Traffic Analysis Toolbox Volume VII:

Predicting Performance with

Traffic Analysis Tools

PUBLICATION NO. FHWA-HOP-08-055 March 2008

Office of Operations

1200 New Jersey Avenue, SE

Washington, DC 20590
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 Documentation Page 

1. Report No. 

FHWA-HOP-08-055 

2. Government Accession No. 

 

3. Recipient’s Catalog No. 

4. Title and Subtitle 

Predicting Performance with Traffic Analysis Tools - Final Report 

5. Report Date 

March 2008 

 6. Performing Organization Code 

7. Authors 

T. Luttrell (SAIC), W. Sampson (University of Florida), D. Ismart (Louis 

Berger Group), D. Matherly (Louis Berger Group) 

8. Performing Organization Report 

No. 

 

9. Performing Organization Name and Address 

Science Applications International Corporation (SAIC) 

1710 SAIC Drive 

McLean, VA 22102 

 

10. Work Unit No. (TRAIS) 

 

11. Contract or Grant No. 

DTFH61-06-D-00005; Task 25 

12. Sponsoring Agency Name and Address 

United States Department of Transportation 

1200 New Jersey Avenue, S.E. 

Washington, DC 20590 

13. Type of Report and Period 

Covered 

14. Sponsoring Agency Code 

HOTM 

15. Supplementary Notes 

Mr. John Halkias (Task Manager) 

Mr. Grant Zammit (Resource Center Technical Expert) 

Mr. Barry Zimmer (COTR) 

16. Abstract 

This document provides insights into the common pitfalls and challenges associated with use of traffic analysis tools 

for predicting future performance of a transportation facility. It provides five in-depth case studies that demonstrate 

common ways to ensure appropriate results when using an microsimulation tool, and also includes “how to” material 

that allows users to address common challenges associated with microsimulation analysis.

Key Words 

Traffic Simulation Modeling, Traffic Analysis Tools, 

Microsimulation, Operational Analysis

18. Distribution Statement 

No restrictions. This document is available to the public 

from: The National Technical Information Service, 

Springfield, VA 22161. 

19. Security Classif. (of this 

report) 

Unclassified 

20. Security Classif. (of this page) 

Unclassified 

21.No of Pages 

52 

22. Price 

N/A 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.
PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

Table of Contents 

EXECUTIVE SUMMARY ......................................................................................................... vi 

INTRODUCTION..........................................................................................................................1 

CASE STUDY 1: I-494 AND TRUNK HIGHWAY 7 IN MINNEAPOLIS, MINNESOTA .4 

Project Description.......................................................................................................................4 

Characteristics and Inputs........................................................................................................5 

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

Analysis and Results.....................................................................................................................6 

Evaluation Measures................................................................................................................7 

Congested Conditions ..............................................................................................................7 

Conclusions and Recommendations .............................................................................................8 

Extensions and Guidance..............................................................................................................9 

Modeling Process.....................................................................................................................9 

Analysis of Results ................................................................................................................10 

Potential Simulation Model Issues.........................................................................................10 

CASE STUDY 2: I-15 RECONSTRUCTION IN OGDEN, UTAH .......................................11 

Project Description.....................................................................................................................11 

Analysis and Results...................................................................................................................12 

Results....................................................................................................................................13 

Conclusions and Recommendations ...........................................................................................13 

Extensions and Guidance............................................................................................................14 

CASE STUDY 3: S.R. 826-PALMETTO EXPRESSWAY INTERCHANGE 

OPERATIONAL ANALYSIS REPORT, FLORIDA. .............................................................15 

Project Description .....................................................................................................................15 

Description .................................................................................................................................16 

Year 2004 (Post-Construction) Existing Traffic Volumes ....................................................17 

Mainline and Ramp Traffic Volumes ....................................................................................17 

Intersection Traffic Volumes .................................................................................................17

Year 2000 (Pre-Construction) Traffic Volumes ....................................................................18 

Analysis and Results...................................................................................................................19 

Ramps, Mainline and Intersection Level of Service Analysis Comparison ..........................19 

Eastbound Ramp Merge / Diverge Level of Service Analysis Comparison..........................19 

Mainline Level of Service......................................................................................................20 

Micro-simulation Results.......................................................................................................20 

Highway Capacity Manual Software Results ........................................................................21 

Conclusions and Recommendations ...........................................................................................22 

Extensions and Guidance............................................................................................................22 

Level of Effort........................................................................................................................22 

Traffic Analysis Software Selection ......................................................................................23 

Modeling Process...................................................................................................................23 

Data Development .................................................................................................................23 

Measure of Effectiveness Issue..............................................................................................23 

CASE STUDY 4: I-25 AND UNIVERSITY BOULEVARD IN DENVER, COLORADO..24 

Project Description.....................................................................................................................24 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

Conditions Prior to Construction ...........................................................................................25 

SPUI Selected ........................................................................................................................25 

Operations Analysis...............................................................................................................26 

Analysis and Results...................................................................................................................27 

Conclusions and Recommendations ...........................................................................................28 

Extensions and Guidance............................................................................................................29 

CASE STUDY 5: CHICAGO STREET NETWORK ..............................................................30 

Project Description .....................................................................................................................30 

Characteristics and Inputs......................................................................................................31 

Data Collection ......................................................................................................................31 

Analysis and Results...................................................................................................................31 

Evaluation Functions .............................................................................................................31 

Calibration..............................................................................................................................32 

Throughput Comparison ........................................................................................................32

Stop Time Comparisons.........................................................................................................32 

Prediction and Validation ......................................................................................................32 

Spillback ................................................................................................................................33 

Lane Distribution ...................................................................................................................33 

Conclusions and Recommendations ...........................................................................................33 

Extensions and Guidance............................................................................................................34 

Cross-Cutting Findings...............................................................................................................35 

PRACTICAL GUIDANCE ON THE APPLICATION OF TRAFFIC SIMULATION AND 

ANALYSIS TOOLS FOR TRANSPORTATION-INVESTMENT DECISIONS.................38 

Issues and Pitfalls of Implementation (So Why Didn’t My Analysis Work?) ..........................38 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

v 

List of Figures 

Figure 1: Case Study Locations and Types of Projects ...................................................................2 

Figure 2: Intersection of Interstate 494 and TH 7 in Minneapolis, Minnesota................................4 

Figure 3: I-494 and TH7 Interchange Detector Locations...............................................................6 

Figure 4: I-494 and I-394 Interchange Detector Locations .............................................................6 

Figure 5: LOS Observations by 15 Minute Time Period.................................................................8 

Figure 6: I-15 Corridor in Ogden, Utah.........................................................................................11 

Figure 7: SR 826/Palmetto Expressway at NW 67th (Left) and NW 57th Avenue (Right)..........16 

Figure 8: Existing Conditions Geometry (EB Improvement Project Post-Construction) .............17 

Figure 9: Year 2000 (Pre-Construction) A.M. and P.M. Peak Hour Traffic Volumes..................19 

Figure 10: I-25 Interchange in Denver, Colorado..........................................................................24 

Figure 11: Test Bed Network.........................................................................................................30 

Figure 12: Evaluation Steps...........................................................................................................34 

List of Tables 

Table 1: MNDOT’s Peak Hour Simulation Estimates versus Actual Conditions..........................7 

Table 2: Historical AADTs and Reduction Factor.......................................................................18 

Table 3: Year 2000 (Pre-Construction) and Year 2004 (Post-Construction) Comparison 

Freeway Ramp Merge / Diverge Analysis Derived from the HCM ..............................20 

Table 4: Year 2000 (Pre-Construction and Year 2004 (Post-Construction) Comparison 

A.M. and P.M. Peak Hour Freeway Mainline Micro-simulation Analysis Summary ...21 

Table 5: Year 2000 (Pre-Construction and Year 2004 (Post-Construction) Comparison 

A.M. and P.M. Peak Hour Freeway Mainline HCM Analysis Summary ......................22 

Table 6: Simulation-Generated Delay and Levels of Service ......................................................26 

Table 7: HCM-Generated Delay and Levels of Service...............................................................27 

Table 8: ▲ Model Compared with ▲ Field.................................................................................32 

Table 9: LOS Designation in the Highway Capacity Manual (1994) ..........................................33 

Table 10:Comparison of Findings by Site.....................................................................................35 

 
PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

EXECUTIVE SUMMARY 

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

transportation professionals to relieve congestion. Use of traffic simulation and analysis tools has 

become the standard approach for evaluating transportation design alternatives, operational 

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

The purpose of this study is to assess and provide an understanding on how well simulation and 

traffic analysis tools predict performance, and identify elements and issues which practitioners 

should be aware of to effectively apply these tools. In order to support recommendations for use by 

practitioners, information was gathered and five locations were chosen for in-depth analysis. These 

sites include: 

1) I-494 and Trunk Highway 7 in Minneapolis, Minnesota: Analysis of proposed freeway 

segment and interchange improvements. 

2) I-15 Reconstruction in Ogden, Utah: Analysis of maintenance of traffic and reconstruction 

closure scenarios. 

3) S.R. 826-Palmetto Expressway Off-Ramps near Miami, Florida: Analysis of proposed offramp improvements and the addition of an auxiliary lane. 

4) I-25 and University Boulevard in Denver, Colorado: Estimation of performance of replacing a 

full cloverleaf interchange with a single point urban interchange (SPUI). 

 

5) Traffic Signal Network in Chicago, Illinois: Study of key issues in the validation of a 

microsimulation analysis of a complex arterial network signal timing project. 

The five cases were selected to test a variety of software model tools across a range of applications 

and settings, illustrative of problems as well as best practices, to derive lessons learned. During 

these investigations, much was learned that increases the understanding of current practice and 

provides insights into improving future analyses. The summary and recommendations at the end 

of this report discuss examples of lessons learned, and how the modeling process can account for 

the types of modeling challenges identified. The information presented in the case studies, 

along with conclusions drawn from the analyses and experience of the authors, are the 

foundation for a practical set of guidelines and suggestions for overcoming common 

shortcomings, unreliable assumptions and similar problems. The checklist included as 

Attachment 1 provides a summary guide to a host of issues that contribute to deviations between 

simulations and observed conditions. 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

INTRODUCTION 

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

transportation professionals to relieve congestion. Use of traffic simulation and analysis tools has 

become the standard approach for evaluating transportation design alternatives, operational 

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

are being utilized by agencies to assess the performance of existing operations as well as the 

prediction of future operations. As reliance on these tools increases it is critical to understand how 

well they predict performance under actual as well as assumed conditions. 

Purpose 

The purpose of this study is to assess and provide an understanding on how well simulation and 

traffic analysis tools predict performance, and identify elements and issues which practitioners 

should be aware of to effectively apply these tools. Often, misapplication of a tool can change the 

results enough to significantly impact the decision making process. Since the modeling process is 

often used to support investment decisions on higher cost projects, misapplication of a tool might 

result in significant cost implications. 

Rationale for the Cases 

 

Information was gathered on more than 20 potential case study locations. In order to determine the 

sites that would provide the best information, site selection criteria were developed and applied. 

These criteria include the following: 

 Availability of “after” data, preferably sites where an “after” study was performed 

 Diversity in traffic analysis tools used (may include more than one) 

 Diversity in type of improvement or operational strategy modeled or proposed 

 Geographic dispersion across sites 

 Facility type 

 Location type (e.g., rural, urban, downtown) 

 Location characteristics (e.g., percent of local traffic versus through traffic, percent trucks, percent 

commuter traffic) 

 Typical volume/capacity or level of congestion 

 Agency’s experience with the use of traffic analysis tools 

Information Gathering 

Once the site selection criteria was applied and a list of the five top sites was generated, contact was 

made with local project representatives (often both in the public and private sector) to extract 

information on the potential case study. For sites with “after” studies, summary reports were 

gathered to assess any completed comparisons of model results with actual field conditions. For sites 

where the strategy or construction was already implemented, investigations were performed into the 

potential for data gathering at the site to facilitate analysis of field conditions. Where appropriate, 

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2 

agencies provided actual files from the tool used. These files were analyzed to support the 

conclusions drawn within this report. 

Scope of Each Case Study 

This study was carried out through in-depth discussions with traffic operations practitioners who had 

relied on a transportation model to assist them in designing a technical solution to a traffic 

engineering problem, only to find that the reality of the traffic and operation of the project was 

somewhat or significantly different from that projected by the model. More than 20 potential cases 

were initially evaluated and narrowed to five cases for in-depth analysis. 

The five cases are shown in Figure 1 and briefly described below. 

Figure 1: Case Study Locations and Types of Projects 

1) I-494 and Trunk Highway 7 in Minneapolis, Minnesota: Modeling for the widening of I-494 

from 4-lanes to 6-lanes From Valley Creek Road to TH 55 in the west metro area. 

2) I-15 Reconstruction in Ogden, Utah: Investigation of various I-15 reconstruction closure 

scenarios to model and quantify the impact on travelers during the project. 

3) S.R. 826-Palmetto Expressway Off-Ramps near Miami, Florida: Documentation of the

operations of off-ramp improvements and the addition of an auxiliary lane. 

Chicago, IL: 

Arterial Network 

Signal Timing 

Improvements

Miami, FL: 

Interchange 

Operational 

Analysis

Denver, CO: Single 

Point Urban 

Interchange 

Upgrade Analysis

Ogden, UT: 

Modeling of 

Construction 

Alternatives

Minneapolis, MN: 

Freeway and 

Interchange Upgrade 

Analysis 
PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

4) I-25 and University Boulevard in Denver, Colorado: Estimation of performance of replacing a 

full cloverleaf interchange with a single point urban interchange (SPUI). 

 

5) Traffic Signal Network in Chicago, Illinois: Study of key issues in the validation of a 

microsimulation analysis of a complex arterial network signal timing project. 

The five cases were selected to test a variety of software model tools across a range of applications 

and settings, illustrative of problems as well as best practices, to derive lessons learned. FHWA’s 

Traffic Analysis Toolbox provides information on the process for carrying out a microsimulation 

analysis project. Some of the processes used in each case study listed are very similar to the process 

outlined in Volume 3 of the Traffic Analysis Toolbox; however, formal site-specific processes for 

modeling and simulation may not be as clearly defined as the FHWA-developed 

process. Practitioners from all the sites studied and highlighted in this document used some or most 

elements of the Toolbox process according to their own established procedures and the procedures 

required for the particular software that was employed, sometimes without specific reference to a 

defined process. Many of the elements of the process are clear to users and must be applied to 

perform a study, while others, and the appropriate techniques for application, may not be. 

Each case study that employed use of an appropriate tool that leads to the metric of Levels of Service 

(LOS) modeled conditions during the worst 15 minute period (peak flow rate). The cases that used 

microsimulation went well beyond the peak 15 minute flow rate typically used for LOS, and usually 

encompassed multiple time periods including the peak hour and often the peak period, as discussed 

in the case studies. Additional metrics that were analyzed in one or more of the case studies included 

queuing, delay (signals), density (freeways), bottlenecking, spillback, following patterns and lane 

distribution, and crash rates. 

During these investigations, much was learned that increases the understanding of current 

practice and provides insights into improving future analyses. The summary “Issues of 

Implementation” at the end of this report discusses examples of lessons learned, and how the 

user can anticipate / adjust / compensate for the types of modeling challenges identified (“So 

what should a user do?”). These cases and summary are the foundation for a (forthcoming?) 

practical set of guidelines and suggestions for typical studies to overcome common shortcomings, 

unreliable assumptions similar problems. 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

CASE STUDY 1: I-494 AND TRUNK HIGHWAY 7 IN 

MINNEAPOLIS, MINNESOTA 

Project Description 

In 2006, the Minnesota Department of Transportation (MNDOT) planned, designed, and 

constructed an expanded section of Interstate 494 in Minneapolis. The project involved 

widening I-494 from 4-lanes to 6-lanes From Valley Creek Road to TH 55 in the west metro area. 

To support the analysis of alternatives, MNDOT used a traffic simulation tool to model future 

conditions along I-494 along with interchanges. For one specific interchange, I-494 and Trunk 

Highway (TH) 7, MNDOT modeled two build scenarios including a full cloverleaf interchange 

and a partial cloverleaf interchange, along with a no-build scenario. The area of study is shown 

in Figure 2. The interchange is located southwest of Minneapolis in a suburban area. 

Figure 2: Intersection of Interstate 494 and TH 7 in Minneapolis, Minnesota 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

5 

At the time of the analysis, the existing interchange was a full cloverleaf. MNDOT was 

concerned with the potential impacts on the loops from the freeway widening project and gave 

additional scrutiny to the I-494/TH7 interchange. 

ar design timeframe. MNDOT opened the upgraded interchange to traffic

on August 31, 2006. 

ed to 

emonstrate peak period performance better than the peak 15 minute measures often used. 

urated conditions for the freeway to see how well the analysis predicted 

ctual field conditions. 

 Cookbook: Discover the Magic of Data Extraction” based on 

e following MnDOT guidance: 

days with traffic incidents during the AM or PM peak period (7 to 8 AM and 4 

torm 

resulting in visibility of 1⁄4 mile or less, smoke or haze, blowing snow, and tornado). 

Characteristics and Inputs 

The planning and design of the project included a simulation model analysis for year opening 

(2006) and the 20 ye

Data Collection 

MNDOT maintains an electronic repository of data for all freeways in the Twin Cities 

Metropolitan area. MNDOT archives the data and provides public access to the data via a 

website. The system archives volume, speed, headway, and occupancy data from loop detectors 

and calculates metrics such as density and flow rate. These advanced metrics are intend

d

Data were downloaded from the system for analysis and comparison with the simulation model 

output and results to test how well the simulation process performed. We focused the analysis 

on freeway and loop operational characteristics for the I-494/TH7 interchange area. Since the 

metrics showed acceptable levels of service, we also chose one additional location where the 

model predicted oversat

a

To this case study assessment, after data during a period from September 2006 through June 

2007 was obtained. This timeframe was selected based on data guidelines from MnDOT’s 

publication titled “Data Extraction

th

 Eliminate weekends, Mondays, and Fridays 

 Eliminate holidays and the days before and after holidays 

 Eliminate bad weather days (days with snow or more than 0.20 inches of rainfall) 

 Eliminate 

to 5 PM) 

 Eliminate days with special weather incidents (e.g., fog or mist, fog reducing visibility to 

1⁄4 mile or less, thunder, ice pellets, hail, freezing rain or drizzle, duststorm or sands

Weather information was also obtained for the Twin Cities area via http://www.weather.gov, 

combined with an inquiry about historical traffic incident information to identify any effects due 

to recurring and nonrecurring incidents. MnDOT recently phased out the Metro Incident 

Selection Tool (MIST); therefore, incident archives were not available for analysis. While 

incidents may have occurred on some of the analysis days, no incidents were accounted for due 

to the lack of data. 
PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

Analysis and Results 

Once the specific dates were selected for analysis, we downloaded archived traffic data 

for the PM peak period (4-5PM). The following figures show the seven detector 

locations studied. 

Figure 3: I-494 and TH7 Interchange Detector Locations 

Figure 4: I-494 and I-394 Interchange Detector Locations 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

Evaluation Measures 

Results for simulated throughput were compared with actual flow rates. The average field measured 

flow rate was consistently higher than the simulated throughput due in part to an issue with a 

previous version of this model in overestimating impacts from bottlenecks. Additionally, travel 

demand data may have been underestimated by the planning model used, especially since true 

“demand” is higher than loop detectors can count – given that they only see departure volumes – 

during congested conditions. Since this project was a partial reconstruction of I-494, the freeway 

lane configuration reduces from six to four lanes further downstream compared with the 

configuration prior to construction During the original analysis and prior to construction, field 

observations by MnDOT’s analysis team highlighted issues with the operating conditions on each 

ramp due to the constrained right-of-way. The simulated speeds for the ramps were fairly similar to 

the actual detector speeds, with the actual speeds slightly higher in the field for 5 of the 7 study 

locations. The model results helped MnDOT better understand the projected operating conditions and 

the speed profiles helped them decide to expand the existing full clover leaf design within the 

constrained right of way compared with the partial clover leaf alternative. Increasing the volumes to 

try and predict true demand through sensitivity analysis may have been helpful in determining break 

points for acceptable versus unacceptable operating conditions. 

Table 1 compares simulated measures with actual field conditions. Since the ramp loop detectors are 

located at the mid points of the ramps, density and LOS do not apply for the ramps. Influences from 

merge and diverge areas were accounted for in the simulation but not directly reported as they would 

be from a Highway Capacity Analysis. All of the ramp roadways have peak hour volumes that are 

below capacity.

. 

Table 1: MNDOT’s Peak Hour Simulation Estimates versus Actual Conditions 

Simulation Statistics Average Field Conditions 

Location Throughput Speed Density LOS Throughput Speed Density LOS 

NB I-494 Off Ramp 518 29 N/A N/A 517 25 N/A N/A 

SB I-494 Off Ramp 323 29 N/A N/A 249 27 N/A N/A 

I494 On Ramp from 

EB TH7 723 20 N/A N/A 613 25 N/A N/A 

I494 On Ramp from 

WB TH7 82 22 N/A N/A 66 26 N/A N/A 

SB I494 Mainline 4221 67 14 B 3589 75 16 B 

NB I494 Mainline 5131 62 16 B 4629 73 22 C 

NB I494 at I394 5544 42 58 F 4302 65 29 D 

Congested Conditions 

The freeway segment northbound on I494 at the I394 interchange was selected to test one location 

that MnDOT projected to have failing operating conditions. As shown in Table 1, the average actual 

density for this segment was 29 passenger cars per hour per lane (pcphpl), while the model predicted 

58 pcphpl. However, several 15 minute time periods of actual field data showed a density greater 

than 45, indicating LOS F based on the Highway Capacity Manual. The model also included 

consideration of truck traffic impacts based on truck operating parameters that differ from those of 

passenger cars, and the MnDOT data archives also account for truck percentages. In this case, the 

7 
PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

8 

model results helped MNDOT better understand and enhance the justification for the need to widen 

the freeway, even though high densities were projected for the widened section in the future as well. 

The six lane section would enhance traffic operations compared with the four lane section. 

Additionally, this area would likely be influenced by the nearby downstream weaving segment which 

could involve a different analysis to determine operating performance. 

Figure 5 highlights the LOS categories within which each individual 15 minute density value 

falls, as observed from actual field conditions. The model’s average density was higher than the 

field-measured average density. However, several periods with LOS F were observed. As 

MnDOT successfully proved, agencies should understand the level of detail being analyzed to ensure 

a full understanding of the operating conditions of the facility. Often, temporal descriptions of 

results are needed to help decision makers understand operations. For example, LOS F should 

be accompanied by the time period the LOS is observed, how long it lasts, and if an 

improvement reduces density or delay but does not change level of service. 

Conclusions and Recommendations 

The purpose of this analysis was to analyze how the application of a micro-simulation tool 

provided support to decision makers and determine how well the tool predicted future operating 

conditions for the freeway and interchange and how it influenced the decision. Although right of 

way constraints and time limitations for reconstruction drove the decision to keep the existing 

loops, use of the model enhanced the decision making process. The analysis adequately 

predicted future freeway operating conditions and overall helped MNDOT understand the 

potential impacts from the freeway reconstruction project. Variation in results for several 

measures may have mainly been due to error in demand projections based on expanded capacity. 

Based upon discussions with the Minnesota DOT team, they believe that a sensitivity analysis 

should be built in to determine, based on the level of confidence in the data projections, the point 

at which additional traffic may make the facility reach congested conditions or conditions 

degraded below a desired threshold. 

Figure 5: LOS Observations by 15 Minute Time Period 

0

10

20

30

40

50

60

70

80

90

100

LOS A LOS B LOS C LOS D LOS E LOS F

Level of Service Bins

Number of 15-minute Observations

NB I494 

at I394
PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

Extensions and Guidance 

The following observations and guidance are offered as a result of the analysis and discussions 

with project personnel. 

Level of Effort – Being a significant corridor level analysis (this report focuses mainly on one 

interchange within the larger study), the level of effort for this particular simulation project is 

estimated to be approximately 1500 person-hours over three months. This level of effort is based 

on users with significant simulation modeling experience and who have access to an excellent 

data repository from which data can be extracted electronically. Additionally, users had access 

to data processing tools, such as a Visual Basic interface tool developed prior to this study that 

processes simulation output files and organizes the output data into a spreadsheet for ease of 

analysis and reporting. A similar study with less experienced modelers and more time 

consuming data collection and data gathering would expand the level of effort beyond what is 

estimated for this study. 

Needs that would add level of effort to this particular magnitude of project include: 

 Field data collection or data gathering from another agency such as planning data from a 

Metropolitan Planning Organization (MPO). 

 Additional time needed for less experienced users to familiarize themselves with the model. 

 Additional time needed for any expanded sensitivity analyses that may be needed to 

determine the future break point demand for a facility. This type of analysis would enhance 

the results and decision making process. 

 Setting up the model and coding the geometrics normally takes less time than data gathering 

and input, especially without links to electronic data repositories such as the MNDOT data 

tools website. Users should allow for adequate time to gather data. 

 Tools can be developed, similar to the one mentioned in this case, to automate data input and 

output processing to save time and cost over multiple simulation projects. Early investments 

may be needed to lower future level of effort. 

Modeling Process 

Users should use simulation modeling to get verifiable results, not simply a set of quantified 

results from a completed analysis. The results should be reasonable and based on model 

calibration. Much of the process FHWA has published for using simulation models was 

developed in parallel with the process used by MnDOT. One area that is evolving for MnDOT is 

model calibration. With added experience, modelers can enhance their understanding of the 

parameters that, when altered, will have the most impact on the results. 

Model results should be used to support decision-making processes and therefore need to be 

adequately communicated to decision-makers. Additionally, users should not simulate every 

idea proposed, but should use discussion and other tools to narrow the list to a few alternatives 

that are the most promising and that can best support decision-making. 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

Analysis of Results 

Users should be careful when comparing information across traffic analysis tools. For example, 

MNDOT used simulation-produced freeway densities within the Highway Capacity Manual’s 

level of service thresholds. FHWA does not promote nor encourage the reporting of LOS based 

on an alternative tool’s results due to the differences in the way these metrics are calculated. The 

HCM uses passenger-car equivalents and peak flow rates to determine density, and the level of 

service thresholds were designed for use with this particular procedure for calculating density. 

Results should be displayed in an appropriate way to allow decision-makers to view the results 

within the context they were designed for. Simulations should further define latter (later??) steps 

in the process. 

Potential Simulation Model Issues 

Since this analysis was performed in 2002, MNDOT used an older version of the simulation 

model than is currently available. The older version of the model had difficulties in predicting 

impacts from lane drops on freeways. This issue has been alleviated in the current version of the 

model. However, the earlier model’s tendency to overestimate impacts from lane drops and 

underestimate the ability of the traffic stream to recover from such congested conditions may 

have been responsible for the model overestimating the congestion and underestimating the 

throughput compared with the field data. Users should be familiar enough with potential issues 

to properly calibrate the model and should validate the findings as much as possible to ensure 

accurate results.

 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

CASE STUDY 2: I-15 RECONSTRUCTION IN OGDEN, UTAH 

Project Description 

The Utah Department of Transportation (UDOT) uses a program-level traffic analysis framework 

called “User Impact Planning” to help facilitate implementation of construction programs and to 

help determine operational treatments during construction to alleviate impacts. Modeling helps 

UDOT better understand future operational characteristics and allows for enhanced planning 

activities. For example, the Statewide Transportation Improvement Program (STIP) may have 

multiple projects proposed for the same year. The UDOT modeling program helps decisionmakers determine the timing for the construction projects at the system level and helps with 

scheduling decisions and letting timeframes. 

The UDOT process includes three stages, where the second stage involves planning level analysis 

to evaluate three alternatives – no build, traditional build, and fast track design-build. The analysis 

at this level includes quantitative estimates of measures of effectiveness including total vehicle 

hours of delay per alternative. The trade off between traditional build and design build lies in the 

typically slow progress with moderate impacts versus fast progress with shorter, more intense 

impacts, respectively. In this case, the design build alternative significantly compressed the 

construction schedule. 

 

UDOT and the University of Utah 

performed a study in 2005 to evaluate 

the impact of various I-15 

reconstruction closure scenarios on 

the travelers in the Ogden area. The 

purpose of the research was to 

investigate the impact of the 

scenarios during and after they were 

implemented on I-15 during the 

reconstruction period and facilitate 

decisions about future maintenance 

of traffic during other reconstruction 

projects. While this study was not 

performed prior to construction to 

assist with decision-making, it 

provided an “after” assessment of 

how well the model could predict the 

impacts from design-build decision 

using real-time field data. The I-15 

corridor is shown in Figure 6. 

Figure 6: I-15 Corridor in Ogden, Utah For the I-15 project, the study team 

Special thanks to Kevin Sommers of the Minnesota Department of Transportation and MnDOT’s 

consulting team for providing details and insights for use in these comparisons.

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

analyzed a paper developed by the University of Utah Traffic Lab that focused on the modeling 

results and associated impacts of the various I-15 reconstruction closure scenarios. The purpose of 

the research was to investigate the impact of the scenarios as the traffic management plans were 

implemented, and facilitate future decisions. Data from the Wasatch Front Regional Council 

(WFRC) transportation planning model were converted from a planning-level tool format to an 

operations-level format. However, much of the analysis relied on planning-level data. The 

converted model was calibrated and validated for each scenario. 

UDOT places priority on reconstruction closure scenarios that efficiently use alternate routes and 

minimize user delay along the network. The overall UDOT user impact planning process uses 

traffic data from Metropolitan Planning Organizations (MPO), provides overlap throughout the 

entire process from planning to design to construction, and keeps decision-making processes at the 

regional level. UDOT uses the early analysis results to make decisions as early as possible in the 

overall process. Based on the modeling outcomes, UDOT applies the necessary innovative 

contracting techniques including incentive-disincentive, lane rental, and A+B bidding. 

Analysis and Results 

The design-build alternative allowed for construction to be completed faster, but the traditional 

build alternative would have lowered the extent of the disruption and spread it out over a longer 

period of time. Under traditional build, UDOT would have let up to ten separate contracts for 

design and construction, with construction covering a span of nearly ten years. 

In consultation with UDOT project managers, the University of Utah Traffic Lab developed 

reconstruction closure scenarios for each alternative for the I-15 project and compared them with 

actual field data. Modelers executed traffic assignments for each scenario for multiple time periods, 

and reported measures of effectiveness for two spatial levels: area wide and corridor specific. 

UDOT designed the study to help quantify benefits and determine a process for selecting the best 

overall alternative for future projects – no build, traditional build, or design build. For the I-15 

reconstruction, the no-build alternative assumes no additional capacity on I-15 through 2020. The 

no-build future alternative was primarily used as a baseline to determine how it compared with the 

benefits of the other two alternatives. 

Within these alternatives, several maintenance of traffic plan components were modeled. The 

corridor level analysis included I-15 and several alternate routes. The main measures of 

effectiveness studied were vehicle hours of delay, vehicle-kilometers traveled, travel time, and 

average network congestion (percentage of links with a V/C ratio greater than 0.9). The model 

used produces metric such as average speed, travel time, and volume to capacity ratio. 

UDOT experts provided some assumptions for use in the traditional build scenario, including: 

 Ten year construction staging for traditional build compared with 4 1⁄2 years for design 

build 

 Two lanes per direction on I-15 would remain open throughout construction. 

 The freeway can close completely at night only. 

 Several other alternates would remain open at all times. 

 When ramps are closed, ramps at consecutive interchanges would remain open. 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

13 

Some planning tools account for the impacts of signalized intersections on arterials by reducing capacity 

on those links in the network. A micro simulation analysis tool will produce more realistic delay values 

compared with planning tools. In using any level of tool (planning versus simulation),the availability of 

high quality travel demand data is important to producing the best analysis results. 

 Construction time for a single interchange would last at least two years (three years for a 

major junction or pair of interchanges). 

The modeling exercise included five time periods: AM peak (6-9am), PM peak (3-6pm), daytime 

period (9am-3pm), evening period (6-10pm), and the nighttime period (10pm-6am). The study 

focused on the average V/C ratios for the PM peak hour only. 

Results 

The model estimated savings of approximately 60 million hours of delay by using Design Build 

versus Traditional Build, for a fifteen year analysis period. Vehicle kilometers of travel did not 

differ significantly between the three alternatives; however, the model estimated that the no-build 

alternative would experience significant congestion and VKT would likely increase as motorists 

seek alternate routes that may increase their trip length. The design-build alternative proved to be 

the best alternative based on this “after” analysis. 

Assessing the field data, it was confirmed that the analysis failed to estimate acceptably accurate 

V/C ratios on the corridors. Correlation coefficients were high at the average daily traffic 

comparison level, but when the analyst developed correlation coefficients based on data at the peak 

hour level they were much lower. Ultimately, however, the analysis produced data that matched 

fairly closely with the local MPO travel demand forecasts. 

The other corridor-specific measures were found to be comparable with the field observations. 

The model also showed a lack of ability to reproduce accurate saturation rates for the major 

arterials. This inaccuracy in estimating saturation rates is inherent in the limitations of 

transportation planning models. The planning models must be designed to accurately distribute 

traffic demand over all links in the real street network. Additionally, the model included several 

different types of facilities including freeways, major and minor arterials, and collector roads. In 

addition, transportation planning models rarely include signals in their modeling procedures. 

Conclusions and Recommendations 

The purpose of this analysis was to highlight some of the issues experience by UDOT in 

assessing traffic control alternatives for a major reconstruction project. Since data were already 

available in analyzed format, the study team expanded on existing findings to provide insights to 

agencies interested in similar analysis processes. The major finding from this study lies in the 

potential limitation of planning tools to accurately predict future demand and volume to capacity 

ratios. 

The University of Utah study concluded that either the arterial capacities or throughput estimates 

are overly reduced by the model, or the demand on the links in the model’s network is 

overestimated. The University study also concluded that LOS values would likely be much 

higher than values obtained from a micro simulation or signal optimization tool, further 

supporting the finding that users should be careful in how they compare results, as they may not 
PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

be directly comparable across different models or simulation tools. Other results from the study 

are more or less comparable with the field observations. 

Extensions and Guidance 

The issue with V/C ratios (discussed under “Results”) was due in part to limitations in the use of a 

transportation planning application with a desired end result being a detailed operational analysis 

of traffic patterns. Agencies are often faced with the challenge of predicting future traffic patterns 

with enough accuracy to evaluate operational-level conditions, even though the tool used may be 

designed mainly for the planning level. The task of determining how well traffic control 

alternatives will function is especially difficult, given the need to have highly accurate demand 

information. Additionally, a sensitivity analysis could be used to determine break points for 

congested conditions by providing a range of potential V/C ratios that might be expected, 

especially due to likely potential error in demand forecasts. 

At the planning and operations levels, model networks should be large enough to include all 

traffic that may potentially be impacted by construction. For example, UDOT developed a 

network model that included major alternate routes to I-15. State agencies should also 

coordinate with local agencies as appropriate to ensure appropriate network coverage and to 

gather appropriate data to use within the model. Consequently, UDOT owns and maintains 

many of the alternate routes, including signalized arterials that might likely be owned by cities or 

counties in other states. 

Since a majority of the urban population in Utah lives along the I-15 corridor and Wasatch Front, 

the model developed in this project will be extremely useful for future analysis without the 

original level of effort required to initially build the model. The overall study in this case cost 

$93,000, with approximately 75% of the total used in setting up the model and the remainder 

used in analyzing the results. During the period of performance for the original study, the Utah 

Traffic Lab was being constructed and therefore ultimately provided the University with direct 

traffic data links to the UDOT traffic management system. As with the MNDOT example, 

access to electronic data in near real-time was a convenient and cost-saving measure for this 

study. 

Special thanks to Doug Anderson, UDOT and Aleksandar Stevanovic of the University of Utah Traffic 

Lab for providing details and insights for use in these comparisons.

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CASE STUDY 3: S.R. 826-PALMETTO EXPRESSWAY 

INTERCHANGE OPERATIONAL ANALYSIS REPORT, 

FLORIDA. 

NW 57th and NW 67th Avenues Eastbound Off-Ramps 

Project Description 

Case Example 3 documents the comparative findings of pre- and post-construction operational 

level of service for the eastbound (EB) off-ramps and the auxiliary lane along the SR 826-

Palmetto Expressway east-west corridor for the NW 67th and NW 57th Avenues interchanges. 

The purpose of this ‘before and after’ analysis was to provide the Florida DOT with a detailed 

analysis that documents the improved traffic operations (density and level of service) due to the 

eastbound off-ramp improvements and the construction of an eastbound auxiliary lane. The 

eastbound interchange improvement project began on January 22, 2001 and was completed 

March 20, 2002. FDOT conducted the study to assist them in determining if similar ramp and 

auxiliary lane improvements for the westbound direction would be warranted. 

The eastbound SR 826 Palmetto Expressway ‘before and after’ study was initiated due to the 

Florida DOT design team completing an Interchange Operational Analysis Report (IOAR) which 

was submitted to Department in December 2004 for the westbound (WB) off-ramps at the NW 

67th and NW 57th Avenues interchanges. The report recommended construction of a continuous 

WB auxiliary lane, and widening of the off-ramps to two lanes, along with operational 

improvements to the intersections. The Scoping Committee requested a study of the recently 

improved eastbound off ramps, at NW 67th and 57th Avenues, in order to verify whether the 

objectives of that project have been accomplished. The recommended improvements included 

widening of the off-ramps and adding a continuous auxiliary lane between both interchanges. 

Due to the similarities in configuration and recommendations, it was decided that an evaluation 

of before and after conditions in the EB direction (field review and microsimulation 

comparisons) would be the best way to establish whether the proposed WB improvements in the 

IOAR (December 2004) are sound and worth pursuing. 

Although the eastbound interchange improvements existed when the ‘before and after’ study was 

initiated, the microsimulation analysis was conducted to quantify the eastbound ramp 

improvements contribution to the SR 826 Palmetto Expressway operations. Growth in the 

Expressways traffic as well as not having field data prior to the construction of the eastbound 

interchange improvements made it difficult to assess the extent of the operational improvements 

by field observation alone. The comparison was conducted by applying a microsimulation 

software analysis to determine what the changes in the level of service would be due to 

interchange improvements. The interchange improvements were also evaluated by using a 

deterministic Highway Capacity Manual software package. A comparison was made between the 

final analysis of the microsimulation software, the Highway Capacity Manual procedure, and the 

actual condition. The EB interchange improvement project began January 22, 2001 and was 

completed March 20, 2002. 

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Figure 7: SR 826/Palmetto Expressway at NW 67th (Left) and NW 57th Avenue (Right) 

The Florida DOT design team completed an Interchange Operational Analysis Report (IOAR) 

which was submitted to Department in December 2004 for the westbound (WB) off-ramps at the 

NW 67th and NW 57th Avenues interchanges. The report recommended construction of a 

continuous WB auxiliary lane, and widening of the off-ramps to two lanes, along with 

operational improvements to the intersections. The Scoping Committee requested a study of the 

recently improved eastbound off ramps, at NW 67th and 57th Avenues, in order to verify whether 

the objectives of that project have been accomplished. The recommended improvements 

included widening of the off-ramps and adding a continuous auxiliary lane between both 

interchanges. Due to the similarities in configuration and recommendations, it was decided that 

an evaluation of before and after conditions in the EB direction (including actual and 

microsimulation comparisons) would be the best way to establish whether the proposed WB 

improvements in the IOAR (December 2004) are sound and worth pursuing. 

Description

The SR 826-Palmetto Expressway is a high-speed limited access facility with a posted speed of 

55 mph. The mainline facility has a typical section consisting of six lanes divided from west of 

NW 67th Avenue to east of NW 57th Avenue. Travel lanes are approximately 12 feet wide with 

7-foot inside shoulders and outside shoulders approximately 10 feet wide. A one-way frontage 

road (NW 167th Street) is located on the north and south sides of the mainline facility. The 

frontage road provides two lanes in each direction with a posted speed of 40 mph. Figure 8 

illustrates the existing conditions geometry for both interchanges after the construction of the EB 

improvement project. 

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Figure 8: Existing Conditions Geometry (EB Improvement Project Post-Construction) 

Year 2004 (Post-Construction) Existing Traffic Volumes 

For the purpose of this comparative analysis, the existing year (2004) A.M. and P.M. peak hour 

volumes were taken directly from the SR 826-Palmetto Expressway IOAR (December 2004), 

referenced above. 

The method used in the IOAR (December 2004) to develop the A.M. and P.M. peak hour 

volumes for the mainline, ramps and intersections is briefly described below. For additional 

details such as traffic flow patterns, origin-destination survey and historical crash data, refer to 

the IOAR (December 2004). 

Mainline and Ramp Traffic Volumes 

Year 2003 Average Annual Daily Traffic (AADT) volumes for SR 826-Palmetto Expressway 

mainline and ramps were obtained from the FDOT’s 2003 Florida Traffic Information CD. The 

2003 mainline and ramp volumes were projected to year 2004 volumes by applying a growth 

factor of 0.5 percent. This applied growth factor was based on the historical records from the 

Department’s traffic counting stations. The growth rate was computed from a regression analysis 

using the Department’s Trends Analysis Spreadsheet. The A.M. and P.M. peak hour traffic 

volumes were estimated by applying appropriate K-factors to the AADT. 

Intersection Traffic Volumes 

Turning movement and seventy-two hour continuous traffic counts were collected at the 

signalized intersections for both A.M. (7:00 to 9:00 A.M.) and P.M. (4:00 to 6:00 P.M.) peak 

hours during typical weekdays in April 2004. 

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Year 2000 (Pre-Construction) Traffic Volumes 

A comparison of historical AADTs from the 2004 Florida Department of Transportation Traffic 

Data CD was done based on key stations along the corridor. Table 2 provides a summary of this 

comparison. 

As shown in the table, the 2001 AADTs declined from the previous year AADTs, due to 

construction. For this reason, volumes for the year 2000 were determined to be representative of 

the most recent entire pre-construction year along the corridor. Also shown in Table 2 below, is 

the average reduction factor of 0.91 applied to the Year 2004 (Post-Construction) volumes taken 

from the IOAR (December 2004) to develop the Year 2000 (Pre-Construction) volumes. 

Table 2: Historical AADTs and Reduction Factor 

Reduction Factor Site Description 2000 2001* 2004 

'00 - '04 '01-'04

9060 SR 826 W NW 67 Ave 116000 111500 135467 0.856 0.823 

0554 SR 826 W NW 57 Ave 123500 118500 116500 1.060 1.017 

0405 SR 826 E NW 57 Ave 136000 134000 149000 0.913 0.899 

0038 SR 823 (NW 57th Ave) N NW 159th St 52500 47000 59500 0.882 0.790 

1190 SR 823 (NW 57th Ave) S NW 173rd Dr 54000 54500 63000 0.857 0.865 

AVERAGE REDUCTION FACTOR 0.914 0.888 

 * Beginning of the construction year 

Figure 9 illustrates the Year 2000 (Pre-Construction) traffic volumes that were developed for the 

A.M. and P.M. peak hour periods for the mainline, ramps, and signalized intersections. 

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Figure 9: Year 2000 (Pre-Construction) A.M. and P.M. Peak Hour Traffic Volumes 

Analysis and Results 

Ramps, Mainline and Intersection Level of Service Analysis Comparison 

For the purpose of comparing the traffic conditions before and after construction at the eastbound 

off-ramps at NW 67th and NW 57th Avenue, the results of a micro-simulation model for SR 826 – 

Palmetto Expressway IOAR (December, 2004) for A.M. and P.M. peak hours were obtained by 

Florida DOT. The existing geometry was reviewed and assumed for the Year 2004 (PostConstruction) conditions. The geometry and traffic volumes were revised for the Year 2000 (PreConstruction) conditions. Listed below are revisions made to the geometry. 

 NW 67th Avenue - reduced to one-lane eastbound off-ramp 

 NW 57th Avenue – reduced to one-lane eastbound off-ramp 

 Removal of eastbound auxiliary lane between NW 67th and NW 57th Avenues 

Eastbound Ramp Merge / Diverge Level of Service Analysis Comparison 

The eastbound off-ramps at the interchanges of NW 67th Avenue and NW 57th Avenue were 

improved from one-lane to two-lanes with a full auxiliary lane between the NW 67th Avenue 

interchange on-ramp and NW 57th Avenue interchange two-lane off-ramp. 

Ramps were analyzed utilizing the ramp module of the Highway Capacity Manual and other relevant 

methodology on ramps from the Highway Capacity Manual. The hourly volumes were converted to 

peak flow rates by applying truck factors, peak hour factors (PHF), driver population parameter, and 

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passenger car equivalents as described in Highway Capacity Manual. Table 3 summarizes results of 

the ramps merge/diverge analyses. 

Table 3: Year 2000 (Pre-Construction) and Year 2004 (Post-Construction) Comparison 

Freeway Ramp Merge / Diverge Analysis Derived from the HCM 

Interchange AM Peak Hour PM Peak Hour

Location Direction Number of Lanes Volume Ramp LOS Volume Ramp LOS 

Year 2000 (Pre-Construction)

SR 826 at NW 67 EB off 1 490 C 640 C th

Avenue EB on 1 1,160 D 760 C 

SR 826 at NW 57 EB off 1 510 D 640 C th

Avenue EB on 1 740 D 640 C 

Year 2004 (Post-Construction)

SR 826 at NW 67 EB off 2 540 B 700 B th

Avenue EB on 1 1,270 B 830 B 

SR 826 at NW 57 EB off 2 560 B 700 B th

Avenue EB on 1 810 D 700 C 

As seen in Table 3 and in the Highway Capacity Manual methodology output, a comparison of 

pre and post construction conditions indicate improvements in the level of service, even when 

considering that the traffic in the segment has grown almost 10% between 2000 and 2004. 

Mainline Level of Service 

Levels of service analyses from the micro-simulation model and the Highway Capacity Manual 

software were conducted for the mainline for Year 2000 (Pre-Construction) and Year 2004 

(Post-Construction). Tables 4 and 5 summarize the comparative results for A.M. and P.M. peak 

hour freeway mainline in the eastbound direction for pre-construction and post-construction 

conditions. 

Micro-simulation Results 

The A.M. peak hour results from the model do not indicate significant differences or marked 

improvements in the post construction condition, with the exception of west of the N.W. 67th

Avenue off-ramp. The eastbound mainline segment between the NW 67th Avenue on-ramp and 

the NW 57th Avenue off-ramp indicated to be within the acceptable level of service standard for 

pre-construction at LOS C but for post-construction the level of service dropped below the 

standard level of service to LOS E. This is primarily due to the traffic increases that have 

occurred since the construction took place, which have caused congestion at the intersections and 

the mainline, congestion which then spills back into the ramps under study. 

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Table 4: Year 2000 (Pre-Construction and Year 2004 (Post-Construction) Comparison 

A.M. and P.M. Peak Hour Freeway Mainline Micro-simulation Analysis Summary 

A.M. Peak Hour 

From To 2000 (Pre-) 2004 (Post-) 

Density LOS Density LOS 

West of NW 67th Avenue NW 67th Avenue Off-Ramp 19.27 C 17.42 B 

NW 67th Avenue Off-Ramp NW 67th Avenue On-Ramp 17.74 B 19.59 C 

NW 67th Avenue On-Ramp NW 57th Avenue Off-Ramp 23.47 C 38.17 E 

NW 57th Avenue Off-Ramp NW 57th Avenue On-Ramp 21.49 C 23.63 C 

NW 57th Avenue On-Ramp East of 57th Avenue 23.72 C 26.66 D 

P.M. Peak Hour 

From To 2000 (Pre-) 2004 (Post-) 

Density LOS Density LOS 

West of NW 67th Avenue NW 67th Avenue Off-Ramp 19.72 C 17.92 B 

NW 67th Avenue Off-Ramp NW 67th Avenue On-Ramp 17.31 B 18.95 C 

NW 67th Avenue On-Ramp NW 57th Avenue Off-Ramp 20.52 C 17.54 B 

NW 57th Avenue Off-Ramp NW 57th Avenue On-Ramp 17.62 B 19.30 C 

Highway Capacity Manual Software Results 

Given the shortcomings of the microsimulation model, which produced spillbacks that did not 

allow the proper isolated evaluation of the auxiliary lane, the freeway module of the Highway 

Capacity Manual software was chosen as an alternative tool to better analyze and isolate 

conditions relevant to the auxiliary lane project. Table 5 provides the summary of the analysis. 

Based on the Highway Capacity Manual software freeway analysis, a comparison of ‘before and 

after’ conditions indicates generally unchanged levels of service and decreases in density along 

key sections of the mainline, even considering that the traffic in the section has grown almost 

10% since the eastbound auxiliary lane was completed 

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Table 5: Year 2000 (Pre-Construction and Year 2004 (Post-Construction) Comparison 

A.M. and P.M. Peak Hour Freeway Mainline HCM Analysis Summary 

A.M. Peak Hour 

From To 2000 (Pre-) 2004 (Post-) 

Density LOS Density LOS 

West of NW 67th Avenue NW 67th Avenue Off-Ramp 21.90 C 18.00 C 

NW 67th Avenue Off-Ramp NW 67th Avenue On-Ramp 18.90 C 20.80 C 

NW 67th Avenue On-Ramp NW 57th Avenue Off-Ramp 25.90 C 21.30 C 

NW 57th Avenue Off-Ramp NW 57th Avenue On-Ramp 22.80 C 25.00 C 

NW 57th Avenue On-Ramp East of NW 57th Avenue 27.30 D 30.10 D 

P.M. Peak Hour 

From To 2000 (Pre-) 2004 (Post-) 

Density LOS Density LOS 

West of NW 67th Avenue NW 67th Avenue Off-Ramp 22.20 C 18.30 C 

NW 67th Avenue Off-Ramp NW 67th Avenue On-Ramp 18.30 C 20.10 C 

NW 67th Avenue On-Ramp NW 57th Avenue Off-Ramp 22.90 C 18.90 C 

NW 57th Avenue Off-Ramp NW 57th Avenue On-Ramp 19.10 C 21.00 C 

NW 57th Avenue On-Ramp East of NW 57th Avenue 22.90 C 25.20 C 

Conclusions and Recommendations 

The purpose of this analysis was to evaluate how much the construction of the expanded freeway 

eastbound ramps and auxiliary lanes contributed to improving the freeway’s operations. As 

expected the microsimulation model accurately represented the existing freeway operations and 

properly forecasted spillbacks from downstream. By replicating the spillbacks, however, the 

analysis of the auxiliary lanes as an isolated improvement was not possible to distinguish. Under 

these circumstances the HCM software was applied in order to evaluate the auxiliary lane as an 

isolated improvement in order to determine the benefits of the improvement. The HCM 

comparative analysis indicated that the eastbound improvements yielded moderate improvements 

in the freeway and ramp operations. Therefore a recommendation was made to construct similar 

improvements in the westbound direction. 

Extensions and Guidance 

Level of Effort 

Case Study 3’s geographic scope is relatively limited to several interchanges and the supporting 

frontage roads and arterial system feeding into the interchange areas. Due to the limited roadway 

system that was under study, the required amount of time to input the network and traffic data for 

the microsimulation model was modest. If the study area was wider in scope then the setup time 

would be significantly higher. For the deterministic HCM model, the setup time was minimal. 

HCM models are relatively simplistic in their data and network requirements and require 

minimal labor for their application. 

 

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Traffic Analysis Software Selection 

An aspect of any traffic analysis study is the selection of the proper software package that best 

meets the objectives of the study. In Case Study 3, the objective of the project was to identify 

how well individual roadway improvements improved the freeway traffic operations. Many 

times as was the case in the Palmetto Interchange Analysis, spillback traffic as shown in the 

microsimulation model masked the operational improvements of the expanded ramps and 

auxiliary lane. By using the deterministic HCM model, the spot improvements due to the 

expanded ramps could be evaluated without being masked by the system deficiencies. An 

important lesson from Case Study 3 is for traffic engineers to select the best package that will 

fulfill the specific objectives of the study. When evaluating specific spot improvements such as 

individual ramps or limited merge areas simpler traffic analysis tools such as the HCM models 

may be more appropriate and effective than microsimulation models. 

Modeling Process 

An important lesson to learn in this case study is that microsimulation models can be used and be 

quite useful for conducting before and after studies even though a formal before study was not 

completed prior to the construction of a transportation improvement. 

Data Development 

Typical applications of traffic analysis software require existing as well as future traffic such as 

traffic volumes and turning movements. In this case study rather than forecasting the future, the 

model was used to recreate the traffic operational conditions prior to the completion of the 

improved ramps and auxiliary lanes. Traffic data for the Year 2000 prior to the construction 

project was developed based on traffic reduction factors established by mainline freeway 

historical counts. The case study demonstrated that through the development of traffic reduction 

factors sufficient traffic information can be developed for practical application to 

microsimulation models. 

Measure of Effectiveness Issue 

In the conduct of this study two separate software packages were applied; microsimulation and a 

deterministic HCM model procedure. Each program has their unique procedures for determining 

the level of service and measure of effectiveness. As demonstrated in this study, if the HCM 

procedure of Level of Service definition as used in the deterministic HCM model will be the 

standard, then the microsimulation results must be converted into an equivalent measure of 

effectiveness as defined in the HCM. For this case study all microsimulation results were 

converted into density and the equivalent Level of Service to match the results of the HCM 

procedure. Whenever a microsimulation model is used it is imperative that an HCM equivalence 

be established as was done in this study when using HCM Levels of Service. 

 

Special thanks to Florida Department of Transportation District 6 and the Consulting Team for 

providing details and insights for use in these comparisons.

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CASE STUDY 4: I-25 AND UNIVERSITY BOULEVARD IN 

DENVER, COLORADO. 

Converting a Cloverleaf Interchange to a Single-Point Urban Interchange 

Project Description 

This case study evaluates the modeling that was part of an overall evaluation of alternatives and 

estimation of performance on a full cloverleaf interchange design to mitigate the substandard ramp radii 

with tight curves, short weaving areas, and inadequate acceleration lanes. Alternatives included 

upgrading the cloverleaf to current standards, to several partial cloverleaf alternatives, to a diamond 

interchange, to a single point urban interchange. Factors affecting the selection of the final interchange 

configuration included cost, right-of-way availability, and traffic operations, including concerns about the 

additional congestion a signal might cause on the already-overloaded arterial. The focus of this review is 

on the comparison between future conditions as estimated by the modeling with actual operations after 

the interchange was built. 

The single point urban interchange (SPUI) (as shown below in Figure 10) was selected as the preferred 

alternative at this location because it provided the best traffic operations on University Boulevard without 

significant right-of-way or environmental impacts. The cost was higher than that for a diamond 

interchange, but the improved traffic operations were given more weight in the selection of the 

interchange alternative. Also, this provided a solution that added only one signalized intersection, which 

alleviated a concern of the public over adding signals to the already congested arterial. 

Simulations were used extensively through the analysis to help decision-makers visualize what projected 

traffic operations might look like; multiple models were employed to accomplish individual tasks with the 

most appropriate tool. A signal timing tool was used to optimize signal timing for the SPUI and compute 

capacity at each signalized intersection along University Boulevard within the immediate arterial, 

including Buchtel and Evans, as well as the unsignalized Buchtel intersection with the Park-and-Ride 

main access. Two additional models were utilized to optimize cycle length and offsets for coordination 

Figure 10: I-25 Interchange in Denver, Colorado 

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through the corridor. Finally, a separate simulation tool was used throughout for delay and Level of 

Service (LOS) estimates for consistency in comparing alternatives. 

Conditions Prior to Construction 

The University Boulevard Interchange with I-25 was a typical cloverleaf configuration, with direct 

ramps serving each of the eight movements in the interchange. University Boulevard was two 

lanes per direction with 12-ft lanes in the interchange area. I-25 was three lanes per direction 

through the interchange. 

 The interchange had a number of deficiencies, including the northbound I-25 to northbound 

University ramp with poor sight distance caused by substandard geometric conditions with 

no acceleration lane at University Boulevard where 12-ft lanes were being narrowed to 11-

ft lanes; the southbound I-25 to southbound University ramp with poor sight distance 

caused by substandard geometric conditions with the ramp terminus within several hundred 

feet of the intersection of University Boulevard and Buchtel Boulevard where queuing 

created stop-and-go conditions; and, the weaving areas on I-25 experienced congestion 

associated with the short weaving distance through a vertical crest which limited sight 

distance. 

 There were also several capacity deficiencies related to the interchange, including the 

northbound weave on I-25 operated at LOS F during both peak hours; the southbound 

weave on I-25 operated at LOS E during both peak hours; and the weave between the 

southern ramp termini and the Buchtel Boulevard intersection operated poorly during both 

peak periods due to the short weaving distances. 

SPUI Selected 

The SPUI and diamond interchanges provide the following safety benefits: 

 The northbound I-25 to northbound University exit ramp moves south, allowing for an 

improved design with a better acceleration lane and sight distance. 

 The southbound I-25 to southbound University ramp moves away from the Buchtel 

Boulevard intersection, allowing for a lengthened weaving section. 

 The signal(s) meter southbound University Boulevard traffic for larger weaving gaps. 

 The northbound University to southbound I-25 ramp moves away from Buchtel Boulevard, 

increasing the weaving distance. 

 The weaving sections on I-25 are eliminated. 

The SPUI was selected as operationally superior to the diamond interchange, with better mid-block 

operations and better interchange LOS. The SPUI provides the additional benefits of adding only 

one signal to University Boulevard and increasing signal spacing between the interchange and 

Buchtel. This information is intended to provide background on the ultimate project, but our focus 

will be on the SPUI as built. 

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Operations Analysis 

For the purposes of this review, opening day estimates were the focus in order to compare model 

predictions with actual operations. The build scenario volumes were distributed to the SPUI. 

The following traffic analyses were conducted: 

 Optimized individual intersection timings; 

 Optimized corridor for coordinated operations; and 

 Simulated the interchange area using the optimized signal timing. 

Delay values were extracted from simulation to determine levels of service (LOS) for the various 

alternatives for both AM and PM peak periods. The effects of a park-and-ride lot on Buchtel just 

off University were made part of the analysis for projected use of the LRT station there. Levels 

of service were obtained for signalized locations using a capacity analysis tool as shown in Table 

6. It should be noted here that while earlier in the original study, when analyzing individual 

signalized intersections, HCM procedures were used, the use of LOS is not entirely appropriate 

in this table given that the delays were generated from simulation. 

Table 6: Simulation-Generated Delay and Levels of Service 

AM PM 

Opening Day Delay LOS Delay LOS 

SPUI 

Northbound 47 D 50 D 

Westbound 41 D 41 D 

Southbound 25 C 25 C 

Eastbound 31 C 31 C 

Buchtel 

Northbound 187 F 208 F 

Westbound 41 D 44 D 

Southbound 12 B 13 B 

Eastbound 169 F 190 F 

Evans 

Northbound 85 F 83 F 

Westbound 301 F 309 F 

Southbound 294 F 302 F 

Eastbound 69 E 73 E 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

Analysis and Results 

Some issues that are apparent when reviewing this particular study raise several questions: 

A. Did the use of multiple tools and the ways they were integrated and compared help or 

hinder the accuracy and comprehensive coverage of the results? 

B. Was data collected using conventional stop-bar turning movement counts that show 

departure flow rates instead of measuring demand by quantifying arrival rates? 

C. Were oversaturated conditions properly modeled incorporating the unmet demand into 

delay computations in multiple-period analyses? 

D. Were the effects of the downstream signalized intersections at Buchtel and Evans 

properly considered, including spillback during peak periods? 

E. Was the use of simulation instead of HCM methods to ascertain LOS for the various 

components appropriate? 

To address these questions, current “after” data were obtained from the City of Denver for 

analyzing the current conditions to compare with “opening day” estimates. The data provided by 

the City included demand flows for all movements of the now-operational SPUI and the 

signalized intersections on University at Buchtel and Evans. Multiple-period analysis runs were 

made for both AM and PM peak periods using these flow rates, the as-built geometric conditions 

and current signal timing, using HCM procedures. 

Results from modeling the “after” data in still another model using HCM procedures as 

independent verification of the delay, LOS and queue estimates are shown here in Table 7 as 

representative of current, as-built operations. 

Table 7: HCM-Generated Delay and Levels of Service 

AM PM 

Opening Day Delay LOS Delay LOS 

SPUI 

Northbound 37 D 37 D 

Westbound 40 D 42 D 

Southbound 28 C 33 C 

Eastbound 42 D 42 C 

Buchtel 

Northbound 503 F 28 C 

Westbound 35 D 35 D 

Southbound 112 F 100 F 

Eastbound 50 D 503 F 

Evans* 

Northbound 334 F 372 F 

Westbound 843 F 704 F 

Southbound 179 F 302 F 

Eastbound 29 C 73 C 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

When compared with the original study predictions shown in Table 7, the major discrepancy is the 

estimate of delay and LOS for the southbound approach at Buchtel (with the northbound approach 

at Evans another inconsistency). The study predicted LOS B, but the current conditions are clearly 

LOS F. This is most likely due to the spillback from Evans, which could have been foreseen from 

its LOS predictions. (One other discrepancy was the PM estimate for the NB approach at Buchtel, 

which is created because the current capacity analysis did not account for the spillover from the 

short left-turn bay there.) 

Conclusions and Recommendations 

After comparing the original case study results with our current analysis, each question raised in 

the Analysis of Results is addressed below. 

A. The original study effectively used multiple analysis tools to take advantage of the 

strengths of each. One tool was used to quantify capacity and LOS at each intersection, 

including the SPUI, as well as signal timing for that intersection. Another tool was 

utilized to determine signal coordination and to confirm delay and LOS for the signals 

operating as a corridor. Finally, another tool was used to simulate the operations of the 

corridor to obtain delay estimates consistent within one model. This worked out to be an 

effective use of multiple tools for this application. 

B. Although it is not clear from the original report, existing count data did not seem to 

include unmet demands judging from the delay and LOS results yielding such good 

operations. This may have shown a better than reality situation for the current conditions, 

but did not have a negative effect on the alternative selection process and of course was 

not a factor in the future condition analysis. 

C. Projection estimates were not constrained by actual counts and did appear to properly 

consider the appropriate demand levels for the future operating situations. Therefore, the 

analyses using these projections did model oversaturated conditions showing some 

extreme delay levels in some cases as proved to be the case after reviewing the “after” 

conditions. 

D. Multiple models showed the excessive queue estimates and predicted the spillback from 

the Buchtel and Evans signalized intersections to be an issue. While these tools 

quantified the magnitude of these queues, some were not effective in modeling the effects 

of the bottleneck on adjacent intersections. The scope of the original study and this 

review was limited to the southbound approaches to Buchtel and Evans as potentially 

affecting the SPUI. The southbound flow rates simply seem to overwhelm the available 

capacity in all analyses, to the point additional lane capacity would be needed to mitigate. 

E. The use of simulation overcame the shortcomings of standard capacity analysis 

procedures for these spillback situations. The results did accurately predict the 

bottlenecks, particularly at Evans, spilling back through Buchtel and affecting the SPUI, 

which actual “after” counts confirm. 

The quote from the initial study shows that this was recognized in the analysis: “However, [the 

spillback] had only a minor effect on the new southbound I-25 to southbound University 

Boulevard ramp, and no effect on the SPUI intersection. The minor queuing on the southbound I25 to southbound University Boulevard ramp due to the southbound University Boulevard 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

queuing did not affect freeway operations because this ramp is fed by the proposed 

collector/distributor roadway, and not directly by the interstate.” 

The failure along this corridor was accurately predicted and acknowledged in the study, with 

agreement that the collector-distributor ramp length would prevent freeway problems. 

Extensions and Guidance 

In summary, results show that the new interchange is operating well when considered 

individually, and this is confirmed in discussions with City engineering staff. However, the 

oversaturated conditions at the downstream signals create a spillback situation for the SB I-25 to 

SB University movement; impeding the flow from this off-ramp and backing it up during peak 

hours. A contributing factor is the pedestrian demand at Buchtel from the University of Denver 

activity, which forces timing to be more limited for vehicles traveling SB on University than 

would be used for vehicular traffic only. 

A more comprehensive analysis would have extended to recommend improvements to the 

Buchtel and Evans signalized intersections, especially Evans since it is apparent the southbound 

bottleneck originates here. Alternative analyses at the Evans signal could have provided 

mitigating improvement choices to resolve the congestion along the corridor. The conclusion in 

the study that these queues did not significantly affect the subject of the study (the SPUI) were 

based on the fact that the southbound exit ramp was extremely long and has enough storage to 

prevent backing up to the freeway. 

While this is true and actual current operations bear this out, it does not address the still existing 

congestion along the corridor and even on this ramp. Solving the problem at Evans would have 

indeed prevented the poor operations for this important leg of the SPUI, thereby improving the 

level of service at the subject of the study. There could have been multiple-period HCM 

analyses performed at the Evans signal to at least generate future improvements to address the 

source of the only real problem found at the SPUI. 

As for Buchtel, investigating strategies to overcome the interference by the large numbers of 

pedestrians accessing the University of Denver could have been explored. Closing two 

crosswalks and/or double cycling the pedestrian call to maximize the vehicular efficiency might 

have been worth modeling to more effectively use the green time for vehicular 

traffic. Another, more costly but still worth a look, could have been a pedestrian overpass, 

including approaching the University for possibly sharing in its expense. 

While these signalized intersections were modeled, the results showing the bottleneck could have 

been used to further investigate alternatives to resolve the problem. A broader look at the 

corridor might have prevented the problem that still exists today. 

Special thanks to Paul Brown from the Colorado DOT’s consulting team for information on the case 

study, and to Amy Rens and Paul Bountry of the City of Denver for providing detailed “after” data for 

use in these comparisons. 

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CASE STUDY 5: CHICAGO STREET NETWORK 

Project Description 

Previous case studies have examined simulation models for before and after construction projects. 

This case study summarizes an academic validation exercise using extensive before and after 

data on a complex arterial network signal timing improvement project, compared with microsimulation projections. The purpose of this study was to investigate key issues in the validation 

of transportation models and to advance an effort to address them. Many of the issues described 

are common to models and modelers in all areas of science and engineering. 

 

A test bed was used to provide a mechanism for validating simulation models. This test bed is a 

microscopic simulator in an application to assess and select signal timing plans on an important 

street network in Chicago, Illinois. 

 

For the computer simulation model to fulfill its purpose, two crucial questions must be 

addressed: 

A. How well does the model reproduce existing field conditions? 

B. Can the model be trusted to represent reality under new, untried conditions, such as 

revised signal timing plans? 

Internal Network

Expressway Connector

Figure 11: Test Bed Network 

The test bed for the study is the network depicted in Figure 11. The internal network is defined as Orleans 

to LaSalle and Ontario to Grand. Traffic in the network flows generally south and east during the morning 

peak and north and west in the evening peak. This demand pattern is accommodated by a series of highcapacity, one-way arterials such as Ohio (eastbound), Ontario (westbound), Dearborn (northbound) and 

30 
PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

Clark and Wells (southbound), in addition to LaSalle (northbound and southbound). Traffic generally 

flows south and east in the morning and north and west in the evening through this signalized intersection 

network. The question being addressed was to quantify how well simulation, with proper calibration, 

reproduces the patterns as experienced in reality. 

Characteristics and Inputs 

Microscopic simulation represents single vehicles entering the road network at random times 

moving second-by-second according to local interaction rules such as car-following logic, lane 

changing, response to traffic control devices, and turning at intersections according to prescribed 

probabilities. The network has 112 1-way links, 30 signalized intersections, and about 38,000 

vehicles moving through it per hour. Streets are modeled as directed links with intersections as 

nodes. 

There are a variety of inputs or specifications that must be made, either directly or by default 

values provided. Signal settings are direct inputs and were singled out as controllable factors 

since altering these inputs to produce improved traffic flow drives the study. For validation, the 

signal plan will be the one in the field. For finding optimal fixed-time signal-timing plans, the 

signal parameters will necessarily be manipulated. 

Data Collection 

Initial field data for the network were collected on a single day, 7 am to 10 am and 3 pm to 6 pm 

with the analyses limited to the three one-hour periods, 8 am to 9 am, 4 pm to 5 pm, and 5 pm to 

6 pm. This covered the peak periods and a “shoulder” period. Traffic volume data were collected 

manually and by video recording. 

There were very few pedestrians, and they had no discernible effect on traffic. Incidents were not 

included, but because illegal parking was an endemic condition, the network was coded to 

account for its effect. Free-flow speed was selected on the basis of posted speed limits. Signal 

timing plans and bus routes were collected directly in the field. 

Analysis and Results 

The interest in the model here is its value in assessing and producing good time-of-day signal 

plans. Comparisons between the field and model results were made through selected evaluation 

functions to deal with the issues raised. 

Evaluation Functions 

Stop time (stopped delay) was chosen on approaches to intersections as the primary evaluation 

function as the typical measure by which intersection level of service (LOS) is evaluated. Other 

criteria such as throughput, delay, travel time, and queue length are all highly correlated with 

stop time. Drivers on urban street networks are particularly sensitive to stop time, spurring traffic 

managers to seek its reduction. In fact, the Highway Capacity Manual’s (HCM) selection of 

stopped delay for LOS designation is meant to reflect the user’s perception of the intersection’s 

quality of service. 

 

The quantities for STV (stop time per vehicle) or STVS (stop time for vehicle stopped) for 

aggregations of approaches (routes or corridors) are very difficult to obtain, requiring the 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

tracking of individual vehicles. By summing over the individual links, a “pseudo stop time” is 

created for the corridor. This will be close to a real stop time, provided vehicles turning off of or 

onto the corridor exhibit no difference from those traveling through. 

Calibration 

Calibration is adjusting input parameters to match model output. Two types of calibration were 

done in the test bed example. The first addressed the blockage of turns at two intersections and 

the subsequent gridlock. The network was altered to facilitate the bypass of the blockage without 

affecting throughput. The second was necessary because of a substantial difference on one link 

(at the LaSalle/Ontario intersection) between the field and the model stop times. This difference 

was largely resolved by changing the free flow speed from 30 miles per hour (mph) to 20 mph to 

be consistent with the observed (from video) speed of vehicles as opposed to the speed limit. 

Throughput Comparison 

A net change in internal total throughput indicates discrepancies showing less output in the 

morning and more output in the evening. This is due to the garage effect: vehicles disappear to 

the parking lots in the morning and reappear from them in the evening; since the morning and 

evening runs do not span the entire day, there are invariably differences in the counts. The 

means of 100 replicated model runs are close to the observed counts. 

Stop Time Comparisons 

The distribution of stop time at each approach shows definite discrepancies at some locations. 

Examination of video and model animation exposes the key cause: the model does not fully 

reflect driver behavior. Lane utilization in the model is not consistent with lane utilization in the 

field. Vehicles joining long queues where they are briefly stopped may not appear in the 

simulation as having stopped. This accounts for smaller STVS in the field than in the model. The 

STVS is larger in the model because it counts only vehicles that completely stopped, so the 

average time per stop is higher However, the key measure of how long truly stopped vehicles are 

delayed appears to match what is seen in the field quite reasonably. 

Prediction and Validation 

A new signal timing plan was put in place in September. Under these new circumstances, 

predictions were to be made and data collection designed for a day in September expected to be 

similar to the date of the first data collection in May. The model was run with the May input, 

except for the updated signal timing. After 

the data were collected in September, the 

results were compared on several key links. 

This showed that throughput and stop time 

performance (STVS) were reasonably close. 

However, when the effect of change in 

demand was checked, it was noted that the 

model has difficulty dealing with storage of 

vehicles on short, congested links just 

downstream of a wide intersection. 

Table 8: ▲ Model Compared with ▲ Field 

Link ▲ Model ▲ Reality 

SB Ohio at LaSalle 0 3 

SB LaSalle at Ohio -11 -10 

NB LaSalle at Ontario -9 -5 

NB Orleans to Freeway 13 15 

NB Orleans at Ontario 1 -2 

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The model differences (Δ = September STVS -

May STVS) were compared to the corresponding 

change in the field values. Even though the 

model’s predictions were not always accurate, 

the differences are close. This is particularly 

important for comparing the performance of 

competing signal plans. A difference of 5 

seconds in stop time can be minor but a 

difference of 15 seconds may be major. One 

starting point may be a comparison of the field 

and model-predicted LOS. The 1994 HCM 

thresholds for LOS were used to be consistent 

with the use of stopped delay, which was more easily measured. 

Table 9: LOS Designation in the 

Highway Capacity Manual (1994) 

Level of 

Service 

Stopped time per Vehicle 

STV: Seconds/Vehicle 

A STV < 5 

B 5 < STV < 15

C 15 < STV < 25

D 25 < STV < 40

E 40 < STV < 60

F STV > 60

Spillback 

A major difficulty is the model’s propensity to turn spillback into gridlock; inadequately 

modeled driver behavior led to intersection blockage far too frequently. (This was corrected by 

modifying the network.) The model tended to stop more vehicles than indicated in the field. In 

reality, drivers coast to a near stop then slowly accelerate through the signal, but the behavior is 

much more abrupt in the model. This flaw manifested itself in disparate stop rates but did not 

seriously affect stopped time per vehicle stopped (STVS). 

Lane Distribution 

It was found that the model was effective but flawed. The model did not accurately model lane 

distribution of traffic, especially regarding following buses in traffic. Lane selection in reality 

was much more skewed (drivers refused in general to follow the buses because of the frequent 

stops) than in the model which showed that most vehicles would follow the buses. 

This had some effect but mostly affected the links and not the intersection approaches that would 

have changed the signal operations. 

Conclusions and Recommendations 

It was found that the model was effective but flawed, despite properly applied methods and 

calibration. The process of properly calibrating the simulation is effective and generally 

applicable when test bed conclusions were derived from the two questions: Does the model 

mirror reality when properly calibrated for field conditions? Does the model adequately predict 

traffic performance under revised signal plans? 

The approach was to focus on key input parameters, such as external traffic demands, turning 

proportions at intersections, and effective number of lanes (for example, due to illegal parking), 

using the model default values for other inputs. 

Overall, despite its shortcomings, the model effectively represented field conditions. There is 

virtually no difference in the estimated levels of service between the field and the model. 

However, as detailed earlier, there were discrepancies documented with spillback levels, car 

following and lane distribution, and the “garage effect” skewing the throughput comparison. 

The predictability of the model was assessed by applying revised (September) signal plans to the 

May traffic network. The model estimates of STVS were reasonably close to field estimates and 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

the model LOSs were, for the most part, similar to those observed in the field. More importantly, 

the model successfully tracks changes in traffic performance over time: on five links for which 

field data were available, two links exhibited a reduction in STVS, one link an increase, and two 

had no significant change; the model’s predictions were the same. 

In summary, a candid assessment of the model is that with careful calibration and tuning, the 

model output will match field observations and be an effective predictor of operational 

performance. 

Extensions and Guidance 

This case study is an excellent example illustrating some 

potential limitations of simulation and the importance of 

calibration and validation for practitioners. In this 

analysis, there were particular shortcomings of the 

model exposed when comparing its simulation with 

reality. Some could be overcome with adjustments to 

the parameters within the model and some were 

accepted as normal variations. 

This points to the need for calibration guidance for users 

in order to minimize the limitations or flaws in any 

simulation tool. There were particular steps taken in this 

study to overcome specific discrepancies. More general 

guidance could be offered to users to assist their 

calibration procedures. 

Users of simulation models need to fine-tune all inputs 

that are related to the driving behavior and vehicle 

characteristics by comparing and adjusting some 

absolute measures. This procedure for calibration and 

validation should include: 

1 SIMULATION MODEL SETUP

Determination of Measure of Effectiveness

Field Data Collection

Network Coding

2 INITIAL EVALUATION

Default Parameter Set

Multiple Run

6 PARAMETER CALIBRATION

Genetic Algorithm

Multiple Run

7 EVALUATION OF PARAMETER SET

Statistical Test

Visualization Check

8 MODEL VALIDATION

Multiple Run

FEEDBACK

3 INITIAL CALIBRATION

Identification of Calibration Parameters

Experimental Design

Multiple Run

4 FEASIBILITY TEST

X-Y Plots

Statistical Test

Identification of Key

Parameters

5 ADJUSTMENT

Adjustment of Key

Parameters

 Comparing the results from the model with 

actual field conditions; 

 Assessing the applicability of the initial set of 

parameters, defaults and assumptions; 

 Defining acceptable ranges for all parameters for 

the given analysis characteristics; 

 Scrutinizing the results from multiple runs to 

compare model results with field data; 

 Viewing animation against known field 

operations for any unrealistic conditions; 

 Figure 12: Evaluation Steps 

 Validating new data by comparing results with new field conditions 

The flow chart shown in Figure 12 is from “Microscopic Simulation Model Calibration and 

Validation Handbook October 2006,” Byungkyu (Brian) Park and Jongsun Won. This is an 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

excellent tool to guide users through the calibration and validation process necessary for the 

appropriate use of simulation models. 

Cross-Cutting Findings 

The examples outlined in the case studies provide some insights into common challenges facing 

transportation agencies today in the arena of microsimulation. While the models sometimes have 

limitations that might make results differ from actual field conditions, practitioner misapplication 

or misinterpretation of the results may provide greater variance between model output and field 

conditions than any limitations built in to the model. If agencies use a defined process for 

application and interpretation of results and follow the logical steps, many of the common issues 

can be alleviated. If issues are known, practitioners should be able to understand such 

limitations and apply the results in a way that ensures sound decisions are made. 

Table 10: Comparison of Findings by Site 

Case Type of Analysis Initial Analysis 

Purpose 

Major Challenge 

Discovered: Comparing 

Actual to Projections 

Major Lesson 

Learned 

I-494 & 

Hwy.7, 

Minneapol

is, MN 

Freeway and loop 

operations analysis 

Compare two build 

(full cloverleaf and 

partial cloverleaf) 

and no-build 

scenarios 

Model underestimated 

flow due to overestimating 

bottleneck downstream 

Compensate for 

known weaknesses in 

models, perform 

sensitivity tests if 

necessary 

I-15 

Reconstruc

tion, 

Ogden, UT 

Delay comparisons 

for alternative 

construction closure 

scenarios 

Compare no-build, 

conventional build, 

and design-build 

construction 

Planning model used for 

analysis failed to estimate 

acceptably accurate V/C 

ratios during peak periods 

Transportation 

planning models are 

limited in providing 

accurate detailed 

operational analysis 

S.R. 826 – 

Palmetto 

Expwy. 

Off-Ramps 

near 

Miami, FL 

Pre- and postconstruction 

operational LOS for 

EB off-ramps and 

auxiliary lane: 

Microsimulation 

and HCM 

Assist in determining 

if similar 

improvements would 

be warranted for WB 

direction 

Microsimulation 

accurately projects 

spillback from increased 

traffic, and so does not 

project the isolated 

improvement from the 

project 

The EB treatment was 

found to be effective 

as an isolated 

treatment; FDOT 

chose to implement 

the WB improvement 

as well for the 

isolated benefit

I-25 & 

University 

Blvd. in 

Denver, 

CO 

Multiple models 

used to evaluate 

potential conversion 

of a cloverleaf 

interchange to a 

SPUI 

Alternatives analysis 

comparing cloverleaf 

upgrade, partial 

cloverleafs, diamond 

interchange and SPUI 

Simulations projected 

downstream spillback but 

alternatives to fix the 

downstream cause were 

not developed 

A new interchange 

may operate well in 

isolation but should 

usually be viewed in 

its larger context

Traffic 

Signal 

Network 

in 

Chicago, 

IL 

Signal timing 

microsimulation 

projections on 

complex urban 

arterial network 

Academic validation 

of before and after 

signal timing 

improvement project: 

How effectively does 

a well calibrated 

simulation reproduce 

reality? 

Generally effective, 

discrepancies in spillback, 

stopping behavior, lane 

distribution (e.g. 

reluctance to follow 

buses), & “garage effect” 

for throughput 

Demonstrates 

importance of 

calibration and 

validation; all driver 

behavior and vehicle 

inputs need finetuning

Special thanks to Nagui Rouphail of North Carolina State University and Brian Park of the University of Virginia 

for providing details and insights for use in these comparisons.

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Freeway widening projects, such as the one in Minnesota, can often shift a bottleneck 

downstream to another location. Bottlenecks in the roadway system can significantly affect 

operating conditions. Modeling can help determine the needed expanse for the widening project 

to avoid major impacts from a bottleneck shifted downstream. 

Modelers should be aware that level of service guidelines were designed for use with the 

Highway Capacity Manual procedures. Metrics such as density and delay that are derived from a 

microsimulation tool do not provide for direct comparison with such HCM thresholds since they 

were produced using different procedures. Field measured metrics can be directly compared 

with HCM LOS thresholds. 

Insights into the magnitude and duration of poor operating conditions are important. Often, 

temporal descriptions of results are needed to help decision makers understand operations. For 

example, LOS F should be accompanied by the time period over which it occurs, how long it 

lasts, and if an improvement reduces the metric in question but does not change level of service 

overall. 

Microsimulation projects can require large amounts of data and information. Modelers should 

build in realistic timeframes that are based on anticipated project-by-project requirements for 

data collection, processing, and information gathering. 

Transportation agency processes often make use of microsimulation models to help determine a 

particular course of action. More than one “build” alternative may be considered along with the 

“no build” alternative. Less frequently, microsimulation models are used to predict future 

performance of the maintenance of traffic alternatives. Expanded use of these tools to predict 

future construction zone operating conditions can help agencies design a Maintenance of Traffic 

Plan and help minimize impacts from during construction, as shown in the Utah Case Study. 

Microsimulation is not necessarily needed for every potential idea for a project. Agencies should 

prioritize and identify, based on appropriate information and data, the top alternatives to focus on. 

Modeling can be useful in supporting investment decisions. The modeling exercise is typically 

consistent with the scale of the overall project investment. 

Tools are available to assist practitioners at the micro, meso, and macro levels (ordered in 

decreasing levels of detail). For example, a macro level analysis tool is useful in determining 

future demand for a facility, while a micro level tool allows for additional detail in quantifying 

impacts at the individual vehicle level. Appropriate timelines and levels of detail should be 

matched with the appropriate tool. 

As shown in the Palmetto Expressway Interchange Case Study, microsimulation can aggregate 

impacts from multiple improvements. Modelers should determine whether or not they need to 

understand the benefits and impacts from each individual improvement prior to developing the 

model. If so, it may be useful to investigate other tools to gain insights into the direct 

performance of individual improvements. The Denver Case Study was also a good example of 

how to make use of multiple tools in order to take advantage of the strengths of each. 

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Prior to model development, data collection plans should be designed to account for unmet 

demand. Automatic traffic detection equipment typically does not account for demand caused by 

queuing, but occupancy levels and other measures can be used to determine whether or not 

saturated conditions may exist. As shown to potentially have occurred in the Denver Case Study, 

lack of information on unmet demand can significantly affect modeling results. 

Queuing is normally adequately accounted for and the effects can be understood using 

microsimulation. As illustrated in the Denver Case Study, the Highway Capacity Manual 

procedure is limited in that it does not include the effects of queue spillback at an intersection 

approach. As shown in the Chicago Case Study, it is important to properly calibrate a simulation 

model to ensure adequate impacts from queuing are reported and included in the analysis of 

alternatives. 

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PRACTICAL GUIDANCE ON THE APPLICATION OF 

TRAFFIC SIMULATION AND ANALYSIS TOOLS FOR 

TRANSPORTATION-INVESTMENT DECISIONS 

The remainder of this document provides insights into some common issues in the application of 

microsimulation tools. These issues are followed by specific actions that can be taken within a 

defined modeling process to either alleviate the issue or to allow practitioners to make decisions 

with a solid understanding of the potential impacts of the issue. These ideas were supported 

based on findings from interviews with local practitioners at each case study location. 

Issues and Pitfalls of Implementation (So Why Didn’t My Analysis 

Work?) 

The most important concept for a user to remember is that software modeling tools are nothing 

more than mathematical equations that are attempting to replicate human behavior. 

Deterministic models try to simulate actions of aggregated drivers and micro-simulation models 

try to replicate individual driver movements. In either case aggregate behavior or individual 

behavior will vary due to a wide degree of variables including but not limited to age, driver 

experience, geographic area, time of day, weather conditions, congestion, and trip purpose. It 

requires an experienced traffic engineer and software model user to avoid the many pitfalls that 

will result in a traffic analysis not replicating existing or future driver behavior. 

Based on the case studies, interviews with dozens of traffic engineers, and the authors’ personal 

experiences, the most common pitfalls to avoid for a successful application of traffic operational 

software are as follows: 

A. Overuse of Default Values (Or It’s in the Model – It Has to Be Right) – The 

practice of using software packages without verifying the default values is very 

common. The tendency is for users to simply to apply the default values because of 

the high cost and limited project budgets to verify the values. 

The odds of taking a software package out of its shrink wrap and successfully 

replicating existing ground conditions are very remote. Many software packages 

are developed outside of the United States. The software developers will use 

default values that are suitable for drivers of their particular countries. For 

example, many of the roundabout software packages overestimate the capacity by 

20% when applied to US conditions (reference NCHRP Report 572 for Project 3-

65, “Roundabouts in the United States”). One reason for this overestimation may 

be the values used in the packages are based on international drivers who are more 

familiar with the operations of a roundabout and this is reflected in their default 

values for variables such as critical gap time. 

The type and range of default values may be extensive. Values such as critical gap 

times, follow up times, car following and lane changing algorithms, saturation 

flows, pedestrian walking speeds, lost time, and design vehicle characteristics such 

as acceleration and deceleration rates are just a few of the variables a user of traffic 

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PREDICTING PERFORMANCE WITH TRAFFIC ANALYSIS TOOLS

operations software packages needs to consider. 

So What Should a User Do? 

Before using a software package, it is critical for the user to determine and review 

the default values that are contained within the package. The review should also 

look at the mathematical equations which are included as the model algorithms. 

For example, in the Highway Capacity Manual as part of the procedure for 

determining the impact of parking on intersection capacity, the equation assumes it 

takes 18 seconds to park a car. For determining the impact of buses on the system it 

assumes that the buses are stopping for 14.4 seconds. The user must determine if 

these values are reasonable for their analysis. This determination should be done 

before the model is applied as part of a project analysis. 

But there are so many default values! And how do you determine what the values 

should be? In the end, it is the experience of the user that is absolutely critical 

when establishing what reasonable values are. And that experience must be applied 

on a project by project basis. However, experience must be supported by 

verification, and that leads to Overcoming Pitfall B ... 

B. Calibration (Or Trust the Model But Verify) – Many interviewees indicated that 

calibrating the traffic operations model is absolutely critical. As indicated in Issue 

A using the default values from the software packages means that the models will 

most likely not simulate field conditions. All models from microsimulation to 

deterministic must be calibrated in order to simulate correctly. 

 

For example, in a recent environmental study conducted in Washington, DC, as part 

of an operational analysis the levels of service for an unsignalized intersection were 

determined. The HCM based model predicted that the intersection would 

experience delays of up to 300 seconds on the minor road. Yet when a field check 

was conducted, the typical minor street delays were in the range of 30 seconds. A 

thorough review of the analysis indicated that the volume counts were correct and 

all data had been inputted correctly. So the team looked at the default values used 

in the model and tried to verify them in the field. As a result, it was determined that 

the typical critical gap times of the drivers at the intersection were 1 to 1.5 seconds 

lower than the gap times used in the model. Lower critical gap times mean higher 

capacity and less delays experienced by the drivers. As a result the critical gap 

times were lowered and the model replicated the 30 seconds of typical delay 

experienced by drivers at the unsignalized intersection. 

So What Should a User Do? 

Models that are being applied in a region or study area for the first time should be 

calibrated. If the study is for a future site, the model should be applied to nearby 

existing intersections or roadway sections and the user should verify that it can 

replicate existing traffic operations. 

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The real question is how we select the variable or variables that should be modified 

so that the model may be calibrated. Again the answer lies in having an 

experienced traffic engineer who is familiar with the software and the sensitivity of 

the model variables to conduct the calibration effort. Even with an experienced 

user, the calibration of the model may require a significant level of effort. 

C. Queuing Analysis (Or How Far Back Will That Queue Go?) – During the 

interviews several users recognized that queue analysis was absolutely critical for 

determining the operational characteristics of the intersection or roadway. In fact 

several interviewees indicated that they were more concerned with the models 

accurately predicting the queues than the level of service. 

In at least one of the case studies the major breakdown in the analysis was not 

accurately predicting the backup conditions downstream from the project. The 

existing analysis and the improvement simply moved the problem downstream and 

the backup interfered with the project's operation. 

So What Should a User Do? 

The first step the user should take is to validate and calibrate the model(s) as 

discussed in Pitfall (?) B. If the model variables have been reviewed and the model 

is still not simulating the queues accurately it very well may be that the area 

coverage is not large enough. The system analysis must cover the entire affected 

area. In some cases the project boundaries are too small and do not include areas 

which may influence the model results. For example, a major improvement beyond 

the project study area may affect the traffic volume or cause traffic to divert. The 

application of the model in the limited project area may not recognize the potential 

change in traffic patterns. 

D. Unusual Geometrics and Conditions (Or This Is Above and Beyond the Call of 

Duty) – Many of the models are limited in their capability of analyzing unusual 

geometric or traffic conditions. Examples are 3 lane or 5 lane sections, rail lines 

through intersections, heavy pedestrian movements, complex signal controls, five or 

six legged intersections, oversaturated conditions, etc. 

So What Should a User Do? 

The various software packages all have strengths and weaknesses and different 

capabilities for analyzing unusual geometrics and conditions. The first step a user 

must take is to match the software’s capabilities with the given conditions to be 

analyzed. 

Sometimes field conditions are so complex that one software package cannot meet 

the analytical requirements for a study involving complex field conditions. Under 

these conditions, a combination of software packages might be considered. For 

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example, in a recent corridor study in Vermont, the project involved a series of 

signalized intersections, arterial sections, roundabouts, unsignalized intersections, 

freeway ramps, multilane facilities, rail operations in the corridor, and a need for 

demonstrating operations to the public through animation. No one software 

package has the capability of meeting all the analysis requirements and therefore a 

combination of four different packages were selected for the analysis. 

E. Inaccurate Field Data (Or What Was That Field Crew Doing Out There?) – 

Sometimes the models are not inaccurate, but the analysis is conducted with poor 

field data. The model user must always consider if the field data they have is 

accurate. Was the data collected on a typical day? Was there a breakdown of the 

counting equipment? Was there human error? The user must always remember 

that traffic varies from day to day, which means there will always be some variation 

between the model simulation and typical traffic conditions. 

How the data is collected could also affect the results. For example, at intersections 

field crews often count traffic by recording the number of vehicles entering the 

intersection - a reasonable technique as long as you do not have saturated 

conditions. However, only counting the vehicles that go through the intersection 

means by definition the analysis should never show volume exceeding capacity. If 

saturated conditions exist and you do not count the queued vehicles, the models will 

never replicate field conditions. 

So What Should a User Do? 

When conducting a traffic operations analysis not only should you look at the 

model parameters but a thorough analysis of how the data input was collected 

should be considered. Again, the most important element of an accurate project 

analysis are the users’ experience and their ability to know what makes sense and 

what does not when reviewing field data. 

F. Inaccurate Travel Demand Forecasting (Or the Forecast Is Right, We Just Got 

the Wrong Year) – The travel demand models have built-in inaccuracies which are 

passed on to the traffic operations software that uses their traffic volume forecasts 

as part of future year operations analysis. The demand models many times are in 

error due to land use changes or population and employment forecasts that are 

simply wrong. 

So What Should a User Do? 

When developing design year traffic, it is a good practice not to use raw traffic 

assignments from the traffic demand models. Rather develop growth factors based 

simulating existing and future traffic assignments. The growth factors should then 

be used to expand existing ground counts. The result is a much more accurate 

future traffic forecast and a more accurate operational analysis.

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G. Inaccurate Origin-Destination Information (Or Tell Me Where They Are

Coming From and Where They Are Going) – More advanced microsimulation 

models typically require the user to not only input traffic counts but also where 

these vehicles are coming from and where are they going. The microsimulation 

models use the origin/destination information to route the traffic through the 

system. Improvements in the transportation network may result in the model 

rerouting the traffic as part of its analysis. However, the original origins and 

destinations for the traffic entering into the system will not change. Most software 

packages have the origins and destinations as inputs that do not vary and do not 

analyze potential future changes even if within the project there are significant 

travel time improvements that may change the traffic patterns. This insensitivity to 

forecast changes in the origins and destinations may result in significant errors. 

So What Should a User Do? 

Microsimulation software is constantly being updated. The next generation of 

microsimulation software very well may be interconnected to regionwide travel 

demand models. The next generation of traffic software will allow the user to 

incorporate an iteration process where the results of an operational analysis will be 

reiterated back through the regional travel demand models. 

However, until the software development catches up to the demand analysis 

requirements, for analysis of major regionwide transportation improvements the 

analysts could manually reiterate the results back through the regional travel 

demand forecasting model and rerun the microsimulation traffic operations 

software. A reiteration model application should be used only with a great deal of 

caution because it is only necessary for the largest regional transportation 

improvements. For smaller size improvements once is enough and the iteration 

process will make only an insignificant improvement in the results. 

 

H. Sanity Checks (Or It Must Be Right It Comes From a Computer) – There is a 

tendency to over-rely on the results of the traffic operations models. Sanity checks 

should be made by the experienced user to determine if the results make sense. Too 

many times the user believes the model is working because it produces numbers. 

So What Should a User Do? 

Sometimes a user cannot see the forest because of the trees. A simple but effective 

way to conduct a sanity check is to get a second opinion. Look at the final results 

and if the model says we are handling 3000 vehicles per hour per lane and they are 

moving at 60 miles an hour it’s time to get a new model. Look at the results and 

see if the traffic forecast is physically possible. 

I. Demand Data (Or Do You Know How to Count Traffic?) – Most turning 

movement counts at signalized intersections are performed by counting vehicles as 

the pass through the intersection and ignoring the unmet demand. This produces 

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43 

unrealistic data in oversaturated conditions that do not represent the true demand at 

the intersection. Performing capacity analyses using data collected this way can 

severely underestimate the delay and back-of-queue results and yield inaccurate 

levels of service. For congested signals, arrival demand (not departure flows) must 

be used for the capacity analysis to accurately match field conditions. 

So What Should a User Do? 

Counting the unmet demand at the end of each period for adding to the departure 

flows yields the more appropriate arrival demands that should be used in these 

analyses. This can be done by estimating the length of the queue at the beginning 

of the red phase at the end of each period for all movements. These distances can 

be converted into vehicles per lane and added to the through volumes to produce 

arrival demand for each period. It is important to proportion the queued vehicles at 

the same turning rates as the through count for that period, and to count the unmet 

demand only once (subtracting it from the subsequent through count). For 

signalized intersection capacity analyses, congested conditions require multipleperiod analyses using the unmet demand for each period as the initial queue for the 

subsequent period to accurately compute the third delay term (d3). 

J. Different Definitions of Level of Service (Or I Can't Define the LOS F But I 

Know It When I See It) – Different software packages have different definitions 

of level of service. Although the Highway Capacity Manual defines the measures 

of effectiveness for various levels of service most traffic operations software 

packages use their own definitions of level of service. For example, when 

analyzing levels of service for roundabouts, some packages use the level of service 

criteria for signalized intersections versus unsignalized intersections. Some 

microsimulation packages may not even calculate density and have no direct 

correlation to the HCM level of service 

 

So What Should a User Do 

 

When first using a software package, the user must review what and how the 

package is developing levels of service. The review should look at how the 

software is treating different types of facilities such as freeways, ramps, weaving 

areas, multilanes, signalized intersections, and unsignalized intersections. If the 

definitions of levels of service are different than the HCM definition then an 

equivalency should be established. Sometimes this is established by using the 

microsimulation to optimize the highway operations and then reanalyzing the 

facility by using an HCM based software package. 

 

The user should also be careful when relying simply on a visual display of a 

software package to get a sense of what the level of service may be. It is critical for 

the user to review the evaluation output files rather than rely on the visual display to 

get a true understanding of the facility’s level of service operations. 
Office of Operations

Room E86-205

1200 New Jersey Avenue, SE

Washington, DC 20590

www.ops.fhwa.dot.gov

FHWA-HOP-08-0