The Use of Data in Planning for 

Operations: State-of-the-Practice Review 

PUBLICATION NO. FHWA-HRT-15-071 JANUARY 2016

Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296
FOREWORD

Planning for operations provides the foundation for proactively operating the transportation 

system. It is a joint effort between planners and operators to improve transportation system 

performance, with a focus on the integration of management and operations strategies into 

planning activities. The Federal Highway Administration (FHWA) promotes the use of a 

performance-based approach for the integration of operations into the transportation planning 

and investment decisionmaking processes at metropolitan and State levels. Access to 

comprehensive, high-quality data on the operation of the transportation system is vital to fully 

applying this approach. 

This report reviews the state-of-the-practice among metropolitan planning organizations in using 

data to support planning for operations activities. Planners and operators can use this report to 

generate ideas to advance their efforts to use data to plan for operations. This review was 

conducted at the outset of an FHWA project to develop a prototype framework for sharing 

planning and operations data between State and local transportation agencies from multiple 

sources within a region. The results of the review were used to inform the development of the 

framework.

Paul Pisano

Acting Director, Office of Operations 

Research and Development

Notice

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

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

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

or regulation.

The United States Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear herein only because they are considered essential to the objective 

of this document.

Quality Assurance Statement

The Federal Highway Administration provides high-quality information to serve Government, 

industry, and the public in a manner that promotes public understanding. Standards and policies 

are used to ensure and maximize the quality, objectivity, utility, and integrity of its information.

FHWA periodically reviews quality issues and adjusts its programs and processes to ensure 

continuous quality improvement.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-15-071

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

4. Title and Subtitle

The Use of Data in Planning for Operations: State-of-the-Practice 

Review

5. Report Date

January 2016

6. Performing Organization Code

7. Authors

Jocelyn Bauer, Julie Evans, Krista Jeannotte, and Anita Vandervalk

8. Performing Organization Report No.

9. Performing Organization Name and Address

Leidos

11251 Roger Bacon Drive, 3rd Floor

Reston, VA 20190

Cambridge Systematics, Inc.

100 Cambridge Park Drive, Suite 400

Cambridge, MA 02140 

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-12-D-00020

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101

13. Type of Report and Period Covered

14. Sponsoring Agency Code

HOP

15. Supplementary Notes

Mr. Gene McHale, Mr. Govindarajan Vadakpat, Mr. John Halkias, Federal Highway Administration

16. Abstract 

The purpose of this state-of-the-practice review is to identify the current use of data by metropolitan planning 

organizations to perform planning for operations activities. This information will be used to develop the virtual 

data access framework and to help establish requirements for the data that should be accessible by planning 

agencies via the framework. In addition to data availability and use, this review also looks at the barriers to the use 

of data so that the project team can design the framework to help address some of these barriers to increase use of 

the resulting framework. This report was developed in conjunction with another state-of-the-practice review on 

data access, sharing, and integration.

17. Key Words

Data, planning for operations, management and 

operations, metropolitan planning organization

18. Distribution Statement

No restrictions. This document is available to the 

public through the National Technical Information 

Service, Springfield, VA 22161.

http://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No of Pages

62

22. Price

N/A

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

TABLE OF CONTENTS

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

SUMMARY OF OVERARCHING PROJECT..................................................................... 1

OBJECTIVES OF THE STATE-OF-THE-PRACTICE REVIEW .................................... 2

RELATIONSHIP TO DATA SHARING STATE-OF-THE-PRACTICE REVIEW ........ 2

ORGANIZATION OF STATE-OF-THE-PRACTICE REPORT....................................... 2

CHAPTER 2. USE OF DATA IN MONITORING TRANSPORTATION 

OPERATIONAL PERFORMANCE AND TRACKING PERFORMANCE 

OBJECTIVES ..........................................................................................................................3

DATA NEEDS FOR MONITORING PERFORMANCE—PERSPECTIVE FROM 

NATIONAL LITERATURE ............................................................................................. 3

CHAPTER 3. EVALUATING M&O STRATEGIES IN PLANNING ....................................7

M&O STRATEGIES CURRENTLY EVALUATED ........................................................... 7

OVERVIEW OF DATA NEEDS FOR ANALYSIS TOOLS AND METHODS................ 7

Sketch Planning Methods: Input Data .................................................................................... 9

Postprocessing Methods: Input Data ...................................................................................... 9

Simulation or Multiresolution and Multiscenario Methods: Input Data................................. 9

DATA NEEDS AND USE IN AMS OF SPECIFIC M&O METHODS OR 

STRATEGIES..................................................................................................................... 9

Data Needs in AMS of TIM.................................................................................................. 10

Data Needs and Use in AMS of ICM ................................................................................... 10

ASSESSING IMPACT OF IMPLEMENTED M&O STRATEGIES ............................... 15

Overview............................................................................................................................... 15

CHAPTER 4. BARRIERS TO USE OF DATA IN PLANNING FOR OPERATIONS .......17

CHAPTER 5. CASE STUDIES ..................................................................................................19

REGIONAL TRANSPORTATION COMMISSION (RTC) OF SOUTHERN 

NEVADA (LAS VEGAS)................................................................................................. 19

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives.................................................................................................. 19

Evaluating M&O Strategies in Planning............................................................................... 21

Assessing Implemented M&O Strategies............................................................................. 21

H-GAC (HOUSTON) ............................................................................................................. 21

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives.................................................................................................. 22

Evaluating M&O Strategies in Planning............................................................................... 23

Assessing Implemented M&O Strategies............................................................................. 24

Data Barriers......................................................................................................................... 24

BAY AREA MTC (SAN FRANCISCO)............................................................................... 24

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives.................................................................................................. 25

Evaluating M&O Strategies in Planning............................................................................... 28

SPC (PITTSBURGH)............................................................................................................. 37
iv

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives.................................................................................................. 37

Evaluating M&O Strategies in Planning............................................................................... 39

Assessing Implemented M&O Strategies............................................................................. 39

Data Barriers......................................................................................................................... 40

SANDAG (SAN DIEGO)........................................................................................................ 40

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives.................................................................................................. 41

Evaluating M&O Strategies in Planning............................................................................... 41

Assessing Implemented M&O Strategies............................................................................. 42

Data Barriers......................................................................................................................... 42

PORTLAND METRO............................................................................................................ 42

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives.................................................................................................. 43

Evaluating M&O Strategies in Planning............................................................................... 43

Assessing Implemented M&O Strategies............................................................................. 44

Data Barriers......................................................................................................................... 44

WILMINGTON AREA PLANNING COUNCIL (WILMAPCO)..................................... 45

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives.................................................................................................. 45

Evaluating M&O Strategies in Planning............................................................................... 46

Assessing Implemented M&O Strategies............................................................................. 47

Data Barriers......................................................................................................................... 48

PIMA ASSOCIATION OF GOVERNMENTS (PAG) (TUCSON)................................... 48

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives.................................................................................................. 48

Evaluating M&O Strategies in Planning............................................................................... 49

Assessing Implemented M&O Strategies............................................................................. 50

Data Barriers......................................................................................................................... 50

REFERENCES.............................................................................................................................51
v

LIST OF FIGURES

Figure 1. Chart. Data collection methods to support operations performance measures ............... 5

Figure 2. Chart. Capability of analysis tools/methodologies to address M&O strategies.............. 8

Figure 3. Chart. Data needs for each model type to analyze TIM strategies................................ 10

Figure 4. Chart. Sample data requirements for AMS for ICM ..................................................... 12

Figure 5. Flowchart. The eight-step CMP framework used by RTC............................................ 20

Figure 6. Screen capture. Nevada FAST Web site’s interactive dashboard ................................. 20

Figure 7. Screen capture. Houston TranStar color-coded traffic map .......................................... 23

Figure 8. Screen capture. PeMS online system............................................................................. 26

Figure 9. Screen capture. MTC 511 system.................................................................................. 27

Figure 10. Diagram. Project performance bubble chart by project type....................................... 30

Figure 11. Diagram. Project performance bubble chart: all road projects.................................... 30

Figure 12. Line graph. Mobility: average weekday hourly delay................................................. 32

Figure 13. Line graph. Reliability—buffer index ......................................................................... 33

Figure 14. Illustration. Heat map of bottleneck locations and extents.......................................... 34

Figure 15. Screen capture. TEMS................................................................................................. 36

Figure 16. Bar graph. Comparison of corridor segments by delay per vehicle per mile .............. 38

Figure 17. Line graph. Variability in speed caused by incidents as a measure of 

nonrecurring congestion.......................................................................................................... 39

Figure 18. Chart. Policy-level performance targets...................................................................... 43

Figure 19. Map. The a.m. peak intersection level of service and travel speed ............................. 46

Figure 20. Map. Identified congested corridors............................................................................ 46

Figure 21. Map. The p.m. peak travel speed changes, 2004–2011............................................... 48

Figure 22. Map. PAG relative congestion .................................................................................... 49

LIST OF TABLES

Table 1. FHWA urban congestion report performance measures and input data........................... 4

Table 2. Benefits and costs quantified in the project-level benefit-cost assessment .................... 29

Table 3. Data sources for AMS integration test in Tucson........................................................... 50

1

CHAPTER 1. INTRODUCTION

SUMMARY OF OVERARCHING PROJECT

In 2012, the Federal Highway Administration (FHWA) initiated a 3-year project entitled “Virtual 

Data Access (VDA) Framework” to develop a prototype framework for sharing planning and 

operations data between State and local transportation agencies from multiple sources within a 

region. The framework will bring together many types of transportation data to give planners and 

operators a multifaceted view of transportation performance both over time and by location. The 

purpose of the VDA Framework is to improve the breadth of available data and reduce the 

barriers to the use of that data so that transportation agencies across a region can advance their 

decisionmaking and performance reporting capabilities in the area of operations. Through greater 

access to data collected in a region, metropolitan planning organizations (MPOs) and State 

transportation departments will be better equipped to conduct performance management and 

performance-based planning and programming as emphasized in the Moving Ahead for Progress 

in the 21st Century Act (MAP-21).

FHWA’s project focused on using the VDA Framework to support planning for operations,

including the use of data from the framework as input to planning for operations analysis and 

simulation tools. Planning for operations is a joint effort between planners and operators to 

integrate management and operations (M&O) strategies into the transportation planning process 

for the purpose of improving regional transportation system efficiency, reliability, and options.

(1)

In addition, the data will be used to support reporting operational performance measures.

The prototype framework will undergo a proof-of-concept test in the Kansas City region, where 

transportation operating and planning agencies have a high level of collaboration, a need for 

easily accessible integrated transportation operations data, and the capability to use that data for 

planning for operations. The framework is being developed in coordination with the proof-ofconcept partners, the Mid-America Regional Council and Kansas City Scout, as well as other 

stakeholders from across the United States. To support the proof-of-concept test, the FHWA 

project team is developing a Performance Measurement and Analysis Tool (PMAT) that 

demonstrates one of the many options for using the integrated data from the VDA Framework.

The PMAT will access public and private sources of data through the VDA Framework and 

calculate performance measures according to user-specified parameters. The performance 

measures will be displayed on a map of the region as well as through the use of data tables and 

charts. The PMAT will be a map-based web application that allows users to view travel time 

reliability, delay, and traffic volume throughput on arterials and freeways and extract calculated 

performance data in the form geographic information system (GIS) shape files. In addition, users 

will be able to export the data to Network EXplorer for Traffic Analysis (NeXTA), an analysis, 

modeling, and simulation (AMS) data hub that was developed by FHWA to support the 

exchange of data among multiple resolutions of AMS tools. The data exported from PMAT will 

support the analysis for operations strategies in a dynamic traffic assignment (DTA) modeling 

tool as part of NeXTA.
2

The VDA Framework and PMAT are being developed using primarily open source software and 

will be available to other regions to leverage. The results of the proof-of-concept test will be 

documented and shared as part of technology transfer webinars near the end of the project. 

OBJECTIVES OF THE STATE-OF-THE-PRACTICE REVIEW

The purpose of this state-of-the-practice review was to identify current use of data by MPOs to 

perform planning for operations activities. This information will be used to develop the VDA

framework and to help establish requirements for the data that should be accessible by planning 

agencies via the framework. In addition to data availability and use, this review also looks at the 

barriers to the use of data so that the project team can design the framework to help address some 

of these barriers to increase use of the resulting framework.

RELATIONSHIP TO DATA SHARING STATE-OF-THE-PRACTICE REVIEW

This report was developed in conjunction with another state-of-the-practice review on data 

access, sharing, and integration. The data access, sharing, and integration review focuses on 

current data sharing and integration practices among State and local agencies, such as data 

environments, technical integration formats, and business rules for integration and sharing. It 

supports the project by establishing a foundation of previous experiences that can be used in 

defining how the data integration and sharing elements of the framework are designed, whereas 

the planning for operations review will be used to determine the data that should be input to the 

framework to support common or emerging planning for operations practices.

ORGANIZATION OF STATE-OF-THE-PRACTICE REPORT

This report covers information from national literature and research in the first several chapters

and then provides detailed information for eight metropolitan areas in case studies in the last half 

of the document. The first chapter of this report helps acquaint the readers with the purpose of 

the project and this report. Chapters two through four cover three key components of a 

performance-based approach to planning for operations that require data: monitoring system 

performance and tracking objectives, evaluating M&O strategies in the planning process, and 

assessing the impact of M&O strategies post-implementation. These three chapters contain 

information from national-level research and publications. Chapter five summarizes the barriers 

and challenges that have been identified through conversations with planning for operations 

practitioners and literature reviews. The last chapter contains case studies of eight MPOs and 

their use of data in the three key data-driven components of planning for operations: monitoring, 

analysis for planning, and post-implementation evaluation.
3

CHAPTER 2. USE OF DATA IN MONITORING TRANSPORTATION OPERATIONAL 

PERFORMANCE AND TRACKING PERFORMANCE OBJECTIVES 

DATA NEEDS FOR MONITORING PERFORMANCE—PERSPECTIVE FROM 

NATIONAL LITERATURE

Monitoring the operational performance of elements of the transportation system is becoming 

more prevalent, particularly in large metropolitan areas on freeways. Transportation agencies in 

major metropolitan areas often have large amounts of archived or real-time data collected 

through road sensors and their central control systems, although there are significant limitations 

to the use of this data for performance monitoring.(2) Typically, the sensors cover a limited 

portion of the freeway network, and the failure rate for in-pavement and roadside traffic sensors 

is high (e.g., 35 percent for the California Freeway PeMS loop detectors). In addition, arterial 

roads are either not covered by these sensors or the data collected is not useful for travel-time 

analysis.

(2)

The recent emergence of probe data is creating more opportunities for transportation operating 

agencies and MPOs to monitor and analyze travel times according to elements of the network 

and routes and to aggregate origin-destination (O-D) travel times geographically. In 2013, 

FHWA made a national set of average travel time probe data freely available to States and MPOs 

to use for their performance management activities. This data set is referred to as the National 

Performance Management Research Data Set. There are also several commercial probe data 

systems available based on cell phone location, Global Positioning System (GPS)-equipped 

vehicles (usually fleets), and cell phones or other personal devices with GPS systems.(2) Prior to 

the release of the National Performance Management Research Data Set, data from these systems 

were typically only available for purchase through the private sector. Instead of purchasing this 

data, one of the MPOs interviewed for this state-of-the-practice review found it much more cost 

effective to deploy its own sensors.

As outlined in SHRP2 L05 Incorporating Reliability in the Transportation Planning Process –

Technical Guidance, data used to monitor current travel time reliability conditions and examine 

past reliability trends by planning and operating agency departments include the following:

(3)

• Automated spot traffic data (e.g., loop, acoustic, radar traffic detectors) including 

volume, speed, occupancy and other data[.]

• Travel time data (probe data)[.]

• Incident logs[.]

• Crash data[.]

• Operational data (e.g., logs of messages displayed on variable message signs, 511 calls or

alerts)[.]

• Weather data.
4

On the national level, the Texas Transportation Institute (TTI) produces the Urban Congestion 

Report for FHWA on a quarterly basis to disseminate congestion and reliability trends for the 

United States as a whole and for cities with freeways instrumented with traffic sensors.

(4) The 

report provides results for three primary operations performance measures: congested hours, 

travel time index, and planning time index. Table 1 contains the definitions of these key 

performance measures and the input data for the measures.

Table 1. FHWA urban congestion report performance measures and input data.(4)

Performance 

Measure Definition Input Data

Congested 

hours

The average length of time each day that roads in 

a particular city are congested.

• Five-min sectionlevel speeds and 

vehicle-miles traveled

(VMT).

Travel time 

index

The extra time spent in traffic during peak traffic 

times as compared to light traffic times. In 

mathematical terms, it is the peak travel time 

divided by the free-flow travel time. If using 

speed in calculations, it is the free-flow traffic 

speed divided by the peak traffic speed. Both 

calculations will yield identical travel time index 

values.

• Average free-flow 

speeds for each 

section during offpeak times.

• Five-min sectionlevel speeds and 

VMT for peak traffic 

periods 

(6–9 a.m., 4–7 p.m.).

Planning time 

index

The extra time cushion needed during peak traffic 

periods to prevent being late. In mathematical 

terms, it is the near-worst case travel time 

(95th percentile) divided by the free-flow travel 

time. If using speeds in calculations, it is the freeflow traffic speed divided by the near-worst case 

traffic speed (5th percentile).

• Average free-flow 

speeds for each 

section during offpeak times.

• Five-min sectionlevel speeds and

VMT for peak traffic 

periods 

(6–9 a.m., 4–7 p.m.).

The National Transportation Operations Coalition developed a set of 12 key operations 

performance measures in 2005, and in 2007, those measures were pilot tested and refined for 

National Cooperative Highway Research Project 20-7, Guide to Benchmarking Operations 

Performance Measures.

(5) The report was developed by the University of Maryland in 2008 and 

documents the pilot testing and review by State transportation departments, cities, and MPOs and 

the resulting set of performance measures. The refined set of operations performance measures 

include the following:

(5)

• Customer satisfaction.

• Extent of congestion—spatial.

• Extent of congestion—temporal.

• Incident duration (i.e., the time elapsed from the notification of an incident until all 

evidence of the incident has been removed from the incident scene).
5

• Recurring delay vehicle.

• Speed.

• Throughput.

• Travel time.

• Travel time—reliability.

• Travel time—trip.

The pilot test sites also tested data collection and compilation procedures, and the report contains 

a pertinent table showing the data collection method, basic data obtained from the data collection 

method, and the performance measure that the data collection supported. The table from this

report is presented in figure 1.

Figure 1. Chart. Data collection methods to support operations performance measures.(5)

The data needed to support key traffic incident management (TIM) performance measures—

including roadway clearance time, incident clearance time, and secondary crashes, as well as the 
6

data needed to support TIM modeling and analysis as defined in the 2012 FHWA report,

Analysis, Modeling, and Simulation for Traffic Incident Management Applications—is listed as 

follows:

(6)

• Incident type (crash, disabled vehicle, fire, debris, abandoned vehicle).

• Incident collision type (fixed object, overturn, vehicle/side, vehicle/head-on, 

vehicle/rear-end).

• Incident “timeline” data (time stamps for start of incident, detection, 

verification, on-scene arrival, lane/shoulder open, all clear).

• Incident severity (KABCO injury scale).

• Incident location (route, travel direction, milepost or GPS coordinates).

• Incident blockage.

o Cross section feature affected (lane, partial lane, right shoulder, left 

shoulder, median, off maintained way)[.]

o Lane type (through/general purpose, through/high-occupancy vehicle 

(HOV), auxiliary, on-ramp, off-ramp)[.]

o Begin/end time.

• Number of involved vehicles.
7

CHAPTER 3. EVALUATING M&O STRATEGIES IN PLANNING

M&O STRATEGIES CURRENTLY EVALUATED 

In general, it is not yet common practice among MPOs to quantitatively evaluate and forecast the 

potential impacts of M&O strategies on transportation system performance, safety, and other 

measures. The majority of cases that were examined for this review did not forecast impacts of 

M&O strategies as part of the planning process. The examples of M&O strategy evaluation were 

primarily performed as part of a Federal initiative, such as an integrated corridor management 

(ICM) AMS study or an FHWA AMS data integration project. A notable exception was the Bay 

Area Metropolitan Transportation Council (MTC), which used an activity-based model to 

evaluate express lanes, congestion pricing, intelligent transportation systems (ITS)

improvements, HOV and bus rapid transit, and contraflow lanes. In addition, MTC was involved 

in the evaluation of signal optimization, freeway service patrols, and transit and travel demand 

management (TDM) improvements.

Through an FHWA AMS data integration project, the Portland, OR, MPO (Metro) evaluated ITS 

improvements, signal systems, ramp meters, and transit and truck priority along a corridor.(7) The 

San Diego Association of Governments (SANDAG) is continuing to use the modeling approach 

from the ICM AMS work to evaluate proposed operations improvements on other corridors.

OVERVIEW OF DATA NEEDS FOR ANALYSIS TOOLS AND METHODS 

Many different tools and methods are available to support the quantitative analysis of M&O 

strategies in planning and programming, and most of the MPOs interviewed for this state-of-thepractice review reported that they were initiating or advancing their use of tools for evaluating 

M&O strategies to support planning and investment decisions.

Using the categories established in FHWA’s Applying Analysis Tools in Planning for Operations

brochure, the following tools can be used for analyzing M&O strategies:

(8)

• Sketch planning and prioritization tools.

• Travel demand models.

• Analytical and deterministic tools (Highway Capacity Manual (HCM) based).

• Simulation models including microscopic, mesoscopic, and macroscopic models.

• Traffic signal optimization tools.

In addition, archived operations data can be used to analyze M&O strategies. More advanced 

analysis methods are being studied and integrated into operations modeling and analysis, 

including the following:

• DTA.

• Multiscenario methods.

• Multiresolution modeling.
8

Currently, only multiresolution or multiscenario analysis methods have the capability to analyze 

all of the M&O strategies commonly recognized.(9) The M&O strategies that may be analyzed by 

a specific tool or methodology are shown in figure 2 from the 2012 FHWA Operations 

Benefit/Cost Analysis Desk Reference, which provides additional information on each of the 

tools listed in the figure.

(9)

Figure 2. Chart. Capability of analysis tools/methodologies to address M&O strategies.

(9)

The FHWA Operations Benefit/Cost Analysis Desk Reference and the Strategic Highway 

Research Program 2 (SHRP2)—L05 Incorporating Reliability Performance Measures into the 

Transportation Planning and Programming Processes: Technical Reference both contain the 

same three broad categories of analysis tools and methods: sketch planning methods, postprocessing methods, and simulation (or multiresolution and multiscenario) methods.(9,3) The 

SHRP2—L05 report also contains a fourth category, monitoring and management tools and 
9

methods, which is not included in this discussion because those tools and methods primarily 

focus on assessing past conditions. Sketch planning methods have fairly limited data 

requirements, whereas post-processing methods require more specific data, and simulation (or 

multiresolution and multiscenario) methods have more comprehensive data requirements.

The SHRP2—L05 report, Incorporating Reliability Performance Measures into the 

Transportation Planning and Programming Processes: Technical Reference, provides a 

summary of the input data needed for each major type of analysis tool or method.(3) The 

descriptions of the general needs are followed by examples that illustrate the specific use of data 

for analyzing M&O strategies as part of the planning, programming, or project development 

phase prior to implementation of the strategies.

Sketch Planning Methods: Input Data

Most sketch-planning methods can be used when only limited data is available. They require 

segment free-flow speed and distance at the most basic level. Average travel time is input as the 

next step, and the travel time can be obtained through measuring it in the field, extracting it from 

a model, or estimating it based on segment volume and capacity.(3)

Postprocessing Methods: Input Data

The input data needed for model postprocessing methods includes link-level data available from 

most regional travel demand models or simulation models. These data include basic facility 

capacities, basic geometric data, and loaded roadway volumes that may represent peak hour, 

peak period, or daily analysis. Information on the probability of various weather conditions (e.g., 

number of days or year of rain) and road work is needed for assigning weights to scenarios if 

multiscenario approaches are used.(3)

Simulation or Multiresolution and Multiscenario Methods: Input Data

At a minimum, simulation or multiresolution and multiscenario methods require a regional travel 

demand model as well as the data needed to develop and calibrate a simulation model. The data 

required for a simulation model generally include detailed roadway geometry, traffic signal 

timing, and discrete data on travel speeds and volumes. A condition occurrence distribution for a 

multiscenario analysis would require archived data on demand, incidents, and weather as well as 

the distribution of the likelihood of each scenario.(3) 

DATA NEEDS AND USE IN AMS OF SPECIFIC M&O METHODS OR STRATEGIES

There are a few national transportation publications that have recently been issued or drafted that 

address AMS techniques for more specific M&O strategies or transportation management 

methods, including FHWA’s Analysis, Modeling, and Simulation for Traffic Incident 

Management Application; the U.S. Department of Transportation’s (USDOT) Traffic Analysis 

Toolbox Volume XIII: Integrated Corridor Management Analysis, Modeling, and Simulation

Guide; USDOT ITS Joint Program Office’s Use of Mobile Data for Weather-Responsive Traffic 

Management Models; and FHWA’s Assessing the Effectiveness of Transportation Management 

Plan (TMP) Strategies. (See references 6, 10, 11, and 12.) The following sections provide 

information on the data needs for analyzing the potential impacts of TIM strategies and ICM.
10

Data Needs in AMS of TIM

The FHWA report Analysis, Modeling, and Simulation for Traffic Incident Management 

Application provides pertinent information regarding data needs for evaluating TIM strategies 

using several types of models: sketch planning models, deterministic (HCM-type macroscopic) 

models, mesoscopic simulation models, and microscopic simulation models, as shown in

figure 3.

(6) Again, the sketch planning models have fairly low data requirements, whereas the 

mesoscopic and microscopic models are much more data intensive. One of the primary 

improvements in TIM AMS applications recommended by the authors is the need for a data 

dictionary and guidelines to ensure consistent data collection and archiving of TIM data to 

enable comparisons and data integration across agencies.(6)

Figure 3. Chart. Data needs for each model type to analyze TIM strategies.(6)

Data Needs and Use in AMS of ICM

Recently, USDOT developed an AMS methodology for use in evaluating and forecasting the 

impact of ICM on several key integrated corridor performance measures: delay, travel time 

reliability, and throughput. This methodology has been demonstrated by three ICM sites in the 

United States: San Diego, CA; Dallas, TX; and Minneapolis, MN. ICM integrates transportation 

management techniques across facilities and modes along a corridor to balance demand, 

coordinate management strategies, reduce congestion, and improve the overall operational 

performance of the corridor. The methodology required to assess the effects of ICM is 

significantly more complex than traditional transportation investments. The assessment of ICM 

typically includes freeways and arterials as well as multiple modes and potentially road or 

parking pricing strategies. In addition, the impacts of ICM need to be evaluated in several 

operations scenarios, including incidents, weather, special events, and other non-recurring events 

that disrupt normal travel conditions. The ICM AMS methodology is flexible and requires 

significant tailoring to meet the needs of individual corridors. The methodology integrates up to 
11

three classes of modeling tools (microscopic, mesoscopic, and macroscopic) as well a mode shift 

model and a transit travel time estimation model, interfaces between the tools, and a performance 

measurement or benefit-cost module.(10)

The ICM AMS methodology has substantial requirements for data that is high-quality, reliable, 

and collected continuously for at least 6 to 12 mo. The data should be collected from all sources 

during the same time period. Archived data sources are generally preferred over manually 

collected data, and data collected during different operational conditions allow greater 

opportunity to model the impacts of ICM during different operational scenarios. Long-term 

archived data enables congestion patterns over many days to be analyzed.(10)

USDOT’s Traffic Analysis Toolbox Volume XIII: Integrated Corridor Management Analysis, 

Modeling, and Simulation Guide contains an extensive list of sample data requirements for 

conducting AMS for ICM, as seen in figure 4.

(10)
12

Figure 4. Chart. Sample data requirements for AMS for ICM.(10)

Dallas Area Rapid Transit (DART) and U.S. 75 Dallas ICM Team conducted an AMS 

assessment of the effects of several ICM strategies on the performance of the U.S. 75 corridor as 

documented in a 2010 report from USDOT.(13) The U.S. 75 corridor in Dallas is a critical, 

regional corridor in which freeway and arterial expansion is not possible. As outlined in 

“Integrated Corridor Management,” the corridor includes the following:

(14)
13

• Continuous frontage roads[.]

• HOV lanes on U.S. 75 and IH-635[.]

• Dallas North Tollway[.]

• 167 Miles of Arterials[.]

• DART Bus network including express service[.] • DART Light Rail (Red and Blue Lines)[.]

The AMS approach used for U.S. 75 included using the North Central Texas Council of 

Governments’ TransCAD travel demand model, a mesoscopic simulation model (DIRECT), a 

time of departure choice element, and an analysis of mode shift and transit. The ICM strategies 

that were assessed for the U.S. 75 corridor include the following:

(13)

• “Comparative travel time information (pretrip and en-route)[.]

• Incident signal retiming plans for arterials[.]

• Incident signal retiming plans for frontage roads[.]

• Light-Rail Transit (LRT) smart parking system[.]

• Red Line capacity increase[.]

• LRT station parking expansion (private parking)[.]

• LRT station parking expansion (valet parking)[.]”

Several data elements were used as input or for calibration and validation of the various 

modeling components used in the assessment. The North Central Texas Council of 

Governments’ travel demand model provided the primary source of vehicular trip tables and 

networks that were used as input to DIRECT. The DART on-board survey provided the data to 

estimate the transit O-D trip table also used as input to DIRECT.

The following data were used for the validation and calibration of the model:

(13)

• “Traffic flows at individual links, as well as on screenlines across the arterial, 

freeway, and transit components of the ICM Corridor[.]

• Travel times along critical segments of the ICM Corridor freeway and arterial 

components[.]

• O-D surveys, identifying travel patterns along the freeway and arterial 

components of the ICM Corridor[.]”

Queue observations were made along critical segments of the ICM Corridor freeway 

and arterial components. An extensive AMS method was also applied to the I-394 

corridor in Minneapolis an east-west commuter route that connects the Minneapolis 

Central Business District to the western suburbs. This assessment focused on the 

impacts of the following ICM strategies:

(15)

• “Earlier Dissemination of Traveler Information[.]

• Comparative Travel Times[.]

• Parking Availability at Park-and-Ride Lots[.]
14

• Incident Signal Retiming Plans[.]

• Predefined Freeway Closure Points[.]

• HOT Lanes Open to All[.]

• Transit Signal Priority.”

The AMS for the corridor relied on a combination of the region’s travel demand model in TP+, 

travel demand forecasting software package, and a mesoscopic simulation model, DynusT,

developed by the University of Arizona. DynasT includes a mode shift model. As with the Dallas 

pilot, the Twin Cities regional travel demand model developed in TP+ was used to extract 

networks and trip tables for the analysis for the corridor. Additional data entered into the model 

included signal timing plans and intersection lane geometry configuration. The traffic flow 

model was calibrated using the speed-density relationship in the field data, and the O-D tables 

were calibrated using link count data. Detector data were also used to develop time-dependent 

O-D tables. Collected travel time validated the calibrated model. Automated passenger count and 

route ridership data were used to create the transit O-D tables.(15)

The following data were used for model validation:

• Traffic counts.

• Travel times collected by probe vehicles.

• Link volumes and speeds.

• Speed space-time contours and volumes from field sensors (for incident scenario 

validation).

In San Diego, the ICM AMS was performed on the I-15 corridor, which is a primary artery for 

the movement of commuters and goods from northern San Diego County to downtown. It is an 

8- to 10-lane freeway with two reversible managed (high-occupancy toll (HOT)) lanes. The 

AMS tested several ICM strategies, including the following:

(16)

• “Pre-Trip Traveler Information[.]

• En-Route Traveler Information[.]

• Freeway Ramp Metering[.]

• Signal Coordination on Arterials with Freeway Ramp Metering[.]

• Physical Bus Priority[.]

• Congestion Pricing on Managed Lanes.”

The AMS approach used by the San Diego team consisted of a TransCAD-based regional travel 

demand model and a TransModeler-based corridor microsimulation model. As input to the 

microsimulation model, the roadway network, and an a.m. peak period, O-D trip table for the 

I-15 corridor study area was extracted from the regional travel demand model. The 

microsimulation model was calibrated using a three-step strategy to calibrate on capacity, route 

choice, and system performance. The California Department of Transportation (Caltrans)

Performance Measurement System (PeMS) database provided much of the data for the 

calibration and validation, including speed data. As outlined in Annex 3. ICM AMS Results for 
15

the I-15 Corridor, the set of data used to calibrate and validate the microsimulation model 

included the following:

(16)

• Traffic flows at individual links along the I-15 corridor[.]

• Speed profiles along critical segments of the corridor[.]

• Queue observations along critical segments of the corridor freeway and arterial 

components.

ASSESSING IMPACT OF IMPLEMENTED M&O STRATEGIES

Overview

This section addresses another key component of a performance-based approach to planning for 

operations that requires data: assessing the impact of M&O strategies post-implementation. This 

review did not systematically identify the data needed to evaluate each M&O strategy postimplementation but instead identified examples during conversations with MPOs.

There are several more examples of M&O strategies that were evaluated post-implementation 

than were found for evaluation during planning. Traffic signal coordination or retiming projects 

were frequently mentioned by MPOs as an M&O strategy that is evaluated for improvements via 

the collection of travel time data. Other examples include a cost-benefit analysis of the Houston, 

TX, area TIM program and the use of traffic count data to conduct before-and-after studies of 

implemented M&O strategies by the Southwestern Pennsylvania Commission (SPC). In addition, 

the Houston TranStar partnership (including Houston-Galveston Area Council (H-GAC)) 

conducts public surveys to assess the use and impacts of the TranStar traveler information 

Web site.

17

CHAPTER 4. BARRIERS TO USE OF DATA IN PLANNING FOR OPERATIONS

During conversations with MPOs regarding this review, many of the same issues were reported 

regarding barriers or challenges to using data in planning for operations. They typically fell into 

the broad categories of transportation system coverage, data quality, data format or resolution, 

and costs in terms of funding and staff time.

There were typically very limited data on arterial performance and gaps of coverage on 

freeways. One MPO reported having difficulty obtaining transit data from the 10 transit 

operators in the region. Some regions obtained private sector, third-party data for coverage on 

arterials, but one region mentioned concerns that it may not represent the typical traveler. Other 

regions conducted special travel time runs on arterials but collected data manually. This typically 

does not allow for the modeling of M&O strategies during different operational conditions (e.g., 

weather, incidents, and special events).

Issues with data quality were also mentioned as a barrier, including loop detector and tube 

failures. MPOs reported having to spend considerable effort in postprocessing data before being 

able to use it in models or performance measures. Specifically, MPOs mentioned probe travel 

time data and traffic count data as requiring significant processing. In addition, the temporal 

resolution of data that the MPOs received was often not fine enough for their models, 

specifically the newer models. The MPOs needed data in 15-min increments or less but were 

receiving data in 24- or 1-h increments. 

The cost of obtaining system performance data was also a key barrier to use of data in planning 

for operations. For example, in Houston, the toll-tag readers collecting freeway performance data 

were too expensive to deploy across the entire network. The region is looking to move to 

Bluetooth®, but the communications necessary to link the devices across such a large region are

costly. Purchasing data from commercial vendors was also reported as expensive.

In addition, MPOs cited the time required to integrate data from multiple operating agencies 

across the region to create a more comprehensive view of the region’s performance as a 

challenge. This is one of the main challenges that this VDA framework aims to address in this 

project.

Chapter 6 includes eight case studies that provide a closer view of the efforts to use data for 

planning for operations in fairly advanced regions across the United States.

19

CHAPTER 5. CASE STUDIES

REGIONAL TRANSPORTATION COMMISSION (RTC) OF SOUTHERN NEVADA 

(LAS VEGAS)

RTC of Southern Nevada, which serves as the MPO and transit authority for Southern Nevada, 

collects a wide variety of transportation operations data from freeways, arterials, and transit 

facilities for use in planning and operations activities. RTC records transit boardings, ridership, 

and operational efficiency but does not maintain regular route-specific transit travel patterns. The 

Freeway and Arterial System of Transportation (FAST) is a regional ITS organization 

administered by RTC and funded by RTC and the Nevada Department of Transportation

(NDOT). FAST aggregates large quantities of data for both planning as well as distribution to 

travelers. Using side-fire radar, inductive loops, closed-circuit televisions (CCTVs) and video 

image detection, FAST collects the following data elements:

• Average annual daily traffic (AADT).

• Average travel time.

• Average travel speed.

• VMT.

• Incidents and delays.

• Vehicle classification.

• Volume and occupancy.

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives

RTC currently uses the data collected by FAST to generate a graphic that depicts relative 

congestion (as volume-to-capacity ratio) to automatically identify the most congested corridors 

to define systems/networks of interest, which is depicted as step 3 in the congestion management 

process (CMP) framework in figure 5.

(17) Although FAST has no formal process to identify 

corridors for operations investment, relying instead on operators observations, the next Long 

Range Transportation Plan and Transportation Improvement Plan updates will incorporate 

congestion reliability data in investment decisions. Furthermore, FAST has begun tracking 

performance using performance-based goals and targets and recently completed its first benefitcost analysis of operations and mobility projects.
20

Figure 5. Flowchart. The eight-step CMP framework used by RTC.

(17)

FAST collects travel time, speed, VMT, incident, delay, classification, volume, and occupancy 

data, which are streamed to the Nevada FAST Web site, where an interactive dashboard displays 

live and archived conditions for use by travelers, as shown in figure 6.

(18)

©RTC.

Figure 6. Screen capture. Nevada FAST Web site’s interactive dashboard.

(18)

Gr

Gr

LGr

LGr

LR

Or

DR

R

Y Or

R

Or

R

Gr

Y

Y

LR

LGr

PGr

DY

R

LGr

LGr

LGr

Gr

LGr

LGr

LGr

Y

Y

Gr

Gr

Gr

Gr

LGr
21

Evaluating M&O Strategies in Planning

To supplement the volume-to-capacity ratio, RTC plans to incorporate analytical tools such as 

travel forecast models, signal optimization tools, and simulation models to quantify and validate 

the benefits of the proposed projects. 

The CMP prioritizes improvements that address congestion at a list of sites that have been 

determined to be in need of improvement based on the following four components of 

congestion:

(19)

• Intensity—Based on both volume to capacity ratio for freeways, interstates, and ramp 

links as well as percent reduction in speed for arterial and collector links.

• Duration—The number of hours in which congestion exceeds the intensity threshold.

• Extent—The number of persons or vehicles affected by congestion, calculated based on 

car/truck volumes and estimated occupancy rate.

• Reliability—Based on crash rates and non-crash-related incidents, obtained from the 

Freeway Service Patrol operated by NDOT.

Current evaluation of strategies in planning does not include quantitative considerations of 

collected data but rather a qualitative hierarchy to prioritize demand reduction and mode shifts 

over highway capacity expansion. 

Assessing Implemented M&O Strategies

In the 2011–2014 Transportation Improvement Report, RTC recommends periodic assessment of 

implemented strategies but has not yet completed these evaluations. RTC has enlisted contractors 

to conduct several before-and-after studies to examine the effectiveness of transit, arterial, and 

parking projects. Many of these projects are still being implemented, and data collection has not 

yet concluded.(20,21)

H-GAC (HOUSTON)

H-GAC houses the MPO for an eight-county region surrounding Houston and serves more than 6 

million people across 7,800 mi2

.

(22) H-GAC has access to various sources of real-time and 

archived operations data and is updating its CMP to incorporate this data to support better longterm planning activities. 

H-GAC is advancing in its use of operational performance data available in the region. It has 

access to fairly extensive freeway data collected and used in real-time by TranStar, a multiagency coalition and traffic management center (TMC) focused on the management and 

operation of the region’s transportation system. The region has a network of toll-tag readers that 

collect speed and travel time data on freeways and toll roads. The Texas Department of 

Transportation (TxDOT) is in the process of replacing the toll-tag readers on its freeways with 

Bluetooth® readers because they provide comparable data collection capabilities at a 

significantly lower cost.
22

H-GAC also has access to freeway incident management information from TxDOT’s Regional 

Incident Management System (RIMS). RIMS is the central database where data for the majority 

of freeway incidents within Houston and surrounding areas are recorded. The information 

gathered by TxDOT on incidents includes incident detection, verification, response time, and 

clearance time.

Like most MPOs, H-GAC has faced more challenges in measuring arterial performance than 

freeway performance. H-GAC purchased 2009 travel time and speed data from a private, thirdparty data provider to help assess system performance. H-GAC has been working with TTI to 

conflate the third-party data with H-GAC’s modeling network, which would assist in validation 

of travel speed for the model. This model will allow for a before-and-after review of how 

specific projects may have affected travel times and speeds.

Efforts in the region have been underway to implement Bluetooth® technology to provide 

continual real-time traffic data on its roadway network not just to capture and use traffic 

information but also to control the quality of the data. Houston has implemented Bluetooth®

readers on a 60-mi2 test section on its arterial system to capture arterial speeds and has been 

approved for funding through H-GAC to fund deployment of additional Bluetooth® readers 

across the remainder of its arterial network. Several other communities have also implemented 

Bluetooth® readers on arterials in the region but not to the extent of the City of Houston’s 

proposed network. Once fully operational, H-GAC and the city will be able to capture real-time 

traffic conditions on arterials. In addition, the information from the system will benefit real-time

operations by helping operators identify system slowdowns, determine causes, and dispatch 

resources to address the problem.

H-GAC has access to traffic count data primarily from TxDOT, which usually collects 5-year 

saturation counts with tubes. TxDOT is the primary count source for H-GAC’s travel demand 

model, and the data ares provided to H-GAC at no cost. However, a location’s count is only 

registered for a single 24-h period, and any construction, incident, or counter malfunction will 

not result in a second attempt to collect a count. It would be cost-prohibitive for the region to 

conduct a continual, comprehensive count program with a road network as extensive as the 

Houston-Galveston area, which has more than 50,000 network links.

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives

Much of the travel time data collected on the performance of the transportation system are used 

for real-time management and operation of the system by TranStar at the TMC and for providing 

traveler information on the public Web site. TTI collects the toll-tag reader and Bluetooth® data

from passing vehicles and calculates the freeway speeds and travel times. The results are fed into 

a color-coded speed map (figure 7) that displays system performance. This map is used for realtime monitoring and incident management at the Houston TranStar control room and for public 

dissemination on the Houston TranStar Web site.(23)
23

©Houston TranStar.

Figure 7. Screen capture. Houston TranStar color-coded traffic map.

(23)

These data are archived and, to a lesser extent, used for monitoring performance measures and 

objectives. H-GAC is reviewing options for conducting performance reporting using these 

datasets. Houston TranStar produces an annual report that documents measures of annual 

average incident clearance time, changes in measured congestion, and traveler use of 

information.

Evaluating M&O Strategies in Planning

The use of data in evaluating M&O strategies in the planning process has been limited thus far.

In early 2012, H-GAC made its first attempt to quantitatively evaluate some of its ITS project 

submissions to the Transportation Improvement Program (TIP) through IDAS. H-GAC worked 

to evaluate several ITS components in IDAS, including traffic signal system improvements, 

CCTVs, and dynamic message board installations, among others, but had difficulty with the 

process and initially obtained some questionable results. 

The modeling group at H-GAC has used the data from TranStar (i.e., toll-tag readers and other 

detectors) to validate the speed data in its freeway models and has used arterial data from a thirdparty vendor to validate its arterial models.

(G)

(B)

(Y)

(O)

(R)

(N)

(N)

(R)

(Y)

(G)

(Y)

(Y)

(Y)

(Y)

(Y)

(B)

(B)

(B)

(O)

(O)

(Y)

(N)

(N)

(B)

(G)

Unless otherwise indicated, Freeway 

Speeds are moving well at greater 

than 50 MPH (G)
24

Assessing Implemented M&O Strategies

H-GAC will be looking at private third-party data to identify before-and-after impacts of project 

implementations. While some pertinent information may be gathered from this exercise, results 

could be affected by variables that may not be project-related. For example, the economic 

downturn and recovery from 2009–2012 may influence both safety and mobility metrics. HGAC is planning for better post-implementation evaluation as part of its current CMP 

modifications.

H-GAC recently used a contractor to perform a benefit-cost analysis of the region’s incident 

management program investments, and the results showed a high benefit-cost ratio. H-GAC 

intends to use these results to inform decisionmakers in the region about investments in TIM.

The incident management data housed in TxDOT’s RIMS is used to monitor the effectiveness of 

incident management activities on the system. Those data are reviewed on a monthly basis to 

analyze incident management activities on the freeways. Current efforts to enhance incident 

management activities in the region involve increased use of this database to monitor 

performance and provide information to partners and policymakers.

The Houston TranStar partnership conducts surveys on and tracks usage of the TranStar traveler 

information Web site. Surveys show that the Web site has a high benefit to the public, with 

approximately half a million viewers each month. Understanding travelers’ use of information 

enables TranStar to tailor its programs more effectively. 

H-GAC has worked with operations data to learn about how the timing of evacuation decisions 

affects traffic patterns. Evacuations are difficult to model because destinations are often 

unknown and may change en route. In addition, other factors can play into an evacuation, such as 

the impact Hurricane Katrina had on Hurricane Rita’s evacuation over a month later. Archived 

operations data was used to identify issues related to the both the Hurricane Rita and Hurricane 

Ike evacuations. They detected a dual wave of evacuation traffic that could be better addressed 

with additional coordination between evacuation announcements and employer early dismissals.

Data Barriers

H-GAC has encountered several barriers when trying to use data for planning for operations.

Travel time and speed data are predominantly generated by toll-tag readers, which are too 

expensive to implement throughout their region of about 8,000 mi2

. Regional operators, 

including TxDOT and various communities, are investigating or implementing Bluetooth®

readers to capture travel data, which are much more affordable. In the meantime, H-GAC is 

using third-party data to look at system performance, but the data are proprietary, and there are 

concerns that it may not accurately depict normal traffic. Furthermore, traffic count data 

provided by 5-year saturation counts conducted by TxDOT are not accurate enough for 

operations and planning activities. 

BAY AREA MTC (SAN FRANCISCO)

MTC is the transportation planning, coordinating, and financing agency for the nine-county San 

Francisco Bay Area. It has extensive experience in the evaluation of management and operation 
25

strategies in planning. At the long-range planning level, MTC has evaluated M&O strategies 

such as arterial signal coordination, ramp metering, congestion pricing, and express lanes using 

the region’s travel demand model and other post-processing methods. Since 2007, MTC’s 

Freeway Performance Initiative (FPI) has been involved with the development of corridor studies 

that will be used to develop a roadmap for the selection of the best projects and operational 

strategies in the region based on performance and cost-effectiveness. With respect to 

performance monitoring, MTC has been evaluating the potential to use the Caltrans PeMS to 

support their state-of-the-system reporting.(24) In addition, MTC supported the development of 

the Traffic Operations System Equipment Management System (TEMS), a central database for 

inventory and status information for the San Francisco Bay Area ITS and traffic operations 

devices operated and maintained by Caltrans. The following sections describe these examples 

with a focus on the data used for these efforts.

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives

MTC has multiple sources of transportation performance data for monitoring the operational 

performance of the area’s highways. These sources include the following:

• PeMS.

• MTC 511 system.

• Caltrans Highway Congestion Monitoring Program (HICOMP).

• Caltrans Traffic Accident Surveillance and Analysis System (TASAS).

• Private sector data. 

PeMS is an Internet-based data archive system that collects historical and real-time traffic data in 

California to compute freeway performance measures. It collects traffic data from freeway 

detectors such as counts and occupancies and can automatically compute speeds, VMT, vehicle 

hours traveled (VHT), delay, travel time index, and productivity for every detector location every 

5 min. PeMS also aggregates several of the performance measures in time and space. Figure 8

presents a screenshot of the PeMS online system. Users can retrieve data using the standard 

query forms within the system.(25)
26

©2012 State of California.

Figure 8. Screen capture. PeMS online system.(25)

The MTC 511 system (figure 9) is a one-stop source for traffic, transit, ridesharing, and parking 

and bicycling data for the nine-county San Francisco Bay Area.(26) The traffic section ingests 

real-time traffic speed and travel time information on highways and major arterials from a 

private sector data provider. This information is checked against several quality filters that help 

ensure the data are as accurate as possible before it is used by the 511 system to provide traveler 

information to Bay Area travelers. In addition, Traveler Information Center operators input into 

the 511 Traffic/Transit Reporting and Management System information on incidents, planned 

closures, and event data coming from several external data sources, including Caltrans, the 

California Highway Patrol, and the media. The 511 system then provides travel time and incident 

information for user-selected routes based on real-time and historical information. The system 

reports both typical travel time and current travel time as well as roadway incidents and closures 

to aid travelers in determining whether they should consider route, departure time, or mode 

changes. The transit section of 511 provides information about routes, schedules, real-time 

departures, and transit trip planning. Users can obtain complete information, including maps for 

a multimodal trip spanning multiple agencies. The rideshare section of 511 provides users with 

the ability to save time and money by accessing information on traditional, dynamic, and casual 

ridesharing. The bicycling section of the 511 system provides information about bike maps and 

biking infrastructure as well as bike sharing facilities. The parking section of 511 provides static 

and real-time data (when available) about parking at train stations, park-and-ride lots, and public 

and private parking lots and garages. 

Unless otherwise indicated, 

Freeway Traffic is moving at 

greater than 60 MPH (G)

(G) (LG) (Y) (DY) (O) (DO) (R)

(R)

(R)

(R)

(DO)

(Y)

(Y)

(Y)

(DO)

(DO)

(DY)

(DO)

(LG)

(LG)

(LG)

(LG)

(Y)

(LG)

(Y) (O)

(Y)

(LG)

(LG)

(LG)

(LG)
27

©2015 MTC.

Figure 9. Screen capture. MTC 511 system.

(26)

The HICOMP report has been produced by Caltrans since 1987. The HICOMP report is 

produced annually and contains a compilation of measured congestion data reflecting conditions 

on urban freeways in California. Over several years, MTC produced a state-of-the-system report 

and shared this data with Caltrans for the HICOMP report. The data was collected by driving 

specially equipped vehicles along congested freeway segments during peak travel periods. 

Caltrans also performs floating car runs at least twice per year on freeway segments with HOV 

lanes. The HICOMP report includes maps illustrating the congested locations, the duration of 

congestion, and the hours of delay for each congested segment.

TASAS is a traffic records system containing an accident database linked to a highway database. 

The highway database contains description elements of highway segments, intersections and 

ramps, access control, traffic volumes, and other data. TASAS contains specific data for 

accidents on State highways.

MTC purchases private sector speed data collected over a large geographical area for most 

roadways on a regional scale. The private sector data provider aggregates traffic from GPSenabled vehicles, mobile devices, traditional road sensors, and many other sources.

G Y R B G

Unless otherwise indicated, 

traffic is moving under free 

flow conditions

Y

Y

Y

Y

Y

Y

R

R

R

R

Y

R

R

Y

Y

Y

R

R
28

Evaluating M&O Strategies in Planning

Regional Transportation Plan

MTC and the Association of Bay Area Governments adopted Plan Bay Area in 2013. Plan Bay 

Area integrates a new sustainable communities strategy element into the Regional Transportation 

Plan, resulting in an integrated transportation, land use, and housing plan targeted to reduce 

greenhouse gas emissions for cars and light-duty trucks.(27) MTC relied on a performance-based 

approach, focusing on measurable outcomes to help understand how potential transportation 

investments could advance the region’s goals. Regionally significant transportation projects and 

scenarios were evaluated based on their level of support for adopted targets and based on their 

cost-effectiveness (benefit-cost assessment). The adopted performance targets for Plan Bay Area 

predominantly focused on the sustainability goals of the region. Unlike previous regional 

transportation plans, for the first time the majority of the performance targets dealt with issues 

beyond the scope of traditional transportation planning, incorporating issues such as public 

health impacts, the potential for greenfield growth, and economic development.

Travel Model One, the region’s activity-based travel demand model, was leveraged to evaluate 

the performance of both projects and scenarios. For all non-committed projects (i.e., projects 

either lacking full funding or environmental clearance), two assessments were performed to 

determine their usefulness and efficiency in achieving the plan’s objectives. First, each 

transportation project was qualitatively evaluated based on its level of support for the adopted 

targets. Projects could receive an overall targets score ranging from +10 (strongly supporting all 

targets) to -10 (strongly adversely affecting all targets). Second, all major capacity-increasing 

transportation projects with costs exceeding $50 million and/or with regional impacts were 

evaluated using a quantitative model-based benefit-cost analysis. This process went beyond the 

adopted performance targets to consider as many quantifiable benefits as possible, seeking to 

determine which projects are most cost-effective in providing benefits to users and society.(28)

The qualitative criteria for each of the 10 performance targets included the following:

• Climate protection (carbon dioxide (CO2) reduction).

• Adequate housing.

• Healthy and safe communities (particulate matter).

• Healthy and safe communities (collisions).

• Healthy and safe communities (active transportation).

• Open space and agricultural preservation.

• Equitable access.

• Economic vitality.

• Transportation system effectiveness (non-auto mode share/VMT).

• Transportation system effectiveness (maintenance).

All projects were assessed using Travel Model One, creating a level playing field across all of 

the region’s analyzed projects. A no-build model run was conducted to determine the baseline 

future year conditions (e.g., total regional travel time, tons of airborne emissions, non-recurring 

delay, collisions, etc.). After changing the baseline conditions to represent project-related 

improvements, the model was then run again to analyze with-project future year conditions.
29

Table 2 lists the project benefits and costs that were quantified and monetized in the project-level 

benefit-cost assessment. Benefits were based on year 2035 travel model output for a typical 

weekday and, therefore, had to be multiplied by an annualization factor to determine the annual 

benefits. Capital costs were annualized based on the expected useful life of the corresponding 

transportation asset type and then combined with a net annual operating and maintenance cost.

Table 2. Benefits and costs quantified in the project-level benefit-cost assessment.

(28)

Project Benefits Project Costs

• Travel time reduction:

o Auto: free-flow travel time, recurring delay, non-recurring 

delay

o Truck: free-flow travel time, recurring delay, non-recurring 

delay

o Transit: in-vehicle travel time, out-of-vehicle travel time

• Travel cost savings:

o Auto operating costs

o Auto ownership costs

o Parking costs

• Emissions reduction:

o CO2

o Particulate matter less than 2.5 m in diameter

o Reactive organic gases

o Nitrogen oxides

• Collision reduction:

o Fatalities

o Injuries

o Property damage

• Health cost savings due to active transportation

• Noise reduction

• Capital costs

• Net operating and 

maintenance costs

By combining the targets assessment and benefit-cost assessment, MTC staff were able to inform 

policymakers about the merits and limitations of projects. The results of the project-level 

performance assessment, including some operations and management strategies, are summarized 

in figure 10 and figure 11.

(28) Each bubble chart shows the benefit-cost ratio (on the vertical axis) 

and the targets score (on the horizontal axis).

μ
30

©Association of Bay Area Governments and MTC.

Figure 10. Diagram. Project performance bubble chart by project type.

(28)

©Association of Bay Area Governments and MTC.

Figure 11. Diagram. Project performance bubble chart: all road projects.

(28)
31

The following themes emerged from the assessment related to M&O strategies:

• Utilization of ITS technologies and implementation of congestion pricing programs were 

very cost-effective and also supported many of the plan’s targets, in contrast to highway 

expansion projects.

• Express lanes are moderately cost-effective due to the high capital costs, but they 

adversely impact the Plan Bay Area targets by increasing capacity for automobiles.

FPI

Because of limited opportunities for highway expansion, MTC initiated the FPI to try to 

maximize the capacity of the existing roadways through the use of M&O strategies based on 

performance and cost effectiveness. Several corridors were analyzed as part of the FPI program, 

each including a quantitative assessment of existing freeway conditions and development and 

assessment of short-term and long-term congestion relief strategies and projects. The results 

from these FPI corridor studies have been incorporated into Caltrans’ Corridor System 

Management Plans (CSMPs). The California Transportation Commission requires that all 

corridors with a project funded through the Corridor Mobility Improvement Account have a 

CSMP that is developed with regional and local partners. The CSMP recommends how the 

congestion reduction gains from the Corridor Mobility Improvement Account projects will be 

maintained with supporting system management strategies.

Existing Conditions Analysis: The goal of the existing conditions analysis was to perform a 

comprehensive assessment of the existing traffic performance in a corridor, including the

following:

• Mobility—How well the corridor moves people and freight.

• Reliability—The relative predictability of the public’s travel time.

• Safety—The safety characteristics in the corridor, including crashes (i.e., fatality, injury,

and property damage).

• Other—Other measures of interest such as productivity, VHT, person hours traveled, 

transit, and park-and-ride capacity.

New and archived data were used for assessing the traffic performance in the existing conditions 

analyses on the FPI corridors to varying extents, including data from the following:

• PeMS.

• MTC 511 system.

• HICOMP.

• TASAS.

• New and historical probe vehicle runs, vehicle occupancy surveys, and traffic counts.

Figure 12 through figure 14 present some of the performance measures generated for the FPI 

existing conditions analysis using archived traffic data.(29)
32

©Cambridge Systematics.

Figure 12. Line graph. Mobility: average weekday hourly delay.

(29)

0

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400

500

600

700

800

900

1,000

0:001:002:00

3:004:005:00

6:007:008:00

9:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Hour of the Day

Average Daily Vehicle Hours of Delay (@60mph)

2006 Weekday

2005 Weekday

2004 Weekday
33

©Cambridge Systematics.

Figure 13. Line graph. Reliability—buffer index.

(29)

-

5

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0:00

TIME OF DAY

TRAVEL TIME (MIN)

Average Travel Time

Buffered Travel Time (95% On-Time)

Travel Time at 60mph

Travel Time at 35mph
34

©Cambridge Systematics.

Figure 14. Illustration. Heat map of bottleneck locations and extents.

(29)

Analysis of Strategies: FPI corridor studies have involved several types of analysis tools,

including sketch planning tools (e.g., IDAS, California Life-Cycle Benefit/Cost Analysis Model, 

postprocessing), travel demand modeling, macrosimulation, and microsimulation (e.g., 

Paramics). Models were validated using speeds, queue lengths, and travel times identified during 

the existing conditions analysis and traffic count data from PeMS or other sources. Prior to 

identifying potential strategies for the corridor, the cause(s) of the existing congestion were 

assessed. This aids in developing strategies that will actually address the congestion problems. 

The performance measures generated and the project prioritization methods used as part of the 

analysis varied but generally included VHT, VMT, speed, travel time, total delay, miles of 

congestion, reliability, and benefit-cost. As an example, recommended strategies for the I-580 

East FPI corridor in Alameda County, CA, studies included the following: 

• ITS improvements.

• Corridor-wide ramp metering.

• Signal optimization.

• Augmented freeway service patrol.

• Accelerated planned auxiliary lane and ramp improvements. SR87

I-280

Mathilda

Rengstorff

Willow

Holly

3rd/4th

Millbrae

Oyster Pt

SPEED

(mph)

MontegueExpressway

Ellis

Embarcadero/Oregon Xwy

Bottlenecks:

Willow

Hillsdale/92

3rd On

SR84SR92

Peninsula

Millbrae

Oyster Point/Old Bayshore

SR87

I-280

Mathilda

Rengstorff

Willow

Holly

3rd/4th

Millbrae

Oyster Pt

SPEED

(mph)

MontegueExpressway

Ellis

Embarcadero/Oregon Xwy

Bottlenecks:

Willow

Hillsdale/92

3rd On

SR84SR92

Peninsula

Millbrae

Oyster Point/Old Bayshore

SR87

I-280

Mathilda

Rengstorff

Willow

Holly

3rd/4th

Millbrae

Oyster Pt

SPEED

(mph)

MontegueExpressway

Ellis

Embarcadero/Oregon Xwy

Bottlenecks:

Willow

Hillsdale/92

3rd On

SR84SR92

Peninsula

Millbrae

Oyster Point/Old Bayshore
35

• Extended and enhanced HOV/HOT operations.

• Major interchange improvements.

• Additional transit and TDM improvements.(30)

TEMS

Cost-effective management of transportation infrastructure is a critical challenge facing 

transportation agencies, particularly for traffic control devices, ITS, and operations equipment,

which are now relied upon heavily for real-time traffic and incident management. Compounding 

this challenge for MTC as the entity responsible for traveler information and the freeway service 

patrol is Caltrans’ responsibility for deploying, operating, and maintaining the majority of the 

ITS equipment and systems. As such, MTC and Caltrans District 4 initiated the development of 

TEMS, a central database and equipment management system for San Francisco Bay Area ITS

and traffic operations devices. TEMS consolidated the several existing traffic operations system 

databases and spreadsheets and ensures the following:

• An accessible, Web-based, robust software that provides a repository for ITS and traffic 

operations devices information.

• A practical way to manage ITS and traffic operations inventory, status, and maintenance 

information, including a uniform, consistent standard for characterizing equipment.

• Reliable and accurate ITS, traffic operations, and transportation management center 

information.

It includes a database and mapping of inventory and status information for the ITS and traffic 

operations equipment deployed and planned, including changeable message signs, ramp meters, 

mainline meters, detector stations, CCTV, extinguishable message signs, highway advisory 

radio, control cabinets, and associated communications. Figure 15 presents a sample screen from 

TEMS.(31)
36

©Caltrans MTC.

Figure 15. Screen capture. TEMS.

(31)

Data Barriers

Some data barriers and gaps include the following:

• Need to improve detector coverage to minimize detection gaps.

• Need to enhance detector maintenance to improve detector health and confidence in 

the data.

• Insufficient availability of data and performance information on arterial facilities and 

freeway ramps. They are exploring the use of private sector provider data to address this 

need, as well as ways to eliminate the detector gaps on the freeway.

MTC is committed to advancing the use of real-time and archived data for a variety of planning 

for operations activities. One example is that since 2012, it has switched to using a private sector 

data provider as the primary source for regional congestion monitoring instead of using the 

floating car method because it is costly and usually only a few days’ worth of data are obtained. 

MTC has also been promoting enhancements to PeMS that would improve the system’s 
37

usability, data extraction capabilities, and ability to analyze and quantify non-recurrent 

congestion as well as expand the system to include arterial and ramp data.

SPC (PITTSBURGH)

SPC is the MPO for the greater Pittsburgh 10-county area. Its regional operations planning 

efforts were originally based on the Pennsylvania Department of Transportation (PennDOT)

Transportation System Operations Plan and are integrated into the regional long-range 

transportation plan. SPC’s 2011 Regional Operations Plan identified the following seven priority 

areas:

• Traffic signal operations.

• Incident and emergency management.

• Traveler information.

• Operational teamwork.

• Intermodal connectivity.

• Freeway and arterial operations.

• Freight management.

SPC has traditionally collected most of its own traffic data for use in planning for operations, 

including 6,600 traffic counts in a 10-year period from roadways throughout the region.(32) SPC 

also collects travel time, speed, and delay data by conducting travel time runs along regional 

corridors every 3 years and uses GPS receivers and on-board diagnostic tools to inform 

investment decisions and support CMP.

SPC collects its own park-and-ride utilization data as well as most of its traffic counts. The 

organization also receives additional traffic count data and incident data from PennDOT. 

In recent years, SPC has moved away from the traditional floating car method of data collection 

and has integrated new sources of data such as private third-party vehicle probe data, which is 

made available through PennDOT and through the use of Bluetooth® detection devices. These 

Bluetooth® devices are used in temporary installations much like traffic counters, but their 

function is to gather travel time and speed data rather than traffic volume data.

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives

SPC’s corridor travel time run data were aggregated by corridor and evaluated based on 

forecasted congestion levels for a.m. and p.m. peak periods. Comparing observed speeds with the 

posted speed limit, SPC reported performance measures, including delay per vehicle and per 

vehicle mile, as well as total delay and total delay per mile. 

Collected data are used to customize the 25 congestion management strategies in the SPC 

toolbox for targeted corridors to address both recurring and non-recurring congestion.(33)

Rather than setting specific thresholds for acceptable levels of congestion or explicit targets, SPC 

aggregates regional rankings of congested corridors that are addressed in the Regional 
38

Operations Plan, developed in conjunction with PennDOT.(34) These regional rankings are 

depicted in various graphics to show relative performance in accordance with the specified 

metrics. 

Figure 16 depicts delay data for various corridors, enabling SPC to identify congested segments 

and to select targeted operational improvements for congestion mitigation.(35)

©SPC.

Figure 16. Bar graph. Comparison of corridor segments by delay per vehicle per mile.

(35)

Figure 17 illustrates the variability in speeds caused by incidents as a measure of non-recurring 

congestion.(35) This graph identify patterns and problem regions and can help inform regional 

strategies for incident management, special event planning, and work zone management without 

actively measuring non-recurring congestion. 

SPC has begun to update its CMP analysis methodologies and data products in order to use new 

technologies and new sources of data. A comprehensive update of the CMP Web site is 

underway and will incorporate freeway and expressway reliability performance measures that 

account for non-recurring congestion.
39

©SPC.

Figure 17. Line graph. Variability in speed caused by incidents as a measure of 

nonrecurring congestion.

Evaluating M&O Strategies in Planning

SPC recognizes that a stronger connection is needed between the CMP and project selection to 

encourage the implementation of M&O strategies. Engaging outside partners has been a major 

challenge to strengthening connection. 

The commission uses 10 years of traffic counts and real-time information to improve traffic 

flows and reduce transit travel times through adaptive traffic signalization strategies.(34) It also 

uses microsimulation and visualization modeling software to facilitate detailed traffic analysis 

for proposed projects and to generate animated model results for demonstrations to policymakers 

and other relevant stakeholders. 

Assessing Implemented M&O Strategies

SPC’s Traffic Operations and Safety Committee focuses at least once a year on evaluating 

implemented strategies from the Regional Operations Plan initiatives. It also compares M&O 

strategies with capital improvement projects to assess cost effectiveness and overall performance 

improvement of the system.(34)

SPC has used traffic count data from loop detectors to conduct before-and-after studies of 

implemented M&O strategies to evaluate performance, such as the regional traffic signal 
40

program, but not all projects have enough money in the budget for these assessments. The 

organization, in conjunction with its planning partners, also plans to assess a recently completed 

ramp management study to determine its applicability for other sites.(34)

Data Barriers

SPC has documented a lack of available transit operations data from 10 regional transit agencies 

as a barrier to using data for the CMP, but it is currently developing a regional transit smartcard 

program that may enable better collection and coordination of data in the future. The commission 

is also looking to improve its measures of non-recurring congestion through a partnership with 

the new TMC to increase data sharing and develop additional performance measures to support 

the CMP, such as incident clearance time.(34)

SANDAG (SAN DIEGO)

SANDAG is the MPO for San Diego County, serving as the regional decisionmaking body for 

18 cities and county government within its jurisdiction.(36) SANDAG compiles traffic count data 

for significant roadways and all Caltrans routes in the San Diego region, which are collected 

annually and maintained by the 18 jurisdictions, Caltrans, and the county. SANDAG published

this information online from 2006 to 2010 to provide average weekday traffic counts, which are 

two-way, 24-h volumes.(37) SANDAG is in the process of acquiring arterial data from a private 

sector data vendor.

SANDAG also uses Caltrans’ PeMS to access data. PeMS is a real-time archive data 

management system for transportation data in California. It collects raw detector data in real

time, stores and processes these data, and provides a number of Web pages that provide analysis 

of the performance of the freeway system.

The following information is available in PeMS (plots, tabular, and/or mapped):

• An inventory of the freeways and detectors that are in the geographical segment.

• Inventory of routes, corridors, managed facilities, field elements, arterials, transit 

agencies, and freeway service patrol beats.

• Performance measures (actual and predicted), including VMT, VHT, delay, lost 

productivity (congested lane mile hours), travel times, Q (the average speed of a vehicle, 

calculated by dividing VMT byVHT), travel time index, congestion pie, bottlenecks, 

AADT, mobile 6 modeling (measured VMT versus the measured speed), level of service 

(LOS), etc. 

• Detector health.

• Lane closures.

• California Highway Patrol incidents.

• Photolog images.
41

SANDAG uses the PeMS tool for visualization of the data, as well as GIS mapping applications 

available to visualize the traffic information compiled from Caltrans sensors. SANDAG 

generally uses the PeMS tool for planning activities such as post-analysis, planning analysis, 

identification of bottlenecks, and identification of critical areas of travel time savings. Through 

the I-15 ICM project, SANDAG worked with Caltrans to develop a PeMS module entitled 

Corridor-PEMS that allows many of the planning and operations activities conducted with PeMS 

to be done using real-time data.

(38) SANDAG has developed a smart decision support system as 

part of the ICM project that will look at real-time model performance, operational data, and 

historical offline model data to develop, recommend, and implement corridor-level response 

plans in real time. For example, the decision support system will allow the coordination and use 

of freeway ramp meters with arterial signals to manage congestion levels during an incident 

based on real-time and archived operational data.

SANDAG also developed the initial prototype PeMS module, entitled Arterial PeMS, to extend 

PeMS capabilities and functionalities beyond the State Highway System to collect arterial data

and report performance.

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives

SANDAG produces an annual State of the Commute report for the public on the usage and 

performance of freeways, transit facilities, and local roads. The report provides regional travel 

trends, traveler delay data, and key statistics for several travel corridors in the region. The reports 

also include before-and-after analysis for recently deployed projects. Data used for the State of 

the Commute reports include freeway and arterial data from PeMS and transit data from area 

transit providers.(39) The 2013 State of the Commute report introduced arterial corridors to 

the report.

SANDAG monitors regional bottlenecks on an ongoing basis using Caltrans’ PeMS bottleneck 

algorithm, which allows SANDAG to identify major bottlenecks. Recent expansions of the 

detector network for PeMS were developed specifically for the San Diego region to incorporate 

real-time transit and arterial data. SANDAG uses transit travel time and ridership data to track 

improvement toward general target objectives. 

Evaluating M&O Strategies in Planning

SANDAG currently uses archived operations data to validate its models. The organization plans 

to expand the data used to develop its models to include pedestrian and bicycle data to support 

and advance pedestrian and bicycle modal improvements through the Active Transportation 

Grant Program and the Regional Bicycle Plan.

(40,41) Along with the County of San Diego and a 

local college, SANDAG developed a bike counter program to begin capturing bike data. Current 

efforts using this system have focused on establishing initial baseline data collection efforts and 

developing a long-term strategic plan for incorporating bicycle data in future State of the 

Commute reports to support ongoing bicycle data collection efforts.

SANDAG is making the transition to an activity-based model, including an active transportation 

component, and plans to use archived operations data to baseline the new model. SANDAG is 
42

also looking to advance a performance-based management approach to corridor management 

based on real-time and historic data.

As part of the I-15 ICM initiative, SANDAG led the AMS for various ICM strategies for the 

corridor. The AMS approach used by the San Diego team consisted of extracting a subarea of the 

macro regional travel demand model for use in a corridor-level micro-simulation model.

In cooperation with Caltrans, SANDAG has used a similar analysis, simulation, and modeling 

approach to develop CSMPs for other major corridors, including I-805, to identify, evaluate, and 

plan corridor-based system management strategies.(42)

Assessing Implemented M&O Strategies

SANDAG has not historically performed evaluations of implemented operations projects and 

strategies but has begun to aggregate data from the newly constructed express lanes on the I-15 

corridor and will be using that data to compare FasTrak (an electronic toll collection system in 

California) users to normal traffic. SANDAG will use these data to monitor the performance of 

the 20-mi express lanes system and determine operational improvement strategies to best 

maximize overall corridor mobility and operational efficiencies that can be considered over time.

SANDAG provides before-and-after study results for a few significant roadway infrastructure 

and transit improvements in the region as part of each annual State of the Commute report.

Data Barriers

The greatest challenge SANDAG has with data in planning for operations is arterial collection 

where there is not yet sufficient infrastructure, although new equipment is being added to 

supplement the data from PeMS. Currently, SANDAG only has access to enough freeway travel 

data to provide consistent and complete performance reporting.

SANDAG also has had challenges in obtaining data in small enough time increments to validate 

its models. It also has occasional issues with roadway sensor failures when conducting travel 

time analyses.

PORTLAND METRO

Metro is the MPO for the three-county area surrounding Portland, OR. Metro’s 2014 Regional 

Transportation Plan continues the performance-based, outcomes-driven planning approach 

established with its 2035 Regional Transportation Plan, adopted in 2010.(43) Metro recently 

implemented its first round of performance-based planning with a call for projects for the TIP. 

Metro has access to primarily freeway operations data sources but is working on implementing a 

plan for data collection on arterials and determining how this data can be used for planning.

Metro has developed a strong partnership with Portland State University through which they are 

working on new methods for collecting, analyzing, and archiving transportation system 

performance data in the Portal system, an archived data user service for the Portland-Vancouver 

metropolitan region. Through Portland State University’s Portal, Metro has access to traffic 

counts, speed, occupancy, incident, weather, transit, and freight data. The traffic data are
43

collected by loop detectors across the freeway. Metro has also obtained travel time data from a 

private third-party data provider.

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives

During the 2035 Regional Transportation Plan development, Metro and its partners established 

several performance targets, shown in figure 18, to monitor and report out during each plan

update.(44) As a key element of its CMP, Metro began a monitoring program to assess the 

performance of 24 mobility corridors every 2 years and use that information to inform 

incremental land use and transportation project implementation decisions. Metro developed a 

Mobility Corridor Atlas to serve as a baseline that includes land use and performance data for 

regional measures such as travel time, safety, and bike and pedestrian network completion.(45)

This publication will be updated in 2015. Metro uses data from Portal to track performance 

measure targets that are part of the regional transportation plan.

©Portland Metro.

Figure 18. Chart. Policy-level performance targets.

(44)

Evaluating M&O Strategies in Planning

As part of the process for developing its TIP, Metro provides a Data Resource Guide that 

contains updated performance data on several measures implemented along Metro’s 24 mobility 

corridors along with its request for projects. The applicants are advised to use this data in 

completing their project applications and are encouraged to move those performance indicators 

in a positive direction through their projects. The Data Resource Guide includes performance 

information on regional travel options, transit, safety, roadways, and active transportation.(46)

Metro has also developed a Regional Transportation System Management and Operations 

(TSMO) Plan to maximize the benefits of new investments and to facilitate data collection and 

analysis for use in planning—for example, through installation of automated vehicle locators to 

provide transit data and the use of media access control address-reading technology to provide 
44

traffic data.(47) Congestion data on the system are used to help determine which projects will be 

funded using the funds allocated specifically for TSMO. Metro relies on before-and-after data to 

establish projected impacts from the proposed TSMO projects. 

The data used to validate Metro’s transportation models include traffic count data collected by 

local jurisdictions in the region along cuts or screenlines to verify auto assignments. Metro also 

uses an online archive of freeway count data, and the Oregon Department of Transportation

recently purchased private third-party data for comparing travel times and speeds. The purchased 

data cover a large number of arterials, but they are fleet-based and limited to whatever facilities 

those vehicles are traveling on.

Metro is also moving from experimentation to broader use with a DTA model. The 

organization’s DTA model is still fairly new and is in a transition period from development to 

application.

Metro participated in an FHWA demonstration project on the use of a data integration hub to 

easily transfer model and limited field data between AMS tools. The demonstration focused on 

NW 185th Avenue in the Portland area and helped evaluate ITS in the corridor, including ramp 

metering, adaptive signal control, and transit/truck priority. The data sources for the 

demonstration included field-measured 24-h link volumes and speeds, peak period turning 

movement counts, signal timing data, regional travel demand model data, and Bluetooth® travel 

time data.(7)

Assessing Implemented M&O Strategies

Metro is not currently assessing implemented M&O strategies, but the latest version of the 

Regional Transportation Plan notes that they will be evaluated in the near term. 

Data Barriers

Loop detectors have only been deployed on the region’s limited-access freeways, which restrict 

the amount of data available on the system, and the data must be supplemented with simulated 

data from model output for the rest of the system. Evaluation of strategies as part of the larger 

planning process depends on the expansion of data capture along the rest of the freeway and 

arterial system. Metro is now working with private third-party data and using it to update its 

performance measures. 

Assimilating data from a wide variety of sources can be challenging; for example, private thirdparty travel time has to be heavily postprocessed before it can be used. Also, the resolution of the 

volume data Metro receives from local jurisdictions is not sufficient for the needs of newer 

modeling tools: tube count data are aggregated into hourly figures, whereas 5-, 10-, or 15-min 

intervals are more appropriate. There are also significant postprocessing needs for the count data.

In working with the DTA model, Metro has found that it had to spend a great deal of time to get 

Synchro® data into its network. Metro also wants better methods of visually depicting data to 

demonstrate trends and analyze problems.
45

WILMINGTON AREA PLANNING COUNCIL (WILMAPCO)

WILMAPCO is the MPO for Cecil County, MD, and New Castle County, DE. It collects or 

acquires data from its planning partners to use for monitoring transportation system performance 

as part of its CMP and to illustrate congestion visually through maps that highlight areas of 

improved or degraded performance. The area is moving forward in expanding its data collection 

efforts. The area is moving forward in expanding its data collection efforts, including the 

installation of many Bluetooth® devices covering freeways and arterials in 2015. These devices 

are provided and managed by the Delaware Department of Transportation (DelDOT) TMC.

DelDOT is the primary source of the archived data for WILMAPCO. Along the expressway, 

there are radar traffic detectors about every 0.5 mi collecting speed and volume data. The 

University of Delaware Center for Transportation provides WILMAPCO with freeway and 

arterial data that it collects through annual GPS travel time runs. The region also collects traffic 

count data from a variety of sources, which it uses for level of service analysis. In addition, 

WILMAPCO has access to transit usage data from the Delaware Authority for Regional Transit, 

including park and ride utilization data, ridership, and monthly seating capacity. Soon,

WILMAPCO will have Bluetooth® travel time data along the arterials from the DelDOT TMC. 

WILMAPCO supplements archived data from DelDOT with arterial travel time data from a 

private third-party data provider. WILMAPCO intends to collect more data in-house to 

reduce cost.

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives

WILMAPCO uses arterial travel time data to focus on improving the flow of traffic through 

coordinated signal timing. The data are used to analyze volume-based level of service to identify 

congested intersections and target regions for improvement. Two other performance measures 

are monitored using the data, including travel speed versus free-flow speed and the number of 

crashes along a corridor. The performance measures are visually depicted (as in figure 19 and 

figure 20) and tracked over time to identify areas in need of improvement.(48)
46

©WILMAPCO.

Figure 19. Map. The a.m. peak intersection level of service and travel speed.

(48)

©WILMAPCO.

Figure 20. Map. Identified congested corridors.

(48)

Evaluating M&O Strategies in Planning

In 2012, WILMAPCO began including in its congestion management system report an 

Intersection Operational Analysis section that examines the performance of signalized 

intersections and prioritizes congested intersections that can be alleviated by adjusted signal 

timing methods such as traffic responsive signalization (TRS) and those that require capital 

improvements to increase performance.(49) WILMAPCO’s 2014 version of the Intersection 

Operations Analysis section has expanded beyond signal timing adjustments to include the 

following:

• Regional delay/capacity analysis for signalized intersections in the arterial network.
47

• Identification of intersections that can no longer function efficiently due to limited 

capacity.

• Prioritization of intersections in need of capital improvements, minor adjustments, or 

only signal timing improvements.

• Status update on capital improvements and implementation of TRS to demonstrate 

progress to decisionmakers and the public.(49)

Beyond this effort, WILMAPCO’s strategy evaluation is more qualitatively focused, favoring 

those strategies that eliminate trips, create a mode shift, and effect operational efficiency over 

capital improvements. WILMAPCO found that the outputs of cost-benefit analyses were not 

useful for informing decisionmakers, and WILMAPCO no longer conducts the analyses.

Assessing Implemented M&O Strategies

WILMAPCO’s congestion management system does not yet include conducting assessments of 

individually implemented M&O strategies, but it is working with State transportation 

departments on coordinating the data necessary for the analysis. Regional performance 

improvements and degradations can be seen from its system monitoring section (figure 21),

which can be used to inform future decisions. In the assessment of individual strategies, 

WILMAPCO has experienced difficulty isolating the effects of a particular strategy in 

conjunction with local development, where business closures and signal retiming activities 

impact system performance but are not regularly reported to the agency.
48

©WILMAPCO.

Figure 21. Map. The p.m. peak travel speed changes, 2004–2011.

(48)

Data Barriers

WILMAPCO’s barriers include a lack of funding and resources to collect before-and-after data 

necessary for the assessment of individual strategies. 

PIMA ASSOCIATION OF GOVERNMENTS (PAG) (TUCSON)

PAG collects much of its own data, supplemented by data from member jurisdictions and 

contractors. Collecting and compiling data on system performance is part of PAG’s work 

program. PAG aggregates all traffic volume counts on arterials and collectors in the region and 

collects turning movement data. It receives performance data on freeways and other State 

roadways from the Arizona Department of Transportation (ADOT).

Use of Data in Monitoring Transportation Operational Performance and Tracking 

Performance Objectives

Travel time, speed, and intersection delay are used to generate a travel time index and the annual 

delay per traveler to measure system performance as well as traffic counts to track volume-to-
49

capacity ratios, which are depicted graphically to show relative congestion throughout the region 

(figure 22). (50)

©PAG.

Figure 22. Map. PAG relative congestion.

(50)

PAG has set several system performance objectives and performance measures as part of the 

CMP monitoring process in its current regional transportation plan, and the organization has 

begun monitoring those objectives as part of its 2045 Regional Transportation Plan development 

process. PAG will continue to evaluate the relevance of its CMP objectives as it implements 

revisions to the CMP and assesses the requirements associated with performance measure 

reporting required as part of MAP-21 legislation. PAG is working with the University of Arizona

to launch a travel time collection effort using Bluetooth® technology.

Evaluating M&O Strategies in Planning

M&O strategies are generally prioritized based on objectives, goals, and resources that have been 

agreed upon by consensus among member jurisdictions and supplemented by user input, such as 

survey input from participants in the rideshare program.

The PAG region uses a Synchro® model for 600 signals, and this model is used to evaluate 

signal timing projects. In conjunction with the University of Arizona, PAG has begun to develop 

a mesoscopic model that will have the potential to evaluate more M&O strategies. To prove the 

concept, researchers and PAG staff have tested the new model on a bus rapid transit evaluation 

that involves the regional travel demand model, the mesoscopic model, and the microscopic 

model (Vissim).

Recently, Tucson and Portland participated in an FHWA demonstration project on the use of a

system to integrate data between AMS tools as well as limited field data. In addition to 

 

 

 

 

 

 

 

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demonstrating aspects of the AMS system, the test in Tucson focused on the following 

two objectives for the I-10 corridor and its interchanges:

• Identify future capacity requirements for the I-10 corridor.

• Evaluate optimal construction sequencing for the corridor and its interchanges.

The data sources for the test in Tucson are defined in table 3, which is taken from the FHWA 

AMS integration project report. The AMS test used a DTA and VISSIM model.

Table 3. Data sources for AMS integration test in Tucson.

(7)

Source Data Type

AMS 

Tool Source

Regional travel demand model TransCAD PAG

24-h segment counts, intersection turning 

movement counts, I-10 mainline speed 

data

Collected by quality counts for 

ADOT

Regional Synchro® models Synchro® PAG

Blank cell = Not applicable.

Assessing Implemented M&O Strategies

Due to limited staff and funding, most performance evaluation is conducted using Synchro®

analysis rather than post-implementation studies. However, PAG has evaluated selected corridor 

timing projects through a comparison of before-and-after volume and speed data. 

Data Barriers

Performance measures established in the CMP report were focused on low-cost options because 

of funding limitations. PAG would like to conduct more before-and-after studies but lacks the 

staff required to cover all activities.
51

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HRDO-30/01-16(WEB)E