Associates lacks 

licenses and experienced personnel for operating Aimsun. Dowling Associates has not 

proofed or verified these results, but respects the expertise of the people that produced 

these results, and has included these results on that basis. 

 

18 Initial results indicated a problem with the Synchro to Vissim conversion software provided by 

Vissim. There was also a problem with how Dowling had selected the node data collection box in 

Vissim. PTV made the necessary corrections and returned both input and output files to Dowling 

Associates. 
83 

Dynasmart-P was not tested because Dowling Associates lacks licenses and experienced 

personnel on this tool. 

Test Network 

The test network consists of a freeway and an arterial street. They are not connected. 

They operate independently. 

The freeway is a single (right-bound) direction of freeway, 2.75 miles long, 3-lanes wide, 

with a single-lane on-ramp and a single-lane off-ramp (see figure below). 

Figure 1. Test Freeway Facility. 

The arterial street is bidirectional, one-mile-long, with a single lane in each direction, and 

two signalized intersections (see figure below). 

Figure 2. Test Arterial Street. 

At each intersection the approach widens to a single exclusive left turn lane, a single 

exclusive through lane, and a single exclusive right turn. Both signals are 100-second 

cycle fixed time with exclusive left turn phases. Each left and through signal phase gets 

the same amount of green time (see figure below). Right turns on red are prohibited. 
84 

Source: Screenshot of Synchro Input Screen. 

Figure 3. Signal Timing for Example Signal on Test Arterial. 

For the purpose of comparing HCM results to simulation results, the peak-hour factors 

within HCS and Synchro have been set at 1.00 for all movements. The percent heavy 

vehicles was set at 0 percent. 

While some of these assumptions would be unusually simplistic for U.S. practice, they 

have been selected to dampen as much as possible the differences in how the various 

simulation models reflect vehicle behavior and signal operation. The attempt with this 

simple test network is to obtain very simplistic vehicle behavior so that observed 

differences in the MOEs produced by the tools are mostly the result of differences in how 

the MOEs are computed and to a lesser extent the result of how vehicle behavior and 

signal operation are modeled within each tool. 

Comparison of MOEs Produced Under Free-Flow Conditions 

The MOEs computed by the various tools were first tested under free-flow conditions. 

The freeway mainline volume was 900 vph entering the study section. Each freeway onramp and off-ramp carried a volume of 100 vph. Each turning movement of each leg of 

each signalized intersection on the test arterial was assumed to serve a demand of 100 

vph. Thus each intersection carries a total 1,200 vph, summed over all 4 approaches. 
85 

Table 15. Basic Freeway Section Results – Free-Flow. 

Freeway Section HCS Synchro SimTraffic CORSIM Vissim Q-Paramics Aimsun S-Paramics

Vehicles Enter 900 N/A 893 ? ? 911 904 901 

Vehicles Exit 900 N/A 896 907 886 921 886 900 

SMS (mph) 60.0 N/A 52.0 59.4 59.1 62.4 62.4 65.3 

Density (v/ln-mi) 5.0 N/A 5.7 5.1 5.0 5.0 4.7 4.6 

HCM LOS A N/A A A A A A A 

• All models were coded with a 60 mph free-flow speed and a demand of 900 vehicles 

per hour. According to HCM, the 3-lane freeway segment is at 14 percent of capacity 

at this level of demand. 

• All simulation tool results are the average of five runs with varying random number 

seeds. 

• Simulation tools not calibrated. All parameters at default. Note that Aimsun, 

Q-Paramics, and S-Paramics were produced by their respective vendors and not the 

authors of this report. Note that Vissim results are based on corrections to author’s 

initial coding by the vendor. 

• Shaded cells are not directly output by the tool. Entries in these cells were computed 

manually by the analyst based on other outputs provided by the tool. Question mark 

means the data was not readily available in the standard output for a specific link. 

• Synchro does not output freeway performance results. 

Table 16. Freeway Facility Results – Free-Flow. 

Freeway Section HCS Synchro SimTraffic CORSIM Vissim Q-Paramics Aimsun S-Paramics

Vehicles Enter 1,000 N/A 999 ? ? 1,022 1,007 1,029 

Vehicles Exit 1,000 N/A 996 999 987 1,018 956 985 

VMT (v-mi) 2,587 N/A 2,583 2,597 2,599 2,805 2,833 2,700 

VHT (v-h) 43.3 N/A 49.0 43.8 44.3 45.1 45.7 40.7 

SMS (mph) 59.7 N/A 52.7 59.3 58.6 62.2 61.9 64.6 

• All models were coded with a 60 mph free-flow speed and a demand of 1,000 vehicles 

per hour (combination of mainline in and on-ramp volumes). 

• All simulation tool results are the average of five runs with varying random number 

seeds. 
86 

• Simulation tools not calibrated. All parameters at default. Note that Aimsun, 

Q-Paramics, and S-Paramics were produced by their respective vendors and not the 

authors of this report. Note that Vissim results are based on corrections to author’s 

initial coding by the vendor. 

• Shaded cells are not directly output by the tool. Entries in these cells were computed 

manually by the analyst based on other outputs provided by the tool. 

• Synchro does not output freeway performance results. 

Table 17. Signalized Intersection Results – Free-Flow. 

Intersect MOEs HCS Synchro SimTraffic CORSIM Vissim Q-Paramics Aimsun S-Paramics

Vehicles Enter 1,200 1,200 1,200 ? ? ? 1,222 1,198 

Vehicles Exit 1,200 1,200 1,208 1,199 1,225 1,197 1,198 1,201 

Delay/Vehicle(s) 34.9 37.5 38.1 31.8 29.0 26.4 30.6 30.1 

EBT Q 50% (ft) 63 54 61 25 17 29 22 90 

HCM LOS C D D C C C C C 

• All models were coded with a demand of 1,200 vehicles per hour for the intersection 

(100 vph for each of the 12 turn movements. According to HCM, the signal is at 28 

percent of capacity). 

• All simulation tool results are the average of five runs with varying random number 

seeds. 

• Simulation tools not calibrated. All parameters at default. Note that Aimsun, 

Q-Paramics, and S-Paramics were produced by their respective vendors and not the 

authors of this report. Note that Vissim results are based on corrections to author’s 

initial coding by the vendor. 

• Shaded cells are not directly output by the tool. Entries in these cells were computed 

manually by the analyst based on other outputs provided by the tool. 

Table 18. Arterial Street Results – Free-Flow. 

EB Arterial HCS Synchro SimTraffic CORSIM Vissim Q-Paramics Aimsun S-Paramics

Art Speed mph 23.3 21.7 20.0 21.0 19.0 30.6 26.9 26.0 

Art LOS C C C C C A B B 
87 

• For eastbound direction of one-mile-long section of arterial between first and second 

signals. 

• All simulation tool results are the average of five runs with varying random number 

seeds. 

• Simulation tools not calibrated. All parameters at default. Note that Aimsun, 

Q-Paramics, and S-Paramics were produced by their respective vendors and not the 

authors of this report. Note that Vissim results are based on corrections to author’s 

initial coding by the vendor. 

• Shaded cells are not directly output by the tool. Entries in these cells were computed 

manually by the analyst based on other outputs provided by the tool. 

With the exception of SimTraffic and Paramics, the tools are in pretty close agreement on 

freeway operation MOEs under free-flow conditions. The default parameters for 

SimTraffic result in low estimated free-flow speeds for the freeway. The default 

parameters for Paramics result in high estimated free-flow speeds for the freeway. 

The MOEs produced by the tools for arterials under free-flow conditions are in relatively 

close agreement in some respects and far apart in other respects. The mean signal queues 

predicted by the various tools range from under one vehicle to slightly under 3 vehicles. 

The predicted maximum queue length ranges from 145 feet to 188 feet (between 6 and 7.5 

vehicle lengths). The predicted mean arterial speed (eastbound direction only) ranges 

from 19 mph to 31 mph (level of service A to C). 

MOEs When Free-Flow Speed Is Calibrated 

Based on the free-flow tests, it appeared that CORSIM and Vissim did not require 

calibration to match the coded free-flow speeds for the freeway. 

Both Paramics and SimTraffic free-flow test results indicated that some calibration of the 

default parameters in each model would be required to match the target free-flow speed 

input by the analyst. 

Unlike the other tools, Paramics treats the coded free-flow speed as a speed limit that can 

be exceeded. Since Paramics computes delay as the difference between the output speed 

and the input free-flow speed, it is not possible to calibrate the free-flow speed output by 

Paramics without also affecting the computed delay for the facility. If the analyst codes a 

lower free-flow speed to trick Paramics into outputting a lower free-flow speed, the lower 

input value becomes the new basis for computing delay, not the output speed. Technical 

support for Paramics suggested that it would be undesirable to adjust the driver 

aggressiveness to force a lower free-flow speed, because it would impact other behavior as 

well. Consequently no tests were made with a free-flow speed calibrated Paramics model. 

The SimTraffic tool was calibrated to better match the coded free-flow speeds for all links 

by proportionally increasing the “speed factors” (the percentage of the coded free-flow 

speed that each driver type is assumed to target) for each of the 10 driver types in the 
88 

model. The simulation was rerun with the tool calibrated to better reproduce the coded 60 

mph free-flow speed for the freeway and 30 mph for the arterial street. The results are 

shown below. 

Table 19. Basic Freeway Section Results – Calibrated Free-Flow. 

 SimTraffic 

Freeway Section HCS Uncalibrated Calibrated 

Vehicles Enter 900 893 893 

Vehicles Exit 900 896 896 

VMT (v-mi) 930 927.6 927 

VHT (v-h) 15.5 18 15.7 

SMS (mph) 60.0 52 59.0 

Tot Delay(v-h) 0.0 2 1.3 

Delay/Veh (s) 0.0 8.2 5.3 

Density (v/ln-mi) 5 

V/c 0.14 

HCM LOS A 

Table 20. Freeway Facility Results – Calibrated Free-Flow. 

 SimTraffic 

Freeway Facility HCS Uncalibrated Calibrated 

Vehicles Enter 1,000 999 998 

Vehicles Exit 1,000 996 995 

VMT (v-mi) 2,587 2,583 2,601 

VHT (v-h) 43.3 49 43 

SMS (mph) 59.7 52.7 59.9 

VHT Delay (h) 0.2 4.3 2.7 

Delay/Veh (s) 0 16.7 9.7 
89 

Table 21. Signalized Intersection Results – Calibrated Free-Flow. 

 SimTraffic 

Intersect MOEs HCS Uncalibrated Calibrated 

Intersection V/C 0.31 

Delay/Vehicle (s) 34.9 38.1 38.4 

HCM LOS C D D 

Total Delay v-h 11.6 12.8 12.8 

Stop Delay (h) 8.9 10.4 10.6 

StopDel.Veh(s) 26.8 31.0 31.8 

EBT Q 50% (ft) 70 61 58 

EBT Q Mean (veh) 2.8 

EBT Q 95% (ft) 152.5 128 111 

EBT Q Max (ft) 156.3 188 127 

EBT Q Max (veh) 6.3 

Total Stops 900 936 948 

Stops/Veh 0.75 0.78 0.79 

Vehicles Enter 1,200 1,200 1,202 

Vehicles Exit 1,200 1,208 1,206 

Table 22. Arterial Street Results – Calibrated Free-Flow. 

 SimTraffic 

EB Arterial HCS Uncalibrated Calibrated 

Art Speed mph 23.3 20 22.7 

Art LOS C C C 

The calibration improved the match between the mean freeway and arterial speeds 

predicted by HCS and SimTraffic. However, the SimTraffic predicted signal delays, stops, 

and queues generally diverged even further from the HCS results. 

Comparison of MOEs for At Capacity Conditions 

The tools were rerun this time with volumes determined by the HCS software to be the 

capacity for the freeway merge and the signalized intersections (6,900 vph for the freeway 

merge, 4,468 vph for the signalized intersections). 

The simulation tools were run again in their uncalibrated state. The results are shown 

below for HCS, Synchro, SimTraffic, CORSIM, Vissim, and Aimsun. 
90 

SimTraffic appears to have a much lower default freeway capacity than the other tools. 

This is evidenced by the significantly higher freeway facility delay predicted by 

SimTraffic. CORSIM and Vissim appear to be in closer agreement with the HCS results 

than SimTraffic. Calibration of the SimTraffic default capacity for freeway links could 

conceivably reduce or eliminate this discrepancy. 

The simulation tool results for the intersection and the arterial street deviate to a greater 

extent from HCS and Synchro results than before under free-flow conditions. This 

appears to be due to the impact of queue blockages of the left and right turn lanes at the 

signals. HCS and Synchro assume that all of the demand can reach each turn lane. 

SimTraffic, CORSIM, and Vissim found that that was frequently not the case. Thus the 

latter tools predict higher delays and slower speeds than HCS and Synchro. 

Table 23. Basic Freeway Section Results – At Theoretical HCM Capacity. 

Freeway Section HCS Synchro SimTraffic CORSIM Vissim Q-Paramics Aimsun 

Vehicles Enter 6,901 N/A 5,085 6,353 6,822 6,603 6,910 

Vehicles Exit 6,901 N/A 5,032 6,348 6,817 6,601 6,691 

VMT (v-mi) 7,131 N/A 5,254 6,564 7,022 6,818 7,282 

VHT (v-h) 140 N/A 115 121 134 136 142 

SMS (mph) 51 N/A 46 54 53 51 51 

Density (v/ln-mi) 45 N/A 36 39 43 43 44 

HCM LOS E N/A E E E E E 

• All simulation tool results are the average of 5 runs with varying random number 

seeds. 

• Simulation tools not calibrated. All parameters at default. Note that Aimsun, QParamics, and S-Paramics were produced by their respective vendors and not the 

authors of this report. Note that Vissim results are based on corrections to author’s 

initial coding by the vendor. 

• Shaded cells are not directly output by the tool. Entries in these cells were computed 

manually by the analyst based on other outputs provided by the tool. 

• Resource limitations restricted tests of at-capacity conditions to HCS, SimTraffic, 

CORSIM, Vissim, Q-Paramics, and Aimsun. 
91 

Table 24. Freeway Facility Results – At Theoretical HCM Capacity. 

Freeway Facility HCS Synchro SimTraffic CORSIM Vissim Q-Paramics Aimsun 

Vehicles Enter 6,900 N/A 6,990 6,502 6,916 6,603 6,995 

Vehicles Exit 6,900 N/A 5,032 6,438 6,548 6,504 6,585 

VMT (v-mi) 18,830 N/A 14,141 17,619 18,999 18,023 20,357 

VHT (v-h) 363 N/A 1,273 408 362 347 392 

SMS (mph) 52 N/A 11 43 52 52 52 

• All simulation tool results are the average of 5 runs with varying random number 

seeds. 

• Simulation tools not calibrated. All parameters at default. Note that Aimsun, QParamics, and S-Paramics were produced by their respective vendors and not the 

authors of this report. Note that Vissim results are based on corrections to author’s 

initial coding by the vendor. 

• Shaded cells are not directly output by the tool. Entries in these cells were computed 

manually by the analyst based on other outputs provided by the tool. 

• Resource limitations restricted tests of at-capacity conditions to HCS, SimTraffic, 

CORSIM, Vissim, Q-Paramics, and Aimsun. 

Table 25. Signalized Intersection Results – At Theoretical HCM Capacity. 

Intersect MOES HCS Synchro SimTraffic CORSIM Vissim Q-Paramics Aimsun 

Vehicles Enter 4,468 4,468 3,311 ? ? ? 3,340 

Vehicles Exit 4,468 4,468 3,185 3,737 3,460 2,817 2,887 

Delay/Vehicle (s) 86 81 604 142 145 167 397 

EBT Q 50% (ft) 420 256 3,128 2,625 784 559 653 

HCM LOS F F F F F F F 

• All simulation tool results are the average of 5 runs with varying random number 

seeds. 

• Simulation tools not calibrated. All parameters at default. Note that Aimsun, 

Q-Paramics, and S-Paramics were produced by their respective vendors and not the 

authors of this report. Note that Vissim results are based on corrections to author’s 

initial coding by the vendor. 
92 

• Shaded cells are not directly output by the tool. Entries in these cells were computed 

manually by the analyst based on other outputs provided by the tool. 

• Resource limitations restricted tests of at-capacity conditions to HCS, SimTraffic, 

CORSIM, Vissim, Q-Paramics, and Aimsun. 

Table 26. Arterial Street Results – At Theoretical HCM Capacity. 

EB Arterial HCS Synchro SimTraffic CORSIM Vissim Q-Paramics Aimsun 

Art Speed mph 15 19 17 15 16 17 18 

Art LOS D C D D D D C 

• For eastbound direction of one-mile long section of Arterial between First and Second 

Street signals. 

• All simulation tool results are the average of 5 runs with varying random number 

seeds. 

• Simulation tools not calibrated. All parameters at default. Note that Aimsun, 

Q-Paramics, and S-Paramics were produced by their respective vendors and not the 

authors of this report. Note that Vissim results are based on corrections to author’s 

initial coding by the vendor. 

• Shaded cells are not directly output by the tool. Entries in these cells were computed 

manually by the analyst based on other outputs provided by the tool. 

• Resource limitations restricted tests of at-capacity conditions to HCS, SimTraffic, 

CORSIM, Vissim, Q-Paramics, and Aimsun. 

4.3 CONCLUSIONS 

This section highlights the conclusions of our evaluation of the computation of MOEs by 

seven typical traffic analysis tools (HCS, Synchro, DynaSmart, SimTraffic, Vissim, 

Paramics, Aimsun). 

Computation of HCM Level of Service 

Of the analysis tools considered here, HCS, Synchro, and Paramics report HCM level of 

service letter grades. The other tools report MOEs that users may be tempted to convert 

into HCM LOS grades. 

The HCS and Synchro are relatively faithful to the HCM LOS method and criteria since 

they are inherently macroscopic analysis tools. Paramics differs in its LOS letter grade 

assignments because it is a microscopic tool and its outputs are not as directly comparable 

to HCM LOS criteria. The discussion below highlights these differences. 
93 

Freeway LOS – Most microscopic tools output mean vehicle density but not in terms of 

passenger car units. 

Paramics provides for a user input PCE adjustment by vehicle type to convert vehicle 

density into passenger car unit density. Thus the Paramics PCU densities are closer to the 

HCM densities. However the PCE adjustment is universal rather than link-specific (and 

thus not sensitive to grades). 

Intersection LOS – All microsimulation tools tally approach delay for the links 

approaching the intersection. CORSIM is the only one that tallies control delay, which 

includes the delay after passing through the intersection to resume free-flow speed. Also, 

HCM control delay excludes vehicles not going through the intersection that become 

entangled in the queue for the intersection. 

Arterial LOS – Only SimTraffic directly reports the arterial level of service grade. Paths 

can be identified in the other microsimulation tools and the mean speed of through traffic 

reported. 

Two-Lane Highway LOS – Paramics reports percent time delay for two-lane rural roads. 

But, since Paramics (like all of the other microsimulation tools evaluated here) does not 

model passing in opposing lanes, the model is of limited use for modeling two-lane rural 

roads. 

Computation of Volume Capacity Ratio 

Only HCS and Synchro, among the tools evaluated here, output volume/capacity ratios. 

It is not practical for the microsimulation tools to output this measure. It would require 

multiple runs to identify the capacity of a link or node. 

Computation of Speed and Travel Time 

The computation of speed and travel time is generally similar for all of the 

microsimulation tools. The difference is whether or not the delays incurred by vehicles 

denied entry to the network during the simulation are included in the accumulated 

vehicle travel time (VHT) and whether or not that additional delay also is included in the 

computed mean speed for the system. Most developers have elected to exclude vehicles 

not on the network from the computation of mean network speed. 

Some microsimulation tools compute mean link speeds by dividing accumulated vehicle 

miles traveled on the link by the vehicle hours traveled on the link (excluding delays 

incurred by vehicles denied entry to the link). It is unclear whether or not the other tools 

take the simple mean of the vehicle speeds on the link. 

Computation of Delay 

The definition of delay varies among the microsimulation tools. The result is that some of 

the tools generate delay even under free-flow conditions. 
94 

Most microscopic tools compare the actual travel speed to the free-flow speed over the 

length of the link (or trip) to determine delay. However, the mean speed of vehicles under 

free-flow conditions is often five mph below the coded free-flow speed for the link. Thus 

delay is guaranteed under even free-flow conditions. It would be better to compare actual 

speed to the desired speed. 

The computation of mean delay runs into the problem of defining the number of vehicles 

by which to divide the total delay. Most microsimulation tools, in order to save on 

computer storage requirements, do not keep a running tally of the number of vehicles for 

which the various statistics are accumulated each instant within the simulation period. 

So, in order to arrive at the number of vehicles on the link for computing link averages 

most tools use one or more vehicle counts for the link after the simulation is done. 

Some tools use just the number of vehicles exiting the link during the analysis period. 

Others add to that the vehicles remaining on the link at the end of the analysis period. 

Still others add the average of the vehicles on the link at the start of the period and the end 

of the period. This counting issue is illustrated below. 

Distance

Time

8

11

15 14 13 12

22 21 20 19 18 17 16

Link Length

Analysis Period

7654

321

9

10

Figure 4. Counting Vehicles on a Link. 

As shown above, a total of 22 vehicle traces (the diagonal lines in the figure) take place 

somewhere on the link at some time during the analysis period (the square box in the 

figure). 

Some of the microscopic tools may use 22 vehicle traces to accumulate VMT, VHT, delay, 

queues, and stops on the link during the analysis period. Others use only the 11 vehicle 

traces that exit the link during the simulation period. An additional difficulty comes in 

the computation of the mean delay and mean stops. 

A total of 14 vehicles actually entered the link during the analysis period (22 traces minus 

8 already on the link at the start of the analysis period). A total of 11 vehicles exit the link 

during the analysis period. Eleven vehicles remain on the link at the end of the simulation 

period. Only 3 vehicles actually enter and leave the link within the simulation period. 
95 

Some microsimulation tools will divide the accumulated delay by the 11 exiting vehicles. 

Others will divide the accumulated delay by 22, the 11 exiting plus 11 remaining vehicles 

on the link. 

Incidentally, this counting problem also makes it difficult to define the volume (flow rate) 

on a link. If we stand at the start of link we would count 14 vehicles passing by during the 

analysis period. If we stand at the end of the link we would have counted 11 vehicles 

passing by. If we stand in the middle we would have counted 15 vehicles passing by 

during the analysis period. The problem is that we are attempting to apply a point 

measurement (volume) to a link. 

Computation of Queue 

The different microsimulation tools apply different definitions of queuing vehicles. 

SimTraffic and CORSIM use predefined maximum speeds and acceleration rates to 

identify stopped and queuing vehicles. Aimsun, Paramics, and Vissim allow the user to 

select their own definition of queuing vehicles. 

Some tools provide the mean and maximum number of vehicles queued at any instant. 

Others provide the distance to the farthest upstream point of the queue, the back of queue. 

The difference between the two can be seen in the above figure. The maximum back of 

queue within the analysis period is nine vehicles back from the stop bar, however the 

maximum queue at any instant in time is five vehicles. 

The mean queues and 95 percentile queues estimated by microsimulation tools generally 

differ from those provided by HCM type tools for signals in that the microsimulation tools 

measure the average queue across both green and red signal intervals. Traditional HCM 

tools compute the mean queue for signals for only the red signal intervals. 

The difference can be illustrated by Figure 4. Counting Vehicles on a Link. The queue 

does not start until the signal turns red, about half way through the analysis period. 

During this red time the mean queue is about four vehicles long. If we add in the zero 

queues experienced during the green time, the mean queue for the analysis period is two 

vehicles. 

Computation of Stops 

The different microsimulation tools include differing degrees of “slow” vehicles in their 

definition of stopped vehicles. Some tools document these definitions in their Users 

Guides. Others do not. 

Aimsun, Paramics, and Vissim allow the analyst to set their own definition of when a slow 

vehicle is stopped and when it ceases to be stopped. CORSIM and SimTraffic have these 

values preset. 

Again the mean number of stops per vehicle varies according to how the microsimulation 

tool determines how many vehicles should be used to compute the mean. 
96 

Computation of Density 

Most all microsimulation tools output density in terms of mixed vehicles per lane-mile. 

One, Paramics allows the user to apply PCEs by vehicle type to output density in terms of 

passenger car units. These PCEs however apply systemwide and are not link or facility 

type-specific. 

Paramics also adds the density of the vehicles at the end of the simulation period to the 

mean density of vehicles during the simulation period (rather than averaging the two). 

This results in double the density. This double counting can be turned off by the analyst. 

Computation of Travel Time Variance 

None of the tools directly output reliability of travel times (variance of travel times). The 

microsimulation tools can be run several times to obtain some variance estimates. Several 

microsimulation tools have tools for computing the variance from several runs. However, 

the variance built in to the microsimulation tools is generally limited to the pulsing of 

traffic within the simulation period and variations in driver behavior. 

Random day-to-day variations in demand and uncertainty in the estimated mean demand 

are not incorporated in these tools. 

Also, these tools do not incorporate random day-to-day variations in capacity due to 

random incidents. 

Thus the variance in travel times produced by microsimulation tools would be expected to 

be less than that exhibited in the real world. 
97 

5.0 CRITIQUE OF CURRENT MOE ESTIMATION METHODS 

This chapter highlights the limitations of current MOEs and their computation. 

5.1 LIMITATIONS IN HOW MOES ARE CURRENTLY COMPUTED 

Our ability to measure the MOEs in the field and the ability of simulation tools to estimate 

the MOEs are both severely strained by extreme congestion (See Table 27. Strengths and 

Weaknesses of Common MOEs, and Table 28. Common Limitations of MOEs in Practice). 

Under severe congestion conditions it is very difficult to study a large enough geographic 

area over a long enough analysis period to ensure that congestion does not extend back 

beyond the limits of the study area and congestion is not present at the start or the end of 

the analysis period. Thus most simulation modeling of severe congestion conditions 

usually has queues that extend beyond the temporal and physical limits of the model. 

Failure to include in the MOEs the queues that stretch beyond the geographic and the 

temporal bounds of the study area will bias the computation of travel time, speed, and 

delay, making capacity improvements within the study area appear to perform worse 

than no improvements. 

There also are problems with reporting queues that extend beyond the turn bay and/or 

beyond the link. Only one simulation tool will track the buildup of queues beyond the 

subject link, and none of them will track congestion beyond the temporal or geographic 

limits of the model. With one exception, the reported queue is, by definition, never longer 

than the storage capacity of the turn bay or the link. Thus, the analyst cannot rely upon 

the reported queue length to identify queue overflow problems. The analyst must find the 

upstream links and review the reported queues there. 

5.2 INHERENT LIMITATIONS OF CURRENT MOES

Most MOEs are satisfactory measures of traffic performance for uncongested conditions. 

Travel time, delay, queues, stops, density, and travel time variance all increase as traffic 

demand increases relative to capacity and traffic operations worsen. 

Among these MOEs, speed is a less satisfactory indicator of how close a facility is to 

breakdown, because speed is comparatively insensitive to changing traffic flow rates until 

capacity is reached. 

Most all of the MOEs tend to break down under extreme congestion conditions. Speed, 

density, stops, and travel time variance are invariant under “parking lot” conditions 

where no movement is possible (speed equals zero, density equals jam density, and travel 

time variance is zero). The only MOEs that continue to function under parking lot 

conditions are travel time and delay, which continue to increase over the length of the 

analysis period. 
98 

Table 27. Strengths and Weaknesses of Common MOEs. 

MOE Strengths Weaknesses 

HCM Level of Service • Widely known and used. 

• Readily understood by DecisionMakers. 

• Most LOSs can be either estimated 

or measured in field. 

• Totally loses sensitivity once LOS “F”

(capacity) is reached. 

• LOS measures not available for many 

situations (e.g., local residential streets, 

rural highways with signals, etc.). 

• Average delay, average density used in 

LOS, miss hot spots. 

• Misleading on extent of time which given 

LOS is present at location in network (peak 

15 minutes versus 2 1⁄2 hours. 

Volume/ Capacity • Readily understandable. 

• Gives indication of how close to 

breakdown. 

• Expensive to measure in field once v/c1. 

(must gather queue data). 

Travel Time • Can be directly measured in field. 

• Indicates relative benefits of 

alternatives. 

• Must be related to some standard in order 

to determine if result is good or bad. 

• Expensive to measure in field. 

Speed • Can be directly measured in field. 

• Indicates relative benefits of 

alternatives. 

• Must be related to some standard in order 

to determine if result is good or bad. 

• Expensive to measure in field. 

Delay • Can be directly measured in field. 

• Indicates relative benefits of 

alternatives. 

• Must be related to some standard in order 

to determine if result is good or bad. 

• Expensive to measure in field. 

• Definitional issues. 

Queue • Can be directly measured in field. 

• Indicates relative benefits of 

alternatives. 

• Must be related to some standard in order 

to determine if result is good or bad. 

• Expensive to measure in field. 

• Definitional issues. 

Stops • Can be directly measured in field. 

• Indicates relative benefits of 

alternatives. 

• Must be related to some standard in order 

to determine if result is good or bad. 

• Expensive to measure in field. 

• Definitional issues. 

Density • Can be directly measured in field. 

• Indicates relative benefits of 

alternatives. 

• Must be related to some standard in order 

to determine if result is good or bad. 

• Expensive to measure in field. 

• Definitional issues. 

Travel Time Variance • Can be directly measured in field. 

• Indicates relative benefits of 

alternatives. 

• Must be related to some standard in order 

to determine if result is good or bad. 

• Expensive to measure in field. 

• Definitional issues. 
99 

Table 28. Common Limitations of MOEs in Practice. 

Building Blocks of Performance Measurement Systems 

MOE Limitations 

Travel Time • Treatment of delays and travel time for incomplete trips or trips that cannot 

start within the analysis period is an issue. 

• Difficult for a decision-maker to interpret (no value of time is obviously too 

high or too low without knowing more about the trip characteristics). 

Speed • Treatment of incomplete or un-begun trips is an issue, as for travel time. 

• Interpretation is facility specific. Low speed may be quite acceptable on an 

arterial, but not on a freeway. 

• Once all vehicles come to a stop, the mean speed conveys no further 

information on the severity of congestion (speed cannot go below zero). 

• Speed is a poor indicator of how close the facility is to breakdown (capacity). 

Delay • Treatment of incomplete trips or trips that have not started is an issue. 

• Definition of free-flow speed, against which delay is measured, is a problem. 

Some use the posted speed limit for the free-flow speed; others use the mean 

speed under very low flow conditions (can be higher or lower than the posted 

speed limit). 

Queue • Definition of when vehicle joins a queue and when it leaves a queue is a 

problem. 

• Interpretation is road segment and facility specific. Queues are normal for 

signals, but queues that overflow turn bays or block cross streets are not 

desirable. 

• Tallying of queued vehicles unable to enter the road segment is an issue for 

many tools. Many tools cannot report a queue longer than the storage 

capacity of the road segment or turn bay. 

• Tallying of vehicles, which have not been able to enter the road network 

during the analysis period, is an issue. 

Stops • Definition of what minimum speed is a stop is an issue. (One reviewer has 

recommended that stop time be tallied only for full stops, zero mph, to better 

match two-fluid models of traffic flow.) 

• Since tallying of stops is normally suspended while a vehicle is moving up 

within a queue, the definition of a queue is an issue. 

• Once all vehicles are queued on a road segment, further increases in 

congestion have no effect on stops. 

Density • Once all vehicles are queued, then further increases in congestion have no 

effect on density. 

Travel Time Variance • Rarely used due to difficulty of computation. Prediction tools are close to 

non-existent. 

• Once all vehicles are queued on a road segment, the variation in travel time 

tends to drop towards zero. Variance will tend to peak when the volume is 

around capacity. 
100 

Table 28. Common Limitations of MOEs in Practice (continued) 

Indicators of Performance 

MOE Limitations 

HCM Level of Service • HCM LOS is designed to convey how close the facility is to reaching capacity 

or unacceptable LOS. Once LOS “F” reached, HCM LOS letter grades give no 

further information on relative severity of different degrees of congestion. 

• HCM LOS ratings do not exist for several situations: roundabouts, 

collector/distributor roads, local residential streets. 

Volume/ Capacity • Cannot measure in field or simulate volumes passing a given point that are 

greater than capacity (measured v/c is always less than 1.00). In such 

situations, must add to the measured flow rate the buildup in the queue. 
101 

6.0 RECOMMENDED MOES

This chapter presents the recommended set of MOEs along with their recommended 

method of computation and interpretation. 

6.1 RECOMMENDED SYSTEM OF KEY SYSTEMWIDE MOES

Traffic operations analyses can generate a great deal of numerical output. Any one of the 

numerical outputs can be important to the analyst depending upon the purpose and scope 

of the analysis and the alternatives being evaluated. The set of key systemwide MOEs

recommended below is designed to be the “starting set” but not the end all set of MOEs

for any traffic operations analysis. 

This basic set is kind of like the first set of exams given to every patient when they first 

check into the emergency room. It does not matter what the patient’s complaint, the same 

four measurements are gathered and reported for every emergency room patient: 

temperature, pulse, blood pressure, and blood oxygen. These four basic indicators give 

the doctors a basic understanding of the general health of the patient, and some indication 

of general fields to investigate for the source of the problem. But they are not the 

definitive diagnostic tests. They are not the MRIs, X-Rays, or exploratory surgery that 

come later. 

Similarly, our basic system MOEs are designed to be that first round of tests every traffic 

engineer and planner should perform to assess the general health of his or her 

transportation system. They give an indication of the overall health of the system, and 

how serious the problems are. But they do not necessarily tell the engineer/planner 

precisely what is wrong. That is the purpose of additional analyses and additional tests. 

So this basic set of MOEs is not designed to replace the more detailed intersection and 

segment MOEs engineers and planners already are accustomed to perform to diagnose 

and solve traffic operations problems on the transportation system. This basic set is 

designed to help the engineer/planner and decision-maker rapidly assess the state of the 

system and identify key avenues of additional analysis to better identify needed 

improvements. 

This basic set of MOEs also is good for rapidly assessing the benefits of alternative 

improvements at the system level, in a form readily understandable by the decisionmaker. 

The basic set of MOEs for decision-making consists of five basic measures: 

1. Throughput; 

2. Mean Delay; 

3. Travel Time Index; 
102 

4. Oversaturated Freeway Segments; and 

5. Surface Street Intersections with Turn Bay Overflows and Exit Blockages. 

Each of these basic MOEs is highlighted in Table 29. Key Measures of Effectiveness for 

Decision-Making. Their use, definition, computation, reporting, and interpretation are 

presented in this table. 

Table 29. Key Measures of Effectiveness for Decision-Making 

Decision MOE Use/Description/Interpretation 

Throughput 

(Vehicles/hr) 

and 

Percent Incomplete 

Trips 

• Use – Provides a measure of the relative productivity of the system compared to 

an alternative. This MOE is also required to compute mean delay. 

• Definition – The number of vehicles present at the start plus those attempting to 

enter and successfully entering the system during the analysis period. 

• Computation – At start of analysis period tally number of vehicles present within 

system. During analysis period tally number of vehicles attempting to enter the 

system but never successfully enter. Tally number of vehicles that both enter and 

exit the system during the analysis period. Tally number of vehicles entering the 

system during the analysis period that are remaining on system at end of analysis 

period. 

• Reporting – Number of vehicles at start that successfully exit system during 

analysis period (V1). Number of vehicles at start that fail to exit the system 

during the analysis period (V2). Number of vehicles that entered but did not exit 

during the analysis period (V3). Number of vehicle generated but never entered 

the system during the analysis period (V4). Number of vehicles that successfully 

both entered and exited the system during the analysis period (V5). Report the 

percent of incomplete trips as the ratio of (V1+V2+V3+V4)/ (V1+V2+V3+V4+V5). 

• Interpretation – The throughput is compared for different alternatives to 

determine the relative productivity of each alternative. Higher values are desired. 

If the ratio of incomplete trips exceeds 5 percent then the decision-maker should 

consider lengthening the analysis period or investigate the MOEs computed for 

solely the through trips (V5) to see if they might better represent system 

performance. 
103 

Table 29. Key Measures of Effectiveness for Decision-Making (continued) 

Decision MOE Use/Description/Interpretation 

Delay/Trip • Use – Comparison of traveler perceptions of average traffic performance of 

alternatives. 

• Definition – The travel time (for all vehicles entering and attempting to enter the 

system during the analysis period) minus the theoretical travel time at the freeflow speed. This difference is divided by the number of vehicle trips to obtain 

mean delay per trip. 

The free-flow speed is defined as the minimum of the maximum safe speed or the 

analyst coded speed limit. Note that turning vehicles at an intersection would 

have a lower maximum safe speed than through vehicles. 

• Computation – Accumulate distance traveled (VMT) and time expended (VHT) 

each second by vehicles on system (or attempting to enter system) during analysis 

period. Simultaneously accumulate theoretical VHT that would have been 

expended if vehicle had been able to travel the same VMT at free-flow speed. At 

the end of the analysis period the free-flow VHT is subtracted from the 

accumulated VHT to obtain Delay VHY. The Delay VHT is converted to seconds 

and then divided by the total number of vehicle trips over which the delay was 

accumulated (V1+V2+V3+V4+V5). If the number of incomplete trips 

(V1+V2+V3+V4) is greater than 5 percent of all trips, then either the analysis 

period should be lengthened, or Delay VHT should be accumulated separately for 

through trips (V5) and averaged over only the through trips (V5). 

• Reporting – Delay is reported in vehicle hours traveled (VHT) and mean seconds 

per trip. If incomplete trips account for over 5 percent of all trips, that should be 

noted. 

• Interpretation – Subjective. If trips are long in system, then high mean delays 

may be acceptable. If trips are short, even low mean delay may be unacceptable. 
104 

Table 29. Key Measures of Effectiveness for Decision-Making (continued) 

Decision MOE Use/Description/Interpretation 

Travel Time Index • Use – To determine if facility operation during peak periods is unacceptably 

worse than for off-peak periods. 

• Definition – Ratio of observed vehicle-hours traveled (VHT) to theoretical VHT at 

free-flow speed for same VMT. 

• Computation – See notes above on computation of mean delay. The travel time 

index is the accumulated VHT divided by the theoretical free-flow VHT. 

• Reporting – System travel time index plus a few words qualifying the result, such 

as “Good,” “Bad.”

• Interpretation – 

− The TTI minus 1.00 gives the mean delay as a percentage of the mean trip time. 

− The inverse of the TTI gives the mean speed as a percentage of the free-flow 

speed for the system. 

− For an entire metropolitan area (including local streets, arterials, and freeways) 

a peak period TTI of 1.4 or better was typical of 90 percent of the metropolitan 

areas of United States in 2005. 

− For more focused systems of mixed freeway and arterial facilities (no local 

streets) a TTI of under 2.5 is roughly indicative of generally uncongested 

conditions and good signal coordination. 

− For a system of solely unsignalized facilities (freeways, highways, 2-lane rural 

roads) a TTI of over 1.4 is indicative of a facility that is over-capacity for the 

entire length of analysis period. 

− A qualifier of “Good” for TTIs <= 1.5, “Potentially Acceptable” for TTIs 

between 1.5 and 2.5, and “Less Desirable” for TTIs > 2.5 would help the 

decision-makers. 
105 

Table 29. Key Measures of Effectiveness for Decision-Making (continued) 

Decision MOE Use/Description/Interpretation 

Freeways: 

Percent Breakdowns 

• Use – To identify geographic and temporal extent of unsatisfactory operations for 

freeways in system. 

• Description – Breakdown is defined as a density of traffic greater than the density 

typical of capacity flow rate conditions. This is equivalent to HCM LOS F 

definition. Two percents are computed: 

− Maximum percent of directional freeway miles at LOS F at any onetime during 

the analysis period. 

− Percent of analysis period when any directional freeway segment is at LOS F. 

• Computation – Determine maximum LOS “E” density for each directional 

freeway segment in system according to the Highway Capacity Manual. 

Determine PCE values for each vehicle type in system for each directional 

freeway segment according to the Highway Capacity Manual. For each second in 

the analysis period tally number of vehicles on directional segment and multiply 

by appropriate PCE value to obtain equivalent PCUs. At end of model run 

compute 15 minute running averages of PCUs for each directional segment. 

Identify start and end times when 15 minute average PCU exceeds HCM LOS E 

maximum density. Find earliest start time of LOS F and latest end time of LOS F 

for system. The percent duration of LOS “F” is equal to the latest end time minus 

the earliest start time of LOS F, divided by the length of the analysis period. 

The maximum percent LOS “F” is the maximum number of directional segment 

miles operating at LOS “F” (averaged over 15 minutes) at any one time divided by 

the total directional miles of freeway in the system. 

• Interpretation – The geographic and temporal percents LOS “F” are indicators of 

congested freeway operations. The lower, the better. 
106 

Table 29. Key Measures of Effectiveness for Decision-Making (continued) 

Decision MOE Use/Description/Interpretation 

Surface Street: 

Percent of 

Intersections Exits 

Blocked 

• Use – To identify geographic and temporal extent of unsatisfactory operations for 

urban streets in system. Intersection exit blockages due to downstream queues 

extending back to an upstream intersection reduce through capacity and are 

potential safety concerns. 

• Definition – Two percents are computed: 

- Maximum percent of intersection exits that are blocked at any onetime during 

the analysis period. 

- Percent of analysis period when any intersection exit is blocked. 

• Computation – For every directional street link and for each second in the 

analysis period compare the distance of back bumper of last car in any lane of the 

directional street link to the total estimated distance to the nearest upstream 

intersection (If the upstream node has only one link feeding the subject 

downstream link, that node is not an intersection. Abandon the test on the subject 

link and perform the test instead on the upstream link.). Determine if the back of 

queue is within 25 feet of storage length of the directional link. 

- Accumulate number of seconds during analysis period when one or more 

directional links are full. The percent of the analysis period with one or more 

full directional links is the number of seconds when one or more directional 

links are full divided by the number of seconds in the analysis period. 

- Tally number of directional links meeting this “full” criterion at any one time. 

The maximum percent full links is the maximum number of full directional 

links at any one time divided by the total number of directional street links 

(exclude freeway links which have their own LOS F test) in the system. 

• Report – Maximum percent of full directional street links at any one time. Percent 

of analysis period during which one or more directional street links are “full.”

• Interpretation – The geographic and temporal percents of “full” links are 

indicators of potential capacity and safety problems. The lower, the better. 

Since a common goal for designs of new turn bays is to provide storage for the 95-

percentile demand per cycle, a goal of no more than 5 percent of the analysis 

period with full street links would be desirable for a new design. 

There are, of course, many other MOEs that every good planning or engineering study 

also will want to consider, such as, user benefits, user costs, fuel consumption, noise, air 

pollutant emissions, and safety. However, the five basic system MOEs listed above will 

give the decision-maker and the analyst a good idea of how well the system is performing 

and how one alternative improvement compares to another. The text below describes 

them in more detail. 

Throughput 

Throughput is an indicator of the productivity of the system. It tells the analyst how 

many vehicles were processed by the system during the analysis period. A related 

indicator is the percent of trips that were initiated during the analysis period but could not 
107 

be completed during the analysis period. Throughput plus the percent incomplete trips 

tell the analyst how good a job the system is doing at moving vehicles. 

If the analyst’s goal is to move people rather than just vehicles, throughput and percent 

incomplete can be computed based on person trips rather than vehicle trips. It is a 

straight forward conversion requiring only knowledge of the vehicle occupancy (number 

of person per vehicle). 

Throughput is defined as the number of distinct vehicles (or people) able to enter or exit 

the system during the analysis period. To aid its interpretation, the throughput should be 

divided into five components according to whether or not the vehicle entered, exited, 

never entered, or never exited the system during the analysis period (see Figure 5. 

Illustration of Different Components of Throughput). 

Distance

Time

Link Length

Analysis Period

Proposed

Analysis

Domain

Traditional

Analysis

Domain

V1

V5

V3

Figure 5. Illustration of Different Components of Throughput. 

Vehicles Class I = Vehicles that were present at the start of the analysis period and were 

able to successfully exit the system before the end of the analysis period. 

Vehicle Class II (not shown in figure) = Vehicles that were present at the start of the 

analysis period but were unable to successfully exit the system before the end of the 

analysis period. Except for severe congestion cases or very short analysis periods, this 

class of vehicles is usually zero. 

Vehicles Class III = Vehicles that were able to enter the system during the analysis period 

but were unable to successfully exit the system before the end of the analysis period. 

Vehicles Class IV (not shown) = Vehicles that tried to enter the system during the 

analysis period but were completely unsuccessful. While it frequently happens that 

vehicles are temporarily delayed from entering the system due to congestion, only 

extreme congestion (or a very short analysis period) would cause vehicles to be totally 
108 

barred from entering the system during the analysis period. Thus, the number of vehicles 

in this class of vehicles is usually zero as well. 

Vehicles Class V = Vehicles that entered during the analysis period and were able to 

successfully exit the system before the end of the analysis period. 

The percent of incomplete trips will be the sum of vehicle classes 1, 2, 3, and 4, divided by 

the sum of all vehicle classes (1+2+3+4+5). 

If the percent of incomplete trips is greater than five percent of total trips during the 

analysis period, the analyst should lengthen the analysis period so that more complete 

trips are reflected in the computed MOEs for the system. 

If it is not feasible to further lengthen the analysis period, the analyst must carefully 

evaluate the MOEs computed for the incomplete trips against the MOEs computed for the 

complete trips and decide which is the most relevant measure of system operations, or 

select a weighting system for combining the MOEs for incomplete and complete trips. 

Generally, higher throughputs and lower percentages of incomplete trips are desired since 

they reflect the productivity of the transportation system. 

Mean Delay 

Mean delay is an attempt to couch delay measurements in terms that a typical driver 

might understand, mean seconds delay per trip. However, it must be recognized that the 

same number of seconds of delay will be more important for a short trip, and less 

important for a long trips. 

One issue with delay is the yardstick to measure it against. Common practice would be to 

measure actual travel time against a theoretical travel time at free-flow speeds, when 

congestion is nonexistent. However, an agency may have a different standard against 

which it wishes to measure system performance. 

This report has adopted the common measure of free-flow travel time against which to 

measure delay, but other yardsticks are certainly possible. 

Free-flow speed is often defined as the speed of traffic at very low-flow conditions (when 

the flow rate is so low that changes in the flow rate have no significant effect on average 

travel speed). This may require a field measurement of prevailing speeds under low-flow 

conditions, so often planners will simply use the posted speed limit as a proxy for the freeflow speed. 

Free-flow speeds may indeed be higher or lower than the posted speed limit. A poorly 

enforced speed limit may not be obeyed. The maximum safe speed on curves and in turns 

at an intersection also should be taken into account in determining the free-flow speed. It 

is up to the analyst to determine the desired target against which to compute delay. 
109 

The accumulated travel time of all vehicles on the system should be accumulated during 

the analysis period. At the same time the theoretical travel time to go the same distance 

traveled should be accumulated as well. 

At the end of the analysis period the analyst (or software tool) subtracts the accumulated 

vehicle hours traveled at the theoretical free-flow speed (the free-flow VHT) from the 

accumulated vehicle hours traveled (VHT) to get the total delay in terms of vehicle hours 

(the Delay VHT). 

The delay VHT is converted to seconds and then divided by the number of vehicle-trips to 

obtain mean delay per vehicle-trip. 

If the analyst wants person-trip delay, the vehicles are multiplied by the vehicle 

occupancy while the theoretical free-flow and actual VHTs are accumulated. The result is 

person hours traveled (PHT). Delay is computed as before and then divided by the total 

number of person-trips during the analysis period. 

If the percent of incomplete trips (described above under throughput) is greater than five 

percent, then the analyst should extend the duration of the analysis period. If this is not 

feasible, then the analyst should accumulate VHT (or PHT) by the five vehicle-trip classes 

described above under throughput and compute the mean delay separately for each 

vehicle-trip class. The analyst then needs to evaluate the results and decide how to 

present them. 

Generally less delay is preferable, however; specific thresholds of acceptable or 

unacceptable delay are not available. The Highway Capacity Manual publishes mean 

delay level of service thresholds for signals and unsignalized intersections but not for 

segments of streets. The results might be roughly compared to the HCM thresholds, but 

one should not expect an exact equivalence due to differences in the definition of delay 

between the Highway Capacity Manual and the method used here. 

The Travel Time Index (TTI) 

The Texas Transportation Institutes Travel Time Index (TTI) indicates the extra delay 

associated with congestion on the system. The TTI is the ratio of the peak-period travel 

time to the free-flow travel time. Values of TTI for urban areas in the United States range 

from 1.05 (Anchorage, Alaska) to 1.75 (Los Angeles, California). 

Typical TTIs at capacity are shown below for uninterrupted flow facilities, arterials with 

poor signal coordination, and arterials with good signal coordination. 

An individual freeway or conventional highway with a TTI of greater than 1.4 is over 

capacity. An individual signalized arterial with a TTI greater than 2.5 either has poorly 

coordinated signals or is over capacity, or both. 
110 

Table 30. TTIs Per HCM – Freeways and Highways. 

Miles per Hour Hours/Miles 

HCM Facility Type 

Free-Flow 

Speed 

Speed at 

Capacity 

Free-Flow Travel 

Time Rate (T0) 

Capacity Travel 

Time Rate (TC) 

Travel Time 

Index at Capacity 

Freeway 75 53.3 0.0133 0.0188 1.41 

 70 53.3 0.0143 0.0188 1.31 

 65 52.2 0.0154 0.0192 1.25 

 60 51.1 0.0167 0.0196 1.17 

 55 50.0 0.0182 0.0200 1.10 

Multi-Lane Highway 60 55.0 0.0167 0.0182 1.09 

 55 51.2 0.0182 0.0195 1.07 

 50 47.5 0.0200 0.0211 1.05 

 45 42.2 0.0222 0.0237 1.07 

Two-Lane Rural Road 55 40.0 0.0182 0.0250 1.38 

Sources: 

1. Freeways: Exhibit 23-2 HCM. 

2. Multi-Lane Highways: Exhibit 21-2 HCM. 

3. Two-lane Highways: Exhibit 20-2, HCM. 

Table 31. TTIs Per HCM – Arterials – Uncoordinated Signals. 

 Miles per Hour TTI 

Arterial Class Speed Limit (mph) Signals/mi Zero vph Capacity 0vph/lim Cap/0vph Cap/Lim 

1 55 1 32.9 22.4 1.67 1.47 2.45 

1 50 2 31.5 21.7 1.59 1.45 2.30 

1 45 4 20.4 12.9 2.21 1.58 3.49 

2 45 2 29.0 20.5 1.55 1.41 2.20 

2 40 4 19.7 12.6 2.03 1.56 3.17 

2 35 5 16.7 10.5 2.10 1.59 3.34 

3 35 4 18.7 12.2 1.87 1.53 2.87 

3 30 6 14.5 9.0 2.06 1.62 3.35 

4 35 4 18.5 12.1 1.89 1.53 2.89 

4 30 6 14.1 8.8 2.13 1.60 3.41 

4 25 8 10.4 6.5 2.40 1.59 3.82 

Source: FDOT ARTPLAN Modified by Dowling 6/22/06. 

Assumptions: 120 second signal cycle, 0.45 g/c through, no coordination. 
111 

Table 32. TTIs Per HCM – Arterials – Coordinated Signals. 

 Miles per Hour TTI 

Arterial Class Speed Limit (mph) Signals/mi Zero Vph Capacity 0vph/lim Cap/0vph Cap/Lim 

1 55 1 32.9 22.4 1.67 1.47 2.45 

1 50 2 31.5 21.7 1.59 1.45 2.30 

1 45 4 24.9 16.3 1.80 1.53 2.76 

2 45 2 29.0 20.5 1.55 1.41 2.20 

2 40 4 24.0 15.9 1.67 1.51 2.52 

2 35 5 20.5 13.3 1.70 1.54 2.63 

3 35 4 22.5 15.2 1.56 1.48 2.30 

3 30 6 19.0 12.2 1.58 1.56 2.46 

4 35 4 22.2 15.1 1.58 1.47 2.32 

4 30 6 18.3 11.9 1.64 1.54 2.53 

4 25 8 15.5 10.4 1.61 1.49 2.40 

Source: FDOT ARTPLAN Modified by Dowling 6/22/06. 

Assumptions: 80-120 second signal cycle, 0.45 g/c through, coordination if >=4 sig/mi. 

Freeway Segments at Breakdowns 

Freeway breakdown occurs when the demand exceeds the capacity of the freeway. This 

can be most reliably measured by identifying the density per lane of traffic when the 

freeway is flowing at capacity and then comparing the observed density to the density at 

capacity. If the observed density is greater than the density at capacity, then the freeway 

is considered to be oversaturated or in “breakdown” condition. 

The Highway Capacity Manual applies the level of service “F” label to passenger car unit 

densities exceeding capacity that last for at least 15 minutes. For the purpose of this 

system MOE, we have relaxed the requirement that the density persist for at least 15 

minutes, so that we can catch even transient breakdowns in freeway operations. 

The geographic extent and duration of freeway segments operating at Highway Capacity 

Manual Level of Service “F” densities during the analysis period are indicators of the 

severity of freeway congestion on the system during the analysis period. 

This MOE might be more appropriately displayed as a two-dimensional time-space plot 

showing the portion of the time-space box taken up by congestion. However, this report 

attempts to identify two dimensions that might indicate the maximum extent and 

duration of congestion. One measure is the maximum percent of the analysis period 

when any freeway segment in the system is at level of service “F.” The other measure is 

the maximum percent of freeway miles that are congested at any one time during the 

analysis period. The first measure attempts to get at the maximum duration of congestion 

on the freeway system. The second measure attempts to get at the maximum geographic 

extent of the freeway congestion. 
112 

Both measures require the identification of which directional freeway segments are 

operating at LOS “F” densities for continuous 15-minute periods at each second of the 

analysis period (A 15-minute running average density is computed each second of the 

analysis). The number of vehicles must be converted to equivalent passenger car units 

(PCUs) using the passenger car equivalents in the Highway Capacity Manual. These 

equivalents vary by vehicle type and grade (or general terrain: level, rolling, 

mountainous). 

Once the timing and location of level of service “F” conditions have been identified, it is 

then just a matter of processing the data to find the maximum length of time when at least 

one freeway segment is congested, and the maximum number of freeway-miles of 

segments that are congested at any one time during the analysis period. 

To help the decision-maker appreciate the relative magnitude of the problem, the results 

are reported as a percentage of the total freeway-miles in the system and a percentage of 

the total analysis period. 

Generally lower percentages of Level of Service “F” are preferred. 

Surface Street Backups and Overflows 

Surface street backups and turn bay overflows are indicators of the extent to which 

capacity destructive queues are present on the surface streets of the system. These 

excessive queuing situations also are indicative of potential safety problems where 

vehicles queue back into an upstream intersection or vehicles queue back out of a turn bay 

and into a through traffic lane. 

A surface street backup is one where a downstream queue backs up in one or more 

through lanes to an upstream intersection, reducing its capacity. For want of a more 

descriptive term, these are called, “Intersection Exit Blockages.” They occur when the 

number of vehicles queued in any one lane on a street exceed its storage capacity. 

Additional vehicles must wait for the queue to reduce a bit before they can move through 

the upstream intersection. 

Turn bay overflows are situations where the number of vehicles waiting to make a left or 

right turn exceeds the storage capacity of the turn bay. Waiting vehicles must back up 

into the through lanes, reducing through capacity at the intersection. 

As for freeway Level of Service F, this MOE might be more appropriately displayed as a 

couple of two-dimensional time-space plots (one for street backups affecting upstream 

intersections, the other for turn bay overflows) showing the portion of the time-space box 

taken up by excessive queuing. However, this report attempts to identify two dimensions 

that might indicate the maximum extent and duration of excessive queuing. One measure 

is the maximum percent of the analysis period when any turn bay or street segment in the 

system is experiencing queue overflows. The other measure is the maximum percent of 

directional street segments that have queue overflows at any one time during the analysis 

period. The first measure attempts to get at the maximum duration of street and turn bay 
113 

queue overflows on the street system. The second measure attempts to get at the 

maximum geographic extent of the queue overflows. 

Both measures require the identification of which directional street segments and turn 

bays have queues that exceed the storage length at any second in the analysis period. 

Since most simulation tools cannot identify queues that exceed the storage capacity (the 

excess vehicles are reported as queued on the upstream link), the criterion selected is 

whether or not the queue is “close” to storage capacity of the link or turn bay. The 

estimated storage length for one vehicle of 25 feet is subtracted from the storage capacity. 

If the queue exceeds the storage length minus 25 feet, it is considered as if it has actually 

exceeded the available storage length. This measure no doubt slightly over estimates the 

actual occurrences of queue overflows, but it is only off by one vehicle length. 

Once the timing and location of queue overflow conditions have been identified, it is then 

just a matter of processing the data to find the maximum length of time when at least one 

directional street segment or turn bay is overflowing, and the maximum number of 

directional street links or turn bays with overflow queues at any one time during the 

analysis period. 

To help the decision-maker appreciate the relative magnitude of the problem, the results 

are reported as a percentage of the total directional street segments (or turn bays) in the 

system and a percentage of the total analysis period. 

Generally lower percentages of queue overflow are preferred. Since it is common to 

design new turn bays for the 95 percentile traffic surge each cycle, it is suggested that 5 

percent queue overflows during the analysis period might be a good target for a new 

design. No guidance is provided for existing systems, which may routinely experience 

more frequent queue overflows, and still be quite acceptable to the local populace. 

6.2 COMPARISON OF MOES BETWEEN ANALYSIS TOOLS 

Since each analysis tool has slightly different definitions of what constitutes stopped and 

queued vehicles and because the tools also vary in the determination of which vehicles to 

include in the computations (vehicles that both entered and exited during the analysis 

period, vehicles that did neither, or vehicles that either entered or exited but not both 

during the analysis period), it is not feasible for an analyst to take the macroscopic output 

from one tool, apply a conversion factor (or equation) and compare the results to that of 

another tool. The analyst simply does not have access to sufficient data at the macroscopic 

level to be able to compare MOEs across tools. 

The lowest common denominator for all analytical tools is the vehicle trajectories output. 

This is a microscopic output. With this output the analyst can compute macroscopic 

MOEs from any tool in a consistent manner. Then it is indeed possible to compare vehicle 

performance results across different analytical tools, through the computation of 

macroscopic MOEs from microscopic vehicle trajectory data. 
114 

6.3 MOES FOR FUTURE RESEARCH 

The technical review suggested four additional key MOEs that are likely to be valuable to 

decision-makers but could not be included at this time due to several uncertainties about 

them requiring more research and additional practical experience in order to better define 

them. They are: 

• Buffer Index; 

• Fuel Consumption Ratio; 

• NOx Emissions Ratio; and 

• Safety. 

The Buffer Index 

The buffer index shows how much earlier a travel must start the trip (as a percentage of 

the trip duration) to have a 95 percent chance of arriving on time. It is computed 

according to the following formula: 

( ) () ()

( )

100%

minutes

AverageTravelTime

minutes

TravelTime

minutes

95th PercentileTravelTime

Index % ×

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎣

⎡ −

=

Average

Buffer Equation 69

There are a dozen different measures of travel-time reliability (see “Selecting Travel 

Reliability Measures,” by J. Lomax, D.L. Schrank, S.M. Turner, R. Margiotta, Report, Texas 

Transportation Institute, May 2003.) 

The buffer index was selected because it is similar to the decisions a traveler has to make, 

“How much extra time do I have to allow in order to be sure of getting there on time?” 

Related measures include standard deviation, variance, and several other indicators or 

travel-time reliability. 

Difficulties with implementing the buffer index at this time include a lack of 

understanding of the true variability of traffic operations when considering variance in 

demand, probability of capacity reducing incidents, and the inherent variability of driver 

behavior for any given situation. More research is needed to better understand how much 

of this total variability is included in microsimulation models, and how much is still 

missing. 

Fuel Consumption Ratio 

The Fuel Consumption Ratio would be used to identify the excess fuel consumption 

caused by unsatisfactory system operation. It is the ratio of fuel consumed to fuel that 

would have been consumed if all travel could have been made at most fuel efficient speed 

for vehicles without stops. 
115 

Fuel consumption can be expressed as power consumption (Kilowatt hours or British 

Thermal Units). Gallons of gasoline equivalent was selected since it generates a ready 

visual image of fuel consumption for decision-makers. 

While tools do exist to compute fuel consumption from VMT and speed, the key issue 

with this MOE is that tools and procedures do not exist for computing the theoretical 

optimal fuel consumption for a system. 

The NOx Ratio 

The NOx ratio is defined as the kilograms of nitrous oxide emissions divided by the 

theoretical minimum NOx emissions that would occur at ideal vehicle operating speeds on 

the system. 

Vehicles actually emit a wide range of pollutants (Carbon Monoxide, Nitrous Oxides, 

Hydrocarbons, etc.). Nitrous Oxides (NOx) was selected as an indicator pollutant since it 

is the one most likely to be controlled in a nonattainment basin. 

Vehicle-Specific Power (VSP) may be an alternative measure that closely follows NOx

emission rates. VSP requires assumptions about drag and rolling resistance, Weight drops 

out because VSP is normalized by mass. Using typical value of coefficients, in SI units the 

equation becomes: 

VSP (kW/metric Ton) = v * (1.04*a + 9.81*grade(%) + 0.132) + 0.00121*v^3 Equation 70

Where: 

V = Velocity (km/hr). 

A = Acceleration. 

Source: Nam, Proof of Concept Investigation for the Physical Emission Rate Estimator 

(PERE) to be Used in MOVES [report EPA420-R-03-005]. 2003, U.S. EPA (page 7). 

Issues with the NOx ratio or other air pollutant emission proxy such as VSP is the lack of 

agreement on how best to compute air pollutant emissions or proxies from 

microsimulation data. The standard methods generally use average speed and VMT, but 

upcoming methods rely on data that can only come from vehicle trajectories. There also is 

a lack of information as to the accuracy of vehicle accelerations predicted by 

microsimulation models. 

Safety 

Safety is one field where a great deal of research will be needed to develop tools for 

translating traffic operations results into indicators of relative safety. One promising 

approach is to use microsimulation to tally the frequency of conflicts or close encounters 

in the traffic stream. However, this approach is still under development and more time 

will be needed to determine how best to incorporate safety into traffic operations analysis. 
116 

7.0 PRACTICAL APPLICATION RESULTS 

The purpose of the practical application task is to demonstrate through case studies the 

validity of the approach(s) recommended in the previous chapter. The intent is to use 

existing real-world data to demonstrate how transportation professionals should generate 

and interpret the basic system MOEs recommended by this study. 

7.1 SELECTION OF CASE STUDIES 

The original hypothesis of this research was that a set of procedures could be developed to 

convert the MOEs reported for typical microscopic and macroscopic traffic analysis tools 

into a consistent set of MOEs comparable to the Highway Capacity Manual. The 

investigation into how various tools compute the standard MOEs discovered that there is 

a wide range of methods used to compute and average the standard MOEs, and these 

methods diverge at the microscopic level. The reports produced by typical traffic analysis 

tools do not provide sufficient microscopic information to enable the analyst to convert 

the MOEs produced by one tool into the equivalent MOEs produced by another tool. 

Consequently, the recommended approach for achieving consistent MOE results across 

various analytical tools is to compute the MOEs directly from vehicle trajectory data 

produced by the tool, rather than trying to correct aggregate MOE results reported by 

each microscopic or macroscopic tool. 

One advantage of this approach is that vehicle trajectory data is the lowest common 

denominator shared by all microscopic and mesoscopic tools. A single procedure for 

computing MOEs directly from vehicle trajectory data is applicable to all micro and 

mesoscopic tools. The procedures identified in this report can be automated today 

through the use of commonly available analytical tools (ACCESS and EXCEL for example) 

or in the future through procedures built into each tool to process vehicle trajectory data. 

Since none of the macroscopic tools and few of the microscopic tools produce vehicle 

trajectory data readily accessible to the average user, these cases studies use vehicle 

trajectory data measured directly in the field. The FHWA NGSIM program has developed 

three datasets of tenth-second vehicle trajectory data. Two of these vehicle trajectory data 

sets were selected for use in the case studies: 

1. The I-80 Freeway – Emeryville, California; and 

2. Lankershim Boulevard – Los Angeles, California. 

7.2 THE I-80 FREEWAY CASE STUDY 

The I-80 data set consists of tenth-second vehicle trajectories for a 1,650-foot-long section 

of the I-80 eastbound freeway between Powell Street and Ashby Avenue in Emeryville, 

California. The section is a 5-lane plus HOV lane weaving section of freeway with a one 

lane on ramp merge point for Powell Street about 420 feet into the section. The grade is 

nominally 0 percent in this section. 
117 

Vehicle trajectory data was gathered for three 15-minute periods on the afternoon of 

April 13, 2005. The 5:15 p.m. through 5:30 p.m. period was selected for the case study as 

likely to have the most congestion. 

Further details on the data set can be found in: Cambridge Systematics, NGSIM I-80 Data 

Analysis (5:15 p.m. to 5:30 p.m.) Summary Report, December 2005, available on the 

NGSIM web site (http://www.ngsim.fhwa.dot.gov/). Table 33. I-80 Data Dictionary, 

defines the data fields in this data set. 

The MOE results for the I-80 data set are shown in Table 34. I-80 Decision-Maker MOEs. 

A single ACCESS query of the I-80 vehicle trajectory database provided most of the 

decision-maker MOEs. This query identified the first millisecond and the last millisecond 

when the vehicle was present anywhere on the system for those milliseconds within the 

analysis period. The starting and end “Y” position for each vehicle also was identified in 

the same query. 

The query was output to a spreadsheet, which then computed the VMT and VHT for each 

vehicle. Each vehicle and the VMT and VHT it generated was separated into five 

categories: 

1. Vehicles already present at the start of the analysis period that were able to exit the 

system before the end of the analysis period. 

2. Vehicles already present at the start of the analysis period that were still within the 

system at the end of the analysis period. 

3. Vehicles still present on the system at the end of the analysis period, but which were 

not present at the start. 

4. Vehicles completely denied entry to the system during the entire analysis period. (The 

NGSIM datasets do not include these vehicles so they had to be estimated for the 

purpose of the case study.) 

5. Vehicles successfully entering and exiting the system during the analysis period. 

Travel Time Index (Trip Time/Ideal Trip Time) 

Computation Notes – The travel time index was computed by taking the ratio of the sum 

of the VHT traveled for all 5 vehicle categories to the ideal free-flow VHT. The ideal freeflow VHT was computed by dividing the sum of the VMT accumulated by all 5 vehicle 

categories by the posted speed limit of 65 mph. 

The NGSIM dataset does not include vehicles unable to enter the system, so the vehicle 

hours accumulated by vehicles denied entry to the system had to be estimated. Since the 

study section had a mean density of close to 100 vehicles per mile per lane over the entire 

analysis period, it was assumed that the queue extended beyond the start of the study 

segment for the entire analysis period. Based on personal knowledge of the freeway 
118 

section, it was estimated that the queue of vehicles waiting to enter the study section 

extended back about 1 mile and persisted in steady state throughout the entire analysis 

period. The VHT of delay for vehicles temporarily delayed from entering the study 

section was therefore estimated at 100 vehicles/mile/lane x 4 lanes x 1 mile x 0.25 hrs = 

100 VHT (See Table 35. Adjustment to TTI Computation for Delayed Entry to System 

(I-80)). 

The vehicle hours accumulated for vehicles entering the system during the analysis period 

(Vehicle categories 3 and 5) were split into two subcategories. The first subcategory 

represents the VHT accumulated within the study section. The second subcategory 

represents the estimated VHT accumulated by the same vehicles waiting their turn to 

enter the study section during the analysis period. 

The final TTI is then the ratio of the posted speed limit to the final adjusted mean speed 

for all vehicles taking into account the delays to vehicles queued waiting to enter the 

system during the analysis period. 

Interpretation – The value of 18.69 for the TTI is very high, quite a bit higher than the 

metropolitan areawide TTIs reported by the Texas transportation Institute, which in 2005 

ranged from 1.05 (Anchorage, Alaska) to 1.75 (Los Angeles, CA) (Source: The 2005 Urban 

Mobility Report by David Schrank and Tim Lomax of the Texas Transportation Institute, 

May 2005, http://mobility.tamu.edu). 

The TTI also is quite a bit higher than the TTIs at capacity flows computed using the 

Highway Capacity Manual method for freeways. 

Throughput (vph) 

Computation Notes – The system throughput is the total number of vehicles able to exit 

the system during the analysis period, including vehicles present anywhere in the system 

at the start of the analysis period. The throughput is normalized to an hourly rate by 

dividing the number of vehicles by the number of hours in the analysis period. 

The throughput was the sum of vehicle categories 1 and 5, all vehicles able to exit the 

system during the analysis period. 

Interpretation – Throughput is a solely a function of demand until demand starts 

approaching capacity. Then it is a function of solely capacity. A throughput of 6,844 vph 

is equivalent to 1,141 vph/lane for a 6-lane freeway, which is less than 60 percent of the 

potential capacity of this freeway. The throughput is constrained by a downstream 

bottleneck. Throughput is useful for comparing alternative improvements. 

Freeway MOEs – Maximum Extent Breakdowns (%/dir.miles) 

An ACCESS query grouping PCEs present on the segment for each millisecond of the 

analysis period was exported to an EXCEL spreadsheet. Within the spreadsheet the PCUs 

per lane-mile were compared to the Highway Capacity Manual density thresholds for 

LOS “F” on freeways (43 pcu/lane-mile). 
119 

The mean density the full length of the analysis period and across all lanes and for the full 

length of the study section exceeded the LOS “F” density of 43 pcus/lane/mile. Thus the 

study section reached 100 percent LOS “F.”

Freeway MOEs – Duration of Breakdowns (%/Time) 

The total milliseconds where PCUs per lane-mile were greater than the Highway Capacity 

Manual density thresholds for LOS “F” on freeways (43 pcu/lane-mile) was divided by 

the total milliseconds in the analysis period to obtain the percent duration. Since the 

duration was 100 percent of the analysis period, the study section is considered severely 

congested. 

Surface Street MOEs – Street Segment Overflows 

The surface street MOEs are not applicable to the I-80 data set. 

Table 33. I-80 Data Dictionary. 

Field Name Definition Used in Case Study 

1 Vehicle ID Preliminary ID number assigned to each vehicle 

trajectory during first pass recording trajectories. Several 

ID numbers dropped during manual reconnection of 

broken trajectories. 

Yes 

2 Frame ID Frame ID Number. No 

3 Total Frames Number of frames vehicle is visible. No 

4 Global Time Number of milliseconds after Midnight, January 1, 1970 

Greenwich Standard Time. 

Yes 

5 Local X Lateral (X) coordinate of the front center of the vehicle 

with respect to the left-most edge of the section in the 

direction of travel. 

Yes 

6 Local Y Longitudinal (Y) coordinate of the front center of the 

vehicle with respect to the entry edge of the section in the 

direction of travel. 

Yes 

7 Global X CA State Plane NAD83 coordinate. No 

8 Global Y CA State Plane NAD83 coordinate. No 

9 Vehicle Length Feet. No 

10 Vehicle Width Feet. No 

11 Vehicle Class 1 = motorcycle, 2 = auto, 3 = truck. Yes 

12 Vehicle Velocity Feet per second. Yes 

13 Vehicle 

Acceleration 

Feet per second squared. Yes 
120 

Table 33. I-80 Data Dictionary (continued) 

Field Name Definition Used in Case Study 

14 Lane 

Identification 

Lane 1 = farthest left. 

Lane 6 = farthest right. 

Lane 7 = on-ramp. 

Lane 9 = shoulder on the right-side 

Noa

15-18 Additional data on leading vehicle, following vehicle, 

and headway 

No 

Source: Cambridge Systematics – NGSIM Data Dictionary. 

a Although not used in this particular test, it would be handy to have the lane identification in the MOE 

dataset, so that the difference between HOV and non-HOV lane operation could be evaluated. 

Table 34. I-80 Decision-Maker MOEs. 

Decision-Maker MOEs Value Interpretation 

Travel Time Index (Trip Time/Ideal Trip Time) 18.69 Severely congested. Quite a bit higher than 

HCM TTI at capacity. See table below. 

Throughput (vph) 6,844 Operating at about 50 percent of capacity for 6-

lane freeway. 

Freeway MOEs 

Maximum Extent Breakdowns (%/dir.miles) 100% Severely congested. 

Duration of Breakdowns (%/Time) 100% Severely congested. 

Surface Street MOEs 

Maximum Street Segment Overflows N/A 

Maximum Segment Overflows (%/segs) N/A 

Duration Segment Overflows (%/time) N/A 

Maximum Turn Bay Overflows N/A 

Maximum Turn Bay Overflows (%/bays) N/A 

Duration Turn Bay Overflows (%/time) N/A 
121 

Table 35. Adjustment to TTI Computation for Delayed Entry to System (I-80). 

Vehicle Group Component Value 

V1 (vehicles present in system at start of period and successfully exiting 

before end of period) 138 

V2 (vehicles present in system at start of period but unable to exit before 

end of period) 0 

V3 (vehicles entering system during period but unable to exit before end 

of period)) 79 

V4 (vehicles denied entry for entire period) 0 

V5 (vehicles successfully entering and exiting during the period) 1,573 

V (total vehicles) 1,790 

VMT1 (vehicles at start and successfully exiting) 28.25 

VMT2 (vehicles at start unable to exit) 0.00 

VMT3 (vehicles at end, but not at start) 17.90 

VMT4 (vehicles denied entry) 0.00 

VMT5 (vehicles successfully entering and exiting) 461.33 

VMT (total vehicles) 507.47 

VHT1 (vehicles at start and successfully exiting) 2.30 

VHT2 (vehicles at start unable to exit) 0.00 

VHT3 (vehicles at end, but not at start) 7.27 

VHT3A (accumulated on Network) 2.49 

VHT3B (accumulated waiting to enter Network) (manual adjustment) 4.78 

VHT4 (accumulated while vehicle denied entry) 0 

VHT5 (vehicles successfully entering and exiting) 136.38 

VHT5A (accumulated on Network) 41.29 

VHT5B (accumulated waiting to enter Network) (manual adjustment) 95.09 

VHT (total vehicles) 145.94 

MPH1 (vehicles at start and successfully exiting) 12.30 

MPH2 (vehicles at start unable to exit) 0.00 

MPH3 (vehicles at end, but not at start) 2.46 

MPH4 (while vehicles denied entry) 0.00 

MPH5 (vehicles successfully entering and exiting) 3.38 

MPH (total vehicles) 3.48 

VMT = Vehicle miles traveled (VMT1, VMT2, etc., are VMT for respective vehicle group). 

VHT = Vehicle hours traveled. 

MPH = Mean speed (miles per hour). 

TTI = FFS/MPH where FFS equals free flow speed and MPH equals adjusted mean speed for all vehicles. 
122 

7.3 THE LANKERSHIM CASE STUDY DATA SET 

The Lankershim data set consists of tenth-second vehicle trajectories for a 1,600-foot-long 

bidirectional section of Lankershim Boulevard between Valley Heart Drive and the 

U.S. 101 freeway in California. The section includes 4 closely spaced signalized 

intersections. The arterial has three to four through lanes in each direction with left and 

right turn bays many of the intersections. The posted speed limit is 35 mph. 

Adjacent land uses include: Bus Terminal, Metro station, 36 story office building, Metro 

station and office building parking lots and garages, and Universal Studios parking lot. 

Vehicle trajectory data was available for two 15-minute periods on the morning of June 16, 

2005. The 8:30 a.m. to 8:45 a.m. period was selected for the case study as likely to have the 

most congestion. 

Further details on the data set can be found in: Cambridge Systematics, NGSIM I-80 Data 

Analysis (8:30 a.m. to 8:45 a.m.) Summary Report, March 2006, available on the NGSIM web 

site (http://www.ngsim.fhwa.dot.gov/). A data dictionary listing the contents of this 

dataset is provided in Table 36. Lankershim Data Dictionary. 

The MOE results for the Lankershim data set are shown in Table 37. Lankershim 

Decision-Maker MOEs. 

Decision-Maker MOE Results 

A single ACCESS query of the Lankershim vehicle trajectory database provided most of 

the decision-maker MOEs. This query identified the first millisecond and the last 

millisecond when the vehicle was present anywhere on the system for those milliseconds 

within the analysis period. The starting and end “Y” position for each vehicle also was 

identified in the same query. 

The query was output to a spreadsheet, which then computed the VMT and VHT for each 

vehicle. Each vehicle and the VMT and VHT it generated was separated into five 

categories: 

1. Vehicles already present at the start of the analysis period that were able to exit the 

system before the end of the analysis period. 

2. Vehicles already present at the start of the analysis period that were still within the 

system at the end of the analysis period. 

3. Vehicles still present on the system at the end of the analysis period, but which were 

not present at the start. 

4. Vehicles completely denied entry to the system during the entire analysis period. (The 

NGSIM datasets do not include these vehicles so they had to be estimated for the 

purpose of the case study.) 

5. Vehicles successfully entering and exiting the system during the analysis period. 
123 

Since the NGSIM dataset does not include vehicles unable to enter the system, the vehicle 

hours accumulated by vehicles denied entry to the system had to be estimated. Since the 

queues in the system did not fill up the street segments very often during the analysis 

period (less than 12 percent of the time), the vehicle hours of delay for denied entry 

vehicles was assumed to be zero. No adjustment was necessary. 

Table 36. Lankershim Data Dictionary. 

Field Name Definition 

Used in 

Case Study

1 Vehicle ID Preliminary ID number assigned to each vehicle trajectory during 

first pass recording trajectories. Several ID numbers dropped 

during manual reconnection of broken trajectories. 

Yes 

2 Frame ID Frame ID Number. No 

3 Total Frames Number of frames vehicle is visible. No 

4 Global Time Number of milliseconds after Midnight, January 1, 1970 

Greenwich Standard Time. 

Yes 

5 Local X Lateral (X) coordinate of the front center of the vehicle – 

perpendicular to the median of the Lankershim Boulevard. 

Vehicles traveling on the east side of the median have positive 

Local X values, while those traveling on the west side of the 

median have negative Local X values. 

Yes 

6 Local Y Longitudinal (Y) coordinate of the front center of the vehicle 

along the median of the Lankershim Boulevard. The start point is 

at the southern boundary of the study area. 

Yes 

7 Global X CA State Plane NAD83 coordinate. No 

8 Global Y CA State Plane NAD83 coordinate. No 

9 Vehicle Length Feet. No 

10 Vehicle Width Feet. No 

11 Vehicle Class 1 = motorcycle, 2 = auto, 3 = truck. Yes 

12 Vehicle Velocity Instantaneous feet per second. Yes 

13 Vehicle Acceleration Instantaneous feet per second squared. Yes 

14 Lane Identification Current lane position of vehicle. Lane numbering is incremented 

from the left-most lane, except for locations where left-turn or 

right-turn bays exist. Left-turn bays are numbered starting from 

11 and are incremented from the left-most left-turn bay. Rightturn bays are numbered starting from 31 and are incremented 

from the left-most right-turn bay. The left-turn bay mid-block 

between intersections 3 and 4 is numbered 101. 

Yes 

15 Origin Zone No 

16 Destination No 

17 Intersection From south to north, intersection numbers. Yes 

18 Section From south to north, section number. Yes 

19 Direction Moving direction of the vehicle. 1 – eastbound (EB), 2 – 

northbound (NB), 3 – westbound (WB), 4 – southbound (SB). 

Yes 

20 Movement Movement of the vehicle. 1 – through (TH), 2 – left-turn (LT), 3 – 

right-turn (RT). 

No 

21-24 Additional data on leading vehicle, following vehicle, and 

headway. 

No 

Source: Cambridge Systematics – NGSIM Data Dictionary. 
124 

Table 37. Lankershim Decision-Maker MOEs 

Decision-Maker MOEs Value Interpretation 

Travel Time Index (Trip Time/Ideal Trip Time) 2.58 Better than typical for an uncoordinated 

arterial operating at capacity. 

Throughput (vph) 4,212 Useful for comparing the productivity of 

alternative improvements. 

Freeway MOEs 

Maximum Extent Breakdowns (%/dir.miles) N/A N/A 

Duration of Breakdowns (%/Time) N/A N/A 

Surface Street MOEs 

Maximum Street Segment Overflows 2 Two segments to investigate. 

Maximum Segment Overflows (%/segs) 33% Significant fraction of system under stress. 

Duration Segment Overflows (%/time) 20% Problems endure longer than peak 15 minutes 

of hour. 

Maximum Turn Bay Overflows 2 Two problem spots to investigate. 

Maximum Turn Bay Overflows (%/bays) 40% Significant fraction of turn bays have problems. 

Duration Turn Bay Overflows (%/time) 12% Greater than design standard of 5 percent 

implied by 95 Percentile Turn Bay Length 

design standard. 

Travel Time Index (Trip Time/Ideal Trip Time) 

Computation Notes – The travel time index was computed by taking the ratio of the sum 

of the VHT traveled for all 5 vehicle categories to the ideal free-flow VHT. No adjustment 

to the TTI was necessary for vehicles delayed from entering the system during the analysis 

period, because queues did not extend beyond the physical limits of the study area. The 

ideal free-flow VHT was computed by dividing the sum of the VMT accumulated by all 5 

vehicle categories by the posted speed limit of 35 mph. 

Interpretation – The value of 2.58 for the TTI is quite a bit higher than the metropolitan 

areawide TTIs reported by the Texas transportation Institute, which in 2005 ranged from 

1.05 (Anchorage, Alaska) to 1.75 (Los Angeles, California) (Source: The 2005 Urban 

Mobility Report by David Schrank and Tim Lomax of the Texas Transportation Institute, 

May 2005, http://mobility.tamu.edu.). 

According to the Highway Capacity Manual method the TTI for an urban arterial would 

range from 1.9 to 2.1 under free-flow uncoordinated conditions and from 2.9 to 3.3 under 

capacity flow uncoordinated conditions for a Class II/Class III arterial. Since the 

computed TTI of 2.58 falls below the values at capacity for uncoordinated arterials, the 

arterial traffic operation is considered to be generally good. 
125 

Throughput (vph) 

Computation Notes – The system throughput is the total number of vehicles able to exit 

the system during the analysis period, including vehicles present anywhere in the system 

at the start of the analysis period. The throughput is normalized to an hourly rate by 

dividing the number of vehicles by the number of hours in the analysis period. 

The throughput was the sum of vehicle categories 1 and 5, all vehicles able to exit the 

system during the analysis period. 

Interpretation – Throughput is solely a function of demand until demand approaches 

capacity. Then it is a function of solely capacity. A throughput of 4,212 vph is equivalent 

to 702 vph/lane for a 6-lane arterial, which is slightly below the planning capacity for a 

signalized intersection (planning capacity would be 750 vph/ln = 1,500 vphgl * 0.50 g/c). 

Throughput is useful for comparing alternative improvements. 

Freeway MOEs – Maximum Extent Breakdowns (%/dir.miles) 

This MOE is not applicable to the Lankershim data set. 

Freeway MOEs – Duration of Breakdowns (%/Time) 

This MOE is not applicable to the Lankershim data set. 

Surface Street MOEs – Street Segment Overflows 

Computation Notes – A specialized set of segment by segment, direction by direction set 

of ACCESS queries were required to identify street segments where the queues of vehicles 

had filled up one or more through lanes on the street. 

First a query was conducted to identify the maximum and minimum “Y” coordinates for 

each vehicle on each direction of each street segment. The results were exported to an 

EXCEL spreadsheet where a pivot table was used to compute the average maximum and 

average minimum “Y” coordinates for each direction of each street segment. The average 

was used because of slight differences in the starting and end “Y” coordinates for each 

lane of each segment. 

The average maximum “Y” was subtracted from average minimum “Y” for each direction 

of each segment to obtain the mean segment length by direction. Since vehicles that 

cannot fit within the segment are never recorded, the target queue length for determining 

if a segment is full was set at 25 feet less than the actual length of the segment. If the 

queue extends to within 25 feet of the back of the segment, the segment is considered to be 

full, for all practical purposes. 

However, the “Y” coordinates in the data set represent the position of the front of the 

vehicle, not the back, so the target length of the segment (for determining when the queue 

filled up the segment) was reduced by a further 25 feet. Thus the target “Y” coordinate for 
126 

determining if the vehicle queue in any lane had filled up the segment was the “Y”

coordinate of the back of the link minus 50 feet. 

A separate query was then conducted in ACCESS for each direction of each segment. For 

each millisecond of the analysis period, if the “Y” coordinate of any vehicle exceeded the 

maximum “Y” coordinate minus 50 feet for that direction of that segment, and the vehicle 

was traveling at that moment at a speed of less than 7 fps, with an acceleration of less than 

+2 fpss, then one or more lanes of that direction of the segment was considered to be 

“blocked” with queuing vehicles (even if not other queuing vehicle is present in the lane in 

front of the queued vehicle). 

The number of segments “blocked” at any millisecond of the analysis period was tallied to 

obtain the maximum number of blocked directional segments for the analysis period. 

The duration of blocked directional segments within the system during the analysis 

period was obtained by summing up the number of millisecond snapshots where at least 

one directional segment was blocked. 

Interpretation – The percentage of street segments subject to risk of overflow is relatively 

high (33 percent), with at least one segment subject to risk of overflow 20 percent of the 

analysis period. Since standard practice is to design turn bays so that they have less than 

a 5 percent risk of overflowing during the peak hour, the 20 percent overflow for an 

individual street segment would be undesirable for a new design. 

Surface Street MOEs – Turn Bay Overflows 

Computation Notes – Given the tediousness of the approach used to arrive at the segment 

blockages, an alternative approach was tried to identify turn bay overflows. 

An ACCESS query was launched to report all instances of queued vehicles (speeds less 

than 7 fps and acceleration less than +2 fpss) within all turn bays of the system. The 

maximum and minimum “Y” coordinate was reported for the queued vehicles each 

millisecond within the turn bay. The query result was exported to a spreadsheet. 

The difference between maximum and minimum “Y” coordinates for each turn bay for 

each millisecond was compared to the nominal storage length for the turn bay reported in 

the NGSIM data collection report, less 25 feet (50 feet was not subtracted from the storage 

length because several turn bays are only 70 feet long. Subtracting 50 feet from the storage 

length would have resulted in these turn bays being reported as at risk of overflow every 

time a single vehicle entered the bay). 

Note that this method was easier to implement with an ACCESS query, but it only gets the 

maximum number of vehicles in queue in the lane at any one time, not the maximum back 

of queue. Consequently, the method described above for identifying street segment 

backups is preferable because of its superior accuracy at identifying queue overflow 

problems. 
127 

A pivot table was used to tally up the number of milliseconds during the analysis period 

that each turn bay was at risk of overflowing. 

The maximum duration of turn bay overflows was estimated to be the longest duration of 

overflows for the turn bays. The maximum number of turn bays at risk of overflow at any 

one time during the analysis period was estimated by visual inspection of the turn bay 

query results. (It would have been more accurate to plot the turn bay overflows by 

millisecond through the analysis period and identify the maximum impacted at any one 

time and the total number of milliseconds of the analysis period when any of the turn bays 

was at risk of overflowing, however; resource and time limitations caused the author to 

select these two shortcuts for approximating the maximum duration and extent of turn 

bay overflows.) 

Interpretation – The maximum number of turn bays at risk of overflowing at any one time 

is quite high (40 percent). Also, the percent of time when one or more turn bays is at risk 

of overflowing (12 percent) is higher than 5 percent risk that standard practice would call 

for in the design of new turn bays. 

7.4 CONCLUSIONS FROM CASE STUDIES 

The above practical applications have demonstrated how vehicle trajectory data can be 

post-processed to yield key basic measures of effectiveness useful for transportation 

operations analysis and decision-makers. People more familiar with database processing 

than the author can no doubt find betters ways to process the trajectory data than were 

used in these examples. However, the practical applications have illustrated the feasibility 

of using off-the-shelf database programs to generate MOEs from vehicle trajectory data. 
128 

REFERENCES 

Cambridge Systematics, 

2004 

Cambridge Systematics. Guide to Effective Freeway Performance 

Measurement (Version 1), NCHRP 3-68 Interim Report. November 2004. 

Cambridge Systematics, 

2005 

Cambridge Systematics. NCHRP 7-15 Interim Report. May 2005. 

Federal Highway 

Administration (2004a) 

Federal Highway Administration, Traffic Analysis Toolbox, Volume I: 

Traffic Analysis Tools Primer, June 2004. 

http://ops.fhwa.dot.gov/trafficanalysistools/tat_vol1/index.htm. 

Federal Highway 

Administration (2004b) 

Federal Highway Administration, Traffic Analysis Toolbox, Volume II: 

Decision Support Methodology for Selecting Traffic Analysis Tools, June 

2004. 

http://ops.fhwa.dot.gov/trafficanalysistools/tat_vol2/index.htm. 

Federal Highway 

Administration (2004c) 

Federal Highway Administration, Traffic Analysis Toolbox, Volume III: 

Guidelines for Applying Traffic Microsimulation Modeling Software, June 

2004. 

http://ops.fhwa.dot.gov/trafficanalysistools/tat_vol3/index.htm. 

FDOT 2000 Florida DOT, Mobility Performance Measures Brochure (2000). 

http://www.dot.state.fl.us/planning/statistics/mobilitymeasures/

mmbrochure.pdf. 

Shaw, 2003 Shaw, T. Performance Measures of Operational Effectiveness for Highway 

Segments and Systems. NCHRP Synthesis 311. Washington, D.C. 

Transportation Research Board. 2003 
Office of Operations

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