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Technical Report Documentation Page

1. Report No.

FHWA-HOP-12-028

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

4. Title and Subtitle

Operations Benefit/Cost Analysis Desk Reference

5. Report Date

May 2012

6. Performing Organization Code

7. Author(s)

Doug Sallman, Erin Flanigan, Krista Jeannotte, Chris Hedden, and Dorothy Morallos –

Cambridge Systematics, Inc.

8. Performing Organization Report No.

9. Performing Organization Name And Address

Cambridge Systematics, Inc.

555 12th Street, Suite 1600

Oakland, California 94607

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-06-D-00004

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

Office of Operations (HOP)

1200 New Jersey Avenue, SE

Washington, D.C. 20590

13. Type of Report and Period Covered

Final Report

December 2009 to May 2012

14. Sponsoring Agency Code

15. Supplementary Notes

FHWA Contracting Officer’s Technical Manager (COTM) Jim Hunt.

16. Abstract

This Desk Reference is intended to meet the needs of a wide range of practitioners looking to conduct benefit/cost analysis of operations strategies. 

The guidance provided in the Desk Reference includes basic background information on benefit/cost analysis, including basic terminology and 

concepts, intended to support the needs of practitioners just getting started with B/C analysis, who may be unfamiliar with the general process. 

Building off this primer base, the Desk Reference also describes some of the more complex analytical concepts and latest research in order to support 

more advanced analysts in conducting their analysis. Some of the more advanced topics include capturing the impacts of travel time reliability; 

assessing the synergistic effects of combining different strategies; and capturing the benefits and costs of supporting infrastructure, such as traffic 

surveillance and communications.

This Desk Reference is supported by an Operations B/C decision support tool, called the Tool for Operations Benefit/Cost (TOPS-BC). This 

spreadsheet-based tool is designed to assist practitioners in conducting benefit/cost analysis.

17. Key Words

Benefit/Cost Analysis; Cost Benefit Analysis; Economic Analysis; 

Transportation System Management and Operations; Operations 

Planning; Analysis Tools; Lifecycle Cost Analysis; Travel Time 

Reliability Analysis; Multiresolution/Multiscenario Analysis 

18. Distribution Statement

No Restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

119

22. Price

n/a

DOT F 1700.7 (8-72) Reproduction of completed page authorized
Preface/

Acknowledgements

The development of the Desk Reference and TOPS-BC was greatly aided by an Expert Review 

Panel that was formed to provide input and guidance to the project. This Expert Review Panel was 

comprised of individuals representing Federal, state, regional, and local transportation agencies, as 

well as research organizations. The Expert Panel has been invaluable in identifying areas of the 

greatest need for guidance on particular strategies, performance measures and other issues, and in 

reviewing the guidance materials and the TOPS-BC application. Members of the Expert Panel and 

other major contributors included:

Jim Allen (New Jersey DOT)

Sam Beydoun (Virgina DOT)

Rashmi Brewer (Minnesota DOT)

Jesse Buerk (Delaware Valley Regional Planning Council)

Mark Burris (Texas Transportation Institute)

Dong Chen (Florida DOT)

Jim Hogan (formally with New Jersey DOT)

Sarath Joshua (Maricopa Association of Governments)

Mena Lockwood (Virginia DOT)

Laurie Matkowski (Delaware Valley Regional Planning Council)

Andrew Meese (Metro Washington Council of Governments)

Dennis Motiani (New Jersey DOT)

Zoe Neaderland (Delaware Valley Regional Planning Council)

Matthew Neeley (Washington State DOT)

Keith Nichols (Hampton Roads Planning District Commission)

Barry Padilla (Caltrans)

Jason Sims (Kansas City SCOUT)

John Shaw (formally with Wisconsin DOT)

Todd Szymkowski (University of Wisconsin/Wisconsin DOT)

Valentin Vulov (Georgia Regional Transportation Authority)

John Wolf (Caltrans)

David Zavattero (City of Chicago)
U.S. Department of Transportation Federal Highway Administration Office of Operations

FHWA Operations Benefit/Cost Analysis Desk Reference| iii

Table of Contents

Chapter 1. Introduction ................................................................................ 1

PROJECT BACKGROUND AND PURPOSE ............................................................ 1

HOW TO USE THE DESK REFERENCE AND TOPS-BC DECISION SUPPORT 

TOOL................................................................................................................. 6

Chapter 2. Overview of B/C Analysis for Operations ............................... 8

WHAT IS B/C ANALYSIS? ................................................................................... 8

HOW IS B/C ANALYSIS USED IN THE OPERATIONS PLANNING PROCESS?........16

HOW CAN B/C ANALYSIS FOR OPERATIONS STRATEGIES BE INTEGRATED 

WITH B/C ANALYSIS FOR OTHER MORE TRADITIONAL STRATEGIES .................24

CHALLENGES AND LIMITATIONS OF B/C ANALYSIS............................................27

Chapter 3. Operations Strategies and their Impacts (Agency, User, 

and Societal Benefits) .................................................................................32

DEFINITIONS OF OPERATIONS STRATEGIES COVERED IN THIS REFERENCE.....32

WHAT ARE THE IMPACTS AND BENEFITS OF OPERATIONS STRATEGIES?..........36

Chapter 4. Existing B/C Tools and Methods............................................ 53

OVERVIEW OF EXISTING TOOLS AND METHODS FOR B/C ANALYSIS OF 

OPERATIONS ...................................................................................................53

OPERATIONS STRATEGIES AVAILABLE FOR ANALYSIS IN THE TOOLS/

METHODS........................................................................................................67

MOES GENERATED.........................................................................................67

Chapter 5. Conducting B/C Analysis for Operations.............................. 71

SELECTING THE APPROPRIATE METHODOLOGY/FACTORS INFLUENCING THE 

ANALYSIS APPROACH ......................................................................................71

DETERMINING THE APPROPRIATE TIMEFRAME TO USE ....................................82

AVAILABLE RESOURCES FOR IDENTIFYING THE BENEFITS OF OPERATIONS 

STRATEGIES....................................................................................................85

CONFIGURING B/C ANALYSIS TO LOCAL CONDITIONS......................................87

ESTIMATING THE IMPACTS (USER AND SOCIETAL BENEFITS) OF THE 

OPERATIONS STRATEGY..................................................................................89

VALUING THE BENEFITS...................................................................................95

ESTIMATING THE LIFE-CYCLE COSTS ..............................................................96

DISPLAYING AND COMMUNICATING RESULTS .................................................100

ADDRESSING CHALLENGES OF APPLYING B/C ANALYSIS TO TSM&O

STRATEGIES TARGETED AT NONRECURRING CONDITIONS.............................102

ADDITIONAL RESOURCES AND SUPPORT.......................................................106

Appendix A. List of Acronyms ................................................................109
U.S. Department of Transportation Federal Highway Administration Office of Operations

FHWA Operations Benefit/Cost Analysis Desk Reference| iv

List of Tables

Table 1-1. Summary of Guidance on Various TSM&O Strategies........................... 5

Table 2-1. Comparison of Projects Using B/C Ratio and Net Benefit...................... 9

Table 2-2. Common Terminology Used in the Desk Reference.............................12

Table 2-3. Comparing B/C Analysis with Economic Impact Analysis ....................14

Table 2-4. Comparison of ARTIMIS Operational Projects with a Traditional 

Roadway Widening Project.....................................................................23

Table 3-1. Sample EPA Fuel Use Rates by Average Speed for Year 2010 (in 

Gallons per VMT) ....................................................................................40

Table 3-2. Reliability Performance Measures..........................................................46

Table 3-3. Sample IDAS-Derived Travel Time Reliability – Rates for One-Hour 

Peak (Vehicle Hours of Incident Delay per Vehicle Mile) .....................49

Table 4-1. Summary of Existing B/C Tools and Methods for TSM&O...................54

Table 5-1. Analysis Methods/Tools Mapped to Appropriate Geographic Scope ..72

Table 5-2. Analysis Tools and Methods Mapped to their General Resource 

Requirementsa .........................................................................................81

Table 5-3. Real Discount Rates 2011 (Public Investment) (in Percentage)..........85

Table 5-4. Distribution of Operating Conditions in U.S. 75 Dallas .........................89

Table 5-5. Capacity Reduction Based on Nonrecurring Weather Types.............104

Table 5-6. Capacity Reduction Based on Nonrecurring Incidents (In 

Percentage)............................................................................................104

Table 5-7. Capacity Reduction Related to Work Zones (In Percentage) ............105

Table 5-8. Sample Scenario Probabilities – I-580 Corridor..................................106
U.S. Department of Transportation Federal Highway Administration Office of Operations

FHWA Operations Benefit/Cost Analysis Desk Reference| v

List of Figures

Figure 1-1. Capabilities Provided by TOPS-BC.......................................................... 2

Figure 2-1. Sample Display of B/C Analysis Output................................................. 11

Figure 2-2. Typical Benefits and Costs Distribution among Stakeholder Groups..16

Figure 2-3. Objectives-Driven Planning for Operations Approach ..........................18

Figure 2-4. ARTIMIS Program Expansion Plans......................................................22

Figure 2-5. Hypothetical Project Comparison Data – MTC San Francisco Bay 

Area 2035 Transportation Plan...............................................................29

Figure 2-6. Sample View of TOPS-BC Impact Lookup Function ............................30

Figure 3-1. Example Display of User Travel Time MOE..........................................38

Figure 3-2. Use of Recurring Travel Time Measurement versus Actual Traveler 

Experience ...............................................................................................44

Figure 3-3. National Summary of the Sources of Congestion.................................45

Figure 4-1. Example View of Sketch-Planning B/C Analysis Method (SCRITS) 

Data Inputs...............................................................................................58

Figure 4-2. Example View of Caltrans’ CAL-BC Analysis Tool Data Outputs ........59

Figure 4-3. Example of Post-Processing B/C Analysis Method (IDAS)..................61

Figure 4-4. Multiple-Resolution Approach Developed for the ICM Initiative 

Analysis ....................................................................................................64

Figure 4-5. Comparison of ICM Benefit Estimates between Nonincident 

Scenarios and Incident Scenarios (San Diego I-405 Analysis – in 

Millions of Annual Dollars).......................................................................65

Figure 4-6. Available Tools/Methods Mapped to Strategies Analyzed....................67

Figure 4-7. Available Tools/Methods Mapped to MOEs Analyzed ..........................69

Figure 4-8. TOPS-BC Analysis Method Selection Tool (Partial Screenview).........70

Figure 5-1. Different Simulation Analysis Tools are Appropriate to Different 

Geographic Scopes.................................................................................73

Figure 5-2. Use of Representative Corridors in an Analysis....................................74

Figure 5-3. Mapping of Strategies to Analysis Scenarios ........................................75

Figure 5-4. Comparison of Analysis Methods with Various Stages of the 

Planning Process and Need for Analysis Sensitivity.............................77

Figure 5-5. TSM&O Strategies Mapped to Likely Impacts on MOEs .....................78

Figure 5-6. Sample View of TOPS-BC TSM&O Impact Look-up Function ............86

Figure 5-7. Sample View of U.S. DOT ITS Benefits Database Web Site...............87

Figure 5-8. Example Calculations of Incremental Change MOEs ..........................91

Figure 5-9. Sample View of Benefit Valuations Maintained in the TOPS-BC

Repository................................................................................................96

Figure 5-10. Example View of Cost Data Organization for a Ramp Meter 

System......................................................................................................98

Figure 5-11. TOPS-BC Projected Stream of Costs Output .....................................100

Figure 5-12. Innovative Use of Graphics to Display Multiple Data..........................101

Figure 5-13. Sample Graphic Presenting Results over an Analysis Timeline ........102
U.S. Department of Transportation Federal Highway Administration Office of Operations

FHWA Operations Benefit/Cost Analysis Desk Reference| 1

Chapter 1. Introduction

Project Background and Purpose

Due to an increasingly competitive fiscal environment, state, regional, and local transportation 

planning organizations around the country are being asked more than ever to justify their programs 

and expenditures. Transportation System Management and Operations (TSM&O) programs have not 

escaped this scrutiny and are routinely asked to rank their projects against traditional expansion 

projects, as well as conduct other “value”-related exercises.

This requirement can put TSM&O projects at a disadvantage since many specialists in this arena 

have limited experience in performing benefit/cost analysis; and often, many of the established tools 

and data available for conducting benefit/cost analysis for traditional infrastructure projects are poorly 

suited to analyzing the specific performance measures, project timelines, benefits, and life-cycle costs 

associated with operational improvements.

In response to the needs of system operators to conduct these analyses, a number of initiatives have

been undertaken in recent years at the national, state, and regional levels to develop enhanced 

analysis tools, methodologies and information sources to support the conduct of benefit/cost analysis 

for many specific TSM&O strategies. It often remains difficult, however, for practitioners to weed 

through the multiple information and guidance sources in order to understand and apply an 

appropriate methodology for meeting their own specific analysis needs.

The FHWA Operations Benefit/Cost Analysis Desk Reference

Project

The Federal Highway Administration (FHWA) Office of Operations initiated this project in recognition of 

practitioners’ need for relevant and practical guidance on how to effectively conduct benefit/cost 

analysis for a wide spectrum of transportation system management and operations strategies. The 

Operations Benefit/Cost Analysis Desk Reference project is intended to provide practitioners with 

relevant guidance on how to effectively and reliably estimate the benefits and costs of operations 

strategies.

This Desk Reference is intended to meet the needs of a wide range of practitioners looking to conduct 

benefit/cost analysis of operations strategies. The guidance provided in the Desk Reference includes 

basic background information on benefit/cost analysis, including basic terminology and concepts, 

intended to support the needs of practitioners just getting started with B/C analysis, who may be 

unfamiliar with the general process. Building off this primer base, the Desk Reference also describes 

some of the more complex analytical concepts and latest research in order to support more advanced 

analysts in conducting their analysis. Some of the more advanced topics include capturing the 

impacts of travel time reliability; assessing the synergistic effects of combining different strategies; and 

capturing the benefits and costs of supporting infrastructure, such as traffic surveillance and 

communications.
Chapter 1 Introduction

U.S. Department of Transportation Federal Highway Administration Office of Operations

FHWA Operations Benefit/Cost Analysis Desk Reference| 2

This Desk Reference is supported by an Operations B/C decision support tool, called the Tool for 

Operations Benefit/Cost (TOPS-BC). This spreadsheet-based tool is designed to assist practitioners 

in conducting benefit/cost analysis by providing four key capabilities, including the following:

• The ability for users to investigate the expected range of impacts associated with previous 

deployments and analyses of many TSM&O strategies;

• A screening mechanism to help users identify appropriate tools and methodologies for 

conducting a B/C analysis based on their analysis needs;

• A framework and default cost data to estimate the life-cycle costs of various TSM&O 

strategies, including capital, replacement, and continuing operations and maintenance (O&M) 

costs; and

• A framework and suggested impact values for conducting simple B/C analysis for selected 

TSM&O strategies.

Figure 1-1 shows the opening screen of TOPS-BC, which provides navigation to these capabilities 

within the support tool. The TOPS-BC application is supported by a separate, stand-alone User’s 

Manual providing instruction on its proper set up and use.

Figure 1-1. Capabilities Provided by TOPS-BC

Operations Strategies Covered

Together the Desk Reference and the TOPS-BC tool are intended to support the analysis of a wide

range of the available TSM&O strategies. These “strategies” include the direct application of 

technologies and infrastructure to roadside application (e.g., deployment of freeway service patrol 

vehicles), as well as many harder-to-define, nonphysical strategies (e.g., interagency coordination). 

While it is not possible to comprehensively provide guidance on every type and variation in application 

of all the many diverse TSM&O strategies (especially in light of the fact that new strategies and 

technologies are constantly emerging), TSM&O strategies covered in the TOPS-BC tool and/or the 

Desk Reference document include strategies from the following categories (see Chapter 3 for a more 

complete description of the TSM&O strategies and substrategies that comprise each category):

FHWA Tool for Operations Benefit/Cost (TOPS-BC)

What would you like to do today? 

Investigate the Range of 

Expected Values Associated 

with Various TSM&O 

Strategies

Map Different Benefit/Cost 

Methodologies to Your 

Organization's Needs 

Estimate Lifecycle Costs of 

TSM&O Strategies 

Conduct Simple SpreadsheetBased Benefit/Cost Analysis 

for Selected TSM&O 

Strategies 

More Info?
Chapter 1 Introduction

U.S. Department of Transportation Federal Highway Administration Office of Operations

FHWA Operations Benefit/Cost Analysis Desk Reference| 3

Physical Strategies, such as:

1. Arterial Signal Coordination –Improves the coordination of traffic signal timing to improve 

flow and reduce delay.

2. Arterial Transit Signal Priority – Provides the capability to expand or accelerate the green 

time allotted to traffic signals when the transit vehicle is detected approaching the intersection.

3. Transit Automatic Vehicle Location – Uses transponder and Global Positioning System 

(GPS) technologies to track the real-time location of transit vehicles. Compiled information is 

typically used to better manage the transit assets or provide traveler information to 

passengers.

4. Ramp Metering – Applies signals to on-ramp or freeway-to-freeway ramp locations to control 

and manage the flow of vehicles into the merge area.

5. Incident Management – Various combinations of incident detection, location verification, 

communication/coordination, and response strategies designed to lessen the time required to 

respond and clear traffic incidents.

6. Pretrip Traveler Information – Traveler information provided through several different 

available channels (e.g., telephone, web-based, broadcast-media, social-media) intended to 

reach individuals prior to the initiation of their trip so that they may make informed decisions 

on destination, mode, route, time of travel, and even whether to forego the trip.

7. En-route Traveler Information – Traveler information intended to reach the recipients while 

they are traveling. The information may be provided through several different channels,

including telephone, in-vehicle system, roadside Dynamic Message Signs (DMS) or Highway 

Advisory Radio (HAR), or broadcast-media.

8. Work Zone Management – Lessens the congestion, delay, and safety issues associated 

with construction or maintenance work zones.

9. High-Occupancy Toll (HOT) Lanes – Allows single-occupancy vehicles (SOV) to pay a toll 

to use underutilized high-occupancy vehicle (HOV) lane capacity. The tolls charged may vary 

according to time-of-day schedules, or may be dynamically assessed in response to traffic 

conditions and available HOV lane capacity.

10. Speed Harmonization – Involves the implementation of variable speed limits and the 

communication of those limits through roadside signs. The speed limits are modified 

according to congestion levels to lessen stop-and-go conditions and lower the speed of 

vehicles as they approach downstream bottlenecks.

11. Hard Shoulder Running – Involves allowing vehicles to travel on the shoulder facilities of 

roadways, often for isolated sections of roadway or limited times of operation. The availability 

of the shoulder for use is often communicated through the use of overhead gantries or 

roadside DMS.

12. Travel Demand Management – Includes a number of strategies that may be employed to 

lessen travel demand (number of trips). These may include physical strategies (e.g., 

employer-based vanpools), as well as nonphysical, policy-based strategies (e.g., alternative 

work hours).
Chapter 1 Introduction

U.S. Department of Transportation Federal Highway Administration Office of Operations

FHWA Operations Benefit/Cost Analysis Desk Reference| 4

Supporting Strategies, such as:

1. Traffic Surveillance – Seeks to collect, compile, and analyze traffic data.

2. Traffic Management Centers – Physical- or virtually-based centers designed to provide the 

backbone management and operations capability to monitor and operate the deployed 

systems and technologies.

3. Communications – Landline- and mobile-based systems designed to provide 

communication between different roadside components, and provide communication 

between the components and any centralized management structure.

Nonphysical Strategies, such as:

1. Active Transportation and Demand Management (ATDM) – The dynamic management, 

control, and influence of travel demand, traffic demand, and traffic flow of transportation 

facilities. Through the use of available tools and assets, traffic flow is managed and traveler 

behavior is influenced in real time to achieve operational objectives, such as preventing or 

delaying breakdown conditions, improving safety, reducing emissions, or maximizing system 

efficiency. Under an ATDM approach, the transportation system is continuously monitored. 

Using archived data and or/predictive methods, actions are performed in real time to achieve 

or maintain system performance.

2. System Integration – Involves the coordination and integration of two or more strategies to 

allow for the sharing of data or capabilities to provide for the betterment of the combined 

system.

3. Interagency Coordination – The integration of efforts, resources, knowledge, or 

technologies across various agencies, departments, or entities to improve the coordinated 

management and operation of the transportation system.

4. Regional Concepts for Transportation Operations – Involves the coordination of various 

stakeholders responsible for operating one or more components or jurisdictions in order to 

develop sets of policies, procedures, and operating parameters that may be implemented 

according to specific identified conditions to improve the efficiency and effectiveness of 

operational strategies during those conditions.

Chapter 3 of this document provides expanded discussion of these various strategies, as well as 

substrategies and variations in application within the general categories. Chapter 3 also identifies the 

typical benefits and impact measures associated with the deployment of the strategies.

Table 1-1 below summarizes the TSM&O strategies above and identifies if specific guidance in their 

analysis is provided in this Desk Reference, the TOPS-BC tool, or both.
Chapter 1 Introduction

U.S. Department of Transportation Federal Highway Administration Office of Operations

FHWA Operations Benefit/Cost Analysis Desk Reference| 5

Table 1-1. Summary of Guidance on Various TSM&O Strategies

TSM&O Strategy

Discussed 

in Desk 

Reference

TOPS-BC

Analysis 

Capability

Physical Strategies

Arterial Signal Coordination  

Arterial Transit Signal Priority  

Transit Automatic Vehicle Location  

Ramp Metering  

Incident Management  

Pretrip Traveler Information  

En-route Traveler Information  

Work Zone Management  

HOT Lanes  

Speed Harmonization  

Hard Shoulder Running 

Travel Demand Management  

Supporting Strategies

Traffic Surveillance  $

Traffic Management Centers  $

Communications  $

Nonphysical Strategies

ATDM 

System Integration 

Interagency Coordination 

Regional Concepts for Transportation Operations 

 – Guidance or analysis capability provided.

$ – Life-cycle cost estimation capability only.

Expert Review Panel

The development of the Desk Reference and TOPS-BC was greatly aided by an Expert Review Panel 

that was formed to provide input and guidance to the project. This Expert Review Panel was 

comprised of individuals representing Federal, state, regional, and local transportation agencies, as 

well as research organizations. The Expert Panel has been invaluable in identifying areas of the 

greatest need for guidance on particular strategies, performance measures and other issues, and in 

reviewing the guidance materials and the TOPS-BC application.
Chapter 1 Introduction

U.S. Department of Transportation Federal Highway Administration Office of Operations

FHWA Operations Benefit/Cost Analysis Desk Reference| 6

Project Workshops

In order to inform practitioners on the availability of the guidance materials, as well as provide an 

opportunity for additional testing and vetting of the material in a real-world analysis situations, the 

FHWA, as part of the Planning for Operations initiative, has technical workshop opportunities 

available. These workshops cover both the guidance available in this Desk Reference, as well as an 

overview of the proper set up and application of the TOPS-BC decision-support capabilities.

How to Use the Desk Reference and TOPS-BC Decision 

Support Tool

As discussed previously, the guidance in this Desk Reference is intended to be appropriate to a broad 

audience – from the novice to the more seasoned benefit/cost analyst. The first several sections of 

the Desk Reference are intended to serve as an overview primer for practitioners that may be 

unfamiliar with either benefit/cost analysis or the often unique characteristics and benefits of 

operations strategies. Subsequent sections build on this basic information to provide more detailed, 

often step-by-step guidance on particular aspects of conducting B/C analysis for operations planning. 

In overview, the remainder of the Desk Reference is organized into the following chapters:

• Chapter 2, Overview of B/C Analysis for Operations provides an overview of B/C analysis, 

its role in the planning process, basic terminology and concepts, and identification of general 

challenges and limitations.

• Chapter 3, Operations Strategies and Their Impacts summarizes the basic definitions of 

the types of TSM&O strategies covered in this project and maps these strategies to their likely 

impacts/benefits.

• Chapter 4, Existing B/C Tools and Methods summarizes the capabilities along with the 

strengths and limitations of many existing B/C tools and methods to aid practitioners in 

identifying appropriate situations in which to apply these tools. This discussion also includes 

information that details how to obtain more information on the tools, and provides a 

comparison discussion of the level of effort needed to set up and apply the tools.

• Chapter 5, Conduct B/C Analysis for Operations provides more detailed, step-by-step 

guidance on how to successfully conduct B/C analysis for operations strategies, identify

considerations that need to be made, and highlight challenges that may be encountered, as 

well as propose methods to mitigate those challenges.

In parallel with the development of this Desk Reference, the TOPS-BC spreadsheet application was 

developed to provide additional decision-support and analysis structure. A separate, stand-alone 

User’s Manual was specifically developed to guide interested practitioners in the proper set up and 

application of the TOPS-BC spreadsheet tool. It should be noted that capabilities within TOPS-BC are 

often referenced within this Desk Reference document, along with discussions of many other 

applicable analysis tools, when appropriate, but the TOPS-BC User’s Manual focuses exclusively on 

TOPS-BC operation and is the devoted source of information for that resource.

Common Questions and Where to Locate More Information

As summarized above, the Desk Reference is structured to provide basic introductory information on 

the general principles and concepts of B/C analysis in the opening sections, appropriate as a 
Chapter 1 Introduction

U.S. Department of Transportation Federal Highway Administration Office of Operations

FHWA Operations Benefit/Cost Analysis Desk Reference| 7

reference resource for beginning and intermediate B/C analysts. The following represents some 

common overview questions along with a guide on where more information may be found on the 

particular topic:

• What is B/C analysis? (See Chapter 2)

• What is the role of B/C analysis in the planning process? (See Chapter 2)

• How does B/C analysis differ from economic impact analysis? (See page 15 and Table 2-3

for a comparative discussion.)

Other users more seasoned with the basic concepts of B/C analysis may have more focused 

questions on the uniqueness of B/C analysis as it is applied to specific TSM&O strategies; for 

example:

• What are the appropriate measures to consider for particular strategies (See Chapter 3 for a 

description of the TSM&O strategies and for a discussion of common measures of 

effectiveness (MOE), and Figure 5-5 for a mapping of strategies to likely impacts); and how 

can these outputs be quantified and monetized? (See Chapter 5)

• What is the appropriate time horizon that should be used? (See Chapter 5 for a discussion of 

time horizons and the impact of the time value of money.)

• How can life-cycle costs be estimated? (See Chapter 5 for a discussion of life-cycle costs 

and methodologies for estimating these costs.)

Other more complex questions related to emerging performance measures or the analysis of other 

nonphysical strategies with less apparent benefits are provided in the later sections of the Desk 

Reference. The following are some of the questions related to more difficult to quantify benefits 

related to TSM&O covered in this guidance:

• What are appropriate ways to estimate travel time reliability impacts? (See Chapter 3 for a 

discussion of the importance of this measure in operations analysis and an overview of 

available methods for quantifying reliability; and Chapter 5 for a more detailed discussion of 

reliability (nonrecurring congestion) analysis methodologies).

• How can the benefits of nonphysical strategies such as improved interagency coordination be 

assessed? (See nonphysical strategy discussion in Chapter 5)

The TOPS-BC spreadsheet tool is intended to support this Desk Reference by serving as a decisionsupport tool to the document. The TOPS-BC tool is referenced throughout this Desk Reference

where appropriate; and often, the reader may be directed to specific information or a specific 

capability within the tool. A more detailed discussion of the proper set up and application of the 

TOPS-BC tool is provided in the separate, stand-alone User’s Manual that is distributed with the tool.
U.S. Department of Transportation Federal Highway Administration Office of Operations

FHWA Operations Benefit/Cost Analysis Desk Reference| 8

Chapter 2. Overview of B/C Analysis 

for Operations

What is B/C Analysis?

Benefit/Cost (B/C) Analysis is defined as a systematic process for calculating and comparing 

benefits and costs of a project for two purposes:

1. To determine if it is a sound investment (justification/feasibility); and

2. To see how it compares with alternate projects (ranking/priority assignment).1

Benefit/Cost analysis is also commonly referred to as Cost-Benefit Analysis, CBA, Benefit/Cost 

Analysis, and BCA. The analysis is identical despite the naming differences. Benefit/costs analysis is 

one type of economic valuation – an analysis that assesses the relative value of a project in 

monetized estimates. As the name implies, benefit/cost analysis determines the value of a project by 

dividing the incremental monetized benefits related to a project by the incremental costs of that 

project. The result is called the Benefit/Cost Ratio and is often the primary output of the analysis 

process. This output may either be expressed as a ratio (2:1) or a resultant value (2). For example, a 

project producing $150,000 in benefits and costing $100,000 would result in a B/C ratio of 1.5:1 or 1.5 

($150,000 benefits/$100,000 costs). Projects determined to have B/C ratios greater than one are said 

to be Efficient investments; in that, each dollar invested in the project returns more than $1.00 in 

benefits. Projects determined to have a B/C ratio less than one are Inefficient investments since the 

costs of the project are greater than incremental benefits created by the project. Projects with a B/C 

ratio of exactly one – benefits are determined to be exactly the same as costs – are said to be At Cost 

Efficiency.

2

Benefit/cost ratios can be used to compare the relative value of different projects. Various projects 

may be prioritized (in terms of economic efficiency), assessing each project individually and 

calculating the B/C ratio for each project. In comparing the various projects, those projects with the 

highest B/C ratio would be ranked as the most efficient.

A second common output measure from B/C analysis is a project Net Benefit. Net Benefit is 

determined by summing all benefits and subtracting the sum of all costs of a project. This output

provides an absolute measure of benefits (total dollars), rather than the relative measures provided by 

B/C ratio. Net benefit can be useful in ranking projects with similar B/C ratios. Table 2-1 presents a 

hypothetical comparison of three projects showing the project monetized benefits, costs, B/C ratio, 

 1 National Academies Transportation Research Board (TRB) Economics Committee, 

http://bca.transportationeconomics.org/. 2 B/C ratios are nearly always positive, ranging from zero to 15 or higher for some TSM&O strategies. B/C ratios 

may be negative; however. A negative value indicates that the project is expected to generate greater 

disbenefits than actual benefits; meaning that on a net basis, the project would make conditions worse rather 

than better.
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and net benefit. Based on B/C ratio in this example, Project 1 (having a B/C ratio of 4.0) would be 

ranked above Project 2 (B/C ratio of 1.5) and Project 3 (B/C ratio of 2.0). Although the relative 

comparison of B/C ratios shows that Project 1 is more efficient than Project 3, the absolute measure of 

net benefit is much higher for Project 3. Depending on the goals of the analysis (e.g., maximizing the 

efficiency of the investment or maximizing the total amount of the benefit), Project 1 or Project 3 could 

be ranked the highest.

Table 2-1. Comparison of Projects Using B/C Ratio and Net Benefit

Project 1 Project 2 Project 3

Benefits $200,000 $150,000 $400,000

Costs $50,000 $100,000 $200,000

B/C Ratio (Benefits/Costs) 4.0 1.5 2.0

Net Benefit (Benefits – Costs) $150,000 $50,000 $200,000

Benefits in a B/C analysis are calculated by estimating the incremental change in various MOEs and 

then applying an established value to the identified amount of change to monetize the benefit. MOEs

can include a wide range of metrics depending on the anticipated impacts of the various projects 

being analyzed. The MOEs should be identified during the analysis set up, and should be sufficiently 

comprehensive to capture the full benefits (positive impacts) and disbenefits (negative impacts) of the 

identified projects. Chapter 3.0 provides additional detail on many of the traditional and nontraditional 

MOEs used in transportation operations benefit/cost analysis; however, typical measures often 

include:

• Travel time (and the reliability of travel time);

• Crashes;

• Fuel use;

• Nonfuel vehicle operating costs;

• Emissions/air quality; and

• Agency efficiency.

For many projects, there are often tradeoffs between positive impacts to some MOEs weighed against 

negative impacts to other MOEs. Both the benefits and disbenefits should be calculated and the total 

benefit for the project should represent the net effect. For example, a proposal to increase the speed 

limit on a roadway could result in a decrease in travel time for users (a benefit), but simultaneously 

could result in an increased crash risk (a disbenefit). The total benefit for the project should weigh

both these impacts to fully capture the total project benefits.

Similarly, an individual MOE may be both positively and negatively impacted by a single project or 

strategy. For example, a project to implement a ramp metering systems along a corridor may be 

predicted to improve travel time along the mainline roadway; however, the travel times may be 

worsened on the actual on-ramp facilities due to the addition of the impedance of the ramp signal. 

The travel time benefit calculated for this project needs to take into account the net change in travel 

time between these off-setting impacts. Practitioners need to be careful in setting up their analysis to 
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identify and fully capture all the network facilities impacted by the project in order to avoid overstating 

or understating benefits.

In selecting which MOEs to employ in an analysis, practitioners need to strive to capture the 

comprehensive impacts of their strategy; however, caution should also be applied to avoid doublecounting particular benefits. The MOEs selected should be mutually exclusive. For example, if a 

project was predicted to reduce emissions, the analyst would not want to include both the benefit of 

the reduced emissions and the benefit of increased health for residents as a result of the emissions 

reduction. Presumably, the emissions benefits would already account for this health benefit; thus 

including both benefit measures would be double-counting.

Benefit/cost analysis for transportation projects is most typically forward looking, attempting to forecast 

the future changes in MOEs related to a potential project or collection of potential projects. Similar to 

many transportation-planning efforts, data needed to drive the future predictions of benefits are often 

obtained from travel demand or simulation models, or a variety of analysis tools capable of modeling 

changes in traffic performance. (Chapter 4 provides detailed discussion of many of the existing B/C 

analysis tools and methods currently in use.)

Although most typically predictive in nature, B/C analysis may also be backwards looking to quantify 

the benefits accruing from existing deployments. This evaluative B/C analysis is most often performed 

to estimate the relative benefit achieved through a prior deployment, often to provide additional 

justification for the value of continuing or expanding the project. These evaluations of existing projects 

typically rely on real-world data on the incremental impacts of the project, based on “before and after”

comparisons of traffic performance both “with and without” the project, when available. Where 

empirical data is unavailable or unreliable, these evaluation B/C analyses may also rely on modeled 

data to fill critical information gaps. Chapter 5 provides additional discussion of the data needs and 

potential data sources related to B/C analysis of transportation operations projects.

Finally, depending on the particular needs of the assessment, B/C analysis may be conducted using a 

snapshot of traffic performance and project costs to estimate average annual benefits and costs. This 

average annual B/C is best used in situations where the relative benefits and costs are anticipated to 

be relatively stable over time. Other analysis may require the calculation of Net Present Value (NPV), 

which represents the sum of the stream of expected benefits and costs over a selected time horizon 

(e.g., 20 years). The stream of benefits and costs is discounted in future years to reflect the time cost 

of money (i.e., spending a dollar today is not the equivalent of spending a dollar five years from today). 

Chapter 5 presents an expanded discussion on the implications of the time horizon and of the time 

cost of money in generating NPV.

Once a B/C analysis is complete, the results may be displayed in many innovative ways. The format, 

structure, and content of the output display are determined by a number of factors, including the 

following:

• The purpose of the analysis (e.g., comparison of multiple projects or benefits estimation of a 

single project);

• The robustness of the analysis performed;

• The MOEs included;

• The desired output information;
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• The intended audience (e.g., technical staff, policy-makers, public, media); and

• The needs of the project to be sensitive to other nonquantifiable benefits and issues.

Figure 2-1 presents a sample display from a benefit/cost analysis conducted on the KC Scout 

program, which is the traffic operations system for the Kansas City metropolitan region. The display 

effectively presents the strong outcome of the analysis showing a B/C ratio of more than eight (or 

more than $8.00 in benefits for each $1.00 invested).

Figure 2-1. Sample Display of B/C Analysis Output

Subsequent discussions in this overview section provide additional introduction to benefit/cost 

analysis, including the following:

• Common terminology;

• How does B/C analysis differ from other economic impact analyses?

• What are considered “benefits”/what are considered “costs”?

• Who are the various stakeholders in B/C analysis?
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Table 2-2. Common Terminology Used in the Desk Reference

Term Definition

At Cost Efficiency A project determined to have exactly equal benefits and costs (B/C ratio 

equals precisely one).

Benefit/Cost (B/C) Analysis

(Also known as Cost-Benefit 

Analysis, CBA, Benefit-Cost 

Analysis, or BCA)

A systematic process for calculating and comparing benefits and costs of 

a project.

Benefit/Cost Ratio Measure calculated by dividing the incremental monetized benefits related 

to a project by the incremental costs of that project. May either be 

expressed as a ratio (2:1) or a resultant value (2). B/C ratios greater than 

one indicate that a project is efficient (benefits exceed costs). B/C ratios 

less than one indicate that a project is inefficient (costs exceed benefits). 

Capital Costs The upfront costs of implementing a project or improvement, including 

planning, design, construction/installation, and equipment costs.

Constant Dollars

(also known as Real Dollars)

Presenting dollar value estimates of future costs and benefits that are 

expressed in terms of today’s (or a selected base year) prices. Constant 

dollars remove the effects of inflation over time to express constant prices 

compared with the selected base year.

Current Dollars

(also known as Nominal Dollars)

Presenting dollar value estimates of future costs and benefits in the year 

they will actually be incurred or received. Current year dollars will reflect 

price changes due to inflation over time.

Direct Benefits Those measurable benefits that may be directly attributed to the project 

investment.

Discount Rate The rate at which predicted cash expenditures (costs) or inflows (benefits) 

are reduced in future years to reflect the time cost of money. The purpose 

of the discount rate is to convert future values to present value.

Economic Impact Analysis The analysis of the comprehensive regional economic impact related to a 

project. More broadly considers multiplicative productivity, jobs, and 

income benefits caused by changes in transportation performance than 

considered in B/C analysis.

Efficient Projects determined to have benefits greater than their costs (B/C ratio 

greater than one).

End of Project Costs Costs necessary to close down temporary projects or any residual or 

salvage value of equipment at the end of the time horizon of the analysis.

Indirect Benefits Represent those regional production, employment, and income benefits 

attributable to the change in transportation system performance related to 

the project (considered in Economic Impact Analysis/not considered in 

B/C analysis).

Induced Benefits Represent those regional economic impacts related to increased regional 

income being re-spent in the local economy (considered in Economic 

Impact Analysis/not considered in B/C analysis).

Inefficient Projects determined to have benefits less than their costs (B/C ratio less 

than one).

Measure of Effectiveness (MOE) Metric used to evaluate the level of impact of a project.

Net Benefit The sum of a project benefits minus the sum of the project costs.
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Term Definition

Net Present Value The sum of the discounted stream of expected benefits and costs over a 

selected time horizon.

Operations and Maintenance 

(O&M) Costs

The continuing costs necessary to keep the project performing as 

planned, including items, such as power, communications, labor, and 

routine maintenance.

Replacement Costs The cost of replacing equipment that reaches the end of its useful life 

during the time horizon of the analysis.

Time Cost of Money The impact of time on the value of future benefits and costs. Money spent 

or earned today is more valuable than the same amount of money 

promised in a future year since the money earned today can be invested 

and earn additional revenue in the interim years. Therefore, benefits and 

costs accruing in later years of an analysis are often valued at a 

discounted rate.

Transfers Occur if one group or segment of the population enjoys a new benefit, but 

does so at the expense of a new disbenefit or additional cost accruing to 

another group.

How does B/C Analysis Differ from Economic Impact Analyses?

Benefit/cost analysis is often confused with Economic Impact Analysis, which serves to identify and 

monetize the full potential regional or national level economic benefits of a project, including changes 

in regional productivity, employment, and income. B/C analysis is defined differently, however, as the 

benefits and MOEs selected for any given analysis should represent the benefits accruing to users of 

the project as well as benefits to society at large. The real difference between these types of analyses 

has to do with the measures on which they focus. B/C analysis focuses on a summary measure of net 

benefit to society. Economic Impact analysis focuses on measures of impact on economic indicators, 

such as aggregate employment or real GDP, none of which serve as a summary measure of societal 

benefit. 

Direct benefits, considered in B/C analysis are those measurable benefits that may be directly 

attributed to the project investment. B/C analysis does not consider broader indirect and induced 

benefits to the regional or national economy. Indirect Benefits represent those regional production, 

employment, and income benefits attributable to the change in the direct impact. Induced Impacts 

are related to the multiplicative affects of the re-spending of new income within the region, resulting 

from increased regional production or employment. Indirect and induced impacts are considered in 

economic impact analysis, which considers these broader regional economic impacts as shown in 

Table 2-3.
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Table 2-3. Comparing B/C Analysis with Economic Impact Analysis

Type of Benefit Direct Benefit Indirect Benefit Induced Benefit

Example of Benefit Reduction in corridor 

travel times.

New businesses are 

attracted to the corridor by 

the improved corridor 

performance.

Employees of the new 

businesses spend their 

incomes at other regional 

businesses.

Considered in B/C 

Analysis Yes No No

Considered in Economic 

Impact Analysis Yes Yes Yes

The subsequent section provides additional detail on the benefits and costs used in B/C analysis.

What are Considered “Benefits”/What are Considered “Costs”?

Benefits

Within B/C analysis of transportation Operations projects, the “benefits” represent the monetized 

estimates of the changes in the MOEs identified for the project that are directly attributable to the 

project investment. These benefits may accrue to the transportation system users (e.g., travel time

savings, reduction in crash risk, decreased operating costs); the deploying agency (increased agency 

efficiency); or society at large (reductions in emissions). The benefits may be either positive (e.g., a 

net decrease in travel time) or negative (a net increase in travel time) in value. Negative benefits are 

known as disbenefits.

Some B/C analysts improperly assign negative benefits (e.g., an increase in the amount of emissions) 

to the cost half of the B/C equation (denominator); however, as discussed below, the cost measure 

should exclusively represent the investment necessary to implement and operate the improvement. 

All changes in MOEs should be valued and accounted for in the benefit (numerator) portion of the 

equation. This may include changes in agency efficiency (measured in reduced agency costs) or 

productivity as well. For example, if a transit agency deploys a transit vehicle Automatic Vehicle 

Location (AVL) system to track and record the real-time location of buses, the agency may predict an

efficiency gain because it will no longer have the need to conduct some manual data collection 

activities. The cost savings associated with the elimination of the manual data collection should 

properly be treated as a change in benefits; not a change in costs, as it is a direct result of the project. 

Chapter 3 provides an expanded discussion of MOEs and benefits used in assessing transportation 

Operations projects.

Costs

For analyzing TSM&O projects, it is recommended that “Costs” or the denominator value in B/C 

analysis represents the life-cycle costs of implementing and operating the project. This is important 

for TSM&O projects since they typically incur a greater proportion of their costs in years after 

deployment to operate and maintain the system, and replace obsolete equipment, when compared to 

more traditional improvements. These life-cycle costs represent:
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• The upfront Capital Costs of implementing the project or improvement, including planning, 

design, construction/installation, and equipment costs;

• The continuing O&M Costs necessary to keep the project operational, including items, such 

as power, communications, labor, and routine maintenance (excludes replacement costs);

• The Replacement Cost of equipment that reaches the end of its useful life during the time 

horizon of the analysis;

• The End of Project Costs necessary to close down temporary projects or any residual or 

salvage value of equipment at the end of the time horizon of the analysis.

These project life-cycle costs should include an accounting of all public-sector and private-sector 

costs, if applicable. Chapter 5 provides additional detail on identifying and estimating the costs 

associated with a project. In addition, the TOPS-BC application supporting this Desk Reference has 

the capability to estimate life-cycle costs associated with many types of TSM&O strategies. The use 

of these capabilities is discussed in the TOPS-BC User’s Manual.

Who are the Various Stakeholders in B/C Analysis?

There are three general categories of stakeholders to which project benefits and/or costs may accrue 

in a B/C analysis. These include:

1. The direct users of the transportation system;

2. Society at large; and

3. The deploying agency or entity.

In many cases, benefits may impact more than one stakeholder group. For example, a project that 

results in a reduction in the number of fatality crashes would clearly be a benefit to the users of the 

project, as they would be able to directly reduce the risk of pain and suffering for themselves and their 

families. Society at large could also be expected to benefit, however, from the reduction in fatality 

crashes. Fatality crashes result in a loss of a productive member of the community; a loss of 

resources; and a loss of the community’s investment in the crash victim (e.g., investments in the 

individual’s public education). Therefore, there are broader societal benefits, in addition to the user 

benefit, that may accrue from a project that reduces the number of fatality crashes.

Project costs may also be shared by multiple stakeholder groups. For example, an automated toll 

payment collection system may require users to purchase an in-vehicle transponder in order to use 

the system. The private-sector cost of the transponder purchase may be included in the overall 

project cost value used in the B/C analysis.

Figure 2-2 presents a general summarization of the stakeholder groups and how the various benefits 

and costs most typically are distributed.
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Figure 2-2. Typical Benefits and Costs Distribution among Stakeholder Groups

How is B/C Analysis Used in the Operations Planning 

Process?

B/C analysis provides several capabilities that are key in supporting different planning needs 

throughout the Operations planning process. B/C analysis is typically performed to provide one or 

both of the following capabilities:

• To determine if a project represents a sound investment (i.e., that the benefits of the project 

outweigh the costs – and to what degree); and

• To compare alternative projects to identify the most efficient projects for ranking/prioritization 

purposes.

These capabilities are invaluable in supporting planning activities throughout the entire cycle of the 

Operations planning process. As discussed in subsequent sections, the robustness of the B/C 

analysis may be scaled to fulfill different needs within the planning cycle. B/C analysis may be 

performed at a simple sketch-planning level to provide order of magnitude estimates of benefits and 

costs appropriate for early screening of projects, but also may be made much more rigorous to meet 

the more detailed analysis demands of later project prioritization or design activities.

Subsequent sections provide additional detail on the Operations Planning Process and the role of B/C 

analysis in supporting this process.

U.S. DOT Planning for Operations Initiative

Recently released guidance from the U.S. Department of Transportation (DOT), the FHWA Planning 

for Operations initiative introduces the Operations planning process as follows.3

“Planning for operations” is a joint effort between planners and operators to support 

improved regional transportation system management and operations. This term 

 3 Advancing Metropolitan Planning for Operations: An Objectives-Driven, Performance-Based Approach – A 

Guidebook, 2010, http://www.ops.fhwa.dot.gov/publications/fhwahop10026/chap_1.htm#s12.

User Societal Agency

Travel time Environmental/emissions Efficiency

Travel time reliability Health Productivity

Safety Mobility

Vehicle operating costs Productivity (Costs)

Fuel costs Security

Comfort/convenience

(Costs)
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encompasses a variety of activities that lead to improved transportation system operations, 

including the consideration of TSM&O strategies in the transportation planning process. 

Planning for operations also includes collaboration among transportation system operators, 

transit agencies, highway agencies, toll authorities, local governments, and others to facilitate 

improved transportation system operations and to ensure that transportation services are 

delivered in as safe, reliable, and secure a manner as possible. Often times, this 

collaboration is carried out in the context of a regional planning agency and is connected to 

the planning for operations process.

Planning for operations in the metropolitan transportation planning process means developing 

operations objectives to direct the consideration of operational performance during the 

planning process, and incorporating operations solutions into investment decisions that 

support the operations objectives. This approach ensures that operations needs are 

addressed in regional planning and investment decisions.

Operations managers are engaged in the planning process so that system performance 

concerns or challenges and potential operations strategies inform and influence the 

development of the metropolitan transportation plan. Operator involvement further ensures 

that operations informs and influences the planning process so that operations considerations 

are reflected in regional transportation plans. This results in a mix of operations and capital 

projects that optimizes transportation system performance.

Relationship of B/C Analysis to Objectives-Driven, Performance-Based Approach to Planning 

for Operations

In order to develop a planning for operations process that is objectives driven and performance based, 

the approach should include the following elements:

• Developing one or more goals within the Metropolitan Transportation Plan (MTP) that focus 

on the efficient management and operation of the transportation system;

• Developing regional operations objectives for the MTP – specific, measurable statements of 

performance that will lead to accomplishing the goal or goals;

• Implementing a systematic approach to developing performance measures, analyzing 

transportation performance issues, and recommending TSM&O strategies;

• Selecting M&O strategies (within fiscal constraints and to meet operations objectives) for 

inclusion in the MTP and transportation improvement program (TIP);

• Implementing M&O strategies, which may include investments and collaborative activities; 

and

• Monitoring and evaluating the effectiveness of implemented strategies and tracking progress 

toward meeting regional operations objectives.

The approach is iterative with monitoring and evaluation used to refine and adjust operations 

objectives over time.4

 Figure 2-3 presents this process graphically.

 4 Advancing Metropolitan Planning for Operations: An Objectives-Driven, Performance-Based Approach – A 

Guidebook, 2010, http://www.ops.fhwa.dot.gov/publications/fhwahop10026.
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Figure 2-3. Objectives-Driven Planning for Operations Approach

Source: Advancing Metropolitan Planning for Operations: An Objectives-Driven, 

Performance-Based Approach – A Guidebook, 2010, http://www.ops.fhwa.dot.gov/

publications/fhwahop10026.

The capabilities of B/C analysis are critical in supporting many of the steps in this objectives-driven 

approach. Guidance provided in Chapter 3 of the Desk Reference on the benefits of operational 

strategies may be useful in identifying suitable regional objectives and performance measures that 

may be used to assess the degree in which strategies meet these objectives.

As previously mentioned, the robustness of the B/C analysis may be scaled to fulfill different needs 

within the planning approach. The early screening and identification of TSM&O projects that meet the 

identified objectives may be performed using a simple sketch-planning-level B/C analysis to provide:

• Order of magnitude estimates of benefits and costs appropriate for early screening of 

projects; and

• A systematic process to winnow out the most promising projects to carry forward in the 

planning and analysis process.

As the planning process continues into project prioritization phases to rank projects for inclusion in the 

MTP, the B/C analysis methods may be enhanced to provide greater confidence in the outputs and 

the ranking of evaluated projects. This analysis may additionally provide benefit and cost information 

that can be used as justification for funding the TSM&O project in the TIP. These analysis 

methodologies may be further enhanced, introducing rigorous analysis and data from detailed 

microsimulation models and/or real-time archived data systems to support the needs of practitioners,

as the prioritized projects enter the design process and implementation steps.

Regional goals and motivation

Operations objectives

Systematic process to develop and select 

M&O strategies to meet objectives –

Process encompassed by CMP

Metropolitan transportation plan

Transportation improvement program and 

other funding programs

Implementation

M&O Strategies

Monitoring and evaluation
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Finally, B/C analysis can support the monitoring and feedback needs within the planning cycle by 

allowing for the assessment of deployed strategies in order to provide justification for expansion of 

promising applications, as well as supplying enhanced data on project benefits that may be fed back 

into the approach and used in future analysis of similar projects.

B/C Analysis can be Used to Compare Operations Projects One With Another, or Provide

a Level Playing Field to Compare Operations Projects with Other Travel Demand Management

or Capacity Projects

One of the greatest strengths of B/C analysis is that it provides a level playing field for comparing 

projects that may be very dissimilar. The systematic process of B/C analysis, when performed 

correctly, allows for widely varying projects that impact different MOEs to be compared head-to-head 

on an apples-to-apples basis. The monetization of the benefits, compared with the total project costs, 

provides a common basis that allows for this even comparison of the effectiveness.

The capability of B/C analysis to provide this level playing field comparison is what allows for the 

comparison of widely varying project types, such as a roadway widening, a new transit line, a signal 

timing project, and an employer-based travel demand management program; all within the same 

analysis structure. All of these projects would be expected to impact the transportation system in 

different ways – some would serve to increase capacity, others would lessen demand, some may 

promote a mode shift, others would serve to smooth traffic flow – therefore, it would be difficult to 

select a single evaluation metric (e.g., travel time, safety, emissions, fuel use, etc.) to effectively 

compare and rank the projects. The comprehensive evaluation structure of B/C analysis includes the 

full range of potential impacts for all projects; and allows for the cross-comparison of the differing 

projects by monetizing the benefits, in terms of the value of the combined benefits to society and the 

agency, thus, providing a common reference for prioritizing the potential investments based on the 

relative efficiency of each project.

Of course, B/C analysis can also be used to compare and rank very similar projects. For example, an 

agency may have the need to evaluate several traffic signal coordination projects in order to 

determine which particular corridors would provide the greatest benefit. In this case, a relatively 

simple B/C analysis could be conducted by identifying those key measures most likely impacted by 

this type of deployment (e.g., travel time, travel time reliability, fuel use, and emissions); and then 

collecting data or modeling scenarios to estimate the impact of the strategy on the individual corridors. 

The changes to the MOEs would then be monetized for the various corridors by applying an 

established value to the incremental change. The monetized benefit would then be compared with the 

cost for each corridor, allowing for the identification of the most efficient corridors (highest B/C ratio).

Comparing different projects with different likely impacts may often be more complicated than 

comparing similar projects with similar impacts. For example, comparing a roadway-widening project 

with a freeway service patrol – traffic incident management program would provide some analysis 

challenges due to the significant differences between the two strategies, for example:

• The roadway widening project would add base capacity to the roadway and presumably 

serve to improve average or recurring conditions, perhaps mitigating a bottleneck location. 

The additional capacity could result in additional traffic being attracted to the facility, possibly 

impacting (positively or negatively) the number of crashes, emissions, and fuel use in the 

corridor. The improvement in baseline capacity provided by the roadway widening project 

would be available on a 24/7 basis.
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• Meanwhile, the traffic incident management program would have a much less substantial 

impact on recurring, everyday conditions, but could have significant impacts during 

nonrecurring incident conditions. This would likely result in a greater impact on the reliability 

of travel time in the corridor, as opposed to the impact on average recurring travel times. 

Likewise, the traffic incident management program would be less likely to directly impact the 

number of crashes occurring in the corridor (outside of the possible reduction in secondary 

crashes occurring in incident-related traffic queues), but may help to reduce the severity of 

some crashes due to faster response times. The benefits provided by the incident 

management would only accrue when the strategy was being operated (perhaps only during 

peak weekday commute hours), and when incidents had occurred.

The wide variation in the types of benefits of these two projects, combined with when the benefits are 

incurred (during everyday recurring conditions or during unique nonrecurring conditions) adds 

significant complexity to the analysis.

Due to the current transportation improvement funding environment, transportation planners and 

Operations personnel need to make these types of comparisons between more traditional 

infrastructure projects and Operations-oriented strategies, since these different projects are often 

competing for the same funds. Therefore, it is often increasingly necessary to prioritize and rank 

widely varying project types. Fortunately, B/C analysis provides a framework that may be adapted to 

the challenges of this analysis need. In setting up these analyses comparing differing project types, 

more care and effort are often required in setting up the analysis in order to:

• Identify the comprehensive set of MOEs that may be impacted by the range of the 

varying projects. Not all varying projects may impact all of the MOEs, but it is critical to 

identify the full range of benefits in order to provide a meaningfully comprehensive analysis. 

Likewise, the identification of MOEs should not only consider measures that are likely to be 

positively impacted (e.g., reduction in the number of crashes), but also those measures that 

may be negatively impacted (e.g., increase in fuel consumption).

• Identify the sources of data necessary to support the estimation of impacts on the 

identified MOEs. Analysts should strive to identify sources of data that are equally 

applicable to all the different project types, wherever possible.

• Identify the analysis methods and/or modeling techniques/platforms that will be used 

to estimate the incremental impacts on the identified MOEs. Some traffic modeling 

methods may be appropriate for analyzing some types of projects, but not others. For 

example, travel demand models are intended to evaluate changes in travel demand and 

system capacity. As such, a travel demand model would be ideal for evaluating the roadway 

widening project example above; however, since most travel demand model’s analyses are 

based on an “average travel day” that is generally free of crashes and traffic incidents, it is 

less well suited to assess the impacts of the incident management program example. 

Whenever possible, a common analysis approach and tool should be utilized to avoid 

introducing bias caused by differing tool/methods.

• Establish the values (dollar amounts) that will be applied to the incremental change in 

MOEs in order to monetize the benefit. When using B/C to compare similar project types, 

it is less important to establish the values that are applied to the changes in MOEs, as all 

projects would be expected to impact the same MOEs, therefore, any change in a benefit 

valuation would create a relatively equal change in all the project analysis outcomes. When 

evaluating widely varying types of projects, however, it is much more critical to establish 
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accurate and justified benefit valuations, since not all projects will impact all the different 

MOEs; thus, an over- or underestimation in one benefit valuation could greatly skew the 

output results for one or a few projects relative to others in the analysis.

All of these analysis requirements need to be carefully considered in order to provide an accurate 

comparison and avoid introducing bias into the B/C analysis. Subsequent sections of this Desk 

Reference provide additional detail to be considered when making these analysis set-up decisions. 

Chapter 3 provides a discussion of the impacts and MOEs associated with various types of TSM&O 

strategies. Chapter 4 provides an expanded discussion and comparison of various types of existing 

analysis tools and methods. Chapter 5 provides an expanded discussion of the benefit valuations that 

may be used in conducting B/C analysis for TSM&O strategies.

Case Study – Cincinnati Region ARTIMIS Study

The Ohio-Kentucky-Indiana (OKI) Regional Council of Governments, the Metropolitan Planning 

Organization (MPO) for the Cincinnati, Ohio region, recently had the need to assess the benefits of 

their regional traffic management and traveler information program, known as ARTIMIS. The 

ARTIMIS program is responsible for deploying and operating a number of TSM&O strategies in the 

region, including the following:

• Regional Traffic Operations Center;

• Traffic Surveillance (camera and loop detection);

• Incident Management and Freeway Service Patrols;

• Traveler Information (Regional 511); and

• DMS and HAR, among other applications.

Many ARTIMIS applications had been successfully applied to many of the key freeway corridors 

located within the region’s suburban beltway network by the earlier 2000’s; however, there was an 

increasing need to expand these capabilities’ key sections of the beltway and to remaining radial 

freeways. Figure 2-4 shows the ARTIMIS expansion plans. In order to complete this expansion, the 

ARTIMIS program would need to compete directly for scarce funding with many more traditional 

roadway capacity enhancement projects, and would need to provide additional justification to 

decision-makers on the benefits of the program in order to secure the necessary support and funds in 

the regional transportation plan (RTP) and TIP.
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Figure 2-4. ARTIMIS Program Expansion Plans

In response to this need, OKI launched an evaluation project to estimate the benefits and costs of the 

ARTIMIS program; and to compare these relative to other more traditional capacity improvement 

projects proposed for the region. In order to provide comparable benefits and costs within the 

analysis, OKI carefully selected key MOEs to fully capture the benefits of the traditional and 

Operational projects. These measures included:

• Mobility (travel time and travel time reliability);

• Safety;

• Fuel Use; and

• Emissions.

The next step was to select the appropriate analysis tools and methods. OKI weighed several 

alternative methods, but eventually selected a combination of their regional travel demand model 

merged with the Intelligent Transportation Systems (ITS) Deployment Analysis System (IDAS)

software. The linking of these methods provided the needed:

• Analysis consistency, since the basis for the analysis of both the traditional projects and 

Operations strategies was the traffic conditions data from the regional travel demand model; 

and

• Analysis rigor, since the IDAS tool enabled the estimation of additional MOEs (particularly 

travel time reliability and crashes) not available directly from their travel demand model.
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The analysts next reviewed the default parameters used in the analysis for consistency with their local 

conditions. In particular, OKI made several adjustments in the model assumptions regarding:

• The projected reduction in incident clearance time was modified based on data gathered 

during a previous evaluation of the ARTIMIS incident management system;

• The assumed market penetration rates for their traveler information system were modified 

based on internal marketing surveys;

• The benefit valuations were modified to be consistent with standard values typically used for 

B/C analysis in the region; and

• Estimated costs in the model were replaced with actual costs based on procurement records.

The results of the B/C analysis showed the existing ARTIMIS program to be an extremely efficient 

investment returning a B/C ratio of 12:1, meaning that the program was generating $12 in benefits for 

every dollar invested. This finding itself provided strong justification for the regional investment in the 

program. The evaluation further compared the ARTIMIS program with several more traditional 

capacity expansion projects in order to provide a relative ranking of the projects. Table 2-4 shows 

selected measures, benefits, and costs of expanding the ARTIMIS program compared with a single 

corridor roadway widening project.

Table 2-4. Comparison of ARTIMIS Operational Projects with a Traditional Roadway 

Widening Project

Selected Measure ARTIMIS Added Lane Project

Miles of improvements 88 10

Fatality accidents -3.2% +0.3%

Mobility (time savings) 500 Hours 800 Hours

Travel time reliability saving 6,900 Hours 5,800 Hours

Emissions -3.6% to -4.5% +0.3% to +1.4%

Estimated Annual Benefit $53 Million $35 Million

Total Project Cost $40 Million $800 Million

Benefit/Cost Ratio 12:1 1.1:1

The benefit/cost information and project prioritization provided by the analysis were presented to 

decision-makers and the public through an outreach campaign. The results, made more relevant by 

the fact that they were generated through a valid and systematic process, were extremely valuable in 

making the case for investment in ARTIMIS in the region. The ARTIMIS expansion and enhancement 

project was identified as a high-priority project in the transportation plan and provided funding through 

the TIP process.
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How can B/C Analysis for Operations Strategies be 

Integrated with B/C Analysis for Other More Traditional 

Strategies

B/C analysis has long been applied in the planning process to evaluate and prioritize investment in 

traditional capacity enhancing strategies, whether it be investments in highways, bus transit, rail 

transit, or other infrastructure element (e.g., bridges). More recently, the use of B/C analysis has been 

expanded at many agencies to examine the effectiveness of other less capital investment types of 

strategies, such as maintenance levels, replacement cycles, and various transportation programs and 

policies.

The use of B/C analysis for assessing TSM&O strategies is also a more recent addition as increased 

competition for funding and the accompanying need to provide greater justification for projects have

driven the call for systematic processes that can be used to objectively weigh the relative benefits and 

costs of various projects, as well as provide meaningful analysis of projects that may differ greatly in 

their scope, intended outcomes, impacts on the transportation system, and costs.

Due to the long-time use of B/C analysis for more traditional infrastructure project assessment, many 

regions and states already have established procedures for conducting B/C analysis. These 

procedures may range from simple guidance on which MOEs to use, to detailed analysis frameworks, 

specified performance measures, and standardized benefit valuations to be applied. Therefore, 

except in situations where the analyst is only attempting to compare different TSM&O strategies with 

each other, care should be taken to be as consistent as possible with the established B/C analysis 

guidelines and procedures in order to provide for meaningful comparability of results. This 

consistency will ensure that the TSM&O strategies may be effectively and accurately compared and 

prioritized alongside more traditional infrastructure investments without risking the overstating or 

understating of benefits due to the analysis methodology itself.

The issue with maintaining this consistency with established B/C procedures designed for analysis of 

more traditional infrastructure projects is that the existing procedures may not be entirely appropriate 

for analysis of TSM&O projects. Analysts should be aware that existing agency procedures or 

guidelines may serve to limit the full, comprehensive assessment of the benefits of TSM&O strategies 

in one or more of the following ways:

• Existing MOEs may not be sensitive to the unique benefits of TSM&O strategies –

Many established B/C frameworks, designed for more traditional capacity infrastructure 

projects, may not include assessment of some of the key benefit areas often provided by 

TSM&O strategies, such as improvements in travel time reliability or improved agency 

efficiency.

• Specified analysis data may be inappropriate for assessment of TSM&O benefits –

Existing guidelines or procedures requiring the use of particular datasets (e.g., Highway 

Performance Monitoring System (HPMS) data, traffic counts, etc.) as inputs to the analysis 

may result in a bias against TSM&O strategies, unless the data is appropriate to the strategy 

being analyzed. For example, many traditional traffic count datasets only represent time 

periods free of incidents and inclement weather. Using this data as the basis for assessing 

the impacts of an incident management system or a weather information system would likely 

result in the severe underestimation of true benefits.
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• Required analysis methods, tools, or models may not be capable of capturing the full 

benefits of the TSM&O strategies – For example, some regional B/C guidelines may 

specify that the established regional travel demand model be used as the basis for the traffic 

impact analysis. However, many regional travel demand models are focused on average 

traffic conditions, and may be inadequate for assessing TSM&O strategies focused on 

incident, construction work zone, or inclement weather conditions; or may not be sufficiently 

sensitive to travel costs to assess pricing options.

• Cost estimation parameters and framework may be inadequate – Many traditional 

infrastructure projects have large upfront capital costs required for construction and 

implementation, and then much smaller continuing O&M costs spread over a long project life 

(e.g., 30 years). Many TSM&O strategies, on the other hand, have much smaller capital 

outlays required for implementation, but proportionately higher continuing O&M costs. 

TSM&O strategies also typically utilize equipment with much shorter useful life cycles (e.g., 

sometimes as short as two to five years) than traditional infrastructure projects. The cost 

estimation framework in existing B/C analysis procedures designed around long-term 

capacity enhancements may not be sensitive to the quick equipment replacement cycles and 

continuing O&M costs associated with many TSM&O strategies.

While many regions and agencies have made significant efforts to enhance their existing regional B/C 

guidelines and policies in recent years to be more compatible with TSM&O analysis needs – including 

the incorporation of new MOEs (e.g., travel time reliability); updates to modeling and analysis tool 

capabilities; and the inclusion of automated archived data – Operations analysts should still be aware 

of these potential constraints of utilizing existing frameworks, datasets, modeling tools, and cost 

parameters.

The following are advantages of using the existing B/C analysis structure:

• Consistency with established procedures;

• Promotes the comparability of results; and

• Uses a vetted process that is familiar to planning staff and decision-makers.

Therefore, analysts looking to estimate the benefits and costs of TSM&O strategies should attempt to 

work within the existing structure and policies to the degree possible, but should remain flexible, when 

necessary, to avoid the understatement of TSM&O benefits due to an inadequate analysis structure. 

When these situations are encountered, the TSM&O analysts and managers should seek resolution 

through possible efforts, such as:

• Encouraging the regional adoption of objectives and performance measures that are sensitive 

to the unique benefits of TSM&O strategies.

• Identifying or developing new traffic datasets, or the development of systems to capture those 

data (e.g., archived data systems), that provide the needed input required for TSM&O 

strategies.

• Encouraging or developing enhancements to the existing regional modeling capabilities to 

better allow the analysis of the specific traffic impacts of TSM&O strategies, or encouraging 

the adoption of new modeling platforms and techniques to provide this analysis capability.

• Promoting changes to the existing regional analysis framework, parameters, and benefit 

valuations to be more applicable to TSM&O strategies; and improving the consistency and 
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comparability of analysis results between TSM&O and more traditional infrastructure 

investment projects.

The sections below provide discussions of several specific phases of the planning process, where 

opportunities for comparing and prioritizing TSM&O strategies alongside more traditional strategies 

most often exist; and explore issues that the TSM&O analyst should be aware of when conducting 

these activities.

Project Screening

Project screening provides the initial assessment of the viability of various projects. Usually, this 

process is performed at an order of magnitude assessment level, not to specifically rank projects in 

any absolute order, but instead to provide a general categorization of projects (e.g., high, medium, or 

low priority) or winnow out projects likely to not be efficient, so that scarce planning resources can be 

focused in later phases on those projects more likely to provide the greatest benefit.

This analysis for TSM&O projects is often performed using sketch-planning analysis tools or readily 

available methods and data. The TOPS-BC tool, developed to support this Desk Reference,

maintains the ability to conduct screening-level B/C analysis for many Operations strategies, and is 

described in the tool’s User’s Manual. Chapter 4 presents additional discussion of other sketchplanning tools and methods appropriate to the project screening task.

Analysts should take care in evaluating TSM&O strategies alongside more traditional improvements to 

ensure that the MOEs used in the analysis are appropriate to the strategy (see Chapter 3 for more 

information on the likely impacts of TSM&O strategies); and are consistent to the degree possible for 

the traditional and the TSM&O strategy. The input data and the tool/method used for analysis should 

also be made as consistent as possible to avoid introducing bias to the analysis.

Project Prioritization

The project prioritization process often requires more robust analysis than required during the 

preliminary project screening process. As such, project prioritization is more likely to include the 

analysis of project impacts using more rigorous and complex analysis tools and methods. Analysis of 

traditional infrastructure projects is often conducted using the regionally accepted travel demand 

model. As discussed above, however, regional travel demand models may present challenges to the 

assessment of any strategies designed to have greater impact during periods of incidents, inclement 

weather, or construction activity. Therefore, it is critical to be aware of these limitations and modify the 

travel demand model analysis to better incorporate these impacts (see Chapter 5 for additional 

discussion), or consider other compatible methods or combinations of methods that may better 

support the analysis. The analysis tools used will likely provide the majority of the data input into the 

actual B/C analysis framework for monetization of benefits and the comparison with project costs, so it 

is critical that the base analysis tools and methods used are compatible with the unique impacts of 

TSM&O strategies.

Similar to project screening, it is also critical that the MOEs selected and the data identified for input 

into the analysis are consistent with the needs of TSM&O analysis. Failure to properly consider these 

issues could result in an understatement of TSM&O strategies in comparison to more traditional 

capacity improvements.
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Congestion Management Process

The congestion management process (CMP) is a systematic approach applied in a 

metropolitan region to identify congestion and its causes, propose mitigation strategies, and 

evaluate the effectiveness of implemented strategies. The CMP then recommends projects 

and strategies for the plan and transportation improvement program (TIP). In many 

metropolitan areas, the CMP is one of the primary avenues for planning for operations. In the 

CMP, system performance issues are systematically examined and management and 

operations strategies are often included in the set of solutions recommended to address 

congestion. The CMP, guided by specific objectives and integrated into the planning process, 

is an example of this systematic approach. In some regions, the objectives-driven, 

performance-based approach for integrating operations into the plan may be performed within 

the CMP.5

For many regions, the CMP is the focus of activities designed to fully consider and integrate TSM&O 

strategies alongside more traditional transportation capacity projects. The TSM&O analysts and 

managers should strongly coordinate with the CMP process to ensure that TSM&O sensitive MOEs

are considered, and that any analysis structure established within the CMP to assess and compare 

the relative effectiveness and efficiency of various strategies in mitigating the identified regional 

deficiencies.

Additionally, many times within the CMP process, the opportunity exists to move beyond the analysis 

of individual strategies and evaluate various combinations of strategies and their effectiveness in 

mitigating deficiencies and providing efficient management and operations of the transportation 

system. This opportunity may require the analysis of combinations of different types of TSM&O 

strategies, as well as the combination of TSM&O and more traditional strategies, to provide a 

synergistic effect. The combinations of strategies may present analysis complexities. While many 

traditional capacity enhancing strategies have been in use for years and their impacts are well 

documented, many TSM&O strategies have only been more recently deployed, and often have been 

deployed in limited applications. Therefore, it can be difficult to identify the likely impacts of combining 

different TSM&O strategies, particularly those that still represent emerging technologies.

Challenges and Limitations of B/C Analysis

Although B/C analysis provides a robust and comprehensive framework for comparing the relative 

efficiency of different projects, there are many challenges and limitations to its overall use, as well as 

specific challenges in assessing TSM&O projects. These challenges and limitations include:

• B/C analysis provides a key piece of information that may be used in analyzing and 

prioritizing projects, but it is not the only information that should be considered. The 

B/C ratio and net benefit information is a powerful element in the comparison of different 

investment opportunities; however, as discussed below, there are limitations to the analysis 

and many project considerations that may not be able to be captured within a B/C analysis. 

Hard-to-capture benefits, such as improvements in community livability, changes in housing 

values, or impacts to disadvantaged communities, may be difficult to fully assess in the 

analysis. Further, other project prioritization considerations such as political will and public 

 5 Advancing Metropolitan Planning for Operations: An Objectives-Driven, Performance-Based Approach – A 

Guidebook, 2010, http://www.ops.fhwa.dot.gov/publications/fhwahop10026.
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acceptability will not be captured in the analysis, yet may still play a role in determining the 

eventual prioritization of the projects being considered for investment. Therefore, it is critical 

that the results of the B/C analysis be carefully combined with other nonquantifiable inputs in 

making final decisions regarding the relative effectiveness of various projects. Figure 2-5

shows a hypothetical analysis framework being used in an assessment of regional 

transportation projects in the San Francisco Bay Area as part of the Metropolitan 

Transportation Commission (MTC) update of their 2035 Transportation Plan. As can be 

noted in the figure, the B/C ratio makes up a critical, but only one of many eventual inputs to 

the investment decision-making process.

• Many TSM&O strategies have only been recently deployed or include emerging 

technologies, making it difficult to estimate the true impact of the strategies. While 

many traditional capacity-enhancing strategies have been in use for years and their impacts 

are well documented, many TSM&O strategies have only been more recently deployed, and 

often have been deployed in limited applications. Therefore, estimates of the likely impact 

TSM&O strategies, particularly those that still represent emerging technologies, may need to 

be based on limited empirical data of the actual benefits of the strategy within the analysis. 

Chapter 3 contains additional information about the impacts and benefits of various 

Operations strategies. Additionally, the TOPS-BC application maintains a number of look-up 

tables on the observed impacts of many TSM&O strategies related to a number of MOEs, as 

highlighted in Figure 2-6.

• Maintaining consistency in the analysis of TSM&O strategies and traditional capacity 

projects is frequently complex due to varying analysis tools and methods, different MOEs, 

different analysis data inputs and sources, and different cost structures that are typically used 

to assess the various projects. As discussed further in Chapter 2, the analysis of varying 

strategies needs to be carefully planned in order to provide comparable and consistent 

results.

• The quantification of benefits needs to be carefully planned and structured to avoid 

the double-counting of benefits. Double-counting can occur in situations where there are 

overlaps in different benefits, or when a change to one benefit results in a direct change to 

another benefit. For example, a project to replace or upgrade traditional traffic signals to 

more efficient light emitting diodes (LED) signal lighting may be expected to result in a cost 

savings of $150,000 in electricity costs to an agency. In conducting a benefit/cost analysis of 

this project, the analyst should be cautious in not accounting for this impact, both as a benefit 

(a $150,000 gain to the agency), as well as a cost (a reduction of $150,000 in operating 

costs). This would result in a doubling of the actual benefit.
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Figure 2-5. Hypothetical Project Comparison Data – MTC San Francisco Bay Area 2035 

Transportation Plan

Note: Figure 2-5 shows the sample results from an analysis of a completely hypothetical project. The 

estimated B/C ratio for this project is projected to be approximately 1.3. The pie chart displays 

the amount that different benefit categories comprise of the total benefit estimate. This display 

also presents information on the projects projected ability to impact various regional targets, such 

as reducing carbon dioxide (CO2) emissions, or improving housing availability. Many of these 

target assessments are qualitative yet still included in the B/C analysis structure. The display 

also includes an assessment of the equity of the benefits (i.e., which groups of residents receive 

the greatest benefits/disbenefits) from the project. Again, these equity issues are not assessed 

in the B/C ratio, but are an important additional consideration for the agency conducting the 

study.
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Figure 2-6. Sample View of TOPS-BC Impact Lookup Function

• There may be difficulty in assessing hard-to-quantify impacts within the analysis. 

Although B/C analysis should strive to be as comprehensive as possible in the MOEs and 

benefits quantified in the analysis, there are often some measures and benefits that prove 

extremely hard to quantify. In some cases, these hard-to-quantify benefits may include 

emerging measures, where a firm consensus has yet to be reached regarding the 

relationship between a change in transportation system performance and the long-term 

monetized benefit amount. Many agencies struggling to include better assessment of global 

climate change within their B/C analysis have faced this challenge. In other cases, the 

measure or benefit may be somewhat esoteric, complicating efforts to place a value on the 

benefit. As a result, many times the B/C analysis is supported by a more qualitative analysis 

of other impacts seen as benefits or disbenefits in the region, such as impacts on community 

livability or urban sprawl.

• There are often challenges in weighing the analysis comprehensiveness against the 

available analysis resources. Like many other types of analyses, there are substantial 

tradeoffs between the comprehensiveness of the analysis with the resources necessary to 

achieve that comprehensiveness. Likewise, achieving a higher level of confidence in the 

accuracy of the results often requires additional resources be made available for the analysis. 

The analyst must make decisions early in the design and set up of the analysis to balance 

these competing needs. For preliminary screening purposes, it may be acceptable to simply 

consider a few key MOEs – presumably those measures likely to be most significantly 

impacted by the projects being compared – and assessed at an order of magnitude scale. 

For more detailed prioritization and design considerations, the analysis may need to be much 

more rigorous, involving multiple tools to support the assessment of many varying MOEs, and 

providing a high level of confidence in the analysis results. Chapter 4 of the Desk Reference

provides a discussion of the tools and methods available for conducting B/C analysis, and 

previews the level of resources needed to conduct analysis using these methods.

FHWA Tool for Operations Benefit/Cost (TOPS-BC)

Investigate the Range of Impact Values Associated with Various TSM&O Strategies

Navigation Screen 

TSMO Strategy Selected:

Impact Category:

Typical Range of Expected Impacts

Expect 5% to 30% capacity reduction on ramps 

and 7% to 20% capacity increase on freeway. 

Expected increases in volume between 5% and 

30%

Return to OPENING SCREEN

Instructions: Please select the "TSM&O Strategy" and 

the "Impact Category" you are interested from the 

pull down list below. If sufficient data is available, a 

range of expected impacts will be shown below. Click 

on the hyperlink to be taken to a table displaying the 

datapoints comprising the range. 

Noted Impacts

Freeway Management System : Ramp Metering : Traffic Actuated

Throughput
Chapter 2 Overview of B/C Analysis for Operations

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• TSM&O strategies that are targeted at mitigating unique, nonrecurring conditions may 

require additional analysis beyond the “average day” analysis typically used for 

recurring congestion projects. TSM&O strategies, such as incident management systems, 

weather systems, evacuation strategies, and other strategies focused on nonrecurring or 

special events, may require special treatment in the analysis to assess the strategy’s impact 

during these conditions and the likely frequency in which those conditions will be observed in 

order to quantify the benefits of the strategy. This is a major departure from analysis of more 

traditional capacity projects that are generally assessed during a “typical” day or peak period,

and the results are anticipated to be identical on all other days. Chapter 5 provides an 

enhanced discussion of how these strategies impacting nonrecurring conditions may be 

evaluated.

• It can be difficult in developing the B/C analysis framework to decide if particular 

impacts represent a new benefit (to users, society or the agency), or if the impacts 

represent a transfer of benefits from one group to another. In a B/C analysis, transfers

can occur if one group or segment of the population enjoys a new benefit, but does so at the 

expense of a new disbenefit or additional cost accruing to another group. For example, a 

deployment of a HOT lane on a corridor could likely increase the amount of revenue that an 

agency receives. This should not be treated as a benefit in the B/C analysis, however, since 

the added revenue to the agency is directly offset by the additional cost paid by the HOT lane 

users. This impact would be considered a transfer; and since the impacts of the increased 

revenue and the increased costs cancel the other out in the analysis, they should not be 

included in the B/C framework.
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Chapter 3. Operations Strategies

and their Impacts (Agency, User,

and Societal Benefits)

Definitions of Operations Strategies Covered in this 

Reference

This Desk Reference and the supporting TOPS-BC application provide guidance regarding a wide 

range of TSM&O strategies. Additionally, in many cases, the guidance provided can equally be 

applied to nonoperational strategies; however, the focus of this guidance is specifically on TSM&O 

strategies.

The TSM&O strategies covered in this guidance represent a mix of long established, as well as 

recently emerging technologies. The strategies include investments requiring the deployment of 

physical systems and hardware, as well as nonphysical improvements, such as enhancing 

interagency coordination. In general, the strategies are categorized into three groups.

1. Operations Strategies – These strategies involve the deployment of physical infrastructure 

to the roadside or transit assets, and are intended to provide direct impact on transportation 

system performance through their operation. These strategies are often highly visible to the 

traveling public and include a wide range of deployments, such as traffic signal coordination, 

freeway service patrols, 511 traveler information systems, HOT lanes, and DMS, among 

many others.

2. Supporting Infrastructure – Supporting infrastructure includes those backbone capabilities 

that serve to support and enhance the functioning of the roadside Operations strategies. This 

backbone infrastructure includes items, such as traffic detection and surveillance, 

communications, and traffic management centers. Often, by themselves, these strategies 

have no direct intrinsic benefits. The benefits derived from these strategies are the improved 

performance of the roadside Operations strategies enabled by their deployment. For 

example, a traffic surveillance camera by itself has little direct benefit. The benefit is derived 

from the manner in which the information is used (i.e., when the surveillance from the camera 

is used by operators to implement different signal timing patterns, or detect and respond to an 

incident faster). Therefore, the measure of benefit of these supporting infrastructure 

components is often measured by estimating the improved efficiency of the roadside 

components supported by the backbone infrastructure, rather than attempting to directly 

estimate the benefits of the supporting infrastructure itself.

3. Nonphysical Strategies – These strategies represent items intended to improve the 

efficiency and effectiveness of the TSM&O systems and includes strategies, such as 

improved interagency coordination; the development of advanced operational plans (e.g., 

evacuation plans); or system integration. While there certainly can be benefits of these 

activities and there are often costs, there typically is little or no new physical equipment or 
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roadside components. Similar to the supporting deployments, the benefits derived from these 

strategies are typically estimated by assessing the improvement in effectiveness of the 

existing roadside components provided by the strategy or activity.

These categories and the strategies belonging to the various groupings are discussed in the following 

sections.

Operations Strategies

The following are operations strategies discussed in this Desk Reference and included in the 

TOPS-BC guidance:

1. Arterial Signal Coordination – This strategy involves the coordination of traffic signal timing 

patterns and algorithms to smooth traffic flows – reducing stops and delays and improving 

travel times. This strategy can be implemented on a small corridor, a limited grid, or 

regionwide in aggressive deployments. The sophistication of the timing coordination can also 

vary from simple preset timing programs to more advanced traffic actuated corridor systems, 

to fully centrally controlled applications.

2. Arterial Transit Signal Priority – This strategy involves installing transponders on transit 

vehicles to communicate with the traffic signal systems. Traffic signal timing patterns are 

adjusted when a transit vehicle is detected approaching the intersection – either providing an 

early green phase or extending an existing green phase – to allow the transit vehicle to 

reduce stop delays through the corridor, improving travel times and travel time reliability 

(maintaining schedules).

3. Transit AVL – This strategy involves equipping transit vehicles with transponders and 

communication capabilities to allow for the real-time tracking of the vehicles by the transit 

agency. This information is used to better manage the fleet and improve schedule 

maintenance, thus improving travel time reliability for users and asset use efficiency for the 

agency. The information generated by the AVL is also often used to support real-time transit 

traveler information for users.

4. Ramp Metering – This strategy involves the placement of a traffic signal on freeway onramps to meter the flow of traffic entering the mainline facility and smoothing the flow of traffic 

in the merge area. Ramp metering may be implemented with minimal cycle lengths designed 

to simply break up platoons of vehicles entering the facility to smooth the merge operations, 

or may be operated more aggressively with longer cycle lengths designed to hold traffic on 

the on-ramp to maintain lower volumes and higher speeds on the mainline facility. Ramp 

meters may be deployed at single isolated locations, or may be deployed regionwide and are 

intended to improve merge operations and reduce bottlenecks at on-ramp locations, thus 

improving corridor travel times and safety. Similar to arterial signal systems, the 

sophistication of the timing patterns may be determined according to preset, traffic actuated, 

or centrally-controlled patterns.

5. Traffic Incident Management (TIM) – These strategies are often divided into several 

substrategies that may be combined to create a coordinated system. The benefits of these 

systems include a reduction in incident related delay (and associated fuel use and emissions 

impacts), and can include safety benefits by allowing for the faster dispatch and response of 

emergency personnel and assets to injury accidents. The TIM substrategies include the 

following:
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a. Incident Detection and Verification – Involves the implementation of surveillance, 

detection, communications, and algorithms to enhance the monitoring of the 

transportation system to more quickly detect the occurrence of incidents and provide 

more information to system operators to verify the location and severity of the 

incident, so that an appropriate response plan may be developed and implemented.

b. Incident Response – Involves the improved development, communication, and 

implementation of response plans through coordinated response strategies and 

computer-aided dispatch (CAD) systems.

c. Freeway Service Patrols (FSP) – Involves prepositioned or roving “highway helper”

vehicles designed to quickly respond to system incidents and mitigate the situation. 

These vehicles may also provide first responder capabilities for more series crashes 

or incidents until more appropriate emergency assets and personnel arrive on scene.

6. Pretrip Traveler Information – These strategies provide single mode or multimodal 

information to travelers prior to the initiation of their trip enabling the user to make better 

informed decisions on the route, mode, and timing of their travel. Often developed as 

regional 511 programs, these systems may deliver the information content via telephone, 

Internet, kiosks, or mobile devices. The enhanced information provided by these systems 

may also be distributed by existing media outlets (e.g., television or radio).

7. In-route Traveler Information – These strategies may provide a variety of information (e.g., 

travel times, mode change opportunities, construction work zone information, incident 

warnings, and alternative route recommendations) to travelers already using the system,

allowing the travelers to make more informed decisions on travel route and mode choice. 

Traditionally provided through DMS or changeable message signs (CMS), HAR, and through 

distribution by traditional media outlets (radio), the channels for distribution have been 

expanded in recent years to increasingly include in-vehicle and mobile devices.

8. HOT Lanes – Allows SOVs to pay a toll to use underutilized HOV lane capacity. These 

systems most often utilize an in-vehicle transponder to determine lane usage and assess 

tolls. The tolls charged may vary according to time-of-day schedules, or may be dynamically 

assessed in response to traffic conditions and available HOV lane capacity.

9. Speed Harmonization – Involves the implementation of variable speed limits and the 

communication of those limits through roadside signs. The speed limits are modified 

according to congestion levels to lessen stop-and-go conditions and lower the speed of 

vehicles as they approach downstream bottlenecks. The primary benefit of these emerging 

systems is improved safety.

10. Hard Shoulder Running – Involves allowing vehicles to travel on the shoulder facilities of 

roadways often for isolated sections of roadway or limited times of operation. The availability 

of the shoulder for use is often communicated through the use of overhead gantries or 

roadside DMS.

11. Work Zone Management – Involves the coordinated implementation and use of pretrip (e.g., 

511 web-based applications) and en-route (e.g., DMS and HAR) traveler information, along 

with construction traffic management and alternative construction work hours planning to 

mitigate the congestion related to construction work zones.

12. Travel Demand Management – Includes a number of strategies that may be employed to 

lessen travel demand (number of trips) associated with work commuting traffic. These 

strategies may include employee-based subsidies for alternative mode use or alternative 

work hours, or employer-based investment in vanpools or alterative work campuses to lessen 

the number of trips made by their employees.
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Supporting Infrastructure

Supporting infrastructure includes those strategies that provide the backbone infrastructure that 

enables the effective operation of the roadside components. The benefits from these supporting 

infrastructure implementations are often measured by estimating the effectiveness and efficiency 

gains to the roadside components directly supported by the strategy. Supporting infrastructure 

discussed in this Desk Reference includes:6

1. Traffic Detection and Surveillance – Involves the implementation of traffic detection (e.g., 

loop detectors, radar detectors, acoustic detectors) and/or camera surveillance systems; and 

the development of associated data and analysis systems to provide the ability to monitor 

real-time conditions on the transportation system. This data is often used to support and 

enhance the capabilities of many Operations Strategies, including traffic signal coordination, 

ramp metering, incident management and Active Traffic Management (ATM), and traveler 

information systems.

2. Traffic Management Centers – Include the regional traffic and transit management centers, 

as well as the smaller subregional centers, required to monitor, coordinate, and operate the 

deployed Operations Strategies.

3. Communications – Include the landline and wireless communication networks required to 

coordinate and operate the Operations Strategies implemented in a region.

Nonphysical Strategies

Nonphysical Strategies are intended to improve the efficiency and effectiveness of the deployed 

TSM&O systems. The FHWA Integrated Corridor Management (ICM) initiative is an example of a 

project that heavily leverages many nonphysical strategies. This category includes strategies such as:

1. ATDM – The dynamic management, control, and influence of travel demand, traffic demand, 

and traffic flow of transportation facilities. Through the use of available tools and assets, 

traffic flow is managed and traveler behavior is influenced in real time to achieve operational 

objectives, such as preventing or delaying breakdown conditions, improving safety, reducing 

emissions, or maximizing system efficiency. Under an ATDM approach, the transportation 

system is continuously monitored. Using archived data and or/predictive methods, actions 

are performed in real time to achieve or maintain system performance. 

2. System Integration – Involves the integration of two or more systems to allow for the 

improved exchange of data between the systems and/or the coordinated operation of the 

systems. For example, a traffic signal coordination system could be integrated with a traffic 

incident management system to provide additional capacity on parallel diversion routes when 

incidents occur on a freeway facility.

3. Interagency Coordination – Involves the exchange of information and/or agreements 

allowing for the joint operation of various strategies across different agencies or jurisdictions.

4. Regional Concepts for Transportation Operations (RCTO) – The development of regional 

concepts of transportation operations involves the coordination of the various stakeholders 

responsible for operating one or more components in order to develop sets of policies, 

 6 Since many of the Supporting Infrastructure components have no direct benefits – their benefits are estimated 

through the roadside Operations Strategies supported by their implementation – they are not included as 

strategies available for benefit estimation in the TOPS-BC application. However, the ability to estimate the lifecycle costs of these components is provided within TOPS-BC. Section 5 of this Desk Reference provides 

additional detail on structuring the estimation of benefits of these Supporting Infrastructure strategies within a 

benefit/cost analysis framework.
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procedures, and operating parameters that may be implemented according to specific 

identified conditions to improve the efficiency and effectiveness of Operational Strategies 

during those conditions. The development of a coordinated strategy for operating strategies 

during a regional evacuation would be an example of a regional concept of transportation 

operations that might be analyzed using benefit/cost analysis. The FHWA Integrated Corridor 

Management (ICM) initiative serves as another example as the Concepts of Operations 

developed in support of this initiative represent RCTOs.

What are the Impacts and Benefits of Operations 

Strategies?

The operations strategies, discussed in Chapter 3 above, all impact the transportation system, traveler 

behaviors, and agency operations in different ways. For example, implementation and operation of 

different strategies could promote changes in the following:

• Roadway volumes (throughput);

• Roadway speeds;

• The duration of incidents;

• Traveler route choice;

• Traveler mode choice;

• Number of trips made by travelers;

• Number of crashes;

• Severity of crashes; or

• Day-to-day agency procedures, among many other possible changes.

These changes represent the direct impacts of the strategies; however, these changes do not 

necessarily represent the benefits of the strategy. For example, a change in travel mode promoted by 

a strategy does not necessarily represent a benefit. The benefit of the change in mode is estimated 

by measuring the effect that the change in mode has on other more quantifiable metrics, which can be 

more appropriately valued in monetized terms. Therefore, the impact of the change in mode is 

measured by the resulting change in several MOEs, such as vehicle operating costs, fuel use, 

emissions, crash risk exposure, and other measures that may result.

In benefit/cost analysis, the direct and measureable impacts of strategies are often important in that 

they are used as a means of estimating changes to various MOEs; however, within the benefit 

valuation framework of the analysis, the impacts are not valued, but are merely an input to the 

calculations. The sections below provide additional discussion of various MOEs that may be used in 

assessing TSM&O strategies in a benefit/cost analysis. These are segmented according to:

• Traditionally recognized MOEs;

• Emerging MOEs; and

• Hard-to-quantify MOEs.
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Traditionally Recognized MOEs

There are a number of MOEs that are often used in assessing TSM&O strategies. In large part, these 

MOEs are derived from and consistent with many of the measures used in assessing more traditional 

capacity related transportation improvements. This is fortunate since the consistency in many of 

these measures promotes and allows the comparison of the relative benefits across different types of 

strategies. Additionally, the long-term use of many of these measures has often resulted in a greater 

consensus regarding the established valuations that may be applied to monetize the benefit.

The most typically applied benefit/cost MOEs include:

• User travel time savings;

• User vehicle operating costs;

• Crashes; and

• Emissions.

These typically used MOEs are summarized in the discussions below. Chapter 4 of this Desk 

Reference identifies various existing tools and analysis methods that may be used to estimate these 

measures; and additionally, Chapter 5 of this Desk Reference provides specific guidance in estimating 

these MOEs using the various analysis tools and methods.

User Travel Time Savings7

User travel time is the most often used MOE for a wide range of transportation improvements, 

including both TSM&O and more traditional capacity-enhancing projects. At its simplest level, user 

travel time represents the net change in the sum of all person hours of travel (PHT), resulting from 

implementation of the strategy within the defined geographic scope of the analysis. The travel time, 

as measured by PHT, may be differentiated by:

• In-vehicle travel time – PHT incurred in the mode of choice of the individual; and

• Out-of-vehicle travel time – PHT necessary to access the mode of choice.

This differentiation is more often used in analyses involving transit modes and is used to represent the 

time necessary to walk to the transit stop, wait for the transit vehicle, and wait at any transfer locations, 

but may also be used with auto modes to represent the time necessary to access (walk to) the vehicle 

in locations where parking is not immediately adjacent to the desired destination.

Depending on the analysis, the PHT may be estimated separately for different modes. This may be 

done simply to provide an estimate of benefits by mode, or because different benefit valuations will be 

applied to travel time incurred in different modes. Most typically, truck travel time is differentiated from 

auto and transit travel time due to the higher costs associated with truck travel time (costs of the driver 

salary and the carrying costs of the vehicle cargo).

Figure 3-1 shows an example analysis of the daily and annual incremental change in user travel time 

between a baseline and alternative scenario, where the measure of user travel time was broken out 

 7 Note: The use of consumer surplus and travel time reliability in the measure of travel time-related benefits are 

discussed in a subsequent section on “Emerging Measures of Effectiveness”.
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separately for auto, truck, and transit modes. Further, the transit PHTs were also broken out by invehicle and out-of-vehicle travel time.

Figure 3-1. Example Display of User Travel Time MOE

Source: San Francisco Bay Area MTC, 2035 Long-Range Transportation Plan Project Performance 

Analysis, 2011.

Also depending on the benefit valuation scheme to be applied and the rigor of the analysis, the net 

change in PHT may be segmented by trip purpose. The most typical categorization includes:

• On-the-clock travel – Represents those business people traveling to a meeting during work 

hours (e.g., a plumber traveling to the next work site);

8

• Commuter travel –Represents those individuals traveling between their homes and their 

business locations; and

• Nonwork travel – Represents individuals making trips for shopping, school, recreation, or 

other purposes.

The reason for differentiating these trip purposes is to apply a different value of travel time based on 

the nature of the trip. Travel time incurred during on-the-clock trip purposes may often be valued at a 

higher rate than nonwork travel in an analysis due to more the greater direct costs incurred in any 

delay in this type of travel.

Although the user travel time measure is commonly used in benefit/cost analysis, there are limitations 

to using the measure in average, recurring travel time to assess TSM&O strategies. Many TSM&O 

strategies are targeted specifically at reducing nonrecurring travel time – due to incidents, inclement 

weather, excessive demand (special events), and construction activity – therefore, only using 

measurement of recurring travel time will result in the severe understatement of benefits due to 

TSM&O strategies. In recognition of this limitation of using average, recurring travel time, the 

measure of nonrecurring travel time or travel time reliability (variability) has become an important 

emerging trend in benefit/cost analysis. The travel time reliability measure is discussed in a 

subsequent section on emerging MOEs.

Source of Measure Estimation in the Analysis. The method of estimating the net change in user 

travel time in the analysis can range from simple, nearly back-of-envelope type estimations of change 

to complex analysis involving detailed data and models to calculate the expected changes in 

 8 Generally, all truck mode travel time is considered “on-the-clock” travel time, unless valued separately.

Benefit/Cost Baseline Alternative Difference

Annual 

Difference

Auto Vehicle Miles of Travel (VMT) 170,177,633 170,204,412 26,779 6,694,695

Truck VMT 18,128,820 18,136,463 7,643 1,910,870

Average Vehicle Occupancy 1.42

Person Hours of Travel (Auto) 5,923,262 5,922,600 (663) (165,631)

Vehicle Hours of Travel (Truck) 462,006 462,006 - -

Person Hours of In-vehicle Travel (Transit) 40,679,986 40,649,288 (30,698) (7,674,482)

Person Hours of Out-of-vehicle Travel (Transit) 33,925,318 33,935,621 10,303 2,575,640
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transportation system demand and performance. The method and tool used to estimate this MOE 

should be appropriately scaled to the needed rigor of the analysis. Chapter 4 and the TOPS-BC tool 

both provide additional guidance on selecting and applying appropriate analysis tools and methods.

At the simplest level, the change in user travel time may be estimated by applying a factor 

representing the anticipated impact of the strategy to a baseline measure (representing either a 

current or a future forecast of travel times without the strategy). Many of the sketch-planning tools 

described in Chapter 4 provide a slightly advanced application of this methodology.

If greater confidence in the accuracy of the analysis results is called for by the needs of the analysis, 

the estimates of the net change in user travel time may be obtained from more advanced modeling 

techniques, such as travel demand models or simulation models. These models are also often 

capable of estimating the change in travel time by mode and/or by trip purpose, if the analysis requires 

that level of categorization. For detailed evaluation of exiting strategies, archived data sources may 

also be used to generate the input data (with improvement and without improvement) needed to 

estimate the net travel time change. Chapter 4 of this Desk Reference provides additional information 

on the available tools and methods for analyzing user travel time. Chapter 5 provides additional 

guidance on applying these techniques.

User Vehicle Operating Costs

The use of changes in net vehicle operating costs in benefit/cost analysis of TSM&O strategies is 

common practice. This MOE is typically broken out into fuel use and nonfuel use categories; although 

in simple analysis, they may be combined as a single measure. Nonfuel costs typically include 

measures of maintenance, insurance, and depreciation costs, but do not include vehicle registration 

and taxes, as these are often considered transfers in benefit/cost analysis.

Similar to the use of user travel time, measures of vehicle operating costs are also often used in 

assessments of non-TSM&O strategies, increasing the comparability of benefit/cost results across a 

variety of project types.

Source of Measure Estimation in the Analysis. Estimation of vehicle operating costs is usually 

relatively easy to estimate, and is often based on simple valuations applied directly to vehicle miles of 

travel (VMT). For simple analysis, a static rate of average fuel use (gallons per VMT) is applied to any 

net change in VMT to estimate the net change in fuel use. A benefit value (cost per gallon of fuel 

exclusive of fuel taxes) is then applied to the change in the number of gallons of fuel consumed. Note

that since estimated fuel use rates can differ substantially based upon the year of analysis (future year 

vehicle fleets are anticipated to have greatly improved fuel use rates), practitioners are encouraged to 

use rates appropriate to the year of analysis. The U.S. Environmental Protection Agency (EPA) 

provides data on the past and future anticipated vehicle fleet fuel usage rates.9

Although an average fuel use rate may be applied for simple analyses, a fuel use rate sensitive to 

actual travel speeds may be appropriate for more rigorous analyses, since fuel use is directly 

associated with travel speeds. For these analyses, a look-up function needs to be applied to link the 

appropriate fuel use rate to a facility based on the average speed noted for the roadway. The 

appropriate fuel use rate (in gallons per VMT) may then be applied to the roadway VMT. Different 

rates are applied to freeway and arterial facilities for any given speed range.

 9 http://www.epa.gov/fueleconomy/.
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In these analyses, any net change in total VMT or roadway speed attributable to the TSM&O strategy 

will affect a change in fuel use. Fuel use rates supplied by the U.S. EPA and other sources are 

applied according to speed and vehicle type (e.g., auto, gas truck, diesel truck) to obtain total fuel use. 

Table 3-1 presents a sample of year 2010 fuel use rates as specified by U.S. EPA.

Table 3-1. Sample EPA Fuel Use Rates by Average Speed for Year 2010

(in Gallons per VMT)

Facility type Speed Auto Truck Gas Truck Diesel

Freeway 0 0.540 0.650 0.450

5 0.182 0.310 0.696

10 0.123 0.181 0.489

15 0.089 0.135 0.297

20 0.068 0.118 0.185

25 0.054 0.120 0.131

30 0.044 0.133 0.110

35 0.037 0.156 0.112

40 0.034 0.185 0.122

45 0.033 0.223 0.136

50 0.033 0.264 0.153

55 0.034 0.310 0.170

60 0.037 0.374 0.187

65 0.043 0.439 0.204

70 0.052 0.511 0.221

Arterial 5 0.144 0.275 0.383

10 0.091 0.174 0.241

15 0.073 0.140 0.194

20 0.064 0.123 0.171

25 0.059 0.113 0.157

30 0.056 0.106 0.147

35 0.053 0.101 0.140

40 0.051 0.097 0.135

Source: http://www.epa.gov/fueleconomy/.

For nonfuel operating costs, an established value in these costs per VMT are directly applied to the 

change in VMT to estimate the benefit value. Similar to fuel use rates, the per VMT value may be 

sensitive to the forecast year of analysis; however, the valuation is usually not sensitive to differences 

in operating conditions (travel speeds, etc.).

Crashes

The assessment of safety impacts, particularly the risk of exposure to crashes, is typically a critical 

part of any transportation benefit/cost analysis. Many TSM&O strategies have proven to have 

significant benefits in the reduction of the number and/or severity of vehicle crashes. Strategies that 

serve to smooth the flow of traffic at difficult merge points, such as ramp metering systems, have been 

observed to directly reduce the risk of crash exposure at those locations. Meanwhile, applications 

such as traffic incident management that serve to improve the emergency response to crashes have 
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been demonstrated to reduce the severity of crashes by quickening the response times and better 

ensuring the appropriate emergency responder assets are directed to the scene when crashes occur.

The benefit valuations typically applied to a reduction in the number of crashes are often substantial, 

particularly in the case of fatality and injury crashes, ranging into multiple millions of dollars. Often,

these valuations are based on a two-tiered structure to fully capture the benefits of a reduction in the 

number or severity of a crash. Typical crash reduction benefit valuations are based on a combination 

of the following valuation methods:

• Actual costs – This valuation methodology is intended to capture the actual accountable 

costs of the crash. For example, in the case of a fatality crash, these costs would include the 

costs of medical treatment of the victims, the loss of the victim’s wages for the family, and any 

property damage.

• Cost to avoid – Most crash valuations additionally include a measure of the additional cost to 

avoid; or in other words, an estimate of the value that individuals would likely pay to avoid 

being involved in a crash of this severity. This valuation additionally accounts for the potential 

pain and suffering experienced by the crash victim(s) and their families.

Source of Measure Estimation in the Analysis. To estimate the change in the number of crashes 

attributable to a TSM&O strategies, crash rates (typically expressed as crash occurrence per million 

VMT) are applied to the change in VMT in the network or for a particular facility type (freeways versus 

arterials). Different crash rates are also usually applied for different severities of crashes, most 

generally defined as:

• Fatality crash;

• Injury crash; and

• Property damage only crash.

However, many different agencies use different categorization schemes for crashes; often breaking 

injury crashes into more discrete severity categories (minor injury versus severe injuries). Many 

agencies also maintain their own crash rates that are based on local crash records, and are typically 

the best source of rates for use in any TSM&O benefit/cost analysis since they are locally derived and 

consistent with analysis performed for other non-TSM&O strategies.

At the simplest analysis level, a single static rate (for each crash severity) may be applied to the total 

change in VMT for all vehicle categories estimated from the application of the TSM&O strategies. For 

more rigorous analysis, more segmented and dynamic rates may be researched and applied. These 

enhanced rates may be categorized or sensitive to:

• Vehicle types (autos versus trucks);

• Facility types;

• Roadway configuration and geometrics;

• Existing roadway safety installations;

• Facility speeds;

• Facility congestion levels (most often facility volume/capacity ratio); and/or

• Even applied to individual links based on actual historical crash occurrence.
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The level of sensitivity of the crash rates to be applied in any given analysis is typically based on the 

following factors:

• The locally prescribed crash analysis rate;

• The need to maintain consistency with other local analysis;

• The analysis rigor (level of confidence required in the analysis results);

• The type of TSM&O strategy being analyzed (i.e., some TSM&O strategies may be 

anticipated to have more direct crash savings benefits, and thus may require additional 

analysis rigor to quantify these benefits);

• Availability of enhanced rates; and

• Availability of appropriately structured input data (e.g., VMT by facility type and vehicle type) 

to support the crash analysis structure.

Beyond the analysis of crash occurrence based on estimated changes in VMT, many TSM&O 

strategies additionally may influence the actual crash rate that should be applied. For example, ramp 

metering applications serve to smooth traffic merging at the confluence of traffic at the end of the onramp lane. This smoothing of the merge has been observed to result in a meaningful decrease in the 

crash rate (particularly for sideswipe, rear end, and run-off-road accident types that are common in 

merge areas). Previous ramp meter applications deployed nationwide have been observed to reduce 

crashes in the merge area by approximately 7 to 25 percent. This crash rate reduction is in addition to 

any change in the number of crashes resulting from changes in the underlying VMT. Similarly, 

strategies such as speed harmonization applications are intended to smooth the flow of traffic and 

reduce the amount of stop-and-go movements. These applications would likely reduce the risk of 

crashes without any change in the underlying VMT at that location.

In these situations, where the TSM&O strategy has a direct linkage to reduced crashes, the crash rate 

that is applied at locations influenced by the TSM&O strategy should be reduced prior to application to 

the VMT observed for the applicable facility. Using the ramp metering example above, the applicable 

crash rate would reasonably be reduced by 7 to 25 percent prior to application to the VMT estimated 

in the merge area following the application of ramp metering.

Emissions

Emissions are another commonly used MOE for TSM&O strategies, as well as traditional 

infrastructure projects. The inclusion of emissions estimates in the benefit/cost analysis is particularly 

important when projects are being prioritized in competition for several types of funding (e.g., 

Congestion Mitigation and Air Quality (CMAQ) funds); or the project is being considered in an air 

quality nonattainment area.

Emissions categories considered in benefit/cost analysis most often include one or more of the 

following emissions categories, and typically include those emissions categories of most concern in 

the local region:

• Hydrocarbons (HC)/Reactive Organic Gases (ROG);

• Nitrous Oxide (NOx);

• Carbon Monoxide (CO);
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• Carbon Dioxide (CO2);

• Particulate Matter (PM10) or Fine Particulate Matter (PM2.5); and

• Sulfur Dioxide (SO2).

Emissions may represent one of the most complex to estimate MOEs used in many B/C analyses. 

This is due to the many variables that determine the appropriate emission rates. Most emissions 

estimations are based on an application of an emissions rate on a per VMT basis. Depending on the 

emissions category, the appropriate emissions rate to apply may be sensitive to numerous factors, 

including the following:

• Year of the analysis – Future year vehicle fleets are anticipated to produce fewer emissions in 

many categories;

• The mix of gasoline and diesel vehicles in the regional fleet;

• Vehicle speeds;

• The number of cold starts;

• The mix of vehicles in the regional vehicle mix (e.g., autos, light trucks, medium-duty trucks, 

heavy-duty trucks, etc.); and

• Regional weather patterns/climate and other considerations.

Given these many variables impacting emissions rate, the rates for most analysis need to be obtained 

from existing emissions analysis tools specifically configured to the individual region, or derived from 

previously conducted regional analysis. Therefore, caution should be applied in using rates derived 

from other regions or based on averages between different regions.

Source of Measure Estimation in the Analysis. Many urban areas, particularly those in 

nonattainment areas, maintain robust emissions estimation and analysis models and tools. These are 

most typically based on the U.S. EPA tools, including MOBILE or MOVES, for most of the country. In 

California, emissions analysis is based on tools and guidance developed by the California Air 

Resources Board (CARB). These customized and configured tools are typically the best source of 

analysis of emissions impacts, when available. These tools and analyses are typically linked to 

analysis conducted in the regional travel demand model; and in many cases, the emissions analysis is 

performed as a post-processing of the travel model data outputs, or may also be performed as an 

iterative feedback loop with the travel demand model.

In areas when an appropriate regional air quality model is unavailable or situations where running the 

full analysis model is impractical given the time and budget resources available for the analysis, more 

simplified analysis may be necessary. Simplified emissions analysis may be conducted using 

emissions rate tables derived from the regional emissions analysis process combined with a look-up 

function to ascertain the appropriate rate (based on vehicle class and speed) to apply. Several 

sketch-planning and post-processing analysis tool (discussed in more detail in Chapter 4) use this 

simplified approach for their own internal emissions analysis. Practitioners using this simplified 

method need to use caution in selecting rate tables that are appropriate for the region and time 

periods being analyzed.
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Emerging MOEs

In recent years, several new MOEs have been introduced into many B/C analyses, including 

measures of travel time reliability (variability), consumer surplus, and measures of global climate 

change. These measures can often provide more justification for TSM&O projects as these 

measures, particularly travel time variability, are precisely the focus of many Operations deployments.

Travel Time Reliability

Practitioners and researchers have increasingly been assessing the importance of travel time 

reliability when evaluating transportation projects. Historically, average travel time was the primary 

measure most often used for B/C analysis of transportation projects. However, use of this average 

measure often only captures the change in recurring travel time, due to the nature of the measure 

itself and the methods that were often used to assess the measure. As shown in Figure 3-2, use of 

the average travel time may not realistically represent actual traveler’s experiences.

Figure 3-2. Use of Recurring Travel Time Measurement versus Actual Traveler Experience 

Jan. July Dec.

Travel

time

How traffic conditions have

been communicated 

Annual average

Jan. July Dec.

Travel

time

What travelers experience

Travel times vary

greatly day-to-day

What they

remember

Source: http://ops.fhwa.dot.gov/freight/freight_analysis/perform_meas/fpmtraveltime/

traveltimebrochure.pdf.

More recently, significant research and analysis have been performed on the effects of nonrecurring 

travel time delay – travel time delays caused by factors, such as incidents, special events, weather, 

construction work zones, and poorly timed traffic signal systems, among other causes. Research 

completed as part of the Transportation Research Board’s (TRB) Strategic Highway Research 

Program (SHRP 2) and other numerous Federal and state efforts have shown the amount of delay 

caused by nonrecurring congestion is substantial; and leaving this measure out of transportation B/C 

analysis risks severely understating the potential benefits of many improvements. Figure 3-3 presents 

a breakdown of the typical causes of congestion, as estimated from national sources; and shows that 

nonrecurring sources of congestion account for more than one-half of the total delay in a typical urban 

network.
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Figure 3-3. National Summary of the Sources of Congestion

Source: Cambridge Systematics, Inc., and Texas Transportation Institute, Traffic Congestion and 

Reliability: Linking Solutions to Problems, July 19, 2004, http://ops.fhwa.dot.gov/

congestion_report_04/executive_summary.htm.

Travel time reliability measurement seeks to quantify the variability in travel times caused by 

nonrecurring, as well as recurring, congestion sources in order to better estimate the full distribution of 

travel times experienced by the system users. A number of performance measures and indices have 

been developed to help quantify these impacts, as shown in Table 3-2.

10 Many of these measures are 

based on an analysis of the distribution of travel times for a particular segment or facility.

 10 Note: Although many of these performance measures are useful in assessing system reliability, they are not 

appropriate as MOEs to be used in B/C analysis since they provide relative comparisons of reliability levels that 

are not immediately able to be valued (monetized).

Non-Recurring

(e.g., Special Events) 

5%

Bottlenecks 

40%

Traffic Incidents

25%

Work Zones

10%

Weather 

(Snow, Ice, Fog)

15%

Poor Signal Timing

5%
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Table 3-2. Reliability Performance Measures

Reliability Performance 

Metric Definition Units

Planning-Time Index • 95th percentile Travel Time Index (95th percentile traveltime divided by the free-flow travel time), normalized by the 

average travel time

• The difference between the 95th percentile travel time and 

the median travel time, normalized by the median travel 

time

None

Buffer Index (BI) • The difference between the 95th percentile travel time and 

the average travel time, normalized by the average traveltime

• The difference between the 95th percentile travel time and 

the median travel time, normalized by the median traveltime

Percent

Failure/On-Time Measures • Percent of trips with travel-times less than 1.1 * Median 

Travel Time or 1.25 * Median Travel Time

• Percent of trips with space mean speed less than 50 mph, 

45 mph, or 30 mph

Percent

80th Percentile Travel-Time Index • 80th percentile travel time divided by the free-flow travel

time

None

Skew Statistic • The ratio of (90th percentile travel time minus the median) 

divided by (the median minus the 10th percentile)

None

Misery Index (Modified) • The average of the highest five percent of travel times 

divided by the free-flow travel time

None

Source: SHRP 2 Project L03 Final Report: Analytical Procedures for Determining the Impacts of 

Reliability Mitigation Strategies.

In addition to the research work completed on improved measures and analytical methods for 

assessing travel time reliability in a network, many parallel efforts have been undertaken to 

demonstrate the impact that many different types of transportation strategies have in improving travel 

time reliability. Both traditional types of capacity expansion projects and TSM&O strategies have been 

shown to improve reliability. Although most typically focused on recurring congestion issues, 

traditional capacity increasing projects may improve reliability since roadways and transit facilities with 

more available capacity are more easily able to handle unusual spikes in demand or weather the 

impacts of an incident. TSM&O strategies are often more directly targeted at the underlying causes of 

nonrecurring congestion – incidents, weather, construction work zones, special events, and poor 

operations – and can have substantial success in mitigating the delay caused by these sources. 

Therefore, it is important that travel time reliability be considered for traditional capacity projects; 

however, it is often even more critical that this measure be considered for B/C analysis of TSM&O 

strategies, since a much higher proportion of the anticipated overall benefit is expected to be derived 

from these strategies’ impacts on reducing nonrecurring congestion.11

Source of Measure Estimation in the Analysis. To meet the need to be able to better consider 

valuation of travel time reliability benefits within a benefit/cost analysis structure, many new and 

 11 SHRP 2 L03 Final Report: Analytical Procedures for Determining the Impacts of Reliability Mitigation 

Strategies, 2010.
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enhanced methods for quantifying this measure have been proposed, developed, and tested. 

However, as much of this research is still evolving, there are frequently limitations in applying many of 

the techniques. Additional research is ongoing to refine and enhance the approaches; some of which 

are detailed below.

Measurement of standard deviation. For existing deployments where sufficient “before and after”

data is available, the impact on travel time reliability may be estimated by comparing the distributions 

of travel time prior to application of the strategy (the “before” or “without” scenario) with the 

distribution following the application (the “after” or “with” scenario). The net change in the standard 

deviation in minutes of travel time may be calculated from the distributions and multiplied with the 

number of facility users to estimate a temporal measure of travel time reliability savings. A value may 

be applied to this measure to monetize the benefit for use in the B/C analysis. This type of analysis 

assumes, however, that an evaluation of an existing deployment is being performed – the method is 

not as useful as a predictive tool – and that sufficient data is available to support the estimation of the 

travel time distributions.12 Data required to identify the travel time distributions may be obtained from 

long-term data archive systems, multiple manual data collection/monitoring activities, or modeling 

efforts using multiple scenarios representing varying expected traffic conditions (see Chapter 5 for an 

expanded discussion of structuring modeling analysis to consider impacts during varying traffic 

conditions). Regardless of the data source, the analyst needs to ensure that the dataset used to 

estimate the distribution of travel times is sufficiently robust to fully capture travel times during a 

representative sampling of travel time conditions. This may require six months to multiple years’

worth of constant data in some situations, where a high level of confidence in analysis results is 

required.

Measurement of total recurring and nonrecurring delay. The development of the SHRP 2 L03 

Report, Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies, resulted 

in the refinement of an approach to estimate total delay (combining both recurring and nonrecurring 

delay), based on the mean recurring travel time and assuming a normal distribution of travel times. 

The developed analysis method and equation are intended for application in “data poor” situations,

where the robustness of either the archived historic traffic data or the modeled predicted traffic data 

was insufficient to provide meaningful analysis of nonrecurring congestion delay. The function below, 

which is intended to be applied to freeway facilities, was estimated by statistically examining the 

relationship between the total mean travel time delay and the mean recurring travel time estimate (as 

based on long-term data collected and analyzed from multiple regions throughout the nation); and 

allows an adjustment of the mean recurring travel time estimate to include the addition of an estimate 

of nonrecurring travel time.13

MeanTT = 1.0274 * RecurringMeanTT1.2204

The above equation provides a reasonable approximation of the relationship between overall 

congestion and recurring congestion. The method does produce a measure of total recurring and 

nonrecurring delay that may be summed for all facilities and users, compared for a baseline and 

alternative scenario to identify a net change in the measure, and monetized to provide an estimate of 

travel time reliability in the B/C analysis. However, its application in B/C analysis for TSM&O 

 12 Modeling of multiple scenarios representing varying traffic conditions may be used to make this analysis 

methodology more applicable for predictive use. See Section 5.0 for a discussion of the required analysis 

structure. 13 SHRP 2 L03 Final Report, Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies, 

2010.
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strategies is limited, primarily due to the inability to effectively break out recurring travel time from 

nonrecurring travel time (travel time reliability), and the difficulty in assessing the specific impact that 

particular TSM&O strategies may have on reducing specific causes of nonrecurring congestion since 

any change in total mean travel time is only based on changes in mean recurring travel time.

Additional work is currently underway as part of a subsequent SHRP 2 project to allow for more 

effective breakout of the nonrecurring congestion by source; and to develop additional analysis 

methods to estimate the impacts of various TSM&O strategies on the source causes of congestion, 

thus allowing broader application of this methodology.14

Incident related delay. As shown previously in Figure 3-3, traffic incidents account for approximately 

25 percent of all congestion delay and the most substantial proportion of nonrecurring (nonbottleneck 

related) delay sources in most urban areas. As a result, several analysis methods have been 

specifically developed to estimate incident-related delay.

The most prevalent example of these methods is the analysis originally developed and implemented 

as part of the analysis capabilities of the FHWA’s IDAS sketch-planning tool.15 The IDAS incident 

delay analysis involves the use of look-up rate tables containing estimates of the amount of incidentrelated delay likely to be experienced for a facility on a per VMT basis. These rates were predicted 

based upon long-term monitoring and analysis of annual incident delay experience on a number of 

national freeway corridors. These rates are sensitive to several key input factors, including the 

following:

• The number of facility lanes – The incident-related delay rate for freeways with a greater 

number of lanes is less than for facilities with fewer lanes, assuming other factors are also 

equal. This is due to the more substantial blockage of capacity caused by similar incidents on 

roadways with fewer lanes. For example, identical incidents blocking a single lane would 

reduce capacity by one-half of the available lanes on a two-lane facility, but only reduce 

capacity by one-quarter of the available lanes on a four-lane facility.

• The facility volume-to-capacity (V/C) ratio – The level of base congestion (prior to the 

occurrence of the incident) is represented by the V/C ratio. Facilities with a higher V/C ratio 

will have higher incident-related rates associated with them, as it would be expected that 

incidents on these more congested facilities would cause a quicker breakdown in conditions 

and a longer time required to allow the incident-related queue to dissipate once the incident 

was cleared.

Different rates are also estimated based on the length of the analysis period (one-hour, two-hour, 

three-hour, four-hour, or daily). Table 3-3 presents an example of the travel time reliability rates used 

in IDAS. These tables may be employed using a look-up function in an analysis to identify the 

appropriate rate to the VMT estimated for the facility. This estimation would be performed for the 

baseline scenario (without the improvement) and for an alternative scenario (with the improvement). 

The amount of incident-related delay would be expected to change if there are any changes in 

roadway volumes, capacities, or number of lanes as a result of the improvement in the alternative 

scenario as compared to the baseline scenario.

 14 SHRP 2 L05 Draft Interim Report, Incorporating Reliability in the Transportation Planning Process, 2011. 15 www.idas.camsys.com.
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Table 3-3. Sample IDAS-Derived Travel Time Reliability – Rates for One-Hour Peak

(Vehicle Hours of Incident Delay per Vehicle Mile)

Volume/1-Hour 

Level of Service 

Capacity

Number of Lanes

2 3 4+

0.05 3.44E-08 1.44E-09 4.39E-12

0.1 5.24E-07 4.63E-08 5.82E-10

0.15 2.58E-06 3.53E-07 1.01E-08

0.2 7.99E-06 1.49E-06 7.71E-08

0.25 1.92E-05 4.57E-06 3.72E-07

0.3 3.93E-05 1.14E-05 1.34E-06

0.35 7.20E-05 2.46E-05 3.99E-06

0.4 0.000122 4.81E-05 1.02E-05

0.45 0.000193 8.68E-05 2.34E-05

0.5 0.000293 0.000147 4.93E-05

0.55 0.000426 0.000237 9.65E-05

0.6 0.0006 0.000367 0.000178

0.65 0.000825 0.000548 0.000313

0.7 0.001117 0.000798 0.000528

0.75 0.001511 0.001142 0.00086

0.8 0.002093 0.001637 0.00136

0.85 0.003092 0.002438 0.002115

0.9 0.005095 0.004008 0.003348

0.95 0.009547 0.007712 0.005922

1 0.01986 0.01744 0.01368

Source: IDAS User’s Manual – Appendix A, 2008.

In addition to changes in incident-related delay forecast due to predicted changes in volume, capacity,

or number of lanes, some TSM&O strategies have been observed to lessen the frequency of incidents 

or reduce their duration. Strategies such as traffic incident management systems have been 

observed to reduce incident-related delay by up to 40 percent due to the improved and faster 

response provided by the enhanced incident detection, verification, and coordinated response.16 In 

this analysis situation, the hours of incident-related delay for the facility would first be calculated for the 

baseline and the alternative using the look-up analysis described in the paragraph above. Then, the 

resulting estimation of hours of incident-related delay for the alternative scenario would be further 

reduced by 40 percent (or another factor more appropriate to the local conditions) prior to comparison 

with the baseline and valuation in the B/C analysis. Further description of how this analysis may be 

performed is provided in Chapter 5.

 16 www.idas.camsys.com.
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The methodology used in IDAS for estimating incident-related delay has been adopted for use in the 

TOPS-BC tool that supports this Desk Reference. A description of how this analysis is applied is 

described in the TOPS-BC User’s Manual.

Induced Travel/Consumer Surplus

The concept of consumer surplus has increasingly been applied in many recent B/C analyses of 

transportation projects. Consumer surplus is an economic concept intended to enhance the analysis 

of travel time and costs. It is based on the principle of weighing the willingness of individuals to pay (in 

time and money) for a particular trip with the actual cost of the trip, and is capable of more accurately 

assessing the benefits of any induced travel resulting from the improvement.

Each user of the transportation facility is willing to pay a certain price (in terms of time, accident risk, 

vehicle operating costs, tolls, etc.) to travel on that facility. This amount will differ from user to user. 

However, every user typically incurs the same travel time costs. The difference between what a group 

of users is willing to pay in terms of travel costs (travel time, etc.) and what they actually pay is called 

consumer surplus. In a benefit cost analysis, we are concerned with the change in consumer surplus 

attributable to a transportation improvement.

Measurement of consumer surplus is most often used in the assessment of travel time savings in 

place of simple measures of the total change in facility travel time observed between the baseline and 

alternative scenarios. An example of why consumer surplus may be a more accurate picture of travel 

time benefits, as opposed to using an approach that only considers the total change in travel time, is 

provided here. A section of roadway in the baseline scenario carries 6,000 vehicles during a peak 

period and maintains an average travel time of 20 minutes, resulting in 2,000 hours of vehicle travel 

time. In an alternative scenario, the segment travel time is reduced to 18 minutes as a result of a 

deployment; however, this improvement in travel time promotes an additional 800 travelers, who 

previously chose to forego their trips, to now travel, resulting in 6,800 trips on the facility. This would 

result in a total of 2,040 hours of travel time in the alternative scenario. If only total travel time was 

used to estimate the benefits of the improvement scenario, the analysis would indicate that the 

transportation improvement resulted in a disbenefit, since there would be 40 more hours of travel time 

incurred in the alternative scenario as compared with the baseline scenario.

The estimation of a travel time disbenefit in the above example is counterintuitive since all the existing 

travelers incur less travel time to traverse the segment; and the new induced travelers must also be 

benefiting from using the facility, or they would not have chosen to change their behavior. When 

consumer surplus is estimated, the impacts of new, induced travel are treated separately to account 

for this effect. Typically, a linear relationship is assumed in the elasticity of individuals’ sensitivity to 

travel in response to costs, therefore, allowing new traveler benefits to be estimated as one-half 

(50 percent) of the benefits of existing users. The following two formulas would be used to estimate 

consumer surplus benefits for existing and new users of a transportation facility:

Where:

V1 = Baseline volume

V2 = Alternative volume

P1 = Baseline price (travel time)

P2 = Alternative price (travel time)
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Existing users = V1 * (P1 – P2)

New users = 0.5(V2 – V1)(P1 – P2)

Using the hypothetical example provided above, this consumer surplus would be calculated as 

follows:

Consumer Surplus Existing Users = 6,000 * (20 min – 18min) = 200 hours

Consumer Surplus New Users = 0.5 * (6,800 – 6,000) * (20 min – 18 min) = 13.3 hours

The sum of these two values represents the estimated change in consumer surplus due to the 

transportation improvement, or:

Total Consumer Surplus = 200 hours + 13.3 hours = 213.3 hours saved

Thus, the use of consumer surplus in place of standard total travel time benefit in this analysis results 

in a more accurate depiction of the improvement as having positive travel time benefits. Since the 

measurement of consumer surplus is closely linked with induced travel, it is most often used in 

analyses that are anticipated to result in significant numbers of induced trips being made.17 If only a 

small number or no new trips are expected to be made as a result of the improvements being 

analyzed, there is little advantage in using consumer surplus, since the estimation of benefits will not 

vary significantly from the benefits estimated using total travel time.

Note: “New” trips in the analysis are only intended to represent those travelers who previously did 

not make a trip, but were induced to travel by the lowered cost of making the trip. Increased 

volumes of trips resulting from route changes from parallel roadways or mode shifts from 

other forms of transportation should not be treated as induced travel in this analysis method.

Difficult to Quantify MOEs

Beyond the more traditional and emerging MOEs discussed above, a number of additional potential 

benefits of TSM&O strategies have been identified and studied. Meaningful estimation of many of 

these MOEs within the confines of a B/C analysis framework has often proven difficult, however. 

Barriers to developing accurate quantification of these barriers often include:

• Few existing estimation/analysis methods;

• Little established consensus on the appropriate valuation to place on the change estimated 

for the MOE;

• Potential overlap with existing measures (increasing the likelihood of double-counting of 

benefits);

• Quantification of the measures is not supported by generally used transportation analysis 

tools; and

• Little empirical evidence establishing a link between TSM&O strategies and quantifiable 

changes in the measures.

 17 Although the discussion in this section focuses on the effects of induced trips, consumer surplus can be equally 

applied to capture the disbenefits of individuals deciding to forego travel in response to an increase in travel 

time or the cost of travel.
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Several examples of these difficult to quantify MOEs include:

• Community livability – Improvements to the desirability of a community to residents due to 

the more efficient and effective operation of the transportation system. Inclusion of these 

benefits in analysis of TSM&O strategies is often hampered by the lack of a clear consensus 

of how TSM&O strategies affect livability in a community, as well as appropriate methods for 

estimating and valuing this measure.

• Customer satisfaction – Many TSM&O strategies have been shown to have a strong 

linkage to improving customer (traveler) satisfaction. Surveys and customer comments 

related to improvements, such as freeway service patrols and traveler information systems,

are often highly positive and supportive of these types of improvements; however, there often 

remain significant barriers to establishing appropriate valuations of these benefits in a B/C 

analysis.

• Traveler feelings of safety and security – Some TSM&O strategies have been shown to 

enhance travelers’ feelings regarding the safety and security of travel. For example, surveys 

and system user comments have revealed that travelers highly appreciate the availability of 

freeway service patrols (FSP). Even travelers who have never had to use the services firsthand often feel more secure in their travels knowing that they are less likely to be stranded in 

an uncomfortable or dangerous situation given the presence of the FSP. Likewise, strategies 

that serve to smooth the flow of traffic and reduce crash risk exposure (e.g., an application of 

ramp meters at a difficult merge location) may also serve to reduce the anxiety of users 

traveling through the improvement location. This benefit is typically not captured in the 

valuation of crash occurrence, mostly due to difficulties in valuing (e.g., assigning a dollar 

value) to the benefit.

Given the difficulties in quantitatively assessing many of these MOEs, these benefits are more often 

analyzed using more qualitative assessment external to the B/C analysis. As more information is 

gathered on these measures and the linkage of TSM&O strategies to these measures, more 

enhanced quantitative analysis may be possible in the future.
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Chapter 4. Existing B/C Tools

and Methods

Overview of Existing Tools and Methods for B/C Analysis

of Operations

Dozens of individual analysis tools and methodologies designed for conducting B/C analysis of one or 

more TSM&O strategies have been identified to date. These include tools developed by regional, 

state, and Federal agencies, as well as proprietary tools developed by many private-sector 

enterprises; and range from simple methods intended for one-time analysis to more complex tools that 

are continually maintained and updated that form a continuing standardized framework for conducting 

B/C analysis for various agencies. Additionally, several emerging tools/methods are currently 

undergoing development as part of parallel efforts by U.S. DOT, American Association of State 

Highway and Transportation Officials (AASHTO), individual states and regions, and research 

organizations.18

Some of the most widely distributed and applied tools used for conducting B/C analysis of TSM&O 

strategies include those summarized (in alphabetical order) in Table 4-1. This listing summarizes 

those major tools developed by Federal, state, or regional transportation agencies (or affiliated 

research organizations) that are available within the public realm. This listing does not include 

proprietary offerings of private-sector vendors. Specific descriptions of the various tools follow 

Table 4-1.

The following sections provide a brief introductory description of the tools and methods presented in 

Table 4-1.

• BCA.Net – BCA.Net is the FHWA’s web-based benefit/cost analysis tool to support the 

highway project decision-making process, which is supported by the FHWA Asset 

Management Evaluation and Economic Investment Team. The BCA.Net system enables 

users to manage the data for an analysis, select from a wide array of sample data values, 

develop cases corresponding to alternative strategies for improving and managing highway 

facilities, evaluate and compare the benefits and costs of the alternative strategies, and 

provide summary metrics to inform investment decisions.

 18 Note: Although classified as Benefit/Cost tools, few of the evaluated tools were capable of estimating the costs 

of TSM&O strategies. Most tools required the costs to be estimated off-model and entered as a line item in the 

analysis assumptions.
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Table 4-1. Summary of Existing B/C Tools and Methods for TSM&O

Tool/Method Developed by Web Site

BCA.net FHWA http://www.fhwa.dot.gov/infrastructure/asstmgmt/

bcanet.cfm

CAL-BC Caltrans http://www.dot.ca.gov/hq/tpp/offices/eab/LCBC_

Analysis_Model.html

COMMUTER Model U.S. EPA http://www.epa.gov/oms/stateresources/policy/pa

g_transp.htm

EMFITS New York State DOT https://www.nysdot.gov/divisions/engineering/des

ign/dqab/dqab-repository/pdmapp6.pdf

The Florida ITS Evaluation 

(FITSEval) Tool

Florida DOT N/A

Highway Economic 

Requirements System – State 

Version (HERS-ST)

FHWA http://www.fhwa.dot.gov/infrastructure/asstmgmt/

hersindex.cfm

IDAS FHWA http://idas.camsys.com

IMPACTS FHWA http://www.fhwa.dot.gov/steam/impacts.htm

Screening Tool for ITS 

(SCRITS)

FHWA http://www.fhwa.dot.gov/steam/scrits.htm

Surface Transportation 

Efficiency Analysis Model 

(STEAM)

FHWA http://www.fhwa.dot.gov/steam/index.htm

Tool for Operations Benefit/Cost 

(TOPS-BC)

FHWA N/A

Trip Reduction Impacts of 

Mobility Management 

Strategies (TRIMMS)

Center for Urban 

Transportation Research 

(CUTR) at the University 

of South Florida

http://www.nctr.usf.edu/abstracts/abs77805.htm

• CAL-BC – Excel spreadsheet-based tool developed by Caltrans. Originally designed to 

conduct benefit/cost analysis of traditional highway improvements, Cal-B/C has been

subsequently enhanced to be used to analyze many types of highway construction and 

operational improvement projects, as well as some ITS and transit projects. Several 

agencies outside Caltrans have also adapted Cal-BC as the basis for their own tools. Cal-BC 

has been developed in separate versions supporting corridor- and network-wide benefits.

• COMMUTER Model – Spreadsheet-based analysis developed by the U.S. EPA to estimate 

emissions benefits related to a number of employer-based travel demand management 

strategies.

• EMFITS – Benefit/cost analysis methodology developed for New York State DOT and 

incorporated in New York State DOT ITS Scoping Guidance (Project Development Manual).

• FITSEval – The Florida ITS Evaluation (FITSEval) tool is currently under development by the 

Florida DOT. The tool is a travel demand model post-processor designed to estimate B/C of 

ITS from the State’s standardized FSUTMS model structure.
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• HERS-ST – Highway Economic Requirements System – State Version (HERS-ST) was 

developed by the FHWA. Originally designed for assessing the impacts of traditional capacity 

improvements, HERS-ST was updated in 2004 to include analysis of selected management 

and operations strategies through the use of a data preprocessor. The Operations 

Preprocessor modifies the basic characteristics of the HPMS data used by HERS (capacity, 

delay, crash relationships, and incident characteristics). HERS then estimates the impacts 

based on the revised characteristics. The I-95 Corridor Coalition recently used HERS-ST to 

assess impacts of investment in multistate corridors.

• IDAS – The IDAS tools was initially developed by the FHWA in 2001 and has undergone 

multiple updates since. IDAS, a sketch-planning tool operating as a travel demand model 

post-processor, implements the modal split and traffic assignment steps associated with the 

traditional traffic demand forecasting planning model. IDAS estimates changes in modal, 

route, and temporal decisions of travelers resulting from more than 60 types of ITS 

technologies. There are more than 30 state and metropolitan planning organizations (MPO)

applications of IDAS. Although many of the public sector-developed tools and methods 

presented in this section are available free of charge, IDAS is only available for purchase 

through the McTrans Center at the University of Florida at a costs of $795 per seat license.

• IMPACTS – IMPACTS is a series of spreadsheets, related to the STEAM model, developed 

to help screening-level evaluation of multimodal corridor alternatives, including highway 

expansion, bus system expansion, light-rail transit investment, HOV lanes, conversion of an 

existing highway facility to a toll facility, employer-based travel demand management, and 

bicycle lanes. Inputs are travel demand estimates by mode for each alternative.

• SCRITS – Screening Tool for ITS (SCRITS) was developed by the FHWA. The tool is a

spreadsheet application for estimating user benefits of ITS at the sketch-planning level. 

SCRITS provides a highly approximate subset of the capabilities found in TOPS-BC.

• STEAM – Surface Transportation Efficiency Analysis Model (STEAM) uses information 

developed through the travel demand modeling process to compute the net value of mobility 

and safety benefits attributable to regionally important transportation projects. Developed by 

the FHWA, STEAM uses information developed through the travel demand modeling process 

to compute the net value of mobility and safety benefits attributable to regionally important 

transportation projects.

• TOPS-BC – The Tool for Operations Benefit/Cost (TOPS-BC) was developed in parallel with 

this Desk Reference and is intended to support the guidance provided in this document by 

providing four key capabilities: 1) allows users to look up the expected range of TSM&O 

strategy impacts based on a database of observed impacts in other areas; 2) provides 

guidance and a selection tool for users to identify appropriate B/C methods and tools based 

on the input needs of their analysis; 3) provides the ability to estimate life-cycle costs of a 

wide range of TSM&O strategies; and 4) allows for the estimation of benefits using a 

spreadsheet-based sketch-planning approach and the comparison with estimated strategy 

costs. The capabilities of TOPS-BC are highlighted throughout this Desk Reference.

• TRIMMS – Trip Reduction Impacts of Mobility Management Strategies (TRIMMS) model

developed by the CUTR at the University of South Florida. TRIMMS© allows quantifying the 

net social benefits of a wide range of transportation demand management (TDM) initiatives in 

terms of emission reductions, accident reductions, congestion reductions, excess fuel 

consumption, and adverse global climate change impacts. The model also provides program 

cost‐effectiveness assessment to meet the FHWA’s CMAQ Improvement Program 

requirements for program effectiveness assessment and benchmarking.
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In addition to these existing tools, several pieces of ongoing research are anticipated to result in the 

development of tools and analysis methodologies related to operations strategies. Where available 

and appropriate, preliminary findings and methods from these studies have been integrated into this 

Desk Reference and utilized in the TOPS-BC application. Several of these more noteworthy efforts 

include:

• The FHWA pooled funds study of the impacts of managed lanes;

• I-95 Corridor and FHWA study on Traffic Incident Management (TIM) and benefit/cost 

analysis;

• The FHWA-sponsored project conducted by the University of Texas to develop a Project 

Evaluation Toolkit and Guidebook;

• The FHWA study on developing Highway Capacity Manual (HCM) methods for analyzing

ATDM strategies; and

• SHRP 2 Project L05, Incorporating Reliability in the Transportation Planning Process.

The above tools and research efforts represent a sampling of the available methods that may be used 

for supporting and conducting B/C analysis of TSM&O strategies. The capabilities of many of these 

tools and the findings of the research efforts have been weaved into this guidance document, and also 

often forms the basis for the benefit and cost estimation capabilities developed in the supporting 

TOPS-BC tool.

In addition to these established tools, there are many customized methods that have been developed 

to support an individual agency’s needs, or modified from an analysis method to meet the needs of a 

specific analysis. These tools and methods can generally be segmented into three broad categories, 

including the following:

1. Sketch-planning methods – These analysis methods provide simple, quick, and low-cost 

estimation of TSM&O strategy benefits and costs. Often based in a spreadsheet format, 

these methods often rely on generally available input data and static default relationships 

between the strategies and their impact on a limited number of MOEs to estimate the benefits 

of the strategy. A number of established B/C tools, including TOPS-BC, SCRITS, and 

Cal-BC, are classified as sketch-planning methods; however, this category also includes 

scores of individually developed and customized spreadsheet and simple database methods 

configured to support various analyses by single agencies.

2. Post-processing methods – These methods are often more robust than sketch-planning 

methods, as they seek to more directly link the B/C analysis with the travel demand, network 

data, and performance measure outputs from regional travel demand or simulation models. 

Several established tools, including IDAS and the FITSEval application, have been designed 

to directly accept detailed model data as inputs to the analysis. The tools then provide 

additional analysis within their framework to assess impacts to MOEs outside the capabilities 

of typical travel demand models. Outside of these more established tools, these postprocessing methods also include customized applications, algorithms, and routines that may 

be applied directly within a region’s existing modeling framework to produce the required 

MOEs. These methods are often more capable of assessing the impacts of route, mode, or 

temporal shifts than compared to sketch-planning methods.

3. Multiresolution/multiscenario methods – These analysis methods are often the most 

complex of the methods and are typically applied when a high level of confidence in the 
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accuracy of the results is required. These methods are most often applied during the final 

rounds of alternatives analysis or during the design phases when detailed information is 

required to prioritize and optimize the proposed strategies. Multiresolution methods depend 

on the integration of various analysis tools (e.g., linking a travel demand model and a 

simulation model) to provide meaningful analysis of the full range of impacts of a TSM&O 

strategy – capturing both the long-term impacts on travel demand, along with the more 

immediate impacts on traffic performance. Meanwhile, multiscenario methods seek to assess 

strategy performance during varying underlying traffic conditions. In this analysis, the impact 

of a particular strategy may be tested under a variety of conditions (e.g., incident versus noincident, good weather versus rain conditions versus snow conditions) in order to fully capture 

the benefits under all the likely operating conditions. This type of analysis often requires that 

the analysis model be run multiple times to capture these effects. For complex analysis 

requiring a high degree of confidence in the results, multiresolution and multiscenario analysis 

may be performed in concert within an analysis framework.

The general categories listed above largely encompass the range of available analysis methods; 

however, there are often methods and tools that may not fit neatly within single categories. These 

hybrid approaches are often applied when a single method does not completely meet the needs of a 

particular analysis. The subsequent sections provide additional discussion of the method categories,

and provide examples of tools and analysis utilizing those methods.

Note: The TOPS-BC tool developed in parallel with this Desk Reference document contains a 

decision-support tool designed to aid practitioners in identifying an appropriate method for 

conducting B/C analysis based on various input criteria. This capability is described further in 

Chapter 5.

Sketch-Planning Methods

These methods/tools are typically conducted in spreadsheets or simply structured databases, and are 

intended to provide relatively easy and fast analysis of the TSM&O strategy. They represent most 

commonly applied tool category. The methods often require relatively limited input data (e.g., basic 

aggregated volume and speed data); and produce order of magnitude results appropriate for early 

evaluation, screening, and prioritization of various strategies. Benefit analysis is typically based on 

generally linear assumptions regarding the impact of various strategies. These methods are also 

often limited; in that, they are incapable of assessing the impacts of various TSM&O strategies 

deployed in combination with each other (i.e., the tools typically cannot analyze the synergistic effects 

of integrating various systems). These methods are often developed and configured by agencies for 

individual analysis, although a number of existing tools has been developed to serve as ongoing 

templates for conducting analysis, including the TOPS-BC tool developed in parallel to this Desk 

Reference, FHWA’s SCRITS tool (shown as Figure 4-1 below), and Caltrans’ CAL-BC tool (shown as 

Figure 4-2 below), among many others.
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Figure 4-1. Example View of Sketch-Planning B/C Analysis Method (SCRITS) Data Inputs
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Figure 4-2. Example View of Caltrans’ CAL-BC Analysis Tool Data Outputs

Sketch-Planning Method Advantages

The following summarize the general advantages of using sketch-planning analysis:

• Easy to use – Many of the sketch-planning methods may be researched and mastered very 

quickly, limiting the amount of staff resources needed to conduct the analysis.

• Limited data requirements – Sketch-planning methods typically require generally available 

data (e.g., facility volumes, speeds) as inputs to the framework. These data may be obtained 

from real-world counts, model data, or other sources.

• Quick set up and analysis times – Assuming the input data is easily available, the analysis 

times are generally very fast, often allowing multiple alternatives to be analyzed in the period 

of a single hour.

• Low cost – Due to the ease of use, limited data requirements, and quick analysis times, 

sketch-planning methods may often be applied at little resource cost to the performing 
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agency. Additionally, many of the established sketch-planning tools are distributed at no or 

very limited procurement cost.

• Ability to customize – Since many sketch-planning methods are developed in a 

spreadsheet format, it is typically easy to both review and make adjustments to the default 

parameters, assumptions and formulas used in the tools. This allows for the better 

configuration of established tools to the local environment and conditions.

Sketch-Planning Method Challenges

The following represent some of the identified challenges of using sketch-planning methods:

• Order of magnitude outputs – Sketch-planning methods often lack the rigor of more 

advance analysis methods; therefore, the outputs are most appropriate for preliminary 

planning and screening of alternatives when a low or moderate level of confidence in the 

analysis results is appropriate to the analysis phase.

• Limited MOEs – Many sketch-planning methods focus only on a limited set of output MOEs, 

reducing the comprehensiveness of the B/C analysis.

• Linear (Nondynamic) assumptions of user behaviors – Most sketch-planning methods 

assume static, linear reactions of travelers to deployed strategies. Behavioral changes, such 

as route change, mode shift, temporal change, or longer-term changes in travel demand, may 

not be captured in the simplified analysis.

Appropriate Applications for Sketch-Planning Methods

Given the capabilities of sketch-planning methods, as reflected in the advantages and challenges 

presented above, these methods are often most appropriately applied during the early stages of the 

planning process, when there is a large number of potential projects that must be preliminarily 

analyzed in order to prioritize projects that are most deserving of additional, more detailed analysis in 

subsequent planning phases. Sketch-planning methods are also appropriate for use in estimating 

high-level benefits of existing strategies for use in justifying the continued deployment funding or 

expansion of an existing initiative.

Post-Processing Methods

These more complex methods and tools generally include customized user interfaces and analysis 

processes, and are intended to be linked (either directly or indirectly) with traditional transportation 

analysis tools, such as travel demand models, simulation models, or HPMS databases. These tools 

may be developed in various programming languages; and some, such as the FHWA’s BCA.Net, are 

available directly through on-line portals. The customized functionality of these tools often provides 

additional analysis capabilities and additional accuracy of estimated impacts, due to the customization 

and configurability provided beyond simple spreadsheet capabilities. Additionally, some of these tools 

work by modifying the existing analysis capabilities available in their host tools to be more sensitive to 

the specific impacts of TSM&O strategies.

These tools may, however, require more specific data and additional effort to configure and operate, 

and the analysis processes may not be as transparent as some spreadsheet-based tools. The 

additional analysis configurability often allows for the closer inspection of specific impacts related to 

specific MOEs and improves the confidence in the output results, making these tools valuable for 
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prioritizing limited lists of potential deployments. Other examples of these types of tools include the 

FHWA’s IDAS software (shown as Figure 4-3 below) and the Florida DOT’s FITSEval toolset.

Figure 4-3. Example of Post-Processing B/C Analysis Method (IDAS)

In addition to these established post-processing tools designed to be applied by multiple agency, this 

category also includes model modifications by individual agencies intended to develop routines, 

algorithms, and processes to improve the ability to generate better estimates of TSM&O-related 

performance measures (e.g., travel time reliability), or otherwise improve the assessment of TSM&O 

within their existing travel demand or simulation model framework.

Post-Processing Method Advantages

The following summarize the general advantages of using post-processing B/C analysis methods:

• Assessment of traveler behaviors – Since these methods are based on existing models 

calibrated to regional travel conditions, there is often the capability to better assess a wider 

range of potential traveler behaviors, such as route change, mode shifts, temporal shifts, and 

long-term changes in travel demand, thus, providing more adaptive (less linear) reactions to 

strategies than provided in the sketch-planning methods.

• Data availability – Since most urban regions have existing calibrated travel demand models, 

the analysis data and structure necessary to conduct this type of analysis already exists. This 

limits the amount of additional data collection and analysis that may need to be performed to 

support the analysis.
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• Consistency with the regional planning process – Most planning agencies are familiar 

and comfortable with using the regional travel demand model outputs in their planning 

studies. Consistency with the existing processes promotes better understanding and buy-in 

with the results of the analysis.

• Development of a reusable process – Once post-processing methods have been initially 

set up and tested, they may be repeatedly used to provide consistent and low-cost analysis of 

multiple alternatives. The initial set-up process for this method may be onerous, however, as 

discussed below.

Post-Processing Method Challenges

The following represent some of the identified challenges of using post-processing methods:

• Analysis effort – Post-processing methods most typically require either the linkage of an 

existing regional model with an available post-processing tool (e.g., IDAS); or the individual 

development of customized model routines, algorithms, and procedures within the model 

structure. Either way, the complexity of modern travel demand or simulation models means 

that a significant effort will be required to develop, apply, test, and validate these methods. 

Often, a majority of this total analysis effort is required in the early set up and calibration of the 

method.

• Compatibility of tools/methods – Many different modeling platforms from a variety of 

vendors are in use today; and there is often little standardization in their input/export data, 

modeling routines, and assumptions. Likewise, the developed post-processing tools also 

have many limitations on the types and format of data that may be used in the analysis. Prior 

to attempting these methods, research is recommended to assess the compatibility of the 

proposed tools to better ensure success in linking their capabilities.

Appropriate Applications for Post-Processing Methods

Given the capabilities of post-processing methods, as reflected in the advantages and challenges 

presented above, these methods are often most appropriately applied during the middle to late stages 

of the alternative analysis/prioritization planning processes. The higher costs (greater analysis 

resources required) associated with post-processing methods mean that a smaller number of 

alternatives be considered in the analysis, as compared with lower-cost sketch-planning methods. 

These methods may also be appropriately used in the evaluation of existing deployments, as 

highlighted in the Cincinnati region case study presented in Chapter 2.

Multiresolution/Multiscenario Methods

These methods/tools are the most complex of the analysis approaches and are typically only used in 

situations where a limited number of alternatives need to be evaluated, but highly accurate results are 

desired. These methods require the use and integration of multiple analysis tools (e.g., combining the 

analysis capabilities of a travel demand model with a traffic simulation model) to assess the potential 

impacts of the TSM&O strategy. The combination of the modeling capabilities is intended to combine 

the strengths of various transportation analysis models to assess both the short-term impacts (e.g., 

impacts on travel speed under particular travel conditions) and the long-term impacts (e.g., changes in 

travel demand over time) of TSM&O strategies.
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Likewise, multiscenario methods seek to assess strategy performance during variations in underlying 

traffic conditions. In this analysis, the impact of a particular strategy may be tested under a variety of 

conditions (e.g., incident versus no-incident, good weather versus rain conditions versus snow 

conditions) in order to fully capture the benefits under all the likely operating conditions. This type of 

analysis often requires that the analysis model(s) be run multiple times to capture these effects. The 

data required to identify and develop appropriate analysis scenarios (e.g., frequency and location of 

incidents; distribution of fair, rain, snow weather days) are often obtained using archived data systems, 

adding another tool to the integrated analysis approach.

For complex analysis requiring a high degree of confidence in the results, multiresolution and 

multiscenario analysis may be performed in concert within an analysis framework. One of the most

prominent recent examples of multiresolution/multiscenario methods for B/C analysis is the FHWA’s 

ICM Initiative analysis effort.19 Figure 4-4 presents an overview of the multiresolution analysis process 

that was developed in the B/C analysis effort of the ICM initiative, showing the linkage of regional 

travel demand models (which were used to assess the long-term impacts on travel demand of the 

considered strategies) and a refined simulation model (used to identify the more immediate 

operational performance impacts of the strategies). The ICM analysis process was completed for 

multiple iterations to estimate the variation in performance under different operating scenarios (e.g., 

incidents versus nonincident days). Thus, this method represents a combined multiple resolution and 

multiple scenario approach.

Multiresolution/Multiscenario Method Advantages

The following summarize the general advantages of using multiresolution/multiscenario B/C analysis 

methods:

• Assessment of short- and long-term traveler behaviors – Travel demand models have a 

strong capability of assessing long-term impacts of travel demand and analyzing traffic during 

“typical” conditions, while simulation models’ strength is often the dynamic assessment of 

operational performance during discrete time slices and during varying nonrecurring traffic 

conditions. Combining the strengths of these two models provides the ability to assess both 

the short-term, immediate impacts of a TSM&O strategy, but also the longer-term demand 

(change in the number of trips) impacts that may occur as travelers adjust to the changed 

transportation environment. These tools often represent the best solution for fully considering 

the multifaceted impacts of many strategies for both operations and planning purposes.

 19 FHWA ICM – Analysis, Modeling, and Simulation Guide, Draft Report, April 2011.
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Figure 4-4. Multiple-Resolution Approach Developed for the ICM Initiative Analysis

Source: FHWA ICM – Analysis, Modeling and Simulation Guide, Draft Report, April 2011.

Benefit Valuation

User Selection

of Strategies

Cost of Implementing

Strategies

Regional Travel 

Demand Model Trip Generation Trip Distribution Mode Choice

Trip Assignment

• Refined 

Trip Table

(Smaller Zones 

and Time Slices)

• Refined Network

Dynamic 

Assignment

Pivot Point 

Mode Choice

Mesosimulation

and/or Microsimulation

Refined Transit 

Travel Times

Corridor-Level

Performance Measures

• VMT/VHT/PMT/PHT

• Travel Time/Queues 

Throughput/Delay

• Environment

• Travel Time Reliability

• Fuel Consumption

• Benefit/Cost Analysis

• Sensitivity Analysis

• Ranking of ICM Alternatives

• Trip Table

• Network

• Other Parameters

Outputs

Interface 

Peak Spreading

Network 

Resolution
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• Assessment of nonrecurring conditions – This method, particularly when multiscenario 

analysis is applied, is capable of dynamically modeling conditions representing nonrecurring 

situations that would likely to occur. Assessing this full range of conditions provides the ability 

to capture benefits of many TSM&O strategies that otherwise would have been understated if 

only “typical” day conditions had been included in the analysis. Figure 4-5 presents a 

comparison of two analyses performed as part of the ICM initiative analysis. The figure on 

the left represents an estimate of the benefits for a San Diego deployment of ICM that only 

considers “typical” days that are void of incident conditions. The figure on the right 

represents an estimate of benefits that would accrue as a result of the improvements when 

both “typical” and incident days are considered. Clearly, the inclusion of the analysis of 

benefits on incident days boosts the benefits (more than double) and illustrates that failure to 

consider these nonrecurring conditions will often result in a severe understatement of benefits 

for many TSM&O strategies.

Figure 4-5. Comparison of ICM Benefit Estimates between Nonincident Scenarios

and Incident Scenarios 

(San Diego I-405 Analysis – in Millions of Annual Dollars)

Source: FHWA ICM – Analysis, Modeling, and Simulation Guide, Draft Report, April 2011.

• Detail of analysis – Multiresolution/multiscenario analysis has the capability to produce very 

detailed analysis results. The ability of this method to examine many MOEs, particularly 

travel time reliability, is far superior to existing sketch-planning or post-processing methods. 

When set up and calibrated correctly, this method allows a high level of confidence to be 

placed on the accuracy of the output results of the analysis activity.

• Flexibility of the analysis – The highly customizable analysis routines, provided by this 

method, allow practitioners to evaluate the widest range of TSM&O strategies of any of the 

methods, and further provide the capability to look at many MOEs at a level of detail not 

provided by other tools/methods.
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Multiresolution/Multiscenario Method Challenges

The following represent some of the identified challenges of using multiresolution/multiscenario B/C 

analysis methods:

• Model development and analysis effort – The complexity of this analysis process requires 

that a significant effort be undertaken to develop the analysis process. Linking various model 

platforms often requires a substantial amount of staff, expertise, computing, schedule, and 

budget resources. It is critical that the linkage process be carefully planned, and that the 

model processes undergo a rigorous calibration and validation process to ensure the 

accuracy of the results. The model calibration process can be the most time- and budgetconsuming process in the entire analysis. For complex analyses, practitioners can expect six 

months to one year be required to develop the analysis capabilities.20 However, once the 

model is established, the analysis may be repeated for multiple alternatives on a relatively 

quick basis, allowing the initial cost of developing the method to be amortized over multiple 

operations and planning analysis. Further, ongoing research and application of these 

methods by FHWA and other practitioners is yielding new methods and process guidance 

that is reducing the cost of these efforts.

• Compatibility of tools/methods – The linkage of different modeling platforms and tools 

requires careful planning and research prior to undertaking this ambitious effort. Many tools 

are not easily combined, and thus, may require significant effort to manually interface the 

models and data. Again, ongoing research, as well as efforts by individual tool developers 

are reducing this barrier as these methods become more mainstreamed.

• Complexity limits on analysis scope – The large amount of resources required to develop 

the model processes often limits the scope of the analysis that may be performed using this 

methodology. This may represent a geographic limitation – the simulation analysis is often 

limited to a single corridor or small subarea, instead of the entire network – or an alternative 

limitation – the number of alternatives that may be run through the process is likely to be 

limited to a small number given the resources required to analyze each scenario.

Appropriate Applications for Multiresolution/Multiscenario Methods

Given the capabilities of multiresolution/multiscenario methods, as reflected in the advantages and 

challenges presented above, these methods are often most appropriately applied during the final 

stages of the alternative analysis/prioritization planning processes, and during phases where the 

actual operating parameters of the strategy are being refined (design phase). These stages of the 

planning process often require the high level of detail and strong confidence in result accuracy 

provided by this method. The resources required to develop and calibrate the modeling capabilities, 

as well as the effort required to run individual scenarios, often limit the geographic scope of the 

analysis – more likely a corridor analysis than a regionwide assessment – or the number of 

alternatives that may be analyzed, so it is recommended that this method be applied after previous 

prioritization processes have already condensed the number of scenarios to be analyzed.

 20 FHWA Traffic Analysis Toolbox – Volume II, 2004.
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Operations Strategies Available for Analysis in the Tools/

Methods

Many of the different tools and methods discussed in Chapter 4 were designed to analyze one or 

more TSM&O strategies. Only multiresolution/multiscenario analysis methods maintain the flexibility 

to currently analyze all of the generally recognized TSM&O strategies. Figure 4-6 summarizes the 

strategies that may be analyzed by many of these widely distributed available tools.

Figure 4-6. Available Tools/Methods Mapped to Strategies Analyzed

Tool/MethodologyTravel Demand ManagementPublic Transit SystemsArterial Traffic MgmtCVOHOT LanesFreeway ManagementSystemsIncident ManagementSystemsRegional Multimodal Traveler InfoWork Zone Mgmt

BCA.net  

Cal-B/C      

COMMUTER Model 

EMFITS     

FITSEval    

HERS-ST (Preprocessor)   

IDAS         

IMPACTS   

Multiresolution/Multiscenario Methods         

NET_BC

SCRITS     

STEAM    

TOPS-BC         

TRIMMS  

 – Addresses most elements of strategy  – Addresses some elements of strategy

MOEs Generated

Most of the analysis tools and methods introduced in Chapter 4 provide varying capabilities of 

analyzing the impact of TSM&O strategies on different MOEs. Few existing tools are fully capable of 
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estimating the impacts to the comprehensive range of measures that may be impacted by TSM&O 

strategies. Only multiresolution/multiscenario methods come closest to this comprehensive capability, 

and the ability of these methods to produce the full range of benefits is not intrinsic to the method 

itself, but is instead a product of the flexibility of the approach. The user of multiresolution/

multiscenario methods would need to carefully plan and implement their modeling approach to 

produce the desired MOEs. Figure 4-7 summarizes the MOEs that may be analyzed by many of the 

currently available tools.

Selecting the appropriate analysis tool or method is a critical step in the B/C analysis. The selected 

tool must be capable of evaluating the TSM&O strategies and MOEs of interest to the agency. The 

tool must also be appropriate to the scope of the analysis and be able to use with the resources 

available for the study. Chapter 5 provides additional detail on factors that need to be considered 

when developing an analysis approach. Additionally, the TOPS-BC application that supports this 

Desk Reference provides a decision-support tool intended to assist practitioners in prioritizing their 

analysis needs against the capabilities of the available tools. In this TOPS-BC screening process, 

users are asked to indicate their analysis needs in relation to a number of criteria, including the 

following:

• Geographic scope of the analysis;

• Desired level of confidence in the results;

• TSM&O strategies to be analyzed;

• Key MOEs;

• Travel modes to include;

• Level of resources to support the analysis; and

• Data/tools available to support the analysis.

Figure 4-8 presents a partial screen view of the TOPS-BC method selection application.
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Figure 4-7. Available Tools/Methods Mapped to MOEs Analyzed

Tool/MethodologyMobility (Travel Time Savings)Reliability (Total Delay)Safety (Number and Severity of Crashes)Environment (Emissions Reduction)Energy (Fuel Use)Productivity (Public Agency Costs/Efficiency)Vehicle Operating Cost Savings

Notes

BCA.net     

Cal-B/C    

EMFITS     Emissions: HC, CO, NOx

Florida DOT’s FITSEval     

Final performance 

measures still under 

development

HERS-ST      HERS preprocessor

IDAS       

Reliability estimates 

represent incident-related 

delay

IMPACTS     Induced demand, parking 

costs, revenue transfers

Multiresolution/Multiscenario       

NET_BC   

SCRITS    

VMT, Vehicle Hours 

Traveled (VHT), emissions 

(HC, CO, NOx)

STEAM     

Total transportation cost, 

accessibility to jobs, 

emissions (HC, CO, NOx), 

noise and other external 

costs, revenue transfers

TOPS-BC       

Reliability estimates 

represent incident related 

delay

TRIMMS   

 – Primary analysis capability  – Secondary analysis capability
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Figure 4-8. TOPS-BC Analysis Method Selection Tool (Partial Screenview)

FHWA Tool for Operations Benefit/Cost (TOPS-BC)

Guidance on Appropriate Benefit/Cost Methods

INPUT CRITERIA

Suggested Methodologies:

1 What is the geographic scope of the analysis? (Select 1)

No Preference

Statewide Recommended:

Regional Tools meeting ALL criteria

Corridor TOPS-BC *

Isolated Location BCA.net

Other CAL-BC

2 What is the desired level of confidence of the analysis results? (Select 1) EMFITS

No Preference FITSEval *

High (extremely accurate) HERS-ST

Medium IDAS

Low (order of magnitude) MicroBENCOST

3 What TSM&O strategy(ies) do you want to analyze? (Choose Multiple) Redbook Wizard *

No Preference SCRITS

Arterial Corridor Traffic Signal Coordination Strategies SPASM *

Traffic Signal Priority Strategies STEAM

Ramp Metering Strategies Travel Demand Model Methods *

Traffic Incident Management Systems Simulation Methods *

Transit AVL and Automated Scheduling

Pre-Trip Traveler Information

Instructions: Please indicate the needs of your analysis associated with the following criteria then 

press "GO". 

A list of appropriate methodologies will be displayed to the right and will change in response to your 

answers to the input analysis criteria. 

Return to OPENING SCREEN

Go

No Preference

Statewide

Regional

Corridor

Isolated Location

Other

No Preference

High (extremely accurate)

Medium 

Low (order of magnitude)

No Preference

Arterial Corridor Traffic Signal Coordination Strategies 

Traffic Signal Priority Strategies 

Ramp Metering Strategies 

Traffic Incident Management Systems 

Transit AVL and Automated Scheduling

Pre-Trip Traveler Information
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Chapter 5. Conducting B/C Analysis

for Operations

Selecting the Appropriate Methodology/Factors 

Influencing the Analysis Approach

The process of selecting an appropriate method or tool for use in the B/C analysis is a critical step in 

ensuring the eventual success of the effort. As discussed in Chapter 4, there is a wide variety of 

existing tools available for conducting B/C analysis of TSM&O strategies. Likewise, there is often 

great opportunity for practitioners to develop their own customized analysis approach if the available 

tools do not meet the exact needs of their analysis. Often, these custom approaches are built by 

borrowing capabilities from existing tools; and enhancing the process with customized methods 

configured to available data, existing regional analysis tools, and the specific needs of the analysis.

Prior to initiating any analysis, practitioners are encouraged to carefully weigh the factors influencing 

the analysis approach. Careful assessment of the analysis needs and mapping to an appropriate 

analysis method or tool will provide for a better use of scarce analysis resources, and help minimize 

the possibility of discovering that the selected analysis method is incapable of successfully completing 

the analysis when the evaluation is half-finished.

The sections below present expanded discussion of multiple factors that should be considered in 

selecting a methodology and customizing an analysis approach. These factors include:

• Geographic scope;

• Types of strategies;

• Need for/purpose of study;

• Desired MOEs;

• Required level of confidence in results;

• Available tools and data;

• Available analysis resources; and

• Other factors.

Geographic Scope

The geographic scope of the strategy to be analyzed plays a key role in developing the analysis 

approach. Some TSM&O strategies may be deployed and influence only a limited geographic area, 

such as an intersection, a freeway interchange, or a single corridor. Other strategies may be deployed 

on multiple corridors or even regionwide (e.g., 511 traveler information systems). The scope of the 

deployment(s) to be analyzed is a critical factor in determining the appropriate facilities to include in 

the analysis, as well as a determinant in selecting an appropriate analysis tool.
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Another issue to consider when assessing the geographic scope to capture in the analysis is the effect 

of transfers from other regions. For example, an economic analysis may indicate a regional increase 

in jobs as a result of the increased regional productivity provided by an improvement. This would be 

seen as a benefit for the region gaining the jobs, but might be a disbenefit for the region where the 

jobs moved. If the analyst were conducting an analysis of the benefits that could be gained if the 

improvement were funded from 100-percent local funding, they may include these benefits as they 

represent a regional gain. If, however, the analysis were being completed for an improvement that 

was anticipated to be funded through state or national sources, these benefits would represent a 

transfer and should not be included. At the national scale, interstate and interregional transfers should 

not be included – only the net change on a national scale should be considered.

Prior to developing the analysis approach, practitioners should carefully assess the possible impacts 

of the TSM&O strategy to be analyzed, and determine the likely geographic extents of the impacts to 

various regional facilities. It is important that the analysis be designed to fully capture not only the 

impacts at facilities immediately adjacent to the proposed deployment, but also capture broader 

network impacts due to likely traveler changes in route mode or time of travel. For example, an 

analysis of a ramp metering deployment should not only attempt to assess the change in roadway 

performance within the immediate merge area, but also should attempt to include an assessment of 

conditions in the ramp queue, at adjacent intersections, and possibly on parallel arterials or freeway 

facilities, if they are likely to be impacted by the deployment. Failure to include all the likely facilities 

and modes likely to be impact by a strategy risks understating or overstating the benefits of the 

strategy.

The anticipated geographic reach of impacts of a TSM&O strategy also plays a key role in the 

selection of an appropriate analysis method/tool. Different analysis methods and tools are typically 

more appropriately scaled to analysis at different geographic scopes. For example, analysis of a 

TSM&O strategy on a corridor level may be most appropriately assessed using a multiscenario 

simulation, model-based approach; however, if an assessment is required on a regionwide basis, the 

simulation approach may prove to be too resource demanding to be practical. Table 5-1 presents a 

general overview of the tool categories (presented in Chapter 4) and their appropriateness to various 

geographic scales. As a general rule, as shown in the table, increases in the complexity of the 

analysis methods typically results in a smaller appropriate geographic coverage, often due to the 

resources necessary to conduct a very robust analysis on a broad scale.

Table 5-1. Analysis Methods/Tools Mapped to Appropriate Geographic Scope

Analysis Method/Tool Appropriate Geographic Scope

Sketch-Planning Methods Isolated Location

Corridor

Subarea

Regionwidea

Post-processing Methods Corridor

Subarea

Regionwideb

Multiresolution/Multiscenario Methods Corridor

Subarea

a Dependent on the specific analysis tool selected. b Assumes post-processing method is applied to the regional travel demand model.
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In addition to helping determine the most appropriate tool category to apply, the intended geographic 

scope of the analysis may play a factor in identifying specific tools to be considered. For example, a 

multiresolution approach utilizing a simulation model may be proposed for a particular analysis; 

however, different simulation packages/platforms are available that can support analysis at different 

geographic scales. For example, macrosimulation models provide less detailed analysis, but can 

provide simulation over a wider regional scale. Mesoscopic simulation models provide more detailed 

analysis, but are often limited to a subregional geographic scale. Microsimulation models provide the 

most detailed analysis, but are often limited to analysis on a corridor scale, given the data and 

resource requirements necessary to develop these models. In designing the analysis, the practitioner 

must make decisions regarding the tradeoff between the detail available in the simulation model and 

the geographic coverage that will be possible, given the available resources for the analysis. 

Figure 5-1 provides an overview of the different types of simulation tools mapped to their typical 

geographic scales.

Figure 5-1. Different Simulation Analysis Tools are Appropriate to Different Geographic 

Scopes

Source: FHWA Traffic Analysis Toolbox – Volume II, 2004.

In situations where a great amount of operational detail is required, but the analysis resources do not 

allow for full modeling or data collection on all the regional facilities impacted by the deployment, 

practitioners may want to consider the use of representative corridors for analysis. In this approach, 

one or more representative corridors are selected for detailed analysis, and the results are then 

extrapolated to other regional facilities. For example, in a well-known evaluation of the ramp metering

system in the Minneapolis/St. Paul metropolitan region, the benefits of metering were examined 
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regionwide; however, the analysis resources did not allow for detailed data collection and analysis on 

all individual corridors in the region. Instead, the analyst carefully selected four representative freeway 

corridors in the region: 1) a downtown corridor, 2) a radial corridor inside the beltway, 3) a radial 

corridor outside the beltway, and 4) a section of the beltway corridor. Figure 5-2 shows the selected 

representative corridors in relation to the overall regional transportation system in the Twin Cities.

Figure 5-2. Use of Representative Corridors in an Analysis

Source: Minnesota DOT Twin Cities Ramp Meter Evaluation, Final Report, 2001.

Detailed data collection and analysis were performed on the four representative corridors, and then 

the analysis results, in terms of percentage changes in key MOEs, were extrapolated to all other 

corridors in the region based on the type of the corridor being analyzed. Performing the analysis in 

this way allowed for the required detailed analysis of specific benefits, but also allowed for the regional 

compilation of total benefits within the resource requirements of the study.

Types of Strategies

The type of strategy, or combinations of strategies, to be analyzed and compared within the B/C 

analysis is a key factor in determining the analysis approach. At the most basic level, the analyst 

should first determine if the analysis would need to:
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• Only evaluate and prioritize TSM&O strategies compared with each other;

• Compare TSM&O strategies alongside alternative scenarios with more traditional capacity 

enhancements; or

• Evaluate various alternative scenarios containing combinations of TSM&O and more 

traditional capacity enhancements.

A list of the strategies (TSM&O and traditional capacity projects) to be analyzed should be developed 

prior to the identification of the analysis approach. Many practitioners also find it useful to graphically 

map the strategies to be analyzed to individual analysis scenarios that will be analyzed. Figure 5-3 

shows a sample mapping of strategies and scenarios that shows the breakout of strategies to be 

performed, as well as identifying those strategies that will combined and analyzed together. This 

display is useful in identifying analysis that needs to be conducted on more traditional capacity 

enhancements (shown at the “Alternative” level in Figure 5-3), as well as scenarios that include only 

TSM&O strategies and combinations of TSM&O and more traditional strategies (shown as the 

“TSM&O Options” in Figure 5-3). This preliminary identification of analysis scenarios also is useful in 

identifying the number of individual analyses that will need to be performed.

Figure 5-3. Mapping of Strategies to Analysis Scenarios

Source: FHWA IDAS Training Course Participant Workbook, 2010.

This list of alternative strategies/scenarios should then be assessed to project the likely impacts of the 

strategies, including:

• What are the MOEs that may be affected?

• What are the facilities that will likely be impacted?

• Which modes will be impacted?
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• What are the likely traveler behavior changes (e.g., route change, mode shift, temporal shift, 

etc.)?

• What are the time periods that likely will be impacted (i.e., are the strategies intended to be 

operated all day or only during peak periods or special conditions)?

• Will the timing of the strategy’s implementation need to be considered (i.e., are all alternative 

strategies projected to be deployed on a similar time horizon or would the deployment of 

certain alternatives be delayed until future years [See discussions later in this Chapter on the 

Impacts of Selecting Different Time Horizons and the Impact of Time on Monetary Value]).

Answers to these questions will allow the analyst to begin formulating the analysis approach, including 

selecting an appropriate analysis tool(s), as well as identifying preliminary input data needs. The 

assessment of strategies to be analyzed should be weighed against the other factors presented in this 

section to formulate the final analysis approach.

Need for/Purpose of Study

As discussed in Chapter 2, B/C analysis can play a role across a wide range of transportation 

planning processes and phases. The need for the analysis (or the planning objective that the analysis 

is intended to fulfill) is a key factor to consider in developing the analysis approach, and often helps to 

determine:

• The required level of confidence in the accuracy of the results;

• The number of alternatives to be analyzed; and

• The appropriate level of resources needed to conduct the analysis.

In early planning phases, for example the preliminary screening of alternatives, there is often a need 

to quickly conduct high-level, order-of-magnitude analysis of a large number of alternative scenarios. 

This analysis would likely require an approach that could be quickly implemented and repeated for a 

number of scenarios; however, the level of detail in the analysis may not be as critical. Alternatively, at 

the other end of the planning spectrum during the final alternative prioritization and design process, 

there may be the need to conduct very detailed analysis of a small number of scenarios. This analysis 

may require a substantially different analysis approach than used for preliminary screening needs. 

Figure 5-4 provides a comparison of different analysis tools mapped to different phases of the 

planning process, as well as the level of detail provided by the different analysis tool types, developed 

for an FHWA workshop series on Applying Analysis Tools in Planning for Operations. The figure 

shows how the proposed analysis method and/or tool may be influenced by the need for detail related 

to different phases of the planning process.
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Figure 5-4. Comparison of Analysis Methods with Various Stages of the Planning Process 

and Need for Analysis Sensitivity

Source: FHWA Workshop Series – Applying Analysis Tools in Planning for Operations Participant 

Workbook, 2011.

Desired MOEs

Different TSM&O strategies often may have impacts on different MOEs. Likewise, different regions 

may place a higher priority on particular MOEs. For example, a region that is in noncompliance for a 

particular emissions category may have requirements that an assessment of that measure be 

included in all regional analysis. Therefore, the analyst should carefully consider the MOEs to be 

used in the analysis when setting up the approach and making decisions on applicable analysis tools.

Figure 5-5 presents an overview of TSM&O strategies and their primary and secondary impacts on 

various MOEs.

In addition, Figure 4-7, presented earlier, shows the capabilities of a wide range of analysis tools in 

examining particular performance measures. In planning for the analysis, practitioners should:

• Carefully identify the MOEs likely to be impacted by the strategies and projects they want to 

evaluate;

• Prioritize the MOEs to the needs of the analysis and the individual needs of the agency; and

• Select a tool or analysis method that best supports the analysis of the prioritized MOEs.
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Figure 5-5. TSM&O Strategies Mapped to Likely Impacts on MOEs

TSM&O Strategy

Mobility (Travel Time Savings)Reliability (Total Delay)Safety (Number and Severity of Crashes)EmissionsEnergy (Fuel Use)Vehicle Operating Cost SavingsAgency Efficiency

Arterial Corridor Signal Coordination

Preset timing

Traffic actuated timing

Centrally-controlled timing

Arterial transit vehicle signal priority

     

Freeway Management Systems

Ramp metering

Preset timing

Traffic actuated timing

Centrally-controlled timing

     

Advanced Public Transportation Systems

Fixed-route systems

Transit AVL

Transit automated scheduling

Paratransit systems

Transit AVL

Transit automated scheduling

      

Incident Management Systems

Freeway/arterial service patrols

Incident detection and verification

Incident response management

     

Pretrip Multimodal Traveler Information Systems

Web-based 511 Traveler Information Systems

Phone-based 511 Traveler Information Systems

Kiosk-based Traveler Information Systems

    

En-Route Multimodal Traveler Information Systems

In-vehicle 511 Traveler Information Systems

(PDA/Web based or Telephone based)

HAR

DMS

Transit Station Traveler Information Systems

    

Commercial Vehicle Operations

Roadside Electronic Credential and Safety Screening      

Traffic and Demand Management

Congestion Pricing

HOT Lanes

    

Speed Harmonization     

Work Zone Management       

 – Primary MOEs associated with the strategy.

 – Secondary MOEs associated with the strategy.
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Required Level of Confidence in Results

The needs of different analyses often require different standards in terms of the level of confidence 

that is needed in the analysis outputs and results. Preliminary screening of projects may only require 

that order-of-magnitude results be generated to help categorize long lists of projects into high-, 

medium-, and low-priority levels. At the other end of the spectrum, very detailed and accurate results 

may be needed for finalizing design decisions or establishing strategy operating parameters. It is 

critical, therefore, that the level of confidence in the results required by the study needs be carefully 

considered in planning the analysis, and in selecting the appropriate analysis method/tool.

The required level of confidence in the results of the analysis often has a direct influence on the 

analysis resources that will be required. Typically, analysis requiring highly detailed and accurate 

results will demand the application of more robust analysis methods and tools that may necessitate a 

greater application of resources, including analysis budget, schedule, computing resources, and staff 

expertise. The tradeoffs between the required level of confidence and the level of resources need to 

be carefully weighed in order to avoid selecting an inappropriate analysis method. Inappropriate

analysis methods can include both:

• “Underpowered” tools or methods that are not sufficiently robust to produce results with the 

desired level of confidence; and

• “Overpowered” tools or methods are overly detailed for the analysis need and may end up 

requiring more analysis resources than are available for the study.

Either of these above situations has the potential to negatively impact the integrity of the analysis and 

may result in the analysis being scrapped and started over using a more appropriately scaled tool or 

method, often at great cost.

While the level of confidence in the results is largely a product of the analysis tool or method selected, 

it is also related to the amount of effort put into customizing, calibrating, and validating the various 

tools. For very simple analysis requiring order-of-magnitude detail, practitioners may choose to simply 

apply parameters, impact measures, and benefit values based on national averages. As the need for 

greater confidence in the level of accuracy increases, practitioners will want to place greater effort on 

identifying and applying customized analysis parameters within the selected method/tool that more 

closely match the local experience and traffic conditions.

The level of confidence required for any individual analysis is determined individually for each analysis 

based on a number of factors, including:

1. The role fulfilled by the analysis in the planning process;

2. The intended audience for the analysis results;

3. The number of alternatives to be evaluated; 

4. The availability and accuracy of underlying data and models; and

5. Subsequent analysis to be performed based on the analysis results.

Among these factors, the role that the analysis fulfills in the planning process often has the most 

substantial influence on the level of confidence and detail required. Figure 5-4, shown earlier in this 

section, showed that activities occurring earlier in the planning process, such as preliminary screening 

of alternatives, often require a lower level of detailed results, and confidence in the results, while 

activities occurring during later stages of the planning process, such as final prioritization of 
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alternatives or design work, may require highly detailed results that also require a high degree of 

confidence in the accuracy of the results. As shown in Figure 5-4, the need for detail and accuracy 

often drives decisions regarding the appropriateness of different tools and methods.

The level of confidence required in the analysis results is often the most substantial factor determining 

the analysis approach and the level of analysis resources that will be required to meet these needs. 

The following section provides additional discussion on the need to balance the analysis needs with 

the available resources.

Available Analysis Resources

Ideally, every B/C analysis would be conducted with the highest level of detail and rigor to provide:

• Comprehensive analysis of all the MOEs possibly impacted by the selected strategy;

• Analysis on a wide geographic scale to ensure the full capture of all regional benefits; and

• The highest level of confidence and detail of the analysis approach.

The reality of constrained budgets available for analysis, however, means that practitioners must often 

weigh the actual needs of the analysis against the available resources to balance the needs against 

the ability to support those needs.

Different analysis methods and tools often require substantially different levels of resources in order to 

successfully complete the analysis. The resources required for an analysis may include:

1. Budget resources;

2. Schedule;

3. Staff knowledge and expertise;

4. Computing resources;

5. Knowledge and data on local traffic conditions and operations;

6. Data availability and collection capabilities; and

7. Time and costs of utilizing supporting tools, models and analysis.

Some simple sketch-planning tools are designed to be applied at a low cost on a quick-timeframe 

basis. Utilizing the basic default parameters available in many of these tools, along with readily 

available data, a novice analyst could produce a basic B/C analysis in an afternoon. On the other 

hand, many analyses involving highly robust multiresolution/multiscenario methods may require six 

months to a year to complete at a cost of hundreds of thousands of dollars. Table 5-2 presents an 

overview of the general categories of analysis methods and tools compared with their approximate 

relative costs. Dollar budget costs presented in the table are approximate and represent the staff time 

required to complete an analysis (or the equivalent cost of a consultant).
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Table 5-2. Analysis Tools and Methods Mapped to their General Resource Requirementsa

Method/Tool Resources Required

Sketch planning Budget – Low ($1K to $25K)

Schedule – 1 week to 8 weeks

Staff Expertise – Medium

Data Availability – Low

Post-processing methods Budget – Medium/High ($5,000 to $50,000)

Schedule – 2 months to 1-year

Staff Expertise – Medium/High

Data Availability – Medium

Multiresolution/multiscenario Budget – High ($50,000 to $1.5 million)

Schedule – 3-months to 1.5-years

Staff Expertise – High

Data Availability – High

a Estimates are provided for a “typical” analysis. Actual time and budget resources would be dependent on 

the number of alternatives, geographic scope, and effort required to compile the appropriate input data.

Available analysis resources should be identified early in the analysis planning process, along with the 

other factors discussed in this section. The analysts and their managers should then carefully weigh 

the needs of the analysis with the projected costs. The estimate of resources should also consider the 

contingency costs of conducting any additional analysis, or evaluating new alternatives that may arise 

during the project prioritization process. If, during this assessment, the project needs and the 

available resources are found to not be aligned, then the analysts and their managers need to seek a 

balanced resolution, but either cutting scope and/or requirements from the analysis (e.g., lessening 

the number of alternatives to be analyzed or reducing the complexity of the analysis); or by increasing 

the available resources to meet the needs of the analysis. Failure to do so will result in either a cost 

overrun, a poorly conducted analysis that does not meet its original intent, or both.

The TOPS-BC application, developed in parallel with this Desk Reference, provides a decisionsupport tool to aid practitioners in weighing the needs of the analysis with the resources that may be 

required. Additionally, the FHWA initiative on the Traffic Analysis Toolbox also provides guidance on 

the level of resources needed to perform different types of analysis (using different types of analysis 

tools).21

Other Factors

The factors presented above often comprise the most critical determinants in identifying and planning 

a successful analysis approach. As discussed, there is often the need to carefully balance the 

tradeoffs between the various factors (e.g., more robust analysis often requires additional resources to 

complete) in the final selection of an appropriate analysis approach.

In addition to these factors, several additional aspects of the analysis should be considered during the 

early planning and scoping effort in order to effectively establish a suitable analysis approach. These 

additional factors include:

 21 FHWA Traffic Analysis Toolbox – Volume II, 2004.
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• Established B/C analysis procedures and framework – Many regions and agencies have 

long used B/C analysis for assessing and prioritizing various transportation projects. This 

long-term use has often resulted in procedures, benefit valuations, and analysis frameworks 

being established. These established guidelines should be followed to the degree possible in 

order to remain consistent with the local planning process. If the established procedure has 

primarily been used to conduct B/C analysis of more traditional capacity-enhancing projects, 

some modification of the framework may be necessary to make it appropriately sensitive to 

the needs of MOEs used for analysis of TSM&O strategies. See Chapter 4 for an expanded 

discussion of these issues.

• Available data to support the analysis – In regions with rich sources of available data, such 

as a region equipped with a robust and reliable archived data system, some more advanced 

analysis methods may be pursued at substantially less cost than could be considered in other 

more “data poor” regions. The readily available data in the robust data region could be 

tapped to support more advanced analysis methods and tools than in the data poor region, 

where substantial resources may need to be applied to manual data collection efforts in order 

to establish the baseline dataset necessary to support the analysis effort, or calibrate and 

validate the needed analysis tools.

• Available tools to support the analysis – Many analysis tools commonly used to provide 

input data for B/C analyses were originally developed for other purposes. For example, if a 

detailed simulation model has been previously developed, calibrated, and validated for a 

regional corridor, a more advanced and detailed analysis method may be possible at a 

substantially lower cost than would be possible in a corridor, where an existing model did not 

exist. All existing and underdevelopment tools that may be available to support an analysis 

should be inventoried during the analysis planning phase, along with the data outputs 

available and the level of effort required to conduct analysis using the tool, and weighed along 

with the other critical factors in identifying the optimal analysis approach.

Determining the Appropriate Timeframe to Use

As opposed to many standard transportation analyses that examine the system performance related 

to a particular improvement alternative for a specific analysis year (e.g., current year 2011 baseline 

analysis, or future year 2030 analysis), many B/C analysis attempt to capture the comprehensive 

benefits that flow as a result of an improvement over the life cycle of the deployment or other another 

selected time stream (e.g., 10-, 20-, or 30-year time horizons). The purpose of this approach is to 

spread out both the benefits and costs over an appropriate timeframe to allow for a meaningful 

analysis. For example, a practitioner would not want to compare the total costs for a strategy with a 

single-year estimate of benefits.

Therefore, the selection of an appropriate timeframe or time horizon to use in the analysis can take on 

more importance than in other transportation analyses. The implications of selecting and using 

different timeframes for analysis are discussed in the following sections.

Impacts of Selecting Different Time Horizons

The practice of capturing benefits and costs over a defined time horizon in transportation-related 

analyses is common and is largely tied to the analysis of large-scale infrastructure projects, where a 

general useful life of the project is well known. For example, the costs (capital implementation costs 

plus continuing operations and maintenance costs) of a new bridge or roadway could historically be 
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relatively well estimated over the expected life (e.g., 30 years) of the infrastructure or pavement. The 

benefits often were relatively static over the analysis timeline as well, providing for an easy calculation 

and comparison of benefits and costs over the selected timeframe.

Applying this same traditional timeframe approach to TSM&O strategies can add complexity to the 

analysis due to the following factors:

• Benefits of TSM&O strategies can be extremely variable – For example, a TSM&O 

weather management system would be expected to have varying benefits year after year,

depending on the number of inclement weather days. This differs from more traditional 

capacity improvements that would be expected to have nearly the identical benefits year after

year.

• TSM&O strategies often have shorter useful life of equipment and require more 

frequent replacement cycles – Many major infrastructure projects, like adding a lane to a 

regional freeway facility, have a relatively long and predictable useful life (e.g., 20 to more 

than 30 years) before requiring complete replacement. Many TSM&O strategies, on the other 

hand, are technology based and some key components may need replacement on much 

faster cycles (e.g., every 2 to 5 years). As a result, it is more difficult to place an overall 

“useful life” on TSM&O strategies since they have equipment with much shorter useful lives, 

yet are often also assumed to be operated in perpetuity once they are deployed.

• Costs of technology are changing over time – Many TSM&O strategies rely heavily on 

emerging technology; and as the economies of scale of new strategies expand, the costs 

often decrease. Therefore, evaluating a pilot project based on the initial development and 

implementation costs may not be accurate, as the costs could be expected to decline 

significantly in future years as a result of technology improvements.

These factors, combined with the effects of inflation and the time value of money, which impact all B/C 

analyses performed over a time horizon (discussed in a subsequent section), make selecting a time 

horizon for the analysis a critical effort that potentially impacts the results.

Many agencies maintain their own procedures for conducting B/C analysis that specify the length of 

the analysis period that should be applied. This standard should be applied whenever consistency 

with the established B/C framework is required. Likewise, if a B/C analysis is required to fulfill part of a 

funding request, the funding mechanism may specify a specific time horizon be used. For example, 

the recent U.S. DOT TIGER Grant application process required that a 20-year time horizon be used in 

the B/C analysis. The analyst should research any existing agency practices and/or the specifications 

of any funding application prior to initiating the analysis to identify any requirements for a specific time 

horizon.

If no predetermined time horizon exists, the analyst may choose between several options for the time 

horizon for the analysis. If a stream of cost analysis is desired, the time horizon generally is 

scheduled to start with the first project expenditures, and extends through the useful life of the project 

or its most long-lived alternative, or some future time at which meaningful estimates of effects are no 

longer possible.22

If the time horizon selected is shorter than the expected life of one of the analyzed projects, and/or a 

project will need to be redeployed within the time horizon, the analysis may need to consider the 

salvage value or residual value of the equipment or deployment that remains at the end of the time 

 22 http://bca.transportationeconomics.org/setup/time-period-of-analysis.
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horizon. For example, if a 20-year time horizon is selected for a light-rail project, it is likely that the rail 

vehicles and rail infrastructure will still have substantial remaining useful life at the end of the analysis 

period. In this situation, the residual value of this remaining life would need to be determined and 

added back into the analysis. Residual or salvage values are often used less in analysis of TSM&O 

strategies as the faster equipment replacement cycles lessen the salvage value for many of 

technologies and equipment.

An alternative option to using an analysis with a set time horizon is to utilize a single estimate of 

average annual benefits and costs. This measure is most typically used in analysis that seeks to 

compare and prioritize various TSM&O strategies with each other. TSM&O strategies are often well 

suited for analysis using average annual benefits and costs since they have relatively short life cycles,

and are anticipated to be repeatedly redeployed in future years. Use of average annual benefits is 

also useful if only a single-year snapshot view of performance is available for the analysis (e.g., only a 

single forecast year of data is available for analysis).

Many current B/C analysis tools use average annual benefits and costs in their analyses, including the 

IDAS tool and the TOPS-BC tool that supports this Desk Reference.

23 Average annual benefits are 

generally estimated based on a single forecast year, and are anticipated to be identical for all years. 

Average annual costs are estimated based on the capital cost of a piece of equipment divided by the 

anticipated useful life of the equipment. To this capital cost, an estimate of annual O&M cost is added 

to estimate the total average annual costs. For example, if a camera to be used in traffic surveillance 

strategy has:

• A total capital cost of $10,000;

• An anticipated useful life of 10 years; and

• Annual O&M costs of $2,000 per year, the average annual costs would be:

Average Annual Cost = ($10,000 capital/10 years) + $2,000 O&M = $3,000 per year

Impacts of Time on Monetary Value (Inflation and Discount Rate)

Two competing influences on future dollar values need to be considered in B/C analysis: inflation and 

discounting.

1. Inflation is the increase in prices for goods and services over time. It implies a loss in the 

value of money over time, as it erodes the purchasing power of a currency. Benefit/cost 

analysis for public-sector projects generally controls for inflation, using estimates of future 

costs and benefits that are expressed in terms of today’s (or some base year’s) prices.

These are referred to as “constant” or “real” dollars. Consistent with this approach, the 

discount rate used in benefit/cost analysis represents the time value of money after 

adjustment for inflation.24

2. The Discount Rate is the recognition that a dollar today is worth more than a dollar five years 

from now, even if there is no inflation because today’s dollar can be used productively in the 

ensuing five years, yielding a value greater than the initial dollar. Future benefits and costs 

are discounted to reflect this fact.

 23 TOPS-BC is capable of estimating average annual benefits and costs, or projecting the benefits and costs to a 

stream of costs that may be used with a time horizon selected by the user. 24 http://bca.transportationeconomics.org/calculation-issues/inflation.
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Benefit/cost analyses typically ignore inflation because the prediction of future prices introduces 

unnecessary uncertainty into the analysis. Therefore, discount rates are typically based on interest 

rates for borrowing with the inflation component removed, yielding the “real” interest rate. This rate is 

typically calculated by subtracting the rate of inflation (consumer price index) from the interest rate of 

an investment, such as a 10-year U.S. Treasury bill. For example, if the interest on a 10-Year 

Treasury bill is 5.5 percent and the inflation rate is 3 percent, then the discount rate would be 

2.5 percent.25

The discount rate for most projects in based on guidance provided by the U.S. Office of Management 

and Budget (OMB). The rate to be applied is related to the type of the project and the expected 

benefits and costs. If the project is anticipated to have benefits to the general public (societal benefits 

such as travel time savings or crash reductions), the OMB currently suggests a discount rate of 

7 percent, which represents the real discount rate on private investment. If, however, the analysis 

includes benefits and costs exclusively related to the public agency, for example, an analysis of an 

investment that would bring about a cost savings to the agency, the OMB suggests using the real 

discount rate for public-sector investments, which is often lower due to the lower risk associated with 

government borrowing. The OMB publishes “real” interest rates on its web site

http://www.whitehouse.gov/omb/circulars_a094_a94_appx-c/. Table 5-3 presents the real rates 

published on the site in July 2011. Generally, if there is a mix of societal and agency benefits within 

the same analysis, only the private-sector investment discount rate (7 percent) is used.

Table 5-3. Real Discount Rates 2011 (Public Investment)

(in Percentage)

Time Period 3-Year 5-Year 7-Year 10-Year 20-Year 30-Year

Discount Rate (%) 0.0 0.4 0.8 1.3 2.1 2.3

Source: Whitehouse Office of Management and Budget, 2011.

Real discount rates, as shown in Table 5-3, represent rates in which inflation has been removed. 

These real rates are to be used in B/C analysis for discounting real (constant-dollar) flows.

Available Resources for Identifying the Benefits

of Operations Strategies

Often, one of the most challenging aspects of conducting B/C analysis of TSM&O strategies is 

identifying the likely impact or benefit of the strategy. This is particularly true for emerging 

technologies or strategies, where few real-world deployments exist to provide empirical evidence on 

the likely impacts to various MOEs. Several options for researching the likely benefit levels that may 

be used in an analysis are discussed in this section.

The TOPS-BC application that supports this Desk Reference maintains a look-up database of likely 

impacts of various TSM&O strategies related to several MOEs. Figure 5-6 shows a view of the 

navigation screen for this capability in TOPS-BC. To research likely impact levels of TSM&O 

strategies, the user would select a “Strategy” and “Impact Category” from the pull-down menus. 

Based on this input, TOPS-BC would display a “Typical Range of Expected Impacts,” assuming there 

is sufficient data to formulate a range for the selected strategy and impact category.

 25 http://bca.transportationeconomics.org/calculation-issues/discounting.
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Figure 5-6. Sample View of TOPS-BC TSM&O Impact Look-up Function

Source: FHWA TOPS-BC model.

The range of expected impacts is based on a compilation of evaluations and studies that have 

gathered empirical evidence on the impact of these strategies in national and international locations. 

This range also often considers the predicted impacts used in various traffic and B/C analysis tools as 

the default impacts used in the calculation of benefits for the strategy, when available. The user may 

view these individual studies and model data on subsequent worksheets in the TOPS-BC application. 

Descriptions of the observed impacts are brief, but links are provided to the source documentation so 

the user may research further any interesting information.

Another valuable resource for researching the potential benefits of TSM&O strategies is the ITS 

Benefit Database maintained by U.S. DOT Research and Innovative Technology Administration’s 

(RITA) ITS Joint Program Office at http://www.benefitcost.its.dot.gov/its/benecost.nsf/BenefitsHome. 

The database allows users to research benefits information by strategy area, provides a rich resource 

for identifying benefits, and maintains a robust library of studies documenting the benefits of many 

TSM&O strategies. Figure 5-7 presents a view of the navigation page for the database site.

FHWA Tool for Operations Benefit/Cost (TOPS-BC)

Investigate the Range of Impact Values Associated with Various TSM&O Strategies

Navigation Screen 

TSMO Strategy Selected:

Impact Category:

Typical Range of Expected Impacts

Expect 5% to 30% capacity reduction on ramps 

and 7% to 20% capacity increase on freeway. 

Expected increases in volume between 5% and 

30%

Return to OPENING SCREEN

Instructions: Please select the "TSM&O Strategy" and 

the "Impact Category" you are interested from the 

pull down list below. If sufficient data is available, a 

range of expected impacts will be shown below. Click 

on the hyperlink to be taken to a table displaying the 

datapoints comprising the range. 

Noted Impacts

Freeway Management System : Ramp Metering : Traffic Actuated

Throughput
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Figure 5-7. Sample View of U.S. DOT ITS Benefits Database Web Site

Configuring B/C Analysis to Local Conditions

As discussed in Chapter 3, many existing B/C tools and analysis frameworks maintain default 

parameters representing the anticipated impact to various MOEs. These are often based on national 

averages of observed benefits gathered from empirical studies in the case of widely available tools. 

For analysis frameworks that were developed for a specific region, these parameters may be more 

individually customized for that specific region. For simple analysis, for example, a quick, first-cut 

screening of alternatives, analysts may choose to use one of these widely available tools or perhaps a 

framework that was developed for another agency or region, and apply the method locally with little or 

no changes to these default settings. As the level of required confidence in the analysis results 

increases, however, users should increasingly place greater scrutiny on the default parameters, and 

modify these inputs accordingly to better configure the analysis to local conditions.

Ideally, analysts will be able to identify and obtain data specific to the local region that may be 

applicable in supplementing or replacing the default analysis parameters based on national averages. 

This data may be obtained from previously conducted evaluations that were conducted in the region, 

review and analysis of archived data, or other local sources. For example, in the Cincinnati analysis 

case study presented in Chapter 2, the OKI region analysts started the analysis with a thorough 
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review of the analysis defaults. The defaults were scrutinized to assess if they were appropriate to the 

way the region operated its TSM&O strategies, and to identify any local sources of information that 

would be more appropriate. The analysts were able to identify a locally conducted evaluation of the 

reduction in incident duration caused by their FSPs, which they were able to use to replace the default 

parameter in the analysis. Likewise, some local survey results were used to refine the default amount 

of time saved by travelers as a result of DMS-provided information. These modifications substantially 

improved the overall analysis; in that, the parameters were better configured to local conditions. This 

configuration was performed for the local system costs and benefit valuation figures as well.

Where data is not available specifically representing conditions in the local area, the reference 

resources discussed in Chapter 5 may be used to assess the appropriateness of the default 

parameters and make adjustments to better configure the results to the local conditions. For example, 

an analyst working for a mid-sized region located in Midwest may want to review the individual studies 

highlighted in the TOPS-BC and the ITS JPO ITS Benefit Database to find individual studies 

conducted in similarly sized and located regions. If the results from these individual studies differ 

substantially from the national average default parameters, they may want to modify the parameters to 

be more representative of conditions in their region.

Finally, for more detailed multiscenario analysis, where the analysts may be attempting to individually 

model different scenarios representing various nonrecurring conditions, a preanalysis of existing 

conditions and the frequency of various nonrecurring factors may be warranted to develop meaningful 

analysis scenarios representing likely conditions. Robust data from archived data systems is often the 

best source for conducting this analysis for scenario development and identification. Table 5-4 

presents an output of an analysis performed for the ICM Pioneer Site study conducted in Dallas. The 

analysis shows the frequency of occurrence of several factors related to congestion levels, and was 

used to identify likely analysis scenarios that most accurately represented the nonrecurring traffic 

conditions on the U.S. 75 corridor. By basing the test scenarios on the actual observed distribution of 

conditions, the analysis was better configured to local conditions.
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Table 5-4. Distribution of Operating Conditions in U.S. 75 Dallas

Demand Incident

Inclement

Weather

Number

of Hours Percent

Med No No 247 33.9%

Low No No 136 18.7%

High No No 134 18.4%

Med Minor No 79 10.8%

High Minor No 55 7.5%

Low Minor No 55 7.5%

Low No Yes 9 1.2%

Med No Yes 5 0.7%

Med Major No 4 0.5%

Low Major No 2 0.3%

Low Minor Yes 2 0.3%

High Major No 1 0.1%

Med Minor Yes 0 0.0%

High No Yes 0 0.0%

High Minor Yes 0 0.0%

High Major Yes 0 0.0%

Med Major Yes 0 0.0%

Low Major Yes 0 0.0%

Source: ICM System – Analysis, Modeling, and Simulation Guide, Draft Final Report, 2011.

Estimating the Impacts (User and Societal Benefits)

of the Operations Strategy

Estimating the benefits of various TSM&O strategies is tied to the type of project being evaluated. As 

detailed in Chapter 3, TSM&O strategies are often categorized into three groups:

1. Physical operations strategies – These strategies involve the deployment of physical 

infrastructure to the roadside or transit assets, and are intended to provide direct impact on 

transportation system performance through their operation. These strategies are often highly 

visible to the traveling public, and include a wide range of deployments, such as traffic signal 

coordination, freeway service patrols, 511 traveler information systems, HOT lanes, and 

DMS, among many others.

2. Supporting infrastructure – Supporting infrastructure includes those backbone capabilities 

that serve to support and enhance the functioning of the roadside Operations strategies. This 

backbone infrastructure includes items, such as traffic detection and surveillance, 

communications, and traffic management centers. Often, by themselves, these strategies 

have no direct intrinsic benefits. The benefits derived from these strategies are the improved 

performance of the roadside Operations strategies enabled by their deployment. For 
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example, a traffic surveillance camera by itself has little direct benefit. The benefit is derived 

from the manner in which the information is use (i.e., when the surveillance from the camera 

is used by operators to implement different signal timing patterns, or detect and respond to an 

incident faster). Therefore, the measure of benefit of these supporting infrastructure 

components is often measured by estimating the improved efficiency of the roadside 

components supported by the backbone infrastructure, rather than attempting to directly 

estimate the benefits of the supporting infrastructure itself.

3. Nonphysical strategies – These strategies represent items intended to improve the 

efficiency and effectiveness of the TSM&O systems; and includes strategies, such as 

improved interagency coordination, the development of advanced operational plans (e.g., 

evacuation plans), or system integration. While there certainly can be benefits of these 

activities and there are often costs, there typically is little or no new physical equipment or 

roadside components. Similar to the supporting deployments, the benefits derived from these 

strategies are typically estimated by assessing the improvement in effectiveness of the 

existing roadside components provided by the strategy or activity.

Methods for estimating the benefits of these types of strategies are discussed in the subsequent 

sections.

Estimating the Impacts of Physical Operations Strategies

Most of the physical operations strategies impact one or more of the common MOEs. Previously 

presented, Table 5-5 shows the primary and secondary MOEs impacted by the strategies. Primary 

measures are those most closely correlated to the strategy, while the secondary measures may be 

more trivially impacted by the strategy, or may only be impacted depending on how the particular 

strategy is implemented and operated.

The goal in evaluating these types of strategies is to isolate the incremental change in the MOEs 

related to the deployment. In a real-world B/C evaluation of an existing strategy, empirical data 

representing conditions without the strategy (before deployment) is compared to data representing 

conditions with the strategy (after deployment) to assess the incremental change.

In a forecasting study, the “with” and “without” conditions are estimated using models or other 

predictive approaches to assess a baseline (without strategy) and an alternative (with strategy) 

scenario. The difference between the scenarios’ performance represents the incremental change due 

to the implementation of the strategy. In using modeled data for TSM&O strategies, it is important that 

the period of analysis in the model closely approximates the temporal (i.e., time-of-day) operating 

parameters of the strategy. For example, in evaluating a HOT lane strategy that is only anticipated to 

be operational during peak periods, the model used should represent the peak period, as this is the 

only time that benefits would be generated by the strategy. Use of a daily model in this situation could 

result in the overstatement of benefits, as benefits would be estimated to be falsely accruing to the 

strategy during off-peak periods, where it would not be expected to be operational.

The real-world before and after data or the modeled baseline and alternative scenario data are often 

entered into a spreadsheet or entered into an existing B/C analysis tool to provide a framework for 

calculating and documenting the change in the various MOEs. If the data used in assessing the 

incremental change represents a daily or portion of a day analysis (e.g., three-hour peak-period 

analysis), the resulting incremental change in the MOE needs to be annualized by applying a factor 

representing how many of those periods would be expected in a given year. Similar to selecting the 

appropriate time period for the analysis, it is critical that the analyst selects an annualization factor that 
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accurately represents the operating parameters of the TSM&O strategy. For example, if the strategy 

were only operated on nonholiday weekdays, an annualization factor of 250 would be used to factor 

daily changes in MOEs to annual measures. For other strategies that are only operated during 

special events or other nonrecurring conditions, the annualization factor may be even less. Figure 5-8 

shows a sample calculation of the daily change in various MOEs and the application of an 

annualization factor (250) to estimate the annual incremental change.

Figure 5-8. Example Calculations of Incremental Change MOEs

Source: San Francisco Bay Area MTC 2035 Long-Range Transportation Plan Project Performance 

Analysis, 2011.

The example shown in Figure 5-8 represents a relatively simple analysis, where a single-year forecast 

(2035 in this example) is used to represent the likely change in MOEs for all years. This assumes that 

the impacts and benefits of the deployed strategies remain fairly constant throughout the time horizon 

of the analysis. These incremental changes in MOEs may be expected to vary significantly, however, 

if the baseline (without the strategy) traffic conditions are expected to vary in future years. Depending 

on the underlying traffic conditions and the strategy selected, the benefits of the strategy could be 

expected to increase in future years – providing greater benefits as congestion increases – or 

decrease in future years – if the strategies incremental benefits are overwhelmed by increasing 

congestion.

Depending on the level of detail required by the analysis and the extent in which future conditions are 

expected to vary during the timeframe of the analysis, it may be necessary to add an analysis of one 

or more additional forecast years, and then interpolate the incremental change in MOEs between the 

analysis years to more fully capture these changing benefits.

For many typical MOEs (e.g., person hours of travel), the modeling method used as the source of the 

data (e.g., travel demand model, HCM analysis or simulation models) will be able to directly export the 

metric. Other MOEs, such as the number of crashes or amount of pollutant emissions, may be able to 

be directly estimated within the model structure (depending on the robustness of the regional 

modeling structure); or if not, may need to be assessed in a post-processing step. For example, many 

of the existing B/C analysis tools maintain the ability to estimate crashes and emissions based on 

simple outputs from the regional travel demand model.

For some of the emerging MOEs, such as travel time reliability, it is often necessary to employ a 

separate analysis method or structure (e.g., multiresolution- or multiscenario-based methods) to 

accurately assess these benefits as these measures are typically not yet well integrated with many 

regional modeling frameworks. See Chapter 5 for an expanding discussion of the methods that may 

Benefit/Cost Baseline Alternative Difference

Annual 

Difference

Auto Vehicle Miles of Travel (VMT) 170,177,633 170,204,412 26,779 6,694,695

Truck VMT 18,128,820 18,136,463 7,643 1,910,870

Average Vehicle Occupancy 1.42

Person Hours of Travel (Auto) 5,923,262 5,922,600 (663) (165,631)

Vehicle Hours of Travel (Truck) 462,006 462,006 - -

Person Hours of In-vehicle Travel (Transit) 40,679,986 40,649,288 (30,698) (7,674,482)

Person Hours of Out-of-vehicle Travel (Transit) 33,925,318 33,935,621 10,303 2,575,640
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be used to meet the challenges of assessing nonrecurring conditions in the analysis of TSM&O 

strategies.

Once the desired MOEs have been estimated and assembled in the B/C analysis tool or customized 

framework, appropriate benefit valuations (discussed in a subsequent section) are applied on a per 

unit basis (e.g., per hour of travel time saved, per number of injury crashes reduce, per ton of 

emissions reduced) to estimate the monetized user or societal benefit generated by the incremental 

change in the MOE.

Estimating the Impacts of Supporting Infrastructure

Estimating the impacts of supporting infrastructure, such as traffic surveillance and detection systems, 

traffic management centers, or communications systems, is often not as straightforward as estimating 

the benefits related to a physical operations strategy. Many of these supporting infrastructure projects 

have no inherent benefits associated with them. The benefits of these strategies flow from their ability 

to improve the physical roadside operations strategies. For example, the deployment of a camera 

surveillance system by itself would not be anticipated to generate any incremental changes to the 

typical MOEs (e.g., travel time, safety, vehicle operating costs, etc.). The benefit of the camera 

surveillance would be the impact of the additional data on improving other physical operations 

strategies – the camera images could be used to enhance the incident detection and verification 

procedures of the incident management program, or used to monitor an arterial in order to select 

appropriate signal timing strategies. Thus, the benefit does not accrue simply from deploying the 

supporting infrastructure, but from the improvement in operations of the physical roadside strategies 

which it supports.

To estimate the impacts of supporting infrastructure, the analyst first needs to map the supporting 

infrastructure to the roadside physical strategies it supports. An assessment then needs to be made 

regarding the influence of the supporting infrastructure. This assessment should evaluate:

• The degree to which the roadside physical strategy is influenced or enabled by the supporting 

infrastructure;

• Which specific MOEs will be impacted by the improved operation of the roadside physical 

strategy;

• The expected amount of time that the supporting infrastructure will be used to enable the 

roadside strategy (e.g., on a day-to-day basis, or only during infrequent occasions); and

• The proportion of cost of the supporting infrastructure that should be reasonably allocated to 

operating the roadside component (for example, if a camera surveillance system is 

anticipated to equally support an incident management program and the corridor traffic signal 

coordination strategy, the cost of the cameras should be proportionately allocated to both 

roadside strategies).

Based on this assessment, the anticipated impact for the roadside physical strategy should be 

increased to a higher point in the expected range of impacts for those MOEs anticipated to be 

improved by the enhancement provided by the supporting infrastructure.

If the supporting infrastructure and the roadside physical operations strategy are deployed in parallel 

as part of an integrated system, the costs used in the B/C analysis should include the full cost of the 

physical operations strategy and the proportion of the supporting infrastructure that can be reasonably 

allocated to supporting the specific roadside component. If, however, the supporting infrastructure 
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serves to enhance an existing roadside strategy, the costs of that strategy should not be included in 

the analysis since they were paid for as part of another project. In these situations, only the cost of the 

supporting infrastructure that can be allocated to the enhancement of roadside component, and 

possibly any additional minor costs necessary to integrate the supporting infrastructure and the legacy 

roadside strategy should be considered.

Estimating the Impacts of Nonphysical Operations Strategies

Nonphysical strategies are intended to improve the efficiency and effectiveness of the deployed 

TSM&O systems, and involve their own challenges in assessment in a B/C analysis framework. The 

following represent some of the common nonphysical operations strategies, along with a description 

of an analysis approach.

1. System integration – Involves the integration of two or more systems to allow for the 

improved exchange of data between the systems and/or the coordinated operation of the 

systems. For example, a traffic signal coordination system could be integrated with a traffic 

incident management system to provide additional capacity on parallel diversion routes when 

incidents occur on a freeway facility. Similar to the analysis of supporting infrastructure 

deployments, analysts evaluating system integration strategies must carefully assess how 

and the degree which existing capabilities of the strategies to be integrated will be enhanced 

by the improved data availability or coordinated operations. The benefits would be estimated 

by modifying the expected impact on various MOEs to a higher level in the range of 

anticipated impacts to reflect this improvement.

If the system integration involves the linkage of two or more existing legacy systems, the 

baseline scenario becomes the analysis of the physical operations strategies operated at their 

previous, nonintegrated impact level, and the alternative scenario would be the operations 

strategies operated at their enhanced impact level. The incremental benefit would be 

represented by the difference between these two scenarios. It is important if existing 

deployments are included in the analysis that the analyst not include the benefits already 

accruing due to the legacy components, but only include the incremental benefits of the 

enhanced operations due to integration.

2. Interagency coordination – Involves the exchange of information and/or agreements,

allowing for the joint operation of various strategies across different agencies or jurisdictions. 

Similar to the approach for assessing system integration, the analyst and system operators 

will need to conduct an assessment of how and the degree to which the interagency 

coordination will improve existing or proposed physical operations strategies. An alternative 

analysis scenario should then be developed that increases the likely impacts on selected 

performance measures higher in the range of likely values. This alternative should then be 

run through the analysis process to estimate the change in MOEs, and should be compared 

with a baseline that represents the existing and proposed physical operations strategies 

operated at a lower level of impact (representing no interagency coordination).

3. Regional concepts of transportation operations – The development of regional concepts 

of transportation operations involves the coordination of the various stakeholders responsible 

for operating one or more components in order to develop sets of policies, procedures, and 

operating parameters that may be implemented according to specific identified conditions to 

improve the efficiency and effectiveness of Operational Strategies during those conditions. 

The development of a coordinated strategy for operating strategies during a regional 

evacuation would be an example of a regional concept of transportation operations that might 

be analyzed using benefit/cost analysis.
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In the case where regional concepts of transportation operations are developed to simply 

improve day-to-day operations, their analysis may be conducted similar to system integration 

or interagency coordination strategies by estimating the influence the regional concept of 

transportation operations may have on the performance of physical operations strategies, and 

then testing the incremental effect on MOEs as an alternative scenario.

Evaluating the impacts of regional concepts of transportation operations is often made more 

challenging, however, in that the plans and strategies may be targeted at unique, 

nonrecurring events (such as incident-related road closures, severe weather events, 

hazardous materials (hazmat) spills, special events, or evacuations). In many cases, these 

unique conditions, in which regional concepts of transportation operations are applied, may 

also require aspects of the system to be operated in dramatically different ways than normal 

day-to-day operations. For example, an evacuation regional concept of transportation 

operations may allow for reversing the flow on particular roadways. There are generally three 

major steps in conducting a B/C analysis of a regional concept of transportation operations 

plan that must be completed in addition to the normal steps required for typical B/C analysis. 

These steps include:

a. The regional concept of transportation operations plans need to be reviewed by the 

analysts and system operators to identify how and the degree to which the operation 

of the physical operations strategies will be modified by the application of the regional 

concept of transportation operations. This includes an assessment of which MOEs

may be influenced, and making adjustments to default impact parameters to reflect 

these changes.

b. The situations in which the regional concept of transportation operations will be 

applied needs to be evaluated by the analysts and system operators, and any 

underlying analysis models will need to be checked to confirm they are capable of 

analyzing any unique conditions. It is important to check that both the analysis of the 

baseline (without the regional concept of transportation operations application) and 

the alternative scenario (with the regional concept of transportation operations

application) are able to be modeled within the analysis framework. Modifications to 

the model may be necessary to allow for these nontypical conditions. For example, 

in the case of a regional concept of transportation operations targeted at improving 

operations for a special event (e.g., a large sporting event), a baseline travel demand 

or simulation model may need to be adjusted to reflect the added trips destined to the 

stadium location. Other regional concepts of transportation operations applications 

may require more substantial modifications to the underlying model. For example, a 

hurricane evacuation regional concept of transportation operations evaluation may 

require significant changes to the underlying model, including a complete 

modification of normal trip origin and destination patterns, modifications to the road 

network to reflect road closures or reverse flow, and modification of transit system 

operations.

c. Once the first two steps are complete, the analyst may run the models or the analysis 

techniques for both the baseline and the alternative scenarios to estimate the 

incremental change in MOEs. Since the conditions covered by many regional 

concepts of transportation operations are unique or nonrecurring events, the process 

of annualizing the benefits is made more complex. Data will need to be compiled and 

analyzed in order to estimate the frequency or likelihood of the conditions in which 

the regional concept of transportation operations is to be applied. For regional 

concepts of transportation operations designed around events or conditions that are 

anticipated to occur multiple times per year, for example sporting events at a 
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stadium, the incremental benefits identified for a single event are multiplied by the 

anticipated number of events or occurrences of those conditions during a year, or:

Annual Benefit = Incremental Benefit per Event * Number of Events per Year

d. For regional concepts of transportation operations designed around less frequent 

events or conditions, such as hurricane evacuations, the identified benefit per 

occurrence or event is multiplied with a factor representing the overall likelihood that 

an event will occur in any given year. For example, if a region undergoes a major 

hurricane evacuation once every 25 years on average. If the same region conducts 

an analysis of the regional concept of transportation operations developed for guiding 

hurricane evaluations and finds the benefits of implementing the plan are $3 million 

per event, the annual benefit would be calculated as follows:

Annual Benefit = Incremental Benefit per Event * Likelihood of Event in Any Given Year

Annual Benefit = $3M per Event * (1/25 or 0.04) = $120,000

e. In presenting and displaying the benefit results from evaluations of regional concepts 

of transportation operations, it is recommended that both the annual benefit and the 

benefit per event be used in order to avoid confusion among the reviewing audience.

Valuing the Benefits

The TOPS-BC application that supports this Desk Reference maintains a repository of many of the 

benefit valuations, along with identification of their source, that are typically applied in B/C analysis of 

TSM&O strategies. Figure 5-9 shows a sample of the benefit valuations maintained in the TOPS-BC

repository. It is recommended that these benefit valuations be closely reviewed by the analyst prior to 

application in a study to ensure compatibility with local conditions. Where locally preferred valuations 

are prescribed by regional B/C analysis guidelines, the local figures should be applied to ensure 

consistency with those local guidelines and better allow the comparison of B/C analysis results from 

TSM&O strategies with non-TSM&O strategies. If the practitioner is developing the B/C analysis to 

support an application for funding, however, the analyst should check any requirements or restrictions 

on using different valuations other than any specified in the funding application instructions. 
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Figure 5-9. Sample View of Benefit Valuations Maintained in the TOPS-BC Repository

Source: FHWA TOPS-BC, 2012.

Estimating the Life-Cycle Costs

Estimating the costs of TSM&O strategies is often complex. Compared to more traditional 

infrastructure improvements, TSM&O improvements typically incur a greater proportion of their costs 

as continuing O&M costs, as opposed to upfront capital costs. Much of the equipment associated with 

TSM&O strategies also typically has a much shorter anticipated useful life than many traditional 

improvements, and must be replaced as it reaches obsolescence. Further complicating the TSM&O 

cost estimation process is the fact that TSM&O deployment costs are greatly impacted by the degree 

in which equipment and resources are shared across different deployments and jurisdictions.

Despite these difficulties, it is critical that planners fully consider and account for all the costs of 

TSM&O strategies when evaluating and developing deployment and O&M plans. Failure to recognize 

and accurately forecast these costs may result in future funding or resource shortfalls, or worse, the 

inability to properly operate and maintain deployed TSM&O improvements. The cost estimation 

capability developed within the TOPS-BC support tool is intended to assist planners in estimating and 

predicting high-level cost and resource requirements of planned TSM&O strategies.

Benefit Valuations

Recurring Travel Time (per hour)

"On the Clock" Travel Time $ 28.00

Other Auto Travel Time $ 14.00

Truck Travel Time $ 28.00

Non-Recurring Travel Time (per hour)

"On the Clock" Travel Time $ 28.00

Other Auto Travel Time $ 14.00

Truck Travel Time $ 28.00

Crashes (per occurance)

Fatality $ 6,500,000

Injury $ 67,000

Property Damage Only (PDO) $ 2,300

Fuel Use

Per Gallon (Excluding Taxes) $ 3.67

Non-fuel Operating Costs (per VMT) 

Auto 0.25

Truck 0.37
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A good structure to follow for organizing these cost data includes:

• Capital costs – Include the upfront costs necessary to procure and install equipment related 

to the Operations strategy. These costs will be shown as a total (one-time) expenditure, and 

will include the capital equipment costs, as well as the soft costs required for design and 

installation of the equipment.

• O&M costs – Include those continuing costs necessary to operate and maintain the deployed 

strategy, including labor costs. While these costs do contain provisions for upkeep and 

replacement of minor components of the system, they do not contain provisions for wholesale 

replacement of the equipment when it reaches the end of its useful life. These O&M costs will 

be presented as annual estimates.

• Replacement costs – Include the periodic cost of replacing/redeploying system equipment 

as it becomes obsolete and reaches the end of its expected useful life in order to insure the 

continued operation of the strategy.

• Annualized costs – Represent the average annual expenditure that would be expected in 

order to deploy, operate, and maintain the Operations strategy; and replace (or redeploy) any 

equipment as it reaches the end of its useful life. Within this cost figure, the capital cost of the 

equipment will be amortized over the anticipated life of each individual piece of equipment. 

This annualized figure is added with the reoccurring annual O&M cost to produce the 

annualized cost figure. This figure is particularly useful in estimating the long-term budgetary 

impacts of TSM&O deployments.

The complexity of many Operations deployments warrants that these cost figures be further 

segmented to ensure their usefulness. Within each of the capital, O&M, and annualized cost 

estimates, costs should be further disaggregated to show the infrastructure and incremental costs. 

These are defined as follows:

• Infrastructure costs – Include the basic “backbone” infrastructure equipment (including 

labor) necessary to enable the system. For example, in order to deploy a camera (closedcircuit television (CCTV)) surveillance system, certain infrastructure equipment must first be 

deployed at the traffic management center to support the roadside ITS elements. This may 

include costs, such as computer hardware/software, video monitors, and the labor to operate 

the system. Once this equipment is in place, however, multiple roadside elements may be 

integrated and linked to this backbone infrastructure without experiencing significant 

incremental costs (i.e., the equipment does not need to be redeployed every time a new 

camera is added to the system). These infrastructure costs typically include equipment and 

resources installed at the traffic management center, but may include some shared roadside 

elements as well.

• Incremental Costs – Include the costs necessary to add one additional roadside element to 

the deployment. For example, the incremental costs for the camera surveillance example 

include the costs of purchasing and installing one additional camera. Other deployments may 

include incremental costs for multiple units. For instance, an emergency vehicle signal 

priority system would include incremental unit costs for each additional intersection and for 

each additional emergency vehicle that would be equipped as part of the deployment.

Structuring the cost data in this framework provides the ability to readily scale the cost estimates to the 

size of potential deployments. Figure 5-10 provides a sample view of the cost data organized 

according to the defined structure in the TOPS-BC application. Infrastructure costs would be incurred 
Chapter 5 Conducting B/C Analysis for Operations

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FHWA Operations Benefit/Cost Analysis Desk Reference| 98

for any new technology deployment. Incremental costs would be multiplied with the appropriate unit 

(e.g., number of intersections equipped, number of ramps equipped, number of variable message sign 

locations, etc.); and added to the infrastructure costs to determine the total estimated cost of the 

deployment. Presenting the costs in this scalable format provides the opportunity to easily estimate 

the costs of expanding or contracting the size of the deployment, and allows the cost data to be 

reutilized for evaluating other corridors.

Figure 5-10. Example View of Cost Data Organization for a Ramp Meter System

In the ramp meter example shown in Figure 5-10, the Annualized Costs for any individual piece of 

equipment equals the Capital Costs (which represents the total cost to deploy or redeploy that piece of 

equipment) and divided by the Useful Life (to amortize the cost of the equipment over the anticipated 

life), plus the annual cost of operating and maintaining the piece of equipment. This methodology 

FHWA Tool for Operations Benefit/Cost (TOPS-BC)

PURPOSE: Estimate Lifecycle Costs of TSM&O Strategies

WORK AREA 1 - ESTIMATE AVERAGE ANNUAL COST

Strategy: Ramp Metering -- Centrally Controlled 

Equipment Useful Life

Capital / 

Replacement 

Costs (Total)

O&M Costs 

(Annual)

Annualized 

Costs

Basic Infrastructure Equipment

TMC Hardware for Freeway Control 5 $ 22,500 $ 2,000 $ 6,500

TMC Software/Integration 5 $ 200,000 $ - $ 40,000

Labor $ - $ 250,000 $ 250,000

TOTAL Infrastructure Cost $ 222,500 $ 252,000 $ 296,500

Incremental Deployment Equipment

(Per Ramp Location)

Ramp Meter (Signal, Controller) 25 $ 88,000 $ 2,000 $ 5,520

Loop Detectors (2) 25 $ 11,000 $ 500 $ 940

Communication Line 25 $ 750 $ 250 $ 280

TOTAL Incremental Cost $ 99,750 $ 2,750 $ 6,740

INPUT Enter Number of Infrastructure Deployments 1 $ 296,500

INPUT Enter Number of Incremental Deployments (# of Ramps) 3 $ 20,220

INPUT Enter Year of Deployment 2011

Average Annual Cost $ 316,720
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assumes that the piece of equipment is redeployed at the end of its useful life. For example, the 

Annualized Costs for the equipment called “TMC Hardware for Freeway Control” is calculated as:

Annualized Costs = (Capital Costs/Useful Life) + Annual O&M Costs

or

Annualized Costs = ($22,500/5 years) + $2,000 = $6,500

Some equipment does not have upfront Capital Costs, but only has recurring annual O&M Costs, as 

illustrated by the “Labor” costs in the Figure 5-10 example. In these situations, the annualized cost is 

simply the annual O&M Costs.

In the TOPS-BC application, users are able to enter the quantity of Infrastructure and Incremental 

equipment units they want to deploy, as shown in Figure 5-10; and the tool will calculate the total cost 

of the selected deployments based on these entries.26 Average Annual Costs for the ramp metering 

example would be calculated as:

Average Annual Costs = (# of Infrastructure Deployments * Annualized Costs of Infrastructure 

Deployment) + (# of Incremental Deployments * Annualized Costs of Incremental Deployment)

or

Average Annual Costs = (1 * $296,500) + (10 * $5,990) = $356,400

Outputs from this process include the following:

• An Average Annual Cost – A single expected cost compiling upfront capital, ongoing O&M,

and future equipment replacement costs in a single figure.

• A Projected Stream of Costs – An output showing year-by-year expected expenditures over 

the next 30-year timeframe. This stream of outputs will also have the capability to escalate 

future expenditures based on an inflation rate selected by the user. This output can be used 

to calculate Net Present Value (NPV) over any time horizon chosen by the user, if they chose 

to use NPV instead of average annual costs in their analysis.

Figure 5-11 shows a sample view of the Projected Stream of Costs generated for the ramp meter 

strategy illustrated in the earlier Figure 5-10. In this view, the Capital Costs are not amortized over the 

life of equipment, but appear in the year they are incurred. Upfront Capital Costs appear in the first 

year of deployment (as indicated by the user), and Replacement Costs are incurred in future years, as 

equipment needs to be replaced. In the case of the ramp metering example, the full Capital Cost of 

deployment is incurred in year 2010, but only a portion of the Capital Cost are incurred again in year 

2015, since only a portion of the equipment involved in the deployment has reached the end of its 

useful life by this date. Space is provided to view costs up to a 50-year time horizon. The user may 

calculate the NPV of the costs by selecting a time horizon (in number of years) and an appropriate 

discount rate. Defaults are provided, but the user may override those defaults by entering their own 

values.

 26 For most TSM&O strategies, the number of “Infrastructure” deployments will be one, as only one deployment of 

this equipment is necessary to support multiple deployments of the “Incremental” units. However, the user has 

the opportunity to deploy more than one “Infrastructure” unit if their planned deployment is configured in a 

nontypical manner (e.g., managed from multiple Traffic Management Centers).
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Figure 5-11. TOPS-BC Projected Stream of Costs Output

Displaying and Communicating Results

Although the primary output of a benefit/cost analysis is a relatively simple ratio (B/C ratio) and 

estimate of Net Benefits, there are typically many additional data regarding changes to various MOEs 

and costs that roll up into the B/C ratio that provide robust opportunities to present and explain the 

findings in order to promote better understanding and acceptance of the results. Prior to developing 

the presentation of findings, the analysts and project managers should carefully consider the following:

• What is (are) the key audience(s) for the results? If the primary audience is a technical 

one, the analysts should be prepared to present sufficient detail to allow the reviewers to map 

their own conclusions to the ones presented by the performing entity. If the audience is 

anticipated to comprise public or high-level decision-makers, the analyst may want to 

carefully craft a presentation of data to make effective use of graphics to display a high level 

of information in an easily digestible and understandable format. In many cases, the findings 

may need to be presented to differing audiences; and in these cases, different formats and 

data displays may be required.

• What is the key information to present? B/C analysis often results in significant amounts 

of data being generated regarding individual impacts to different MOEs. Attempting to 

present all this information can quickly result in overwhelming the audience and losing focus 

on the key issues. The analyst should attempt to identify the key issue or issues, appropriate 

to the intended audience, and focus the output display on those issues.

• How can graphics and charts be best used to condense multiple data? Charts and 

graphics can go far beyond simple pie charts and line graphs to effectively present 

information representing multiple data simultaneously in an easy to comprehend format –

often times increasing the readers understanding of the relationship between multiple 

variables.

FHWA Tool for Operations Benefit/Cost (TOPS-BC) 

PURPOSE: Estimate Lifecycle Costs of TSM&O Strategies

WORK AREA 2 - PROJECT STREAM OF COSTS AND ESTIMATE NET PRESENT VALUE

Strategy: Ramp Metering -- Centrally Controlled 

Cost Item 2010 2011 2012 2013 2014 2013

Infrastructure Equipment Costs $ - $ 474,500 $ 252,000 $ 252,000 $ 252,000 $ 252,000

Incremental Deployment Equipment Costs $ - $ 307,500 $ 8,250 $ 8,250 $ 8,250 $ 8,250

Total Annual Cost $ - $ 782,000 $ 260,250 $ 260,250 $ 260,250 $ 260,250

Cumulative Cost $ - $ 782,000 $ 1,042,250 $ 1,302,500 $ 1,562,750 $ 1,823,000

INPUT Enter Number of Years in the Analysis Time Horizon 20 Source: TIGER Grant Applicatio 

INPUT Enter the Beginning Year of the Analysis 2011

INPUT Enter Discount Rate 2.0% Source: Office of Managemen 

NET PRESENT VALUE OF COSTS $5,456,374

2011 TO 2031
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Figure 5-12 shows the innovative use of a graphic to simultaneously present multiple pieces of data 

comparing different projects. This graphic concurrently compares:

• The relative benefit/cost ratio estimated for the project (down the left axis);

• The relative size of the net benefit (by the size of the bubble);

• The number of qualitative goals met (across the bottom axis);

• The type of the project (by color); and

• The project name.

Figure 5-12. Innovative Use of Graphics to Display Multiple Data

Source: San Francisco Bay Area MTC 2030 Project Performance Assessment, 2007.
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Graphics can also be effectively used to present data occurring over a timeline for those analyses 

using a defined time horizon. Figure 5-13 presents estimated costs, separated by upfront capital 

costs and ongoing O&M costs shown against the left axis, over an 11-year time horizon. This graphic 

effectively portrays the concept that, as more capital costs are expended to deploy more operations 

strategies, the annual O&M costs continue and increase as a result of more and expanded strategies 

to manage. The right-hand axis of the graphic displays the cumulative capital costs over the project 

time horizon.

Figure 5-13. Sample Graphic Presenting Results over an Analysis Timeline

54

WisDOT Traffic Operations Infrastructure Plan

Deployment Cost Schedule

Key Points 

• All costs presented in 2007 

dollars.

• Capital investments per year 

(blue bars) include multiple 

projects associated with the 

TOIP Corridors listed below.

• Larger scale corridor projects’ 

cost were spread out over 

multiple years (max of three 

years). 

• Costs associated with the 

STOC are not included in this 

estimate; but statewide 

initiatives such as 511 are 

included.

• Details on these costs, 

including assumptions, can 

be found in the WisDOT TOIP 

final report dated May 2008.

$1

$2

$3

$4

$5

$6

$7

$8

$9

$10

$11

$12

$13

1 2 3 4 5 6 7 8 9 10 11

Year

$5

$10

$15

$20

$25

$30

$35

$40

$45

$50

$55

$60

$65

Capital Dollars (Cumulative, Millions)

Capital Investment by Year

(Corresponds with the left-hand scale)

Annual Operations,

Maintenance, and Replacement 

Commitment

(Corresponds with the left-hand scale)

Dollars (Millions per year)

Total Cumulative Capital Investment

(Corresponds with right-hand scale) 

Total capital 

investment after full 

build-out = $64M.

A. Badger

B. Capital

C. Fox Valley

D. South Central

E. Hiawatha

F. Wisconsin River

G. Chippewa Valley

H. Wild Goose

I. Peace Memorial

J. Cornish Heritage

K. Titletown

L. Southern Tier

M. Glacial Plains

TOIP Corridors

Priority and Emerging Priority

A A A

B B B

C C C

D D D

E E E

F

F

G

G G

H

I

J

J J

K K

K

M

L

M M

Staging of projects was driven by 

TOIP corridor prioritization process.

F

Total annual O&M 

impact after full buildout = $12.5M.

Source: Wisconsin DOT Traffic Operations Infrastructure Plan, 2007.

Addressing Challenges of Applying B/C Analysis to 

TSM&O Strategies Targeted at Nonrecurring Conditions

The concepts of travel time reliability and addressing nonrecurring congestion have increasingly been 

receiving attention in recent years. This is particularly important in regards to many TSM&O strategies 

since many are targeted specifically at nonrecurring conditions (e.g., incidents, special events, work

zones, and inclement weather). Many of the traditional tools designed to analyze transportation 

projects, however, have proved to be ill-suited to analyzing nonrecurring conditions. For example, 

traditional travel demand models, which have historically shouldered a large portion of alternative 

analysis needs for many agencies, are typically developed and calibrated to days with good weather, 

no incidents, normal demand, and no work zone activity. This means that these traditional analysis 
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methods do not consider the additional negative impacts (reduced capacity, speeds, increased 

crashes) related to these nonrecurring conditions; and as such, TSM&O strategies analyzed using 

these traditional methods may have their potential benefits severely understated.

As a result of the increasing focus on the significant portion of congestion caused by nonrecurring 

sources, and the ability of TSM&O strategies to effectively improve travel conditions during 

nonrecurring events, much improvement has recently been made in enhancing the analysis of 

nonrecurring conditions. Two national initiatives, in particular, have made significant advances in 

overcoming some of the analysis challenges related to identifying and quantifying nonrecurring 

congestion and the impacts of TSM&O strategies in mitigating the negative impacts. These initiatives 

include the FHWA ICM initiative, which includes the development of an Analysis, Modeling, and 

Simulation (AMS) Guide to aid practitioners at applying the developed analysis methods, and the 

ongoing FHWA development of a Guidebook on Analysis of Active Transportation and Demand 

Management Using Highway Capacity Methods.

These projects both are developing analysis methods related to multiresolution/multiscenario 

methods, which as discussed in Chapter 4, and represent the most complex methods best capable of 

assessing these types of impacts. Although much more complex in their actual application, these 

analyses follow several general steps, including:

1. The identification of the causes of nonrecurring congestion in a region;

2. The identification of the negative impacts of these nonrecurring conditions (e.g., reduced 

capacity caused by rainy conditions);

3. The modification of analysis models and routines to be able to model baseline nonrecurring 

scenarios;

4. The identification of TSM&O and traditional projects impact on these nonrecurring conditions;

5. The identification and incorporation of appropriate MOEs into the analysis that are capable of 

quantifying the benefits;

6. The adjustment and development of modeling tools and methods to support the analysis; and

7. The effective presentation and explanation of results.

Although more complex than many typical analysis that only consider recurring congestion, these 

enhanced analyses may be used to support B/C analysis of TSM&O strategies. The basic premise 

behind this method is to separately analyze recurring and various nonrecurring conditions as different 

scenarios, and then sum the results of all the scenarios, weighted to the frequency in which each 

individual scenario is anticipated to occur in a typical year. To accomplish this, the analyst will need to 

compile data on historic patterns for:

• Demand variability;

• Weather patterns;

• Incident occurrence; and

• Work zones.

In order to develop scenarios representing these nonrecurring conditions, the analyst will need to 

make modifications to the baseline parameters in the model used to reflect the capacity loss of these 

nonrecurring conditions. As part of the development of the Guidebook on Evaluation of Active 

Transportation and Demand Management Using Highway Capacity Methods, a number of baseline 

capacity constraints has been mapped to various nonrecurring conditions based on data in the 2010 
Chapter 5 Conducting B/C Analysis for Operations

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HCM. Table 5-5 presents the capacity reduction factors related to various inclement weather 

conditions. Table 5-6 presents capacity reduction factors related to various incident types. Table 5-7 

presents capacity reduction factors related to various.

Table 5-5. Capacity Reduction Based on Nonrecurring Weather Types

Weather Type

Capacity Range

(Percentage)

Rain 2-14

Snow 4-22

Low temp 1-9

High wind 1-2

Visibility 1-12

Source: FHWA Guidebook on Analysis of Active Transportation and Demand Management Using 

Highway Capacity Methods – Workshop Presentation, adapted from Exhibit 10-15, 2010 HCM.

Table 5-6. Capacity Reduction Based on Nonrecurring Incidents

(In Percentage)

Number of 

Lanes (1 Dir)

Shoulder 

Disablement

Shoulder 

Accident

One Lane 

Blocked

Two Lanes 

Blocked

Three Lanes 

Blocked

2 5 19 65 100 N/A

3 1 17 51 83 100

4 1 15 42 75 87

5 1 13 35 60 80

6 1 11 29 50 74

7 1 9 25 43 64

8 1 7 22 37 59

Source: FHWA Guidebook on Analysis of Active Transportation and Demand Management Using 

Highway Capacity Methods – Workshop Presentation, adapted from Exhibit 10-17, 2010 HCM.
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Table 5-7. Capacity Reduction Related to Work Zones

(In Percentage)

Original Lanes

Work Lanes

1 2 3 4

1 ? N/A N/A N/A

2 67 ? N/A N/A

3 77 54 ? N/A

4 84 65 46 ?

Source: FHWA Guidebook on Analysis of Active Transportation and Demand Management Using 

Highway Capacity Methods – Workshop Presentation, adapted from Exhibit 10-14, 2010 HCM.

The capacity reduction factors, presented in the tables above, may be used to create various baseline 

scenarios that represent one or a combination of these various nonrecurring conditions. The 

development and analysis of additional scenarios representing different nonrecurring conditions needs 

to be carefully considered, however, as each additional scenario will require additional time and 

resources to create and run. In addition, it is important for the analyst to remember that, in order to 

conduct a B/C analysis of TSM&O strategies, each of the scenarios will need to be run twice, once as 

baseline without the strategy and once as an alternative scenario with the strategy deployed. 

Therefore, adding additional nonrecurring conditions scenarios can quickly multiply the number of 

model runs that are required.

It is recommended that the analyst review the data compiled on the frequency of nonrecurring events 

in order to develop a reasonable number of scenarios that may be modeled. Table 5-8 presents a 

sample comparison of the frequency of occurrence of various incident and bad weather conditions 

compared with varying levels of travel demand (presented as percentiles of the volume distribution),

prepared for a sample section of the I-580 corridor in California as part of the development of the 

FHWA Guidebook on Evaluation of Active Transportation and Demand Management Using Highway 

Capacity Methods. These probabilities may be used to first assess the usefulness of analyzing 

particular scenarios – the used may decide to omit analyzing scenarios with very low likelihood of 

occurrence. Secondly, these probabilities may be used to weight the outcomes of individual scenarios 

– the analyst would apply a higher weight on those scenarios with a greater likelihood of occurrence. 
Chapter 5 Conducting B/C Analysis for Operations

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Table 5-8. Sample Scenario Probabilities – I-580 Corridor

Source: FHWA Guidebook on Analysis of Active Transportation and Demand Management Using 

Highway Capacity Methods – Workshop Presentation, 2011.

The probabilities of various scenarios would be expected to vary depending on the region and even 

the individual corridor; therefore, it is recommended that analysts assemble and analyze the 

probabilities of nonrecurring conditions individually for each study. Once this data has been analyzed, 

the analyst can prioritize various scenarios to be developed and analyzed based on their probabilities. 

For example, if resources are not available to run all scenarios, the analyst may want to discard those 

strategies with very low probabilities.

Once all the scenarios have been analyzed for both the baseline and alternative scenarios, the 

incremental change in benefits for each scenario would be weighted according to its probability and 

summed to provide an estimate of benefits across all recurring and nonrecurring conditions.

Additional Resources and Support

The TRB Transportation Economics Committee recommends the following additional resources for 

supporting B/C analysis.27

• Abley, S., P. Durdin, and M. Douglass, 2010, Integrated Transport Assessment Guidelines, 

Report 422, Land Transport New Zealand (www.nzta.govt.nz), at www.nzta.govt.nz/

resources/research/reports/422.

• American Association of State Highway and Transportation Officials (AASHTO), 2010, A 

Manual of User Benefit Analysis for Highways, Third Edition, 

https://bookstore.transportation.org/collection_detail.aspx?ID=65.

• Boardman, A., et al., 2005, Cost-Benefit Analysis: Concepts and Practice, Third Edition, 

Prentice Hall/Pearson, available at www.pohly.com/books/costbenefit.html.

 27 http://bca.transportationeconomics.org/published-guidance-and-references.

Capacity Reduction 5% 

Demand

20%

Demand

50%

Demand

80%

Demand

95%

Demand

Row 

Totals

No Incidents, 

Good Weather 0% 6.04% 15.10% 18.12% 15.10% 6.04% 60.40%

Single Lane Closure, 

Good Weather 42% 2.16% 5.40% 6.48% 5.40% 2.16% 21.60%

Dual+ Lane Closure, 

Good Weather 75% 0.07% 0.19% 0.22% 0.19% 0.07% 0.74%

No Incidents, 

Bad Weather 7% 1.26% 3.15% 3.78% 3.15% 1.26% 12.60%

Single Lane Closure, 

Bad Weather 49% 0.45% 1.13% 1.35% 1.13% 0.45% 4.50%

Dual+ Lane Closure, 

Bad Weather 82% 0.02% 0.04% 0.05% 0.04% 0.02% 0.16%

Column Totals 10.00% 25.00% 30.00% 25.00% 10.00% 100.00%
Chapter 5 Conducting B/C Analysis for Operations

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• Caltrans, 2006, Benefit-Cost Models, Office of Transportation Economics, California 

Department of Transportation, available at (http://www.dot.ca.gov/hq/tpp/offices/eab/

LCBC_Analysis_Model.html).

• Caltrans, 2007, Life-Cycle Benefit-Cost Analysis – Economic Valuations, California 

Department of Transportation, Division of Transportation Planning, Office of Transportation 

Economics, available at http://www.dot.ca.gov/hq/tpp/offices/eab/LCBC_Analysis_Model.html.

• CASA, 2010, Cost Benefit Analysis Procedures Manual, Australian Government, Civil Aviation 

Safety Authority, available at www.casa.gov.au/wcmswr/_assets/main/manuals/

regulate/acm/257rfull.pdf.

• Center for Transportation and the Environment (CTE), 2008, Improved Methods For 

Assessing Social, Cultural, and Economic Effects of Transportation Projects, NCHRP Project 

08-36, Task 66, Transportation Research Board (www.trb.org), AASHTO.

• DFID, 2003, Social Benefits in Transport Planning, UK Department for International 

Development (www.transport-links.org). Describes methodologies for more comprehensive 

transportation project evaluation.

• DfT, 2006, Transport Analysis Guidance, Integrated Transport Economics and Appraisal, UK 

Department for Transport.

• EC, 2002, Guide to Cost-Benefit Analysis of Investment Projects, Evaluation Unit, DG 

Regional Policy, European Commission.

• EDRG, 2007, Monetary Valuation of Hard-to-Quantify Transportation Impacts: Valuing 

Environmental, Health/Safety and Economic Development Impacts, NCHRP 8-36-61, 

National Cooperative Highway Research Program (www.trb.org/nchrp).

• FAA, 1999, Airport Benefit-Cost Analysis Guidance, Federal Aviation Administration. U.S. 

Department of Transportation, Washington, D.C.

• FHWA, 2003, Economic Analysis Primer, Office of Asset Management, Federal Highway 

Administration, available at: http://www.fhwa.dot.gov/infrastructure/asstmgmt/primer.pdf.

• FHWA, 2001, Cost-Benefit Forecasting Toolbox for Highways, Federal Highway 

Administration, U.S. Department of Transportation, Washington, D.C., available at: 

http://www.fhwa.dot.gov/planning/toolbox/costbenefit_forecasting.htm.

• FMT, 2005, Federal Transport Infrastructure Plan 2003: Basic Features of the 

Macroeconomic Evaluation Methodology, German Federal Ministry of Transport 

(www.bmvbs.de).

• Forkenbrock, D. J., and G. E. Weisbrod, 2001, Guidebook for Assessing the Social and 

Economic Effects of Transportation Projects, NCHRP Report 456, TRB (www.trb.org ),

available at: http://onlinepubs.trb.org/Onlinepubs/nchrp/nchrp_rpt_456-a.pdf.

• Goodbody Economic Consultants, 2004, Cost Benefit Parameters and Application Rules for 

Transport Project Appraisal, available at: http://www.transport.ie/upload/general/5830-1.pdf.

• GTKP, 2011, Economic Analysis for Multilateral Development Banks, Global Transport 

Knowledge Partnership (www.gtkp.com), at www.gtkp.com/theme.php?themepgid=121.

• Hyder Consulting, 2008, Understanding Transport Costs and Charges (UTCC) Phase 1, New 

Zealand Ministry of Transport (www.transport.govt.nz ), available at: 

www.transport.govt.nz/research/Pages/UnderstandingTransportCostsandCharges.aspx.
Chapter 5 Conducting B/C Analysis for Operations

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• Litman, T., 2001, What’s It Worth? Life Cycle and Benefit/Cost Analysis for Evaluating 

Economic Value, presented at Internet Symposium on Benefit-Cost Analysis, Transportation 

Association of Canada (www.tac-atc.ca), available at www.vtpi.org/worth.pdf.

• Litman, T., 2010, Transportation Cost and Benefit Analysis, Victoria Transport Policy Institute 

(www.vtpi.org ), available at www.vtpi.org/tca.

• LTNZ, 2010, Economic Evaluation Manual (EEM), Land Transport New Zealand 

(www.landtransport.govt.nz), available at: www.nzta.govt.nz/resources/

results.html?catid=401.

• Maibach, M., et al., 2008, Handbook on Estimation of External Cost in the Transport Sector, 

CE Delft (www.ce.nl ).

• MSU, undated, Benefit/Cost Analysis: Introduction, Mankato State University.

• MNDOT, undated, Benefit Cost Analysis, MN DOT Office of Investment Management.

• Portney, P., undated, Benefit-Cost Analysis, The Library Of Economics and Liberty, available 

at: http://www.econlib.org/library/Enc/BenefitCostAnalysis.html.

• TC, 2003-2007, The Full Cost Investigation of Transportation in Canada (multiple 

documents), Transport Canada (www.tc.gc.ca).

• TRISP, 2005), Economic Evaluation Notes, UK Department for International Development 

and the World Bank (www.worldbank.org), at http://go.worldbank.org/ME49C4XOH0.

• UNIDO, no date, A Guide to Practical Project Appraisal – Social Benefit-Cost Analysis in 

Developing Countries, United Nations, available at: www.unido.org/en/doc/views/3421.

• United Kingdom Department for Transport, Transport Analysis Guidance (WebTAG), available 

at: www.dft.gov.uk/webtag.

• van Essen, et al., 2004, Marginal Costs of Infrastructure Use – Towards a Simplified 

Approach, CE Delft (www.ce.nl), at www.ce.nl/?go=home.download

Pub&id=456&file=04_4597_15.pdf.

• White House, 2003, Circular No. A-94: Guidelines and Discount Rates for Benefit-Cost 

Analysis of Federal Programs, Office of Management and Budget, U.S. Government, Printing 

Office, Washington, D.C., available at: www.whitehouse.gov/omb/memoranda/a94-c.html.

• World Bank, 2008, A Framework For Urban Transport Projects: Operational Guidance for 

World Bank Staff, Transport Sector Board, World Bank (www.worldbank.org), at 

http://siteresources.worldbank.org/INTTRANSPORT/Resources/tp_15_urban.pdf.

• Zhang, A., A. E. Boardman, D. Gillen, and W. G. Waters II, 2005, Towards Estimating the 

Social and Environmental Costs of Transportation in Canada, Centre for Transportation

Studies, UBC, for Transport Canada..
U.S. Department of Transportation Federal Highway Administration Office of Operations

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Appendix A. List of Acronyms

AASHTO American Association of State Highway and Transportation Officials

AMS Analysis, Modeling, and Simulation

ATDM Active Transportation and Demand Management

ATM Active Traffic Management

AVL Automatic Vehicle Location

B/C Benefit/Cost

BI Buffer Index

CAD Computer-Aided Dispatch

CAL-BC Caltrans Benefit/Cost Tool

CARB California Air Resources Board

CBA Cost-Benefit Analysis

CCTV Closed-Caption Television

CMAQ Congestion Mitigation and Air Quality

CMP Congestion Management Process

CMS Changeable Message Sign

CO Carbon Monoxide

CO2 Carbon Dioxide

CUTR Center for Urban Transportation Research (CUTR) at the University of South 

Florida

DMS Dynamic Message Signs

DOT Department of Transportation

EPA Environmental Protection Agency

FHWA Federal Highway Administration

FITSEval The Florida ITS Evaluation

FSP Freeway Service Patrol

GPS Global Positioning System

HAR Highway Advisory Radio

HC Hydrocarbons

HCM Highway Capacity Manual

HERS Highway Economic Requirements System

HERS-ST Highway Economic Requirements System – State Version
Appendix A List of Acronyms

U.S. Department of Transportation Federal Highway Administration Office of Operations

FHWA Operations Benefit/Cost Analysis Desk Reference| 110

HOT High-Occupancy Toll

HOV High-Occupancy Vehicle

HPMS Highway Performance Monitoring System

ICM Integrated Corridor Management

IDAS ITS Deployment Analysis System

ITS Intelligent Transportation Systems

LED Light Emitting Diodes

M&O Management and Operations

MOE Measure of Effectiveness

MOVES Motor Vehicle Emission Simulator

MPO Metropolitan Planning Organization

MTC Metropolitan Transportation Commission

MTP Metropolitan Transportation Plan

NOx Nitrous Oxide

NPV Net Present Value

O&M Operations and Maintenance

OMB Office of Management and Budget

OKI Ohio-Kentucky-Indiana

PHT Person Hours of Travel

PM2.5 Fine Particulate Matter

PM10 Particulate Matter

RCTO Regional Concepts for Transportation Operations

RITA Research and Innovative Technology Administration

ROG Reactive Organic Gases

RTP Regional Transportation Plan

SCRITS Screening for ITS

SHRP Strategic Highway Research Program

SO2 Sulfur Dioxide

SOV Single-Occupancy Vehicle

STEAM Surface Transportation Efficiency Analysis Model

TDM Travel Demand Management

TIGER Transportation Investment Generating Economic Recovery

TIM Traffic Incident Management

TIP Transportation Improvement Program

TOPS-BC Tool for Operations Benefit/Cost
Appendix A List of Acronyms

U.S. Department of Transportation Federal Highway Administration Office of Operations

FHWA Operations Benefit/Cost Analysis Desk Reference| 111

TRB Transportation Research Board

TSM&O Transportation System Management and Operations

TRIMMS Trip Reduction Impacts of Mobility Management Strategies

V/C Volume-to-Capacity

VHT Vehicle Hours Traveled

VMT Vehicle Miles of Travel