Freight Performance Measure Primer

October 2017
Notice

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

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specification, or regulation.

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Front Cover Graphic Source: AECOM.
1. Report No.

FHWA-HOP-16-089

2. Government Accession 

No.

3. Recipient’s Catalog No.

4. Title and Subtitle

Freight Performance Measure Primer

5. Report Date

October 2017

6. Performing Organization Code

7. Author(s)

(in alphabetic order) Richard Easley (E-Squared),

Nicole Katsikides (FHWA), Keith Kucharek (AECOM),

Daniel Shamo (AECOM), and Janie Tiedeman (AECOM)

8. Performing 

Organization 

Report No.

9. Performing Organization Name and Address

AECOM

4 North Park Drive, Suite 300

Hunt Valley, MD 21030

E-Squared Engineering

43969 Tavern Dr. Suite 200

Ashburn, VA 20147

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-11-D-00016-T-13001

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

Office of Operations

1200 New Jersey Avenue, SE

Washington, D.C. 20590

13. Type of Report and Period 

Covered

Primer 2016-2017

14. Sponsoring Agency Code

HOP

15. Supplementary Notes

Jeffrey Purdy (FHWA- HOFM)

16. Abstract

This primer aims to assist States and MPOs with the implementation of performance-based 

freight planning and programming, as established under MAP-21 and the FAST Acts, by 

recommending freight performance measurement practices and appropriate performance 

measures. The primer provides a comprehensive list of freight performance measures 

with their availability and degree of manipulation needed. It includes chapters on data 

collection, data analysis, target setting, identifying challenges, best practices, and possible 

solutions.

17. Key Words

Freight, Performance Measurement,

Freight System Performance, Freight Data

18. Distribution Statement

No restrictions

19. Security Classification 

(of this report)

Unclassified

20. Security Classification 

(of this page) 

Unclassified

21. No. of Pages

120

22. Price

N/A

Technical Report Documentation Page

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

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Freight Performance Measure Primer

TABLE OF CONTENTS

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

CHAPTER 1. INTRODUCTION................................................................................................7

BACKGROUND ON FREIGHT PERFORMANCE MEASUREMENT................................7

WHY MEASURE ASPECTS OF FREIGHT SYSTEM PERFORMANCE? ..........................8

CHAPTER 2. PERFORMANCE MEASUREMENT .............................................................11

FREIGHT PERFORMANCE MEASURES...........................................................................12

LINKAGE OF PERFORMANCE MEASURES TO ECONOMIC IMPACTS .....................14

LEVELS OF ANALYSIS .......................................................................................................16

CHAPTER 3. AVAILABLE DATA............................................................................................23

FREQUENTLY USED FREIGHT DATA ..............................................................................23

CHAPTER 4. COMMON FREIGHT MEASURES................................................................29

FREIGHT DEMAND MEASURES.......................................................................................30

FREIGHT EFFICIENCY – HIGHWAY SPEEDS, AVERAGE SPEEDS, RELIABILITY...31

FREIGHT MODELING .........................................................................................................34

RAILROAD SPEEDS AND RELIABILITY.........................................................................36

FREIGHT SYSTEM CONDITION INDICATORS...............................................................37

FREIGHT ENVIRONMENTAL MEASURES ......................................................................38

FREIGHT SAFETY MEASURES .........................................................................................39

FREIGHT ECONOMIC DEVELOPMENT MEASURES AND ANALYSIS.......................39

FREIGHT ACCESSIBILITY MEASURES ...........................................................................42

INFRASTRUCTURE-BASED AND NETWORK MEASURES..........................................42

AREA-BASED MEASURES.................................................................................................44

FUTURE PROGRAMS (FREIGHT FLUIDITY)..................................................................48

CHAPTER 5. CHALLENGES WITH FREIGHT PERFORMANCE ANALYSIS .............51

SETTING FREIGHT GOALS................................................................................................51

LACK OF CONSISTENCY...................................................................................................51

ABUNDANCE OF INCOMPLETE DATA............................................................................52

FREIGHT Modeling CAPABILITIES AT THE REGIONAL SCALE ..................................52

APPLYING THE MEASURES ..............................................................................................52

DATA CAPTURE ...................................................................................................................52

INTEGRATING DATA...........................................................................................................53

SUSTAINING FREIGHT PERFORMANCE MEASURES ..................................................53
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Freight Performance Measure Primer

CHAPTER 6. BEST PRACTICES AND RECOMMENDED FREIGHT

PERFORMANCE MEASURES ............................................................................55

BEST PRACTICES: PRIVATE SECTOR..............................................................................55

BEST PRACTICES: PUBLIC SECTOR................................................................................56

RECOMMENDATIONS FOR A COMPREHENSIVE FREIGHT PERFORMANCE

MEASUREMENT PROGRAM................................................................................59

REFERENCES............................................................................................................................65

APPENDIX A. HISTORY OF FEDERAL AUTHORIZATION AND ITS

RELATIONSHIP TO FREIGHT PERFORMANCE MEASURES ...................69

APPENDIX B. FREIGHT STAKEHOLDER INTERVIEWEES AND

INTERVIEW QUESTIONS ...................................................................................75

APPENDIX C. FREQUENTLY USED FREIGHT DATA......................................................77

APPENDIX D. ADDITIONAL AVAILABLE HIGHWAY DATA...........................................87

APPENDIX E. TERMS AND DEFINITIONS .........................................................................91

APPENDIX F. TRUCK PARKING METRICS .......................................................................97

APPENDIX G. INNOVATIVE RESEARCH: USING FUEL TAXES TO

TRACK TRUCK MOVEMENT..........................................................................101
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Freight Performance Measure Primer

LIST OF FIGURES

Figure 1. Chart. Florida Department of Transportation performance measures graphic. ............17

Figure 2. Photo. Truck delivery on streetcar tracks, Washington, D.C. .......................................19

Figure 3. Photo. Urban freight delivery, Washington, D.C. .........................................................19

Figure 4. Map. U.S. Department of Transportation major freight corridors. ...............................20

Figure 5. Photo. Congestion due to construction on I-81, Pennsylvania. ....................................20

Figure 6. Map. Philly Freight Finder screenshot showing travel time index

and average speeds for selected time period. ...............................................................................21

Figure 7. Map. European map of major roadways. ......................................................................22

Figure 8. Equation. Travel Time Equivalent. ...............................................................................33

Figure 9. Map. Average truck speeds on select interstate highways, 2010. .................................34

Figure 10. Chart. A taxonomy of the most common area-based measurement approaches. .......45

Figure 11. Equation. Generic form of an area-based measure. ....................................................46

Figure 12. Diagram. Fuel vendor location and average distance of truck travel. ......................102

Figure 13. Diagram. Roadway segments attributed to fuel vendor. ...........................................103

LIST OF TABLES

Table 1. Recommended performance measures. ............................................................................2

Table 2. Key linkages between performance measures and economic factors. ...........................15

Table 3. Frequently used freight data. ..........................................................................................24

Table 4. Average train speeds. ......................................................................................................37

Table 5. Infrastructure-based measures. .......................................................................................43

Table 6. Network coverage and completeness measures. ............................................................44

Table 7. Stated goals for transportation policy. ............................................................................60

Table 8. Recommended performance measures. ..........................................................................61

Table 9. Class 1 Railroads. ...........................................................................................................83

Table 10. Tier I truck parking metrics. .........................................................................................97

Table 11. Tier II truck parking metrics. ........................................................................................97

Table 12. Tier III truck parking metrics. ......................................................................................98

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Freight Performance Measure Primer

LIST OF ABBREVIATIONS AND SYMBOLS

AADTT Annual Average Daily Truck Traffic

AAR Association of American Railroads

AASHTO American Association of State Highway Transportation Officials

ADT Average Daily Traffic

AERR Air Emissions Reporting Requirements

ATRI American Transportation Research Institute

BAC Blood Alcohol Concentration

BCA Benefit-Cost Analysis

BNSF Burlington Northern and Santa Fe

BTS Bureau of Transportation Statistics

CFS Commodity Flow Survey

CMAQ Congestion Mitigation and Air Quality Improvement Program

DOC Department of Commerce

DOT Department of Transportation

EIA U.S. Energy Information Administration

EPA Environmental Protection Agency

F&OS Financial and Operating Statistics

FAC Freight Advisory Committee

FAF Freight Analysis Framework

FARS Fatality Analysis Reporting System

FAST Fixing America’s Surface Transportation (Act)

FDOT Florida Department of Transportation

FHWA Federal Highway Administration

FMCSA Federal Motor Carrier Safety Administration

FPM Freight Performance Measurement

FRA Federal Railroad Administration

GDP Gross Domestic Product

GES General Estimates System

GPS General Pavement Study

GPS Global Positioning System

HPMS Highway Performance Monitoring System
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Freight Performance Measure Primer

I-95 CC I-95 Corridor Coalition

IFTA International Fuel Tax Association

ISTEA Intermodal Surface Transportation Efficiency Act

ITS Intelligent Transportation System

LTPP Long-Term Pavement Performance

MAP -21 Moving Ahead for Progress in the 21st Century

MARAD U.S. Maritime Administration

MDOT Maryland Department of Transportation

MPO Metropolitan Planning Organization

MTS Marine Transportation System

NAFTA North American Free Trade Agreement

NATS North American Transportation Statistics

NCFRP National Cooperative Freight Research Program

NCHRP National Cooperative Highway Research Program

NHFN National Highway Freight Network

NHFP National Highway Freight Program

NHS National Highway System

NHTS National Household Travel Survey

NHTSA National Highway Traffic Safety Administration

NPMRDS National Performance Management Research Data Set

NPTS Nationwide Personal Transportation Surveys

O/D Origin and Destination

ODOT Ohio Department of Transportation

ODOT Oregon Department of Transportation

OPA-90 Oil Pollution Act of 1990

OTC Oregon Transportation Commission

PAR Police Accident Report

PHMSA Pipeline and Hazardous Material Safety Administration

PUM Public Use Microdata

SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act:

 A Legacy for Users

SHRP Strategic Highway Research Program

SPS Specific Pavement Studies

STB State Transportation Board
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Freight Performance Measure Primer

TEA-21 Transportation Equity Act for the 21st Century

TEU Twenty-Foot Equivalent Unit

TOFC Trailer-on-Freight-Car

TRB Transportation Research Board

TSA Traffic Safety Administration

TTI Travel Time Index

USDOT U.S. Department of Transportation

VIUS Vehicle Inventory and Use Survey

VMT Vehicle Miles Traveled

WSDOT Washington State Department of Transportation

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Freight Performance Measure Primer

EXECUTIVE SUMMARY

The U.S. Department of Transportation (USDOT) has been adhering to the Government 

Performance and Results Act of 1993 since its adaptation. The Act requires the development of 

strategic plans, annual performance plans, and annual performance reports. The Moving Ahead 

for Progress in the 21st Century (MAP-21) and the Fixing America’s Surface Transportation 

(FAST) Acts require the USDOT to establish performance measures in several areas, including 

the assessment of freight movement on the interstate system.

The Federal Highway Administration (FHWA) Office of Freight Management and Operations is 

responsible for administering freight performance measurement requirements as outlined in the 

MAP-21 and FAST Acts. The FHWA assists in the development of freight performance measures 

through the advancement and dissemination of freight data and tools to support analysis, plans, 

and project development. FHWA developed this Freight Performance Measure Primer as a tool 

for State departments of transportation (DOTs) and Metropolitan Planning Organizations (MPOs) 

to develop meaningful, effective, and consistent performance measurement practices.

Measuring the performance of the freight system can be effective in determining if goals and 

objectives are being met by State DOTs and MPOs. Performance measure data can be used in 

communicating programs or policies, evaluating success and tracking progress, and establishing 

a decision-making tool to identify plans, programs, policies and investments that support freight 

movement. There are several existing data sources and approaches that States and MPOs can 

use to get the baseline conditions and performance measures. The FHWA data sources include 

truck probe data, National Performance Management Research Data Set (NPMRDS), Freight 

Analysis Framework (FAF) data, the Commodity Flow Survey, the Highway Performance 

Monitoring System (HPMS) data, and truck parking data. Freight data are available for other 

modes, including maritime data, pipeline data, railroad data, aviation data, border crossing data, 

and general economic activity. 

There are other common performance measures used by jurisdictions and other entities. These 

measures include truck travel times, travel time reliability, safety, infrastructure conditions, and 

congestion. Other tools include freight demand measures (such as freight volume and truck 

parking) and freight efficiency measures (such as travel time index, mean travel time index, 

delay, speed, reliability cost, and freight bottlenecks). Similar performance measures are used for 

freight rail, such as railroad speeds and reliability.

State DOTs and MPOs should work together to fulfill the Federal requirements from MAP21 and the FAST Acts. These agencies have varying degrees of expertise in analyzing freight 

data and freight performance management and there are variations among agencies that track 

performance, such as data consistency and differences in policies. Federal, State, and MPO 

practitioners face several challenges in developing freight performance measures. These 

challenges include: developing freight planning goals, understanding the role of States and 

MPOs in freight planning and funding, accessing multi-modal data, addressing inconsistency in 

data quality and quantity, and maintaining reliable freight transportation models. 
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Freight Performance Measure Primer

The goal of this primer is to assist States and MPOs by providing information on the most 

commonly used measurement data and approaches. It will allow practitioners to grow the state 

of the practice of freight performance measurement. The primer includes information on data 

collection, data analysis, target setting, identification of challenges, and possible solutions. 

The recommendations and best practices were developed through interviews conducted with 

numerous freight stakeholders, research on a diverse set of jurisdictions, and extensive research 

of current users of freight performance measurement.

The public sector has an interest in identifying measures, including safety, environmental, 

economic, system efficiency, and security, as well as measuring how regulations and standards 

are being met. The private sector is concerned with economic measures, particularly those 

resulting from policy decisions. Several best practices in the private and public sectors exist, 

including statewide freight plans, coordination among government transportation agencies and 

private partners, and the Freight Fluidity System that spans the freight modes.

States and MPOs can take the initial step in their freight planning efforts by convening a Freight 

Advisory Committee (FAC), consisting of public and private stakeholders. These committees can 

play a role in determining the level of analysis required, thereby selecting the most appropriate 

freight performance measures. They can also play a role in obtaining the data. 

The following table summarizes the potential performance measures that States and MPOs could 

include in their freight planning effort. The measures are listed by mode for five categories:

• Safety.

• Maintenance and Preservation.

• Mobility, Reliability, and Congestion.

• Accessibility and Connectivity.

• Environment.

Category Measure

SAFETY

Highway

Motor carrier crash rate

Motor carrier truck at-fault rate

Number of heavy truck-related fatalities

Capacity of weigh stations – number of trucks processed per hour

National highway system pavement conditions

National highway bridge conditions

National highway system intermodal connector condition

Total cost of freight loss and damage from accidents/Vehicle Miles Traveled 

(VMT)

Table 1. Recommended performance measures.
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Freight Performance Measure Primer

Category Measure

SAFETY

Railway

Total loss and damage from accidents per route-mile

Total loss and damage from accidents per ton moved

Number of at-grade railroad crossings along freight significant corridors such 

as freeways and interregional corridors

Number of rail fatalities

Train derailments per ton moved

Water Value of cargo lost or damaged per ton or value of cargo moved

Containers damaged or lost per containers handled/total containers

Air

Total loss and damage from accidents divided by value of freight

Percent of study airports meeting Traffic Safety Administration (TSA) 

guidelines for general aviation security

Incidents per 1,000 operations at freight-significant airports

MAINTENANCE AND PRESERVATION

Highway

Percent of pavement in good condition on freight significant highways

Number of weight restricted bridges divided by total number of bridges

Percent of bridges that meet good and poor structural condition thresholds

Service life remaining on highway pavement

Benefit of truck weight enforcement on pavement service life

Railway

Miles of track in expected or Federal Railroad Administration (FRA) Class I 

divided by total miles of Class I track

Number of double-stack tunnel restrictions divided by number of tunnels

Water

Percent of tons on river moving though locks with constraints

Unscheduled lock closure time (hours)

Channel depths at the port divided by depths at competitive ports

Air Percent of pavement in fair or poor condition at freight-significant airports

MOBILITY, RELIABILITY, AND CONGESTION

Highway

Percent of interstate providing reliable travel times

Percent of interstate where peak hour travel times meet expectations

Percent of non-interstate National Highway System (NHS) providing reliable 

travel times

Percent of non-interstate NHS where peak hour travel times meet expectations

Annual hours of excessive delay per capita

Urban: Average hours of delay per day for freight vehicles on freightsignificant links

Urban: Travel Time Index (TTI) on freight-significant links (ratio of the peak 

travel time to free-flow travel time)

Table 1. Recommended performance measures (continuation).
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Freight Performance Measure Primer

Category Measure

Highway

Percent of interstate mileage providing for reliable truck travel times

Percent of interstate mileage that is uncongested

Clearance time for incidents, crashes, or hazardous materials

Number of intersections and ramps with inadequate turning radii for large 

trailers on freight significant corridors

Urban: Buffer Index on freight-significant links (ratio of the 95th percentile 

travel time to average travel time or free flow travel time)

Rural: Average hours of delay per day for freight vehicles on freightsignificant links

Number of truck rest areas and their capacities

Rural: Average travel time on freight-significant links

Railway

Tons or ton-miles of freight over relevant period

Average terminal dwell time train-hours of delay

Percent of rail track-miles with 286,000-pound railcar capacity rating

Railroad corridor level of service

Water

Tons of traffic arriving at a port

Twenty-Foot Equivalent Units (TEUs) passing through port (port throughput)

Gate reliability or truck turn time

Ship unload rate (time per container)

Ship load rate (time per container)

Average delay per barge tow on river

Air

Flight frequency by airlines with cargo capacity (number per day)

Average time between flights by airlines with cargo capacity (minutes)

Percent of on-time departures at freight significant airports

Percent of on-time arrivals at freight significant airports

ACCESSIBILITY AND CONNECTIVITY

Highway

Triple trailer VMT as a percent of total freight VMT

Percent of major generators with appropriate roadway access to interregional 

corridors and major highways

Percent of shippers with access to triple network

Railway

Class I: Ratio of unit train carloads (or tons) divided by total carloads (or tons)

Percent of shippers within 50 miles of intermodal trailer-on-freight-car 

(TOFC) facility

Percent of major freight generators with appropriate rail access

Number or capacity of intermodal facilities

Water Shippers within 50 miles of river port (for barge accessibility)

Table 1. Recommended performance measures (continuation).
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Freight Performance Measure Primer

The information in this primer is organized into six chapters. Chapter 1 provides a background 

on freight performance and outlines the need to measure aspects of freight performance; 

Chapter 2 describes the performance measures, their linkage to the economic factors, and the 

level of analysis appropriate for each performance measure; Chapter 3 describes what data are 

available for different modes through which freight moves; Chapter 4 describes common freight 

performance measures; Chapter 5 identifies challenges with freight performance analysis; and, 

Chapter 6 identifies the best practices in measuring performance of the freight network across the 

country in both private and public sectors including Maryland, Ohio, Oregon, Washington, and 

the I-95 Corridor Coalition.

Category Measure

Water Availability of container-handling capability and/or bulk transfer capability

Air

Flight frequency by airlines with cargo capacity (number per day)

Average time between flights by airlines with cargo capacity (minutes)

Average travel time delay for trucks on airport access roads

Number of docks or acres of cargo-handling facilities

ENVIRONMENTAL

All

Total tons of emissions reduced from Congestion Mitigation and Air Quality 

Improvement Program (CMAQ) projects for applicable criteria pollutants and 

precursors

All Pounds of greenhouse gas emissions

All Increase in energy consumed or costs related to energy consumption

All Increase in air pollution impacts/costs

Table 1. Recommended performance measures (continuation).

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Freight Performance Measure Primer

CHAPTER 1. INTRODUCTION

How well does freight move on the Nation’s transportation system? Where does it go or where 

does it come from? Does it travel by rail or by truck or both, and how fast? Where are the 

bottlenecks, and which ones are the worst? And, how does this impact the U.S. economy? 

These are questions that State, regional, and local transportation decision-makers try to answer 

as they determine which plans, programs, policies and investments should be implemented 

to best support freight movement. State and local departments of transportation (DOTs) and 

Metropolitan Planning Organizations (MPOs) make decisions to prioritize their programs and 

projects on a regular basis. These efforts have a direct impact on freight system performance. 

In addition, even though an infrastructure project may not be explicitly freight-specific, it can 

impact freight system performance. Thus, the measurement of how these projects impact the 

performance of the freight transportation system is important.

Two major challenges to freight system measurement are the absence of data and the lack 

of methods of analysis. There are robust data for the movement of people and passenger 

vehicles, but understanding freight movement presents different types of challenges to the 

decision-makers. These movements are based upon supply chain decisions made by individual 

corporations, which can change over time due to economic conditions. Freight often moves 

across numerous jurisdictions and by multiple modes of transport. Data that captures origins and 

destinations and multi-modal performance, as well as methodologies of collecting and utilizing 

data across multiple jurisdictions and modes, are extremely limited for freight.

Currently, decision-makers are only able to use a few data sources that help in identifying trading 

partners among States and regions, commodities, tonnage, and value. While formats vary, 

truck data is the most robust and publicly available information to help understand how freight 

moves and to support thorough freight analysis. The analysis of multi-modal freight flows is not 

commonly performed in the United States.

This primer is a resource for the States and MPOs, as well as county or municipal decisionmakers to use in analyzing freight movement. It provides a collection of the publicly 

available data and methods currently in use with information on the next generation of freight 

measurement. It is intended to help improve the practice of freight performance measurement 

and establish consistency among practitioners throughout the United States. It is also intended 

to be updated as new data sources and methods are developed. Additional work still needs to be 

done to improve the comprehensive and multi-modal understanding of freight.

BACKGROUND ON FREIGHT PERFORMANCE MEASUREMENT

Transportation decision-makers need to understand how freight moves and where inefficiencies 

exist to develop the best policies, plans, and investments that support freight movement. 

Improving freight movement has direct and positive effects on the national economy, and 

may also provide environmental and safety benefits. For over a decade, the Federal Highway 

Administration (FHWA) has led the development of freight data and approaches to better 

measure and plan for freight. For example, the FHWA has identified freight data by working 
8

Freight Performance Measure Primer

with the trucking industry to obtain anonymized truck probe data, which is used to understand 

speeds and reliability and identify bottlenecks on the Nation’s highways to help improve freight 

performance. The FHWA has assisted States and MPOs identify freight needs on the Nation’s 

transportation network. As the focus on freight has grown in the past decade, the FHWA has 

worked to coordinate data, freight analysis, and planning methods which help decision-makers 

understand where needs are greatest.

Improving the state of practice is important for State DOTs and MPOs that have varying 

levels of experience and capabilities in freight performance measurement, data capture, policy 

development, and planning. Many agencies are familiar with performance measurement, such as 

the congestion management process or transit service planning, but may not be as familiar with 

freight performance measurement. Integrating performance measurement across the areas of 

transportation operations, planning, programming, and evaluation means developing innovative 

new practices for many agencies (Florida Department of Transportation [FDOT] 2014). Many 

States and MPOs have little experience with freight programs necessary to comply with Moving 

Ahead for Progress in the 21st Century (MAP-21) and Fixing America’s Surface Transportation 

(FAST) Act requirements. This primer gives States and MPOs the tools necessary to be 

consistent in basic, yet meaningful, measurement practices.

Organizations such as the FHWA, the American Association of State Highway Transportation 

Officials (AASHTO), MPOs, State DOTs, and the National Cooperative Freight Research 

Program (NCFRP) have conducted a significant amount of research providing guidance in the 

establishment of performance measures. The research reveals several common challenges in the 

development of freight performance measures. A major challenge is identifying exactly what 

the public sector should measure to best support the economy and balance other policy priorities 

such as the environment, safety, and energy. Additionally, this research identifies challenges 

in understanding how freight moves from origin to destination and across several modes of 

transport. Other challenges include the lack of multi-modal data and undefined and inconsistent 

approaches to measurement.

Public and private stakeholders agree that the efficient movement of freight is critical to 

supporting and advancing the Nation’s global economic competitiveness and environmental 

stewardship. Population growth and the anticipated growth in freight movement over the next 

20 to 30 years will worsen congestion on an already congested transportation network, hindering 

the efficient movement of freight. By focusing on performance measurement and management, 

public and private freight stakeholders can identify the best means in which improvements can 

be made to the efficient movement of freight.

WHY MEASURE ASPECTS OF FREIGHT SYSTEM PERFORMANCE?

The United States is home to the world’s largest economy and possesses one of the largest freight 

systems in the world. The U.S. freight system comprises over four million miles of public roads, 

140,000 miles of railways, 360 commercial airports, and 12,000 miles of marine highways. 

Additionally, the U.S. freight system is supported by trucking firms, railroad companies, 

maritime companies, aviation companies, and the public agencies that serve and regulate 
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Freight Performance Measure Primer

them. Given the enormity of the freight system and the number of public and private freight 

stakeholders, there is interest in freight performance measures as a tool to better understand 

the relationship between efficient freight movement and the economy, environment, safety, and 

sustainability.

Trucks and rail transport nearly 70 percent of all freight tonnage in the United States. The total 

tonnage of freight moved in the United States is expected to increase by over 50 percent by 2040. 

To accommodate the increase in freight demand, more trucks will be needed, but adding more 

trucks to an already congested road network will worsen mobility. Partnerships within the freight 

community will foster innovative solutions that reduce congestion safely and efficiently. Freight 

performance measurement and freight planning tools are vital to understanding and addressing 

ongoing and future congestion concerns.

While some State DOTs and MPOs have recently begun measuring freight system performance, 

successful and effective implementation of a performance-based system has not been 

widely achieved. The U.S. Department of Transportation (USDOT) has been adhering to the 

Government Performance and Results Act of 1993, which requires development of strategic 

plans, annual performance plans, and annual performance reports, since its adaptation. The 

FHWA began developing freight performance measures in 2002 and continues to improve and 

refine these measures. Today, government agencies are held to a high standard of performance 

and transparency about publicly available information.

Recent MAP-21 and FAST Act legislations call for more aggressive performance measurement 

strategies. MAP-21 Section 1115 “National Freight Policy” requires the development or 

improvement of tools to evaluate and enhance transportation system performance and 

strategically target the investment of Federal funds. Section 1203 requires the Secretary of 

Transportation to establish freight performance measures that must be submitted by all States and 

MPOs. The FAST Act implemented a freight funding program and freight formula program that 

will require freight plans as part of eligibility for program funding. These plans must demonstrate 

a link to performance of freight flows and must describe the policies and programs States use to 

support freight. In response to the Federal legislation, the FHWA Office of Freight Management 

and Operations has advanced several research efforts to improve freight measurement and to 

develop multi-modal measurement systems for freight fluidity in cooperation with a variety of 

stakeholders. Freight-specific performance measures assist in identifying needed transportation 

improvements, monitor their effectiveness, and establish priorities for freight transportation 

investments. This information is important for use in freight planning, project development, 

investment planning, and other needs (Refer to Appendix A for more detail on past and current 

Federal legislation.).

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Freight Performance Measure Primer

CHAPTER 2. PERFORMANCE MEASUREMENT

Performance measurement has evolved over the decades into an effective methodology for 

quantifying goals and objectives and communicating progress toward their attainment. Many 

State Departments of Transportation (DOTs) have integrated performance measurement into their 

business practices. Developing and analyzing freight performance measures can be challenging 

for many reasons, including the large number of possible measures, the extensive and potentially 

expensive data that might be required, and the computational complexity that is often introduced 

(Transportation Research Board [TRB], National Cooperative Highway Research Program 

[NCHRP] 2011).

Transportation agencies tend to place performance measures into one of three categories: 

agency performance, system performance, and the impact on broader social performance 

measures. Agency performance focuses on service delivery, such as projects completed. System 

performance focuses on capacity and conditions of the transportation system as well as issues 

such as travel times, cost, and safety. Social performance measures deal with broader societal 

concerns such as economic development and the environment (McMullen et al. 2010).

Traditionally, performance measures at the State level focus on evaluating key highway or 

transit/rail, and port infrastructure performance such as interstates and national highways, Class 

I railroads, and key ports. Freight performance measures require a multi-modal evaluation of the 

performance including highway, rail, waterway, air, and intermodal connections. Performance 

measure data can be used in several ways:

• Description – They can help describe the effect of a program or policy.

• Evaluation – They can be used to assess progress and diagnose what problems or barriers 

that need to be addressed.

• Accountability – They can be used to set targets for specific staff or programs and can 

measure how well they are doing in reaching those goals.

• Decision-support – They can help inform which approach would support the most 

sustainable outcomes.

• Communication – They can be used to explain to your partners or the public what your 

program or policy is achieving.

There are many elements that go into the successful development of freight performance 

measures, including:

• Allow users to examine performance in detail through at-a-glance reporting linked to more 

extensive performance analysis (Drucker 1995, pp. 1-24; Frigo 2002; Kaplan and Norton 

1998).

• Provide reflection of a broad array of performance concerns, not just certain narrow areas.

• Allow inclusion of data protocols, common definitions, classification, agreed reporting 

cycles, quality control/quality improvement processes, and common consensus among 

users as to the accuracy and efficiency of the measurement system and the data it uses 

(Eccles 1991, pp. 25-45).
12

Freight Performance Measure Primer

Most performance measurement systems are evolutionary and start with the current data 

available. The systems tend to mature, evolve, and improve over time.

Although a universally used comprehensive and consistent set of freight performance measures 

do not exist, important elements of freight performance are captured in Federal data sources. 

These data sources are predominantly available for highway and waterway infrastructure 

conditions, freight volumes, and freight externalities such as air emissions and crashes. Privatesector trade associations often produce robust freight performance metrics that can augment the 

public agency metrics.

There is no single agency or entity that has the mandate or resources to develop and sustain 

a comprehensive freight performance measurement program. Many individual agencies and 

private-sector trade organizations measure components of freight system performance, but there 

is no single agency compiling a comprehensive reporting system. Therefore, the recommended 

framework outlined later in this primer seeks to capture the existing performance measurement 

information from both Federal and private sources.

FREIGHT PERFORMANCE MEASURES

Freight performance measures are tools used to evaluate the level of accountability, efficiency, 

and effectiveness throughout the various freight modes including air, rail, highway, and water/ 

marine transportation. Performance measures provide a way to focus attention on the goals that 

an organization has defined while monitoring whether those goals are being attained. Analyses 

of freight performance measures assist in the prioritization and selection of specific freight 

improvement projects in long-range transportation plans, transportation improvement programs, 

and freight-specific investment programs (McMullen et al. 2010).

Some jurisdictions measure freight performance by linking funding to the existence of these 

measures. Other jurisdictions are proactively tracking freight performance measures because freight 

is integral to their economy and impacts congestion, air quality, roadway safety, and infrastructure 

preservation. Thus, freight performance measures grow in importance each year.

Some jurisdictions measure freight performance to identify priority levels for funding new projects. 

With input from the private sector, public sector jurisdictions can determine potential freight 

bottleneck areas. This partnership can also identify major investments planned by the private sector 

that might result in increased congestion and costly delays to the freight industry and passenger 

traffic in the region. Public agencies can also better understand the economic benefit that could 

occur because of these private sector investments.

Measuring freight performance also allows public agencies to establish transparency for public 

sector funding decisions. Because funding is limited, not all projects can be funded in each fiscal 

cycle. By collecting and sharing freight performance measures, the public can understand the 

justification that was used in the funding priority process.

Public jurisdictions are also recognizing that freight performance measures can provide an 
13

Freight Performance Measure Primer

indication of the return on investment for the funding of capital improvement projects. Projects 

such as adding highway capacity, reconfiguring interchanges, and grade separated railroad 

crossings, can be costly. Capturing existing and future projected freight data allows public agencies 

to determine if the project will improve freight flow and, therefore, justifies selection of the 

performance measures. Alternatively, if the project shows no appreciable positive impact to freight 

flow, the agency can re-evaluate a performance measure.

Surface transportation legislation in the Moving Ahead for Progress in the 21st Century (MAP-21) 

and the Fixing America’s Surface Transportation (FAST) acts calls for freight planning, freight 

performance measurement, and the development of tools for freight analysis and data collection to 

justify eligibility for Federal funding. The Federal Highway Administration (FHWA) has developed 

freight data and analytical tools to support analysis, plans, and project development. However, 

more needs to be done to improve freight analysis so that it allows States and Metropolitan 

Planning Organizations (MPOs) to understand the multi-modal nature of freight.

States and MPOs can use several existing data sources and approaches to get baseline and 

performance measures that support freight planning. This document identifies the most common 

data and analytical methods, which will assist State and MPO practitioners to respond to MAP-21 

requirements.

A region’s ability (or failure) to relieve traffic congestion and provide a reliable freight network 

can impact whether jobs are created locally or are shifted elsewhere. Congestion increases travel 

time for freight movement, which in turn means increased costs for delivery of consumer goods. 

Workplaces and residents that relocate due to congestion within metropolitan areas equates to 

tens of millions of dollars lost to local economies—jobs, schools, housing, taxes, and decreased 

standard of living.

Literature searches on the impacts of truck congestion on the economy have yielded few results. 

Data on commodity flows are limited and the impacts to the economy may vary depending upon 

the commodities transported and the types of delivery locations (e.g., manufacturing facilities, 

urban retail, grocery stores, and restaurants). Many transportation professionals do not have 

the expertise to analyze economic indicators and the impact the market may have on business 

development or business relocations and failures. At the same time, many economists are not 

trained in the field of transportation (multi-modal options, fuel cost impacts, deregulation, 

distribution center strategies, and variable roadway usage taxes). In addition, the freight industry 

is struggling to develop highly reliable statistical freight models that can determine mode choice, 

“The increasing congestion within the freight transportation system poses a threat to the efficient 

flow of the nation’s goods and has strained the system in some locations. Moreover, recent growth 

in international trade has placed even greater pressures on ports, border crossings, and distribution 

hubs – key links in the freight transportation system. Congestion delays that significantly constrain 

freight mobility in these areas could result in serious economic implications for the nation.”

-U.S. Government Accountability Office, Freight Transportation: National Policy 

and Strategies Can Help Improve Mobility; GAO-08-287, p. 1
14

Freight Performance Measure Primer

route choice, and optimal time of day for travel and deliveries. Effective freight performance 

measures can be a valuable tool in determining a correlation between qualities of freight flows 

and economic impacts.

Establishing and using freight performance measures provides many benefits. A jurisdiction 

cannot improve a system if it does not first collect the data necessary to measure and analyze 

the system. However, the process for choosing the appropriate freight performance measures 

can be complex. The performance measure depends greatly upon who is using the measure and 

what the goals may be. It is also important to distinguish between performance goals, measures, 

and targets. Performance goals are broad statements that describe a desired result or outcome 

and set the strategic direction for the performance process. Performance measures are used to 

convey goals in measurable objectives and assess progress toward meeting goals. Performance 

targets represent the expected success level within a specified period toward reaching the stated 

goal. For example, an enforcement agency may have the goal to minimize commercial vehicle 

crashes. Many crashes are due to driver error while other crashes are due to inadequate braking 

systems or other equipment failures. A performance measure used to track this goal may be the 

number of safety inspections of commercial vehicles. The target may be to increase the number 

of safety inspections of commercial vehicles by a yearly percentage. This is a measurable action 

to achieve the goal to reduce commercial vehicle crashes.

However, performance measures can be in conflict, such as certain highway or enforcement 

safety measures and travel times measures. For safety purposes, enforcement officers may want 

to stop as many commercial vehicles as possible to aid in a safe and crash free environment. For 

economic purposes, however, the goal may be to minimize any freight flow delays into and out 

of ports which could have a negative impact on safety. Developing a single set of performance 

measures which will satisfy all agencies and the private sector is not a simple task. What may 

be beneficial to some stakeholders may be detrimental to others given the disparate sets of 

objectives from each stakeholder entity.

LINKAGE OF PERFORMANCE MEASURES TO ECONOMIC IMPACTS

There is a direct link between performance measurement and the economy, as shown in Table 2. 

These relationships were studied in the September 2015 FHWA report “Measuring the Impacts 

of Freight Transportation Improvements on the Economy and Competitiveness”. The authors 

note that while some of the performance measures are closely related to each other, they should 

be considered separately as many times the performance measure is “in the eye of the beholder”. 

For example, it was noted that average speed and transit time performance measures have similar 

economic impacts. The difference in these performance measures is in how they are measured 

and the perspective of the agency using them. The transit time performance measure is more 

often used by shippers, who focus on how long it takes to move a shipment between two points. 

The average speed measure is more often used by transportation planners, who are considering 

how investments in infrastructure can improve the performance of specific segments of the 

transportation network (ICF & Leidos 2015, p. 12).
15

Freight Performance Measure Primer

The report identifies another example of related performance measures: reliability and transit 

time variance. Reliability measures the unexpected congestion or delay on a specific roadway 

segment measured by the variance in travel speeds. This measure would typically be used by 

a transportation planner. Transit time variance provides a measure of performance from the 

perspective of the shipper, measuring the level of unexpected delay for the shipment. Transit time 

differs from reliability because other factors, unrelated to vehicle speeds, can affect the transit 

time of a shipment. One example is a driver that exhausts his or her hours of service and must 

stop before completing the delivery. There would be a delay in the shipment’s transit time that 

was unrelated to the speed at which the vehicle could travel to make the delivery (ICF & Leidos 

2015, p. 12).

Table 2. Key linkages between performance measures and economic factors.

Performance 

Measure Economic Factors

Average speed

Higher average speeds may increase the geographic area from which 

supplies can be drawn and the effective market into which supplies can 

be sold (i.e. size of supply and market areas). A larger supply area can 

mean lower-cost and/or better inputs. A larger market area means greater 

production in each facility, thus greater productivity.1

 It also improves 

access to and connection with the freight network.2

Reliability A greater probability of on-time delivery reduces both production and 

distribution costs, due to lower buffer stocks.

Transit times in 

key freight lanes

Transit time affects the size of supply and market areas for firms in 

regions served by those lanes. Reduced cost of carriage is achieved due 

to improved utilization of truck and driver. This reduces both driver labor 

costs and vehicle operating costs.

Variance in transit 

times

More predictable transit times means more efficient scheduling and 

improved utilization of truck and driver. Also, it creates a higher 

probability of on-time delivery and reduces the cost of reliable service.

Crash rates Crash rates drive insurance costs, loss and damage of goods, and delivery 

failures.

Pavement quality Smoothness of pavement increases speeds, reduces loss and damage, and 

lowers vehicle operating costs.

Vehicle operating 

costs

Lower cost per mile reduces cost to shippers and increases supply and 

market areas.

1 Greater production does not automatically translate into greater productivity, but that economies of scale may 

become possible and attainable. Economies of scale can occur as the cost per unit of output decreases with 

increasing scale. This happens when fixed costs are spread out over more units of output. Economies of scale 

do have many limits, including exhausting nearby supplies of raw materials or saturating local consumption 

markets, requiring finished goods to be shipped further to generate new sales. Transportation costs thus play an 

important role in allowing scale economies.

2 Improving vehicle speeds could improve access to the freight network by reducing the time and cost required to 

access an intermodal rail facility, making intermodal shipments more economically viable.
16

Freight Performance Measure Primer

Some economic factors are easier to measure than others. For example, the average speed and 

transit time performance measures affect vehicle and driver costs for the truck, which in turn 

affect the cost of delivering freight. Estimating driver labor costs is relatively straightforward, as 

time savings can be translated into monetary values using estimates of average driver wages and 

benefits (in those cases where drivers are compensated with an hourly wage rate). In situations 

where drivers are compensated on a per mile basis, the impacts of reducing transit time may be 

more difficult to determine. Data are also available for the capital and operating costs associated 

with the truck. The cost of delay for the freight shipper and receiver is more complex, because 

it depends largely on the type and value of the freight carried, and how the freight shipment is 

being used by the customer. The average cost of delay for freight can mask a wide range of costs 

for different commodities, consuming industries, and customers (ICF & Leidos 2015, p. 13).

Estimating how performance measures affect long term productivity improvements in an 

industry can be more complex. Productivity measurements can include the increase in supply and 

market areas, level of access to lower cost or higher quality suppliers, improvements to inventory 

management, and increase in supply chain efficiencies. Productivity improvements can occur 

when average speed or transit times are reduced. Over the long term, improvements such as these 

may allow for business reorganization, expansion, and increased economies of scale (ICF & 

Leidos 2015, p. 13).

LEVELS OF ANALYSIS

Although a review of available literature provides many potential freight performance metrics, 

the small number of States that do have freight performance measures focus only on a few 

performance metrics. The National Cooperative Freight Research Program (NCFRP) Report 

10 reports that “mature performance measurement States” such as Washington, Missouri, and 

Minnesota use between five and ten measures. It was noticeable that no two States had the same 

measures, and in most cases, there were wide differences in the metrics. Although States reported 

freight performance metrics, most of the metrics were not used to calibrate performance of 

specific State programs except for Missouri’s customer satisfaction with its motor carrier office. 

Generic measures such as travel time in freight-significant corridors were likely a contributing 

factor to State efforts to improve overall travel times. However, it was unlikely for a State DOT 

to use freight performance measures to make decisions. Most of the measures appear to be 

indicators of broad trends of overall transportation system performance (TRB, NCFRP 2011, p. 

32).

As part of the process to gather information for this primer, interviews were conducted with 

freight stakeholders from jurisdictions of varying types and sizes. The purpose of the interviews 

was to get input from practitioners in the field that are working on performance measures for 

their respective organizations. Interviews were conducted with stakeholders from State DOTs, 

MPOs, and municipalities, as well as internationally. Stakeholders were asked general questions 

about their organization’s position on freight performance measures to gather a personal 

perspective on their organization’s freight performance measurement usage. The interviewees 

were told that there were no “right” or “wrong” answers, and that the goal of the interviews was 
17

Freight Performance Measure Primer

to obtain a ‘real world’ understanding of how freight performance measurement is conducted 

(or not conducted) within their organization. The interviews are summarized below. The list of 

interview questions and interviewees can be found in Appendix B.

National

Those interviewed indicated that agreeing to valid national-level freight performance measures 

has been challenging. One challenge is the lack of agreed upon definitions. This not only 

varies from State to State, but also from partners within a State (or corridor, mega region or 

municipality). The development of a “freight report card” from which jurisdictions can measure 

their respective freight performance measures against their peers has been reported to be flawed 

due to differences in jurisdictions, and therefore not suited to comparison. Figure 1 provides an 

example of performance report card used by the Florida Department of Transportation (FDOT). 

Unfortunately, there is no agreed-upon set of measures and definitions that all States use. Thus, 

the metrics cannot be easily compared from jurisdiction to jurisdiction. The data sources used 

also vary from one jurisdiction to 

another, as well as the resources 

available to obtain the data. In the 

event that two jurisdictions are in 

complete agreement on a common 

definition of a freight measure, the 

respective data collection methods and 

frequencies may be vastly different, 

which would yield differing trending 

results. In addition, some data sources 

used for freight performance measures 

may be adjusted (outliers and gaps 

accounted for) while other data 

sources are not.

From a national perspective, it 

is difficult to obtain a consensus 

on many performance measures 

definitions. Travel time reliability, 

safety, and congestion are just three 

examples of performance measures 

that have different meanings to 

different freight stakeholders. In 

addition to unclear definitions, many 

entities have different organizational 

goals. Differing goals result in 

differing measures. While various 

studies have been conducted, 

including those through NCHRP, 

there is still work to be done on the Source: Florida Department of Transportation.

Figure 1. Chart. Florida Department of Transportation 

performance measures graphic.
18

Freight Performance Measure Primer

issue of freight performance measure establishment, uniformity, and universal understanding 

and acceptability. The FHWA Rulemaking process will ultimately clarify specific performance 

measures that States and MPOs will be required to adhere to.

States

All States interviewed indicated that their organizations collected some form of freight 

performance measures, but no two States collected the same measures. The State freight 

performance measures varied greatly and included various combinations of the following: truck 

travel times, truck operating costs, truck emissions, transportation network reliability, on time 

travel performance along freight corridors, maintenance of waterway depths for marine freight, 

safety, infrastructure (conditions of highways and bridges), congestion, mobility, economic 

development, environmental, preservation, and percentage of roadway miles of roadway that are 

upgraded on critical rural freight corridors.

Some States are not measuring freight-specific performance, but are instead applying aggregate 

measures to freight (extracting data on fatalities that involve trucks or linking travel speeds and 

using this for freight travel, for example). Several States reported that their data are not fully 

processed or analyzed. While States often used free data provided by FHWA, all data sources are 

not free, and the data often has gaps and outliers that must be accounted for before an analysis 

can be conducted. Some States lack the tools to analyze their own data and they use contractors 

to analyze raw data, which can limit the ability to experiment with various analytical scenarios. 

If freight office staff is not able to manipulate the data, they must rely on outside sources to 

perform the analytical manipulations. Most State DOTs reported having multiple staff members 

who are exclusively or partially dedicated to freight-related initiatives. This is a departure from 

the limited staff resources that are available to local and regional agencies.

Freight performance measure data are frequently collected at the State level, but are usually 

evaluated annually. States routinely collect data in support of various programs such as 

the Highway Performance Monitoring System (HPMS), which includes data on the extent, 

condition, performance, use, and operating characteristics of the Nation’s highways. FHWA 

provides freight data through the Freight Analysis Framework (FAF). However, this data poses a 

challenge for States that need specific corridor-related data or even targeted regional freight data. 

The required granularity of data is not available from Federal sources. Some States are using 

truck probe data which can be helpful, but this type of data is reported to be poor in certain areas 

due to gaps in data and outliers in the evening and early morning hours.

Depending on the State, there is a large variation in modal freight performance data capture. 

Some States only evaluate truck data, while others may analyze truck, waterway, and rail data. 

Some even include air freight in their evaluation. Performance analysis for rail or pipeline is 

limited because private sector data are difficult to acquire due to the proprietary nature of the 

information, and some States believe they have little impact on private sector investments and 

operational parameters.
19

Freight Performance Measure Primer

All States indicated that the requirements 

of the FAST Act will influence their freight 

performance measure data collection 

processes, and agreed that the uniformity 

in requirements should help as the U.S. 

Department of Transportation (USDOT) 

develop State freight “report cards.” Some 

States noted that it is difficult to develop 

meaningful comparative assessments 

since not all States are collecting the same 

performance measures, or using the same 

data sources. Some States have the funds to 

purchase additional data, while others use 

free sources of data, or data obtained from 

other departments or agencies, to analyze 

and produce performance measures within 

their limited budget. Additionally, the scales 

of infrastructure can vary greatly from State 

to State. One State may not have to perform 

much maintenance on newer infrastructure 

and can plan (and devote resources to) other 

projects to enhance freight traffic. Other 

States must plan projects to maintain current 

infrastructure while keeping the flow of traffic 

running as smoothly as possible on already 

crowded highways and roads. 

Both the FAST Act and MAP-21 recommend 

the establishment of Freight Advisory 

Committees (FACs), so most States or regions 

report forming a FAC that holds either regular meetings or ad hoc meetings/consultations. These 

committees provide valuable stakeholder input to State planners. 

Figures 2 and Figure 3 portray the urban environment in Washington D.C. in which freight 

movement and delivery take place. The figures demonstrate some of the difficulties encountered 

during urban deliveries and the need to develop meaningful and useful data to combat these 

delivery concerns.

Regions

National or State-based freight performance data can be difficult to accurately disaggregate 

down to local or regional levels. Many cities and smaller regional planning organizations do not 

have dedicated staff for collecting and analyzing freight performance measures data. Figure 4

shows the major U.S. freight corridors where there is extensive data available. Some agencies 

collect freight performance data while others evaluate existing traffic data and make assumptions 

Source: E-Squared Engineering.

Figure 2. Photo. Truck delivery on 

streetcar tracks, Washington, D.C.

Source: E-Squared Engineering.

Figure 3. Photo. Urban freight 

delivery, Washington, D.C.
20

Freight Performance Measure Primer

on freight challenges. Others utilize freight 

advisory councils to help identify bottlenecks 

and determine freight related project priorities. 

Many of the performance measures collected 

are not specific to freight.

Those region/city entities that produce freight 

performance reports do so on a variable 

basis. Some produce freight performance 

information as infrequently as every 10 years, 

while others may evaluate performance on 

an annual basis primarily using non-freight 

related data collection results for freight 

applications.

Mega Regions and Freight Corridors

Mega regions and freight corridors are large networks of metropolitan regions connected by 

economic, social, environmental, and transportation systems where freight activity is intense. 

Examples of mega regions would include the Great Lakes mega region, the Northern California 

mega region, and the Texas Triangle mega region. Freight corridors would include the I-95 

Corridor, the I-10 Corridor, the I-5 Corridor, the I-81 Corridor, and the I-35 Corridor. In the case 

of mega regions and high-traffic freight corridors, multiple jurisdictions oversee the performance 

of the network. 

Figure 5 shows congestion on I-81 in 

Pennsylvania due to the construction activities.

Depending on the specific organization (DOTs, 

Freight Coalitions, or MPOs), tracking freight 

performance measures could be as complex as 

collecting data using shared funds and internal 

resources, or it could be a single jurisdiction’s 

responsibility, or a combination of those two 

scenarios. Freight Coalitions often use their 

resources to develop tools that can support 

the member organizations as they collect data 

for their respective jurisdictions within the 

corridor or region.

Freight performance measures examined by corridor coalitions include safety, environmental 

impacts, congestion, economic development, travel time reliability, incidents (such as truck 

crashes), and duration and impact of events (how much time to clear a non-recurring event and 

the impacts on freight flows). Some respondents noted there could be a completely different set 

of freight performance measures that could be used for long haul trucking compared to those that 

Source: Federal Highway Administration.

Figure 4. Map. U.S. Department of Transportation 

major freight corridors.

Source: E-Squared Engineering.

Figure 5. Photo. Congestion due to 

construction on I-81, Pennsylvania.
21

Freight Performance Measure Primer

would be used for urban freight delivery. As is the case with other segments (State, region, MPO, 

city, and county), there is no uniformity in data collection methods, data sources, or definitions of 

the various performance measures.

Depending on the organization, the analysis of the freight performance measurement can be used 

for the following purposes: producing reports for informing member jurisdictions, educating the 

public, developing aggregate reporting tools for freight stakeholders in the region, or prioritizing 

funding for deploying strategic plan projects.

Some organizations do not create any reporting tools; instead they serve as a collection and 

dissemination point for their members’ data collection efforts. The Philly Freight Finder is an 

example of a resource used to explore and track the Philadelphia-Camden-Trenton region freight 

network. Figure 6 provides a screenshot of the Philly Freight Finder online tool that contains an 

assortment of information, including truck travel time index information.

The frequency of performance data collection depends largely upon the source of the data. In 

some cases, the data are collected annually by public agencies (such as State DOTs and city or 

county agencies) for reports, such as traffic counts and classifications. In other cases, the data 

may be collected for periods of time every three to ten years (or more). These data collection 

intervals are often irregular because freight data collection is added to an existing project.

Source: Delaware Valley Regional Planning Commission.

Figure 6. Map. Philly Freight Finder screenshot showing travel

time index and average speeds for selected time period.
22

Freight Performance Measure Primer

International

Results of discussions with international representatives were consistent with those within the 

United States. The freight performance measures examined internationally include waiting times, 

loading times, shipment times, emissions, utilization rates (efficiency of trucks measured in 

weight), and congestion.

The truck efficiency 

performance measures 

and emissions are 

collected and reported 

annually. The data is 

collected by public 

agencies, navigation 

system providers, 

and motor carriers 

themselves. Figure 7

shows major roadways 

of Europe. Most of the 

freight performance 

measures collected are 

for roadways. There 

is little demand for 

collecting performance 

measures on other 

modes even though there is consensus among some freight industry stakeholders that there is a 

strong need. There is less interest in other modes because of minimal government investment in 

non-roadway modes. Border crossing data is abundantly available, primarily for trucks. There is 

limited data availability for rail and water freight transportation.

Figure 7. Map. European map of major roadways.

Source: Google Maps.
23

Freight Performance Measure Primer

CHAPTER 3. AVAILABLE DATA

Stakeholders have discussed the need for improved data. National-level or aggregated 

performance measurement data on what, where, and how much freight moves are available 

publicly in the United States. Only truck probe data is publicly available to measure how freight 

moves. Access to private sector freight performance data is limited due to proprietary reasons. 

Limited public sector data availability provides only partial information of the overall freight 

transportation network, preventing the public sector from adequately planning for freight, 

advancing freight performance measures, and understanding where investments are needed.

Private sector stakeholders focus largely on supply chains, or goods movement from start to 

finish. They have excellent sources of data for freight movement. They can identify freight flows 

down to the shipment and know in real-time where their goods are. They have access to stateof- the-art data and measurement systems and can identify where bottlenecks are occurring so 

they can continuously improve their supply chains. To stay competitive, however, private sector 

stakeholders typically do not release this information.

The private sector has partnered with the public sector, especially in recent years, to investigate 

ways in which they can share data. Many private sector representatives serve on State Freight 

Advisory Committees (FACs) and provide anecdotal information about where bottlenecks are 

occurring. Some have provided detail on the value of commodity being moved, related jobs and 

other information useful to decision-makers. However, this is still only information on what, 

where, and how much freight is moving.

FREQUENTLY USED FREIGHT DATA

Table 3 lists the available data sources currently supporting freight analysis and measurement 

beginning with the truck probe data, National Performance Management Research Data Set 

(NPMRDS), Freight Analysis Framework (FAF) data, the Commodity Flow Survey (CFS), the 

Highway Performance Monitoring System (HPMS) data, and truck parking data, which are most 

commonly used by States and Metropolitan Planning Organizations (MPOs) for performance and 

planning. Mostly publicly available or publicly obtainable data are described in this table, with 

an emphasis on the most common sources. There are opportunities for States and MPOs that can 

provide access to more robust or processed data in cooperation with the private sector.

The primer identifies publicly available data to use in simple yet meaningful metrics and 

analysis. For more details on frequently used available freight data, refer to Appendix C, and for 

additional available highway data, refer to Appendix D.
24

Freight Performance Measure Primer

Data Source Data Description

Publicly 

Available

Privately 

Available

Link

Highway

Truck Probe Data

Provides information 

on speed and reliability, 

identifies bottlenecks, 

and supports State and 

regional level analyses of 

truck movements.

X

http://ops.fhwa.

dot.gov/Freight/

freight_analysis/

perform_meas/

index.htm

National 

Performance 

Management 

Research Data Set 

(NPMRDS)

Provides average travel 

times measured in 

5-minute increments.

X

http://www.ops.

fhwa.dot.gov/

freight/freight_

analysis/perform_

meas/index.htm

Commodity Flow 

Survey (CFS)

Provides national 

and State-level data 

on domestic freight 

shipments.

X

Freight Analysis 

Framework (FAF)

Integrates data from a 

variety of sources to 

create a comprehensive 

picture of freight 

movement among States 

and major metropolitan 

areas by all modes of 

transportation.

X

http://faf.ornl.gov/

fafweb/Extraction0.

aspx

Commodity Flow 

Survey Microdata

Provides access 

to shipment-level 

characteristics while 

continuing to protect the 

confidentiality.

X

http://www.rita.

dot.gov/bts/sites/

rita.dot.gov.bts/

files/publications/

commodity_flow_

survey/index.html

Highway 

Performance 

Monitoring 

System (HPMS)

Provides the analysis 

of highway system 

condition, performance, 

and investment needs 

that make up the 

biennial Condition and 

Performance Reports to 

Congress.

X

https://www.

fhwa.dot.gov/

policyinformation/

hpms.cfm

Table 3. Frequently used freight data.
25

Freight Performance Measure Primer

Data Source Data Description

Publicly 

Available

Privately 

Available

Link

Highway Highway Statistics

Provides annual reports 

containing analyzed 

statistical information 

on motor fuel, motor 

vehicle registrations, 

driver licenses, highway 

user taxation, highway 

mileage, travel, and 

highway finance.

X

http://www.

fhwa.dot.gov/

policyinformation/

statistics.cfm

Maritime

Various Reports

Provides information 

on vessel calls in U.S. 

ports and terminals, 

fleet statistics, and trade 

statistics.

X

http://www.marad.

dot.gov/resources/

data-statistics/

Fleet Statistics Provides fleet statistics. X

http://www.marad.

dot.gov/resources/

data-statistics/

U.S. Maritime 

Administration 

(MARAD) Flag 

Fleet Lists

Provides agricultural trade 

statistics. X

http://www.marad.

dot.gov/resources/

data-statistics/

Marine 

Transportation 

System Data 

Inventory

Provides marine and 

safety statistics. X

http://www.marad.

dot.gov/resources/

data-statistics/

U.S. Army Corps 

of Engineers Data

Provides data on 

commerce, facilities, 

locks, dredging, imports 

and exports, and 

accidents.

X

http://www.navigationdatacenter.us/

data/data1.htm

Table 3. Frequently used freight data (continuation).
26

Freight Performance Measure Primer

Data Source Data Description

Publicly 

Available

Privately 

Available

Link

Pipeline

Oil Pipeline 

Statistics

Provides statistics on 

crude oil, gasoline, diesel, 

propane, jet fuel, ethanol, 

and other liquid fuels, 

including petroleum 

prices, crude reserves 

and production, refining 

and processing, imports/

exports, stocks, and 

consumption/sales.

X

http://www.eia.

gov/dnav/pet/pet_

move_pipe_dc_

R20-R10_mbbl_m.

htm

Pipeline Safety 

Statistics

Provides data about 

federally regulated and 

State-regulated natural 

gas pipelines, hazardous 

liquid pipelines, and 

liquefied natural gas 

plants.

X

http://www.phmsa.

dot.gov/pipeline/

library/data-stats

Rail

Surface 

Transportation 

Board Rail 

Waybill Sample

Provides carload waybills 

for all U.S. rail traffic 

submitted by those rail 

carriers terminating 4,500 

or more revenue carloads 

annually.

X X

http://www.stb.dot.

gov/stb/industry/

econ_waybill.html

Association 

of American 

Railroads

Provides information on 

North American freight 

railroads including 

finances, operations, 

performance, input cost 

indexes, traffic, U.S. 

carloads, intermodal 

traffic, and Class 1 rail 

tons.

X

https://www.aar.

org/pages/freightrail-traffic-data.

aspx

Railroad 

Performance 

Measures

Provides information on 

cars on line, train speed, 

and terminal dwell.

X http://www.

railroadpm.org/

Table 3. Frequently used freight data (continuation).
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Freight Performance Measure Primer

Data Source Data Description

Publicly 

Available

Privately 

Available

Link

Aviation

Air Cargo 

Summary Data

Provides a monthly 

freight summary, 

including both freight 

and mail carried by U.S. 

airlines in all service 

classes.

X

http://www.

transtats.bts.gov/

freight.asp

Landing Weights

Provides information 

on landing weights for 

cargo bearing airports 

throughout the U.S.

X

http://www.rita.dot.

gov/bts/sites/rita.

dot.gov.bts/files/

publications/state_

transportation_

statistics/summary/

index.html

Border 

Crossing

Trans-border 

Freight Data.

Contains freight flow data 

by commodity type and 

by mode of transportation.

X

http://transborder.

bts.gov/programs/

international/

transborder/

Texas A&M 

Transportation 

Institute Border 

Data

Provides average travel 

time of U.S.-bound 

commercial vehicles.

X

http://bcis.tamu.

edu/Commercial/

en-US/queryArchivedData.aspx

Table 3. Frequently used freight data (continuation).

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Freight Performance Measure Primer

CHAPTER 4. COMMON FREIGHT MEASURES

Public agencies do not all use the same freight performance measures, but there are some 

common performance measures that have continually emerged. These include truck travel times, 

travel time reliability, safety, infrastructure conditions, and congestion. This section provides 

information and links to common freight performance measures.

Truck travel times – Truck travel times can measure freight flow speeds as well as identify 

speed trends over time. Truck travel times can be obtained by working with private sector 

members of a Freight Advisory Committee (FAC). Freight stakeholders can supply average 

freight delivery travel time data that can be compiled and analyzed. Also, data sources such 

as National Performance Management Research Data Set (NPMRDS), INRIX and American 

Transportation Research Institute (ATRI) can provide speed data for links to supplement data 

collected directly from FAC members.

Travel time reliability – Travel time reliability is used to estimate on-time arrival performance 

of trucks to their delivery destination. It is measured based on the probability distribution of 

travel times and compares the spread between the median time and 95 percentile time of all trips. 

This provides a Planning Time Index that allows shippers to know how much time to plan to 

ensure a 95 percent on-time arrival performance.

Safety – Safety metrics consist of truck-involved accident reports. This information should 

consider fatalities, injuries, time of day, weather conditions, speeds, type of roadway, type of 

truck, and other vehicles involved. Law enforcement typically produces documentation for 

every truck crash that is reported. While this is a good source of information, it is not uncommon 

for the information collected by law enforcement (involving truck crashes) to be incomplete. 

Education of the need for accurate accident reporting is key to obtaining accurate data to serve as 

performance measures.

Infrastructure (conditions of highways and bridges) – Infrastructure condition data can be 

analyzed on freight routes to determine freight impacts. States are required to collect Highway 

Performance Monitoring System (HPMS) data, Proposal and Estimate System (PES) data, and 

bridge condition inspection data. The data obtained on freight corridors can be used to identify 

freight performance measures with little to no data collection expense.

Congestion – Congestion data are typically collected via Intelligent Transportation System (ITS) 

sensors and converted to information that is disseminated to the public through sources like the 

national 511 system. While this data consists largely of passenger vehicle data in addition to 

truck data, the congestion levels along identified freight corridors can be used to identify freight 

performance measures. There are also private sector sources for this data including systems like 

NPMRDS, INRIX, and ATRI.
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Freight Performance Measure Primer

FREIGHT DEMAND MEASURES

Several of the common freight demand measures are described below.

Freight Volumes

Freight movement has grown considerably over the past decade and will grow steadily over the 

long-term. An understanding of current and future freight volumes is an important measurement. 

The Freight Analysis Framework (FAF) provides comprehensive and multi-modal information 

to understand levels of freight movement and provides projections for the next 20 years for all 

modes that practitioners can use in estimates.

The HPMS truck count data can be used to measure truck volumes and provide averages of daily 

and annual truck traffic. Other modal data can provide baseline information for current demand, 

which can then be compared to FAF data and projections.

Jason’s Law/Truck Parking

Truck parking shortages are a national safety concern. Jason’s law was passed in 2012 as part 

of the Moving Ahead for Progress in the 21st Century (MAP-21) to prioritize Federal funding 

toward construction of safe parking lots for truckers to rest.

Jason’s Law directed the U.S. Department of Transportation (USDOT) to conduct a survey and a 

comparative assessment to:

• Evaluate the capability of each State to provide adequate parking and rest facilities for 

commercial motor vehicles engaged in interstate transportation.

• Assess the volume of commercial motor vehicle traffic in each State.

• Develop a system of metrics to measure the adequacy of commercial motor vehicle 

parking facilities in each State.

These surveys were supplemented by information solicited via customized questionnaires for 

stakeholder community members, including representatives from truck drivers, trucking firm 

logistics personnel, and travel plaza and truck stop owners and operators.

In 2015, the Federal Highway Administration (FHWA) published the Jason’s Law Truck Parking 

Survey as well as a geocoded file with accompanying database of all the public and private 

facilities and spaces throughout the United States in response to MAP-21 requirements. The 

results provided an understanding of truck parking facilities, spaces, usage, and other areas of 

analysis. This information is available at

http://ops.fhwa.dot.gov/Freight/infrastructure/truck_parking/index.htm.
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Freight Performance Measure Primer

Several additional studies have been completed in recent years to analyze the adequacy of truck 

parking and the associated safety risks. Many of these studies documented projected growth of 

truck traffic on the Nation’s highway system, severe truck parking shortages in some regions, 

a lack of adequate information for truck drivers about parking capacity at existing facilities, 

and the challenges associated with routing and delivery requirements and accommodating rest 

periods. The studies’ findings strongly correlate with anecdotal information collected from the 

trucking industry as well. Additionally, stakeholders supported the development of metrics that 

would best measure truck parking. Metrics include:

• Number of public and private spaces per State.

• Number of spaces in relation to National Highway System (NHS) mileage in the State.

• Number of spaces in relation to truck Vehicle Miles Traveled (VMT).

• Number of spaces in relation to Gross Domestic Product (GDP) by State.

Other metrics for demand, driver, economic valuation, and safety are included for truck parking 

in Appendix F.

FREIGHT EFFICIENCY – HIGHWAY SPEEDS, AVERAGE SPEEDS, RELIABILITY

Measuring efficiency can be done at the segment level, facility level, or corridor level. Most 

data used to obtain travel times such as the NPMRDS, probe data, or other travel time data (i.e., 

Bluetooth or ITS) are provided at the segment level. To measure efficiency for larger sections of 

roads, the practitioner will need to aggregate the segments together.

The 2015 “Freight Performance Measure Approaches for Bottlenecks, Arterials, and Linking 

Volumes to Congestion Report” offers the following performance measures for measuring 

congestion and reliability, based on travel time (Cambridge Systematics 2015, p. 46):

• Total Delay (vehicle-hours and person-hours) – Actual vehicle-hours (or person-hours)

experienced in the highway section minus the vehicle-hours (or person-hours) that would 

be experienced at the reference speed. It is only possible to calculate Total Delay if traffic 

volumes have been integrated. If not, unit delay (delay per vehicle) is substituted.

• Mean Travel Time Index – The mean travel time over the highway segment divided by 

the travel time that would occur at the posted, free flow, or normal speed.

• Travel Time Index (TTI) – The desired comparison percentile (often 95th or 80th) travel 

time for a segment divided by the travel time that would occur at a free flow, posted, or 

average travel time speed.

• Hours of Congestion per Year – Number of hours per year that vehicle speeds are below 

the following thresholds:

o Freeways and multi-lane highways: 50 miles per hour.

o Rural two-lane highways: 40 miles per hour.
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Freight Performance Measure Primer

o Signalized arterials: 30 miles per hour.

In addition to the measures recommended above, there are also “second order performance 

measures” recommended that are a direct result of changes in congestion and reliability. To 

simplify, first order performance measures can be directly measured. For example, travel speed 

can be directly measured and could be considered a first order performance measure. An example 

of a second order performance measure could be the loss of worker productivity based on lost 

time at work due to slow congestion related travel speeds (the first order measure). Second order 

performance measures are most commonly used to estimate impacts of bottlenecks and the 

benefits of improving them (Cambridge Systematics 2015, pp. 64, 65). Two examples of second 

order performance measures are delay and reliability costs, which are discussed below.

Delay

Delay Cost – This is the monetized value of delay. It is computed separately for passenger cars 

and trucks using the following formulas.

Annual Passenger Vehicle Delay Cost = Annual Passenger Vehicle-Hours of Delay × 

Value of Person Time × Vehicle Occupancy

Annual Commercial Cost = Annual Commercial Vehicle-Hours of Delay × Value of 

Commercial Time

Where: the annual vehicle-hours of delay is Total Delay above, broken out by passenger 

and commercial (trucks) (Cambridge Systematics 2015, p. 64).

Many studies have attempted to quantify the magnitude of delay and then estimate the costs of 

these delays on an annual basis. One of the most recent studies to address this issue, published 

in April 2016 by ATRI, calculates Total Delay and Delay Cost utilizing four data sources 

(NPMRDS, FAF, the FHWA’s Freight Performance Measurement [FPM] program Global 

Positioning System [GPS] data, and ATRI’s Industry financial data). At the national level, 

delay associated with weekday traffic congestion on the NHS totaled over 728 million hours in 

2014. This amount of delay is the equivalent of 264,781 commercial truck drivers sitting idle 

for an entire working year. Applying the 2014 national average operational cost per hour of 

$68.09 equated to just over $49.6 billion in increased operational costs to the trucking industry. 

Spreading this cost evenly across the 10.9 million registered large trucks in the United States 

results in an increased average cost per truck of $4,546” (ATRI 2016, p. 14).

For this study, ATRI implemented several standardization procedures so that the results could be 

compared to future studies that follow the same standardization procedures. “Incorporating these 

new procedures will allow this 2014 report to become a benchmark of comparison for future 

study years, as well as allow future year-over-year comparisons” (ATRI 2016, p. 2). The ATRI 

standardization procedures included:

• Using publicly available highway usage figures for the year studied.

• Using the national trucking industry cost of operation figure specific to the year studied.

• Establishing a standardized method for quantifying the amount of delay.
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Freight Performance Measure Primer

• Establishing a standardized method for estimating truck volumes (ATRI 2016, p. 2).

For additional information on how ATRI quantified the national average operational cost per hour 

of $68.09, refer to the ATRI report: “An Analysis of the Operational Costs of Trucking: 2015 

Update.”

In ATRI’s 2016 report, “Cost of Congestion to the Trucking Industry,” there is a variety of 

information on congestion costs at various levels. States can contact ATRI directly to request 

congestion figures for their respective metropolitan areas or counties in their State (ATRI 2016, 

pp. 23, 26).

Reliability Cost – In addition to the costs of typical delays, studies have shown that highway 

users also value reliability, or minimizing the variability in travel conditions. The 2013 Strategic 

Highway Research Program (SHRP) study, “SHRP2 Project C11: Reliability Analysis Tool,” 

developed a method for computing both typical congestion costs and reliability portions of 

congestion costs by using a “travel-time equivalent approach.” The report includes equations for 

Travel Time Equivalent for both passenger vehicles and commercial vehicles shown in Figure 8.

Although there have been improvements in data available for determining congested locations, 

local knowledge is still very useful in identifying congestion problems. Anecdotal information 

or observations from field engineers can be used to verify (or eliminate) potential congestion 

locations and to also identify locations that should be studied but may have been missed. This 

information can be gathered through formal, or informal, surveys of local engineers, planners, 

major shippers, and carriers. The survey questions should try to distinguish between commuter 

bottlenecks (weekday peak-period bottlenecks experienced by all vehicles—trucks and cars) and 

primary freight bottlenecks (where trucks are the primary vehicles or suffer the most delay). For 

example, designated port or freight distribution center truck routes can affect truck traffic more 

than commuter traffic. Depending on time of day or number of ships or trains that are loading 

and unloading, designated truck routes may experience peak period delays due to poor signal 

timing, grade crossings at entrances to freight facilities, poor signage, and many other issues that 

can cause disproportionately larger delays to trucks than personal vehicles (or commuter traffic).

Figure 8. Equation. Travel Time Equivalent. 
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Freight Performance Measure Primer

A resource for methods for measuring travel time reliability and understanding the calculations is 

the FHWA Office of Operations, Office of Transportation Management website, http://ops.fhwa.

dot.gov/perf_measurement/reliability_measures/index.htm, which has resources that describe 

measures and gives examples of congestion measures that employ the metrics suggested.

Speed

A common freight performance measure is speed. Freight corridors can have speeds monitored 

and used as a metric. Speed can also be used as a surrogate for time. While it is not always 

possible to measure travel times from origin to destination, it is possible to measure speeds. 

The high cost of congestion and consequences associated with freight delivery delays influence 

location decisions for distribution centers, manufacturing facilities, and other commercial real 

estate development.

The FHWA collects national freight link speed data, presented graphically on Figure 9 below. 

Per USDOT Facts and Figures, the national average truck speed data are collected from 500,000 

trucks at 250 freight-significant highway infrastructure locations annually (USDOT website 

2016). While the national truck speed data graphic provides a national indication of truck speeds 

(and heavy congestion zones), it does not have the granularity necessary to establish regional and 

local priorities based on truck speeds.

FREIGHT MODELING

Freight planning and forecasting employ a variety of tools and techniques, including economic 

flow models, land use and economic input-output analyses, commodity-based models, vehicle or 

trip-based models, and other analytic tools. Typically, data for forecasting models is obtained and 

Source: Federal Highway Administration.

Figure 9. Map. Average truck speeds on select interstate highways, 2010.
35

Freight Performance Measure Primer

maintained by public agencies. A variety of data sources are used, such as local data (including 

traffic counts, traffic forecasts, demographics, and land use), the National Transportation Atlas 

Database, the Commodity Flow Survey, the FAF, Transearch data, and numerous Federal 

sources. In addition to the public data collected, modeling also utilizes a variety of private sector 

data sets. Some of these include private shipper data, purchase orders, bills of lading, and ship 

manifests.

These tools can be used in freight planning and forecasting to document existing demographic 

and employment conditions and characteristics of freight transportation (tonnage, origin/ 

destination, and modes of transport). They can also be used to estimate the future measure of 

transportation (tonnage, origin/destination, and modes of transport) based on projected changes 

in population, employment, development, and economic forecasts.

Current forecasting and modeling tools have their limitations, however. Most commodity flow 

data sources are geared towards larger geographic regions and do not necessarily translate 

effectively to local planning efforts. Additionally, current tools do not accurately depict the 

complexities of the supply chains. Numerous factors influence transportation needs making it 

difficult to link the data resources (land use, demographics, and/or employment) to the freight 

activity that relates to these measures (truck counts, rail activity, and/or vessel activity). Generally, 

transportation forecasting models focus on average trip generation rates; however, freight activity 

does not lend itself to average rates of production and consumption. Lastly, third-party providers 

make freight less visible to shippers and receivers, making it more difficult for the public sector 

to document freight activity through the normal channels (Transportation Research Board [TRB], 

SHRP July 2013).

Valid performance metrics can and should be used as variables in the freight models that exist 

today. Performance metrics such as congestion, safety, and travel speeds will affect freight 

modeling outcomes, which in turn can directly affect infrastructure investment decisions.

Bottlenecks

Freight congestion problems are most apparent on highways that routinely experience recurring 

congestion and traffic backups because traffic volumes exceed highway capacity. Bottlenecks 

are estimated to account for about 40 percent of vehicle hours of delay. The balance, about 60 

percent of delay, is estimated to be caused by nonrecurring congestion, the result of transitory 

events such as construction work zones, crashes, breakdowns, extreme weather conditions, and 

suboptimal traffic controls.

Freight bottlenecks occur on highways that serve high volumes of trucks. They are found on 

highways serving major international gateways like the ports of Los Angeles and Long Beach, 

at major domestic freight hubs like Chicago, and in major urban areas where transcontinental 

freight lanes intersect congested urban freight routes. 

A freight bottleneck is defined by a combination of three features: the type of constraint, the type 

of roadway, and the type of freight route. A freight bottleneck may be caused by congestion at an 

interchange on a freeway serving as an intercity truck corridor, or by poorly timed traffic signals 
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Freight Performance Measure Primer

at intersections on an arterial road that serves as an urban truck corridor. Truck bottlenecks may 

also include “truck specific” delays caused by roadway features that impact truck movements, 

such as steep grades, substandard vertical or horizontal clearances, weight restrictions, delays at 

border crossings or terminals, or truck operating restrictions.

The identification of a freight bottleneck starts with simple methods to scan for potential 

locations. The “Freight Performance Measure Approaches for Bottlenecks, Arterials, and Linking 

Volumes to Congestion” Report suggests the following data upon which scans can be based:

• Vehicle Probe Data – Can be used to identify links with a significant amount of slow 

vehicle speeds, especially if the links are in sequence (indicates queuing).

• Inventory Data (e.g., traffic counts) – Identifies highway sections with high volume-tocapacity ratios.

• Anecdotal Information – State and local engineers and planners, who are usually aware 

of problem locations, can provide valuable information (Cambridge Systematics 2015,

pp. 3, 4).

Most of the methods reviewed attempted to identify recurring bottlenecks, including physical 

characteristics often associated with bottleneck locations such as interchanges, bridge crossings, 

and lane drops that were matched against the data scans. This step was necessary to identify 

other causes of slow speeds, such as long-term work zones or bad weather, and to give the 

bottleneck an identity. Performance measures used in the studies are the usual suite of traveltime-based measures made truck-specific. Delay is a common metric because it can be valued.

The performance of trucks on arterials is important because of first and last mile access to origins 

and destinations, ports and transfer facilities, but it has not been widely studied. This is because 

more serious truck bottlenecks occur on freeways and are due to the limited availability of data 

on port and transfer facility access. A few studies have used vehicle probe data to study port and 

transfer facility access, but this does present some technical concerns. More recent studies, which 

use vehicle probe data to develop detailed travel-time measures, rely on planning-level estimates 

of truck volumes. A step by step guide to measuring bottlenecks is available on FHWA’s website: 

http://ops.fhwa.dot.gov/publications/fhwahop15033/index.htm#toc.

The FHWA recommends that States and Metropolitan Planning Organizations (MPOs) consult 

this reference for guidance in addressing bottleneck analysis and understanding the application of 

the NPMRDS or other probe data for bottlenecks. (USDOT 2013)

RAILROAD SPEEDS AND RELIABILITY

Specific railroad reliability measures are rarely published, unless used to help support 

justification for a project seeking public funds. Public sector practitioners have sought rail speeds 

and reliability hoping to achieve multi-modal measures and identify freight bottlenecks. While 

specifics are not available, practitioners can consult some private sector resources for possible 

aggregated data. One resource is the website www.railroadpm.org, which provides high level 

indicators of performance for the six Class I carriers. 
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Freight Performance Measure Primer

Train speeds are measured by the haul times between terminals, with average speed being 

calculated by dividing train miles by total hours operated (excluding yard and local trains, 

passenger trains, maintenance-of-way trains, and terminal time) (refer to Table 4 for 52-week 

unweighted averages). The six major North American railroads voluntarily report train speed 

on a weekly basis. In addition to a composite speed, the railroads report train speed for various 

components of their network, such as Intermodal, Multilevel, and Coal Unit. Data is only 

presented at the national level; therefore, obtaining estimated speeds for a State or region may be 

challenging.

FREIGHT SYSTEM CONDITION INDICATORS

Bridge Structural Deficiencies

The National Bridge Inventory is the primary source of data for evaluating the conditions of 

over 116,660 bridges on the NHS in the United States. The overall performance trends for NHS 

bridges have improved since the early 1990s. Structural deficiencies as a percentage of total 

bridge deck area on the NHS have dropped from 13.3 percent in 2001 to 9.1 percent in 2010. The 

primary considerations in classifying structural deficiencies are the bridge component condition 

ratings for the deck, superstructure, and substructure. These structural deficiencies are considered 

separately from functional deficiencies such as width, vertical clearance, approach curvature, 

or other factors that may reflect current design standards and not the structural integrity of 

the bridge. Certain structural deficiencies could result in load limits, which can affect freight 

movement. Functional deficiencies may result in vertical or horizontal clearances that hinder 

freight movement, particularly for oversized loads. States are required to report this information 

to FHWA, so States and MPOs would already have an assessment of bridge data.

Intermodal Connector Conditions

The NHS freight connectors are the public roads leading to major intermodal terminals. NHS 

connectors were designated in cooperation with State departments of transportation (DOTs) and 

MPOs based on criteria developed by DOT. Although they account for less than one percent of 

NHS mileage, NHS connectors are key conduits for the timely and reliable delivery of goods. 

Railroad Operating Speed

March 2015 (mph)

Operating Speed

March 2016 (mph)

BNSF 23.2 27.6

Canadian Pacific 25.6 27.7

CSX Transportation 19.4 20.5

Kansas City Southern 26.8 26.6

Norfolk Southern 21.0 23.7

Union Pacific 24.0 26.4

Unweighted Average 23.33 25.41

Table 4. Average train speeds.
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Freight Performance Measure Primer

Hence it is important to evaluate the condition and performance of connectors and related 

investment needs.

Pavement Conditions

States collect extensive pavement condition data as part of their pavement management systems 

and asset management plans. States provide FHWA the following pavement condition data for 

HPMS on the full extent of the NHS and a statistically valid sample of other roadway sections:

• International Roughness Index.

• Present Serviceability Rating.

• Surface type.

• Rutting.

• Faulting.

• Cracking.

• Pavement thickness.

• Last overlay.

The HPMS data are compiled every year and are used for national system monitoring. The data 

are included in FHWA’s biennial Condition and Performance Report to Congress.

FREIGHT ENVIRONMENTAL MEASURES

Emissions

Practitioners can estimate air quality by estimating volumes of pollution from sources of 

emissions. Pollution is typically measured in tons or metric tons and is generally estimated for 

particulate matter, oxides of nitrogen, volatile organic compounds, ozone, and greenhouse gas 

emissions. Data is available from State environmental agencies, State DOTs, MPOs that are part 

of non-attainment areas, and the Environmental Protection Agency (EPA) for all modes. This 

information is useful in State Freight Plans and for project evaluation, but it is also required by 

the EPA.

The EPA promulgated the Air Emissions Reporting Requirements (AERR) in December 2008. 

The AERR consolidated and streamlined previous requirements of several older rules for States 

and local air pollution control agencies to submit emissions inventories for criteria pollutants 

to EPA’s Emissions Inventory System. The EPA uses these submittals, along with other data 

sources (primarily for air toxics), to build the National Emissions Inventory. Many of the States 

voluntarily report air toxics along with the required criteria air pollutants, and these air toxics 

reports are also used to build the National Emissions Inventory.
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Freight Performance Measure Primer

FREIGHT SAFETY MEASURES

Truck Injury and Fatal Crash Rates

Highway injuries and fatalities involving trucks are generally much lower proportionally than the 

percentage of all highway crashes, despite the higher number of miles traveled by these vehicles 

each year.

Although truck traffic has a relatively good safety record, concern over truck safety remains 

significant because of the size, weight, and reduced handling characteristics of trucks compared 

to automobiles. To provide a more stable measure over time of the trucking industry’s safety 

performance, the most common measure used is the number of injury and fatal crashes involving 

trucks per 100 million miles of travel. The National Highway Traffic Safety Administration 

(NHTSA) is the most reliable source of data for this measure. However, a major challenge is that 

States code truck-related incidents differently, so a push for common coding is necessary to truly 

understand the impacts of truck-related injuries and fatalities.

Highway-Rail at-Grade Incidents

Highway-rail at-grade crashes occur when a rail user and a highway user impact at a crossing 

site. This includes motor vehicles and other highway, roadway, and sidewalk users at both public 

and private crossings. Overall, the safety for highway-rail at-grade crossings in the United States 

has improved over the last two decades, most noticeably from 2000 to 2003 and from 2006 to 

2008. Although the number of at-grade crossing incidents has decreased, the Federal Railroad 

Administration (FRA) has prioritized the modernizing of grade crossings and the evaluation 

of public education and awareness strategies to reduce incidents on railroad rights-of-way. 

Railroads operating in the United States are required to submit monthly accident reports to 

FRA and crash data updates are published monthly. The FRA website allows users to query the 

incident database with a wide range of filters, including railroad, State, county, public and/or 

private crossings, and start/end date.

FREIGHT ECONOMIC DEVELOPMENT MEASURES AND ANALYSIS

Origin and Destination Pairs

The “Freight Performance Measure Approaches for Bottlenecks, Arterials, and Linking Volumes 

to Congestion” Report looks at Origin and Destination (O/D) pairs and discusses challenges with 

obtaining quality O/D data. The report notes that past studies have focused on the facility-based view 

of truck impacts but truckers and shippers are usually more concerned with the performance of the 

entire trip. Facility performance is more common since it is at the level at which the data are available 

to State DOTs. True O/D data that defines entire trips are not readily available to public agencies. 

The study recommends that in general, incorporating the user’s perspective into freight performance 

is a key step to advancing the state of the practice. However, both perspectives provide for a 

comprehensive freight performance management program, especially since most improvements that 

can be effected by transportation agencies are facility focused (Cambridge Systematics 2015, p. 3).
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Freight Performance Measure Primer

The GPS readings produce data from on-board or personal devices, but are heavily processed by 

the vendors so that any specific O/D data are aggregated. This is true of the vendors that provide 

the data as speeds assigned to a link, usually defined by the Traffic Message Channel standard. 

When using link data for freight purposes, specific freight truck data (rather than general traffic 

data) is recommended as several studies have confirmed both route and speed differences 

between cars (including taxis and limousines) and freight trucks (Cambridge Systematics 2015, 

p. 3).

A number of methods can be used to understand O/D. First, States and MPOs can talk to their 

freight stakeholders to understand actual business patterns. Anecdotal information is important 

to customize O/D analysis and identify the correct locations of interest. Second, the FAF, 

Commodity Flow Survey, and other economic information, such as the Census Business Patterns, 

are all great resources to identify origins and destinations or key freight nodes, freight generators, 

and their trading partners. For more information, consult the FAF documentation and resources 

on FHWA’s website that illustrate how to apply FAF analysis in an O/D analysis. Additionally, 

ATRI data can aid in the development of anonymous O/D truck trip models and tables. States 

may contact ATRI directly for information on obtaining these data. Other methods to obtain 

performance measure data include working directly with Freight Advisory Committees (FACs) 

and private sector stakeholders to obtain actual O/D data. Practitioners can also investigate using 

ITS technology probe data collected through traveler information systems, and partnering with 

university research centers for collecting O/D survey data from industry representatives.

Economic Evaluation

The U.S. freight network consists of over 985,000 miles of federally aided highways, with most 

national and international freight moving along the interstates. Freight reliability is essential 

along these corridors to promote economic competitiveness and economic growth. However, 

many of these facilities suffer from significant congestion and deteriorating conditions that 

negatively impact the flow of goods and the economy they support.

Understanding the relationship between freight activity and economic influences, such as fuel 

prices, employment rates, inflation, and economic trends, provides valuable information on 

how the freight industry functions and evolves as the economy changes. Understanding these 

trends is key to obtaining the analytics and insight needed for the continued support of the 

freight industry’s role in the U.S. economy. FHWA recently published a reference document 

on how to adapt economic valuation tools for freight analysis. This work focuses on measuring 

economic performance through three tools: 1) benefit-cost analysis (BCA), 2) economic impact 

assessments, and 3) analyses focused on estimating the impact of transportation on industry 

productivity and competitiveness. This document is available on FHWA’s website:

http://ops.fhwa.dot.gov/publications/fhwahop15034/ch1.htm.

The practitioner can develop measures of economic activity or benefit for freight projects 

using these types of measurement tools. These measures can also be used in project or plan 

development.
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Freight Performance Measure Primer

This document also provides a description of how typical performance measures relate to 

economic activity. For example, the average speed and the transit time performance measures 

have similar economic impacts. The difference in these performance measures is in how they 

are measured and the perspective of the agency using the performance measure. The transit time 

performance measure is more often used by shippers, who focus on how long it takes to move a 

shipment between two points. The average speed measure is more often used by transportation 

planners, who are considering how investments in infrastructure can improve the performance of 

specific segments of the transportation network.

Reliability and transit time variance are also closely related performance measures. Reliability 

measures the variability of congestion or delay on a specific roadway segment. This measure 

would typically be used by a transportation planner. Transit time variance provides a measure of 

performance from the perspective of the shipper, measuring the level of unexpected delay for the 

shipment.

There can be significant differences in what these performance measures capture. For instance, 

vehicle speeds are often measured for peak and off-peak traffic. If freight traffic moves 

disproportionately at night, improvements in peak speeds may have a smaller impact on truck 

traffic. Factors other than vehicle speeds on the network affect transit times. The hours-of-service 

rule for truck drivers is one example of this. Drivers who have exhausted their allowable driving 

time are required to stop. Transit time and average vehicle speeds measured on the network are 

thus not always related in linear fashion.

Because some of the performance measures discussed are closely related or overlap, there is also 

some overlap in the economic factors. Some of the economic factors are more easily measured 

than others. For instance, the average speed and transit time performance measures affect 

vehicle and driver costs for the truck, which affects the cost of delivering freight. These costs are 

traditionally measured in BCA. Estimating driver labor costs is relatively straight-forward, as 

time savings can be translated into monetary values using estimates of average driver wages and 

benefits. Data is available to represent the capital and operating costs associated with the truck. 

The cost of delay for the freight shipper and receiver is more complex, depending greatly on the 

type and value of the freight carried, and how the freight shipment is being used by the customer. 

The average cost of delay for freight can mask a wide range of costs for different commodities, 

consuming industries, and customers.

Estimating how performance measures affect long-term productivity improvements in industry 

is even more complex. These effects include increasing the supply and market areas, providing 

access to lower cost or higher quality suppliers, allowing for improved inventory management 

and a more efficient supply chain. Over the long-term, improvements such as these may allow 

for business reorganization, expansion, and increased economies of scale. Table 1, presented in 

the executive summary, summarizes the performance measures and the economic factors that can 

be linked to them.
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Freight Performance Measure Primer

FREIGHT ACCESSIBILITY MEASURES

Two types of freight accessibility measures are infrastructure-based and area-based. 

Infrastructure-based measures focus on the mobility associated with transportation infrastructure 

or services. Area-based measures quantify the accessibility of a given location (Geurs & van Wee 

2004).

These two measures differ in their measurement approach, but are interrelated in their capacity 

to identify and assess freight accessibility problems or constraints. Infrastructure-based measures 

are best suited to identifying the transportation performance factors that may constitute the 

sources of accessibility constraints. Area-based measures quantify the accessibility of a given 

location or user group.

In addition to measures that fit into the more academic categorization of infrastructure- or areabased measures, there is another class of measures that can be classified as network coverage or 

completeness measures. Rather than measuring performance on specific links or at specific nodes 

within the transportation network, these additional measures describe properties of the network 

itself and are addressed along with infrastructure-based measures.

Infrastructure-Based and Network Measures

Infrastructure-based measures capture the level of performance on transportation infrastructure 

that serves a given place. Infrastructure-based measures are applied at the link- or corridor-level, 

and in some cases to nodes within the system. They measure factors that affect accessibility and 

thus overlap considerably with other (non-accessibility) categories of transportation and freight 

performance measures. Common infrastructure-based measures used to characterize factors 

affecting freight access are shown in Table 5, along with a description of the infrastructure 

elements to which they are typically applied, and the other performance areas to which they also 

relate.

Infrastructure-based measures can be applied to any set of selected links and nodes. In some 

cases, the measures are applied to specific corridors, links, or nodes that have been identified 

by planners and freight stakeholders as being particularly important to freight movements. In 

this case, the measures incorporate an implied focus on origins and destinations. For example, 

the American Association of State Highway Transportation Officials (AASHTO) Standing 

Committee on Performance Measurement suggests that targets for their proposed Annual Hours 

of Truck Delay (AHTD) measure and their Truck Reliability Index (RI80) could be established 

“for truck trips on multi-state corridors between major city pairs, and at major international 

border crossings” (AASHTO 2013). Similarly, Virginia experimented with a distance-weighted 

TTI metric for all road links within a 10-mile buffer of key freight nodes (Michael Baker Inc. et 

al. 2012). Infrastructure-based measures, however, do not directly incorporate measures of the 

activities that attract freight movements (i.e., the opportunities afforded by freight accessibility). 

Area-based measures, discussed in the following section, offer this crucial “access to what” 

perspective.
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Freight Performance Measure Primer

Network coverage and completeness measures, like infrastructure measures, capture 

characteristics of the transportation system without directly measuring land-use and activity 

distributions in a geographic space. These measures evaluate network properties related to the 

coverage, directness, and connectivity of the transportation system. In practice, these measures 

are applied to a geographic area and are used to compare network characteristics across different 

target areas. Table 6 presents a set of measures that could be applied to a local or regional 

geography around a point of interest (e.g., a freight generator such as a port or industry cluster). 

This set of measures was developed and tested by the Virginia Department of Transportation 

(VDOT) as part of that agency’s pilot on accessibility measures. The local measures capture lastmile issues, while the regional measures are aimed at issues of network connectivity at the scale 

of a same-day truck delivery market.

Infrastructure-Based 

Measure Infrastructure Application

Travel time (or speed) Travel time (or speed) on a link, corridor, or between a selected

origin-destination pair is a measure of general mobility.

Waiting or transfer 

time.

Waiting or transfer time at border crossings, ports, intermodal

facilities is a specific measure targeted at bottlenecks.

Volume-to-capacity 

ratio

Volume-to-capacity ratio for a specific facility, mapped across a

network, or measuring the number of miles that exceed a certain 

threshold is a measure of congestion.

Travel time index (TTI)

The ratio of a selected upper-bound percentile of travel time to the 

median travel times on a selected path (link, corridor, or between a 

selected origin-destination pair) is a measure of unreliability.

Fluidity indicator

Fluidity indicator as defined by Transport Canada, targeted at

measuring end-to-end supply chain performance, e.g., “total

transit time of inbound containers from overseas markets to strategic 

North American inland destinations via various Canadian gateways” 

(Transport Canada 2014) is a measure of fluidity.

Table 5. Infrastructure-based measures.
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Freight Performance Measure Primer

Area-Based Measures

Area-based measures capture the accessibility of a location or area, relative to a set of other 

spatially distributed activities. Within the academic literature, area-based measures are more 

broadly accepted than infrastructure-based measures as measures that capture the full meaning 

of accessibility—access for whom and to what. They measure accessibility itself, rather than 

measuring performance or composition of the transportation network that enables or constrains 

freight access. Nevertheless, infrastructure-based and network measures can also capture 

elements of accessibility if they are applied to key O/D pairs or to key freight generators. 

There are three key dimensions of an area-based measure that together define the details of any 

measurement approach:

• The user group (defining the perspective of the measure).

• The attractions or destination to which access is being considered.

• The impedances that limit access between the former two.

Figure 10 presents a taxonomy of these three dimensions, with various options presented under 

each of the three headers.

Local Measures Regional Measures

Local measures (e.g., within a 1-mile radius of 

a key node) are:

• Number of intersections (measures 

network connectivity)

• Roadway centerline miles

• Network directness index (a measure 

based on the ratio between straight-line 

and network distances)

Regional measures (e.g., within a 3-hour/100-

mile radius of a key node) are:

• Truck network centerline miles

• Double-stack rail miles

• Rail track centerline miles

• Rail track miles of siding (represents 

increased operational efficiency from 

passing)

• Number of intermodal facilities

Table 6. Network coverage and completeness measures.
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Freight Performance Measure Primer

User Group

The first dimension within this taxonomy is that of the user group. The analyst must select 

the point in space from which impedances are calculated. If the process is to be applied in an 

automated or uniform fashion across an entire region, the most common choice is the centroid 

(weighted or unweighted) of a zone—a transportation analysis zone from a travel demand model, 

for example. An alternate approach is to focus specifically on generators of freight activity such 

as ports, warehousing districts, large box stores, and other freight generators. This approach 

does not attempt to develop a measure of access for every zone in a region; rather, it selects a 

set of freight generators and thus focuses the analysis. This approach appears in State practice. 

For example, the Minnesota Statewide Freight Plan (Cambridge Systematics 2005) reports on 

Source: Federal Highway Administration.

Figure 10. Chart. A taxonomy of the most common area-based measurement approaches.
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Freight Performance Measure Primer

measures of the percent of major generators with appropriate roadway access to interregional 

corridors and major highways. The focus on specific generators is akin to the focus of certain 

infrastructure-based measure approaches on identified subsets of the transportation system that 

are particularly important for freight movements.

Attractions or Destinations

The second dimension is the attractions or destinations to which access is measured. The 

“access to what” component of analysis is central to the idea of accessibility. Accessibility is 

about providing businesses with access, throughout the entire supply chain, to (a) their required 

material inputs that must be transported from the location of relevant suppliers, and (b) markets 

for their products, accessed by means of transport to the location of relevant buyers. Given the 

disaggregated and varied nature of industry requirements, there are a variety of approaches 

available for measuring relevant activities or opportunities. Each approach creates a proxy 

variable at some level of abstraction. Accessibility metrics are necessarily aggregate, and no 

area-based measure will by itself fully capture the level of access an individual business has 

to its potential buyers and suppliers. Nevertheless, there are opportunities for industry-specific 

approaches.

A network access approach uses access to the national freight system (defined as access to a 

highway interchange, rail terminal, or a port) as a proxy for the buyer and supplier markets 

that are more efficiently accessible via the higher-speed and lower-cost “backbone” of the 

transportation system. It focuses on the impedances of so-called “last mile” connections and, in 

some cases, the importance of intermodal services. If measuring access to an intermodal facility, 

the analyst may choose to incorporate an activity variable that captures the level of service 

provided by the other mode (e.g., number of flights or trains per day or number of destinations 

reachable). Adding this weight would result in a functional form more like that of the activity 

access metrics discussed next (refer to Figure 11).

Figure 11. Equation. Generic form of an area-based measure.
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Freight Performance Measure Primer

Activity access approaches seek to quantify the specific activities that are accessible via the 

available transportation system (the Wi

 in Figure 11). The most common approach is to proxy 

activities by counts of population or employment within a given accessible area. This population 

most closely acts as a proxy for final demand, while employment serves as a proxy for buyer 

and supplier firms. This generic approach could also be expanded to an industry-specific 

approach that would, for a given industry, target known categories of input suppliers and output 

consumers.

Impedances

The third dimension of calculating an area-based accessibility measure pertains to the 

quantification of impedances—the barriers between the user group and the identified attractions/ 

destinations. Here the analyst defines the approach along three dimensions: impedance 

quantification, network definition, and the functional form used to incorporate impedance 

values into the overall accessibility metric. Impedance can be quantified in units of travel time, 

travel cost, or generalized cost (which generally incorporates time, cost, and sometimes other 

relevant non-monetary costs of freight movements). These costs must be then calculated on a 

selected network representation of the available transportation system, such as a network within 

a Geographic Information System (GIS) based system, travel demand model, or other routing 

software.

Finally, the analyst must select a functional form with which to incorporate impedance values 

into the overall accessibility metric (the f(cij) in Figure 11). There are two common classes of 

functional forms adopted for accessibility measures: contour measures (also called cumulative 

opportunity, isochronic, or threshold measures) and potential/gravity-type measures. Contour 

measures count the attraction activities within a given impedance threshold from the point of 

origin. Thresholds are usually selected with some behavioral basis, e.g., using a three-hour 

threshold to capture the range for same-day truck deliveries. Potential measures, on the other 

hand, are continuous measures that count all activities in the region, weighted by a function of 

impedance. The chosen functional form of a gravity measure implies a tradeoff or equivalence 

between units of impedance and units of attraction (not necessarily 1:1). Utility-based measures 

are a special form of the more general gravity measure. They are based on a discrete choice 

theory and represent the utility of available choices, with the choice set being the activities 

located in all other zones, and the utility function representing preference-based weighting 

Note to Reader: There is some conceptual overlap between network access approaches and the 

network measures. For example, measuring the number of intermodal facilities located within a 

100-mile radius of a key node is similar in intent to measuring travel time to the nearest intermodal 

facility from a given origin zone. The former quantifies the density of intermodal exchange 

opportunities, while the latter seeks to more directly quantify the experience of a user in search of 

intermodal access. Similarly, a network access measure that quantifies travel time to the nearest 

interstate highway interchange for all zones will tend to move up and down with a network measure 

that counts the number of interstate centerline miles in a region. However, the network measure 

is constructed more as a density indicator (miles per square mile), while the area-based approach 

follows the classical accessibility formulation of “access for whom and to what.”
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Freight Performance Measure Primer

of costs and attractions for a given entity (individual or industry). Utility-based measures are 

derived from the estimation process used in discrete-choice modeling and represent observed 

behavior.

FUTURE PROGRAMS (FREIGHT FLUIDITY)

Transport Canada has developed an innovative approach to measuring freight performance that 

has been an excellent economic development and system improvement tool at the national, 

provincial, and local government levels. Canada’s “Fluidity System” approach incorporates 

multi-modal freight movement data on key freight corridors and infrastructure and reveals 

congestion points and trends. Truck transit GPS travel time data is obtained from a third-party 

provider and this information is used to understand the multi-modal performance of goods 

movement in the United States and across the borders of both Canada and Mexico. (Transport 

Canada 2014). 

This information has been used to identify where operational investments are most needed to 

improve the system and has helped Canada to realize economic and environmental improvement 

opportunities. Most notably, this national effort has been applied to provincial and local 

governments, as the tool can be focused both broadly at the total system level and specifically 

on congestion points. Canada has begun to include select U.S. locations, highlighting the 

importance of considering freight flows throughout North America and the importance to the 

regional and global economy.

The FHWA is leading efforts in the United States to implement a fluidity system of freight 

performance measures and analysis. The FHWA has assembled partners from Canada and 

Mexico, other operating administrations, Federal agencies, academic institutions, the TRB, and 

the private sector to discuss and design a path forward for implementing a fluidity system in the 

United States. 

In addition, the I-95 Corridor Coalition (I-95 CC) and Department of Commerce (DOC) are 

applying the concept of fluidity to major supply chains and corridors in a limited way using only 

truck probe data. A similar effort is underway with the North American Transportation Statistics 

Interchange to apply the fluidity concept using probe data to the North American Free Trade 

Agreement (NAFTA) corridor from Windsor, Ontario to Nueva Laredo, Mexico. These efforts 

focus on the use of truck probe data and will supplement multi-modal data as their projects 

progress to illustrate the performance of multi-modal connections. An application of multi-modal 

freight flows encompassing the full trip of goods (e.g., port to rail to highway to customer) such 

as the one Canada has implemented has not yet been developed in the United States.

FHWA plans to build on the research path designed through TRB efforts and will use the 

results of the I-95 CC and DOC concepts to advance corridor and mega- regional approaches 

to multi-modal fluidity measures. The focus will be on U.S. freight flows, but FHWA intends 

to coordinate with Canada and Mexico to explore a North American application. This project 

is supported by stakeholders, USDOT operating administrations, and Federal economic, 

environment, agriculture, energy, and related agencies. Additionally, this project responds to 
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Freight Performance Measure Primer

calls from the private sector for public sector support for freight infrastructure improvements and 

economic growth.

The MAP-21 and the Fixing America’s Surface Transportation (FAST) Act legislations require 

the USDOT to develop new tools and/or improve existing tools to support an outcomeoriented, performance-based approach to evaluating proposed freight related and non-freight 

related transportation projects. One potential method could be to use fuel tax documentation to 

approximate monthly truck movements.

Using fuel tax documentation to approximate the movement of trucks across the United States is 

based on the premise that all truck movements are a series of trips beginning at a fueling station. 

Each truck travels a certain distance until it runs out of fuel, which is based on the average miles 

per gallon for specific truck sizes. Origins of all truck trips are identifiable as the addresses of the 

fueling stations that are reported on the monthly fuel tax reports. Trucks can only travel within an 

area bounded by the distance allowed on an average fill-up, effectively providing a known area 

where all truck travel associated with each fueling station occurred. For additional detail on this 

potential truck movement tracking method, refer to Appendix G.

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Freight Performance Measure Primer

CHAPTER 5. CHALLENGES WITH FREIGHT PERFORMANCE ANALYSIS

Federal, State, and Metropolitan Planning Organization (MPO) practitioners regularly cite 

challenges in analyzing freight data. These challenges, which are impediments to measuring 

freight, include setting goals and understanding the State or MPO role in freight planning and 

funding, not having multi-modal data, inconsistency in data quantity and quality, and not having 

reliable freight models. The following section describes these challenges in more detail. This 

primer aims to help solve these issues by providing information on the most commonly used data 

and approaches to measurement from which practitioners can work to grow the state of practice 

for freight performance measures.

SETTING FREIGHT GOALS

The identification of goals is the first step in bringing about change and improvements. Goals are 

the desired results—the purpose of a program. Objectives are the methods by which the goals 

will be achieved. Performance measures help agencies evaluate if they are being successful 

in achieving the established goals and objectives. By understanding what the result should be, 

agencies can better select improvement projects or programs that will help the agency achieve its 

goals.

Differences between freight transportation and passenger transportation present challenges 

to setting goals and defining objectives. Freight is most often carried by private, for-profit 

companies, which may have different goals than government agencies. Private companies focus 

on such things as profitability, competitiveness, and customer retention.

Government agencies focus on outcomes that positively impact the public good such as 

increasing safety for the traveling public and reducing congestion and air pollution. To 

understand the differences between public and private goals, transportation agencies should 

gather input from stakeholders with different perspectives to find shared interests that benefit one 

another. The Florida Department of Transportation’s (FDOT’s) outreach for the Freight Mobility 

and Trade Plan is a good example and can be found at 

http://freightmovesflorida.com/wp-content/uploads/2016/11/FMTP-InvestmentElement_2014-09-11.pdf.

LACK OF CONSISTENCY

Freight stakeholders and practitioners continue to investigate how best to measure freight 

performance. Transportation agencies (State departments of transportation [DOTs], MPOs, and 

local governments) have mostly developed their own freight performance measures based on 

existing data available and their agency’s goals.

There is currently no standard set of universally accepted freight performance measures. In 

addition to the inconsistencies in performance measures between different agencies and States, 

there can also be inconsistency between data sets that support freight performance measures. 

Cities, counties, regions, or States may all collect similar data but may do so using different 

methodologies. This makes comparisons of freight performance between them difficult.
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Freight Performance Measure Primer

ABUNDANCE OF INCOMPLETE DATA

There is a large amount of data available that can be used to develop performance measures. 

Data on infrastructure conditions and general travel data aggregated from all modes is widely 

available. Data for measuring freight performance is much more scarce. Many transportation 

agencies use general performance on key freight corridors as a proxy for measuring the 

movement of goods. The partnership between the Federal Highway Administration (FHWA) 

and the American Transportation Research Institute (ATRI), which is providing truck volume 

and speed data collected through a sample of Global Positioning System (GPS) transmissions, is 

making additional freight data available. However, even this data is incomplete. This data sample 

is drawn mostly from large national trucking firms and independent truckers. Recent trends 

indicate the larger companies are handing off deliveries to smaller delivery companies, which 

could mean the data is less reliable for important last-mile connections, as these companies are 

not tracked by ATRI data.

FREIGHT MODELING CAPABILITIES AT THE REGIONAL SCALE

Another challenge is the inability to accurately model the effects of various transportation 

improvements on freight performance. Many regions have truck forecasting models which 

were developed from a patchwork of data. These models do not typically cover all modes 

of freight and are currently incapable of modeling how different improvements or changing 

economic, demographic, or land use conditions might change freight mode share. There have 

been, however, some recent improvements in this area. For example, the Tampa Bay Regional 

Planning Model has a freight component improved by FDOT District 7 to better estimate truck 

travel. However, freight transportation can cover very long distances. In these cases, even a 

strong regional truck model may not accurately capture freight that is passing through the region.

APPLYING THE MEASURES

The 2011 the National Cooperative Freight Research Program (NCFRP) Report 10, 

“Performance Measures for Freight Transportation,” noted most of the agencies that reported 

using freight performance measures did not rely on these measures to guide decision-making. 

The freight performance measures were used as indicators of how freight (typically truck 

freight) was moving through the region, but the measures did not influence the decision-making 

processes or provide the impetus to amend agency goals or objectives.

DATA CAPTURE

It is important to have common definitions of freight performance measures to ensure 

consistency and integrity. Several common terms regarding freight data, such as “merging 

data,” “governing data,” “quality of freight-related data,” and “quantity of freight-related data” 

are not always clearly understood. Definitions for these concepts are included in the glossary 

in Appendix E. Terminology referring to freight performance measures data should always 

be examined and understood before credence is given to any trends identified in performance 

measure analysis.
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Freight Performance Measure Primer

The development of performance measures typically include difficult tradeoffs between their 

predictive or descriptive usefulness, the cost and time involved in acquiring data, and the ability 

for citizens and elected officials to easily understand the measure’s meaning.

INTEGRATING DATA

The FHWA defines data integration as “the process of combining or linking two or more data sets 

from different sources to facilitate data sharing, promote effective data gathering and analysis, 

and support overall information management activities in an organization (Transportation 

Research Board [TRB], NCFRP 2011).

The “Freight Performance Measure Approaches for Bottlenecks, Arterials, and Linking Volumes 

to Congestion” Report (Cambridge Systematics 2015) noted that methodologies for producing 

performance measures from data are very similar, but would benefit from consistent/standardized 

processing procedures. Developing high-level performance measures from low-level data 

requires multiple processing steps, and there are usually multiple ways to perform each step. 

Default values also are often necessary, and these can vary depending on the methodology. The 

result is that different values can result from processing the same basic data.

SUSTAINING FREIGHT PERFORMANCE MEASURES

Much of the literature on performance measures and the experience of the practitioners 

interviewed for the NCFRP Report 10 indicated that performance measuring systems tend to 

mature and improve over time. Input from stakeholders indicated that of the agencies that had 

comprehensive measurement systems, few had those systems from the beginning.

“Begin with what you have” was a common recommendation from the performance management 

practitioners interviewed for the report. It was also acknowledged that there is no one proposed 

freight performance measures system that will meet the needs of all stakeholders. Thus, the 

practitioners predicted that stakeholders would advocate for additional measures that could be 

added over time. A perceived benefit of publishing the “Freight System Report Card” was that 

flaws in the current data would be uncovered as the report card was examined, which should 

result in improvement of the data.

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Freight Performance Measure Primer

CHAPTER 6. BEST PRACTICES AND RECOMMENDED

FREIGHT PERFORMANCE MEASURES

BEST PRACTICES: PRIVATE SECTOR

Billions of tons of freight worth trillions of dollars move in the United States every year. Trucks 

account for over 70 percent of the value of all domestic shipments, while rail dominates the 

long-distance freight market (shipments over 500 miles). Freight performance measures are a 

key ingredient to successfully, efficiently, and safely moving freight within, into, and out of the 

United States.

Performance measures can vary in meaning between the private sector and the public sector. The 

public sector is interested in measuring the effect of policy decisions, such as total shipments, 

commodity flows, and asset management. The public sector is also interested in measuring the 

success of regulations and standards.. These include environmental and safety measures, such as 

emissions of criteria pollutants, employee injuries, and fatalities.

The private sector interests are related but different. Providers are interested in economic 

measures, such as financial performance, equipment performance, loads, employees, and 

customer service. While the private sector is also concerned about fatalities and injuries, they 

also measure the effects of these incidents on insurance costs and liability.

There is a lack of uniformity across the main modes of freight transportation, and less agreement 

among modes as to the most critical performance measures. Although each mode of freight 

transportation has its own performance measurement needs, many of the measures across 

the modes emphasize the financial aspects. Despite the lack of uniformity or consensus of 

performance measures, several measures do tend to represent all modes of freight transport:

• Average length of haul.

• Operating expenses as a percent of revenue.

• Revenue per ton-mile.

• Tonnage (total, all loads).

• Terminal dwell time or empty miles.

The trucking industry is interested in regulations that restrict operational flexibility, rising costs, 

the cost of satisfying regulations, congestion impacts, and improving safety. Trucking industry 

issues generally fall into the following subject areas:

• Hours of service regulations.

• Driver availability and shortages.

• Fuel costs.

• Highway congestion.

• Toll costs.

• Tort and other liability matters.

• Environmental controls.
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Freight Performance Measure Primer

• On-board technology.

The freight transport industry is dominated by Class I railroads, which comprise over 90 percent 

of freight rail revenue. Thus, the rail performance measures focus primarily on those Class I 

railroads. Rail freight performance measures typically consist of the following components:

• Average length of haul.

• Average tons per carload.

• Average tons per train.

• Carloads originated.

• Containers transported.

• Employees.

• Freight cars in service.

• Freight revenue.

• Freight revenue per ton-mile.

• Locomotives in service.

• Net income.

• Operating expense.

• Operating ratio.

• Operating revenue.

• Railroad market share.

• Return on average equity.

• Ton-miles of freight.

BEST PRACTICES: PUBLIC SECTOR

Most State departments of transportation (DOTs) track general performance measures. There 

are few, however, that have a robust freight performance measurement system in place. Several 

States, including Florida, Maryland, Minnesota, Ohio, Oregon, and Washington, have wellestablished freight programs that offer lessons learned. This primer highlights a few of the States 

that have proactively developed their freight programs.

Maryland

The Maryland Department of Transportation (MDOT) has a long history of freight planning 

and freight performance measurement. MDOT, unlike transportation administrations in 

many other States, is the umbrella organization to other business units including the State 

Highway Administration, Maryland Transportation Authority, Maryland Port Administration, 

Maryland Aviation Administration, Maryland Transit Administration, and the Motor Vehicle 

Administration. While each business unit under the MDOT umbrella is a key component to 

MDOT’s freight planning efforts, it is the Office of Freight and Multimodalism that is the lynch 

pin that holds these efforts together.

In 2009, MDOT published the Maryland Statewide Freight Plan. This Plan included a 

comprehensive review of the current and future freight system in Maryland and outlined policies 
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Freight Performance Measure Primer

and projects necessary to ensure the safe and efficient movement of freight in, out, within, and 

through Maryland. The Plan concluded that freight is expected to grow in the region by 100 

percent by 2035 and identified 138 freight-related projects to address future freight demand, 

including 95 highway projects, 26 rail projects, and 17 port projects with a value of over $35 

billion. Many of these projects have begun construction, and more are in the planning and design 

phases.

In 2014, the Office of Freight and Multimodalism developed the Strategic Goods Movement 

Plan, a guiding document for planning and programming. This five-year plan is an update to 

the 2009 Statewide Freight Plan. However, unlike the Statewide Freight Plan that focused 

on projects, the Strategic Goods Movement Plan emphasizes policy positions and strategic 

planning based on qualitative and quantitative analysis. This Plan analyzed trends and long-range 

projections and was developed with support from multiple stakeholders, including the Freight 

Stakeholder Advisory Committee, which assisted in identifying issues and outcomes to improve 

the safety and efficiency of freight transportation. The Freight Stakeholder Advisory Committee 

consisted of freight stakeholders from the private sector, the Federal Highway Administration 

(FHWA), the Federal Railroad Administration (FRA), and the public sector.

Ohio

The Ohio Department of Transportation (ODOT) initiated a statewide freight study in 2013 to 

understand how Ohio’s freight infrastructure is being utilized. The two purposes of the study 

were to 1) plan and prioritize future strategic investments in Ohio’s freight infrastructure, and 

2) guide future economic development activities to make the most efficient use of the existing 

freight infrastructure. The results of the freight study will help inform and guide the State 

transportation plan (Parsons Brinckerhoff 2013).

The ODOT studied the freight system challenges that policy makers should be aware of and be 

prepared to address. The study concluded that truck productivity is down primarily due to driver 

shortages and changes in Federal regulations. It also noted that, although the interstate system is 

the trunk line for shippers and carriers, the U.S. and State routes provide much of the access to 

major customers.

The study also included several observations: manufacturing is making a comeback in the United 

States; air cargo, while still essential to business, has changed and no longer requires major hub 

operations; and Federal investment in locks and dams is inadequate on the inland waterway 

system.

The study evaluated several strategies that would not only benefit Ohio, but would be beneficial 

for many States and Metropolitan Planning Organizations (MPOs). Some of the strategies 

include:

• Develop driver training programs for potential truck drivers.

• Replace diesel fuel with natural gas to reduce fuel costs.

• Public and private air cargo owners should investigate niche markets to convert facilities 
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Freight Performance Measure Primer

from freight hubs to logistics centers based on trucking distribution.

• Investigate use of longer combination vehicles.

• Evaluate cost-benefit of improving local rail lines for 286,000-pound railcar capacity so 

short line users continue to receive cost-effective rail service.

Oregon

In 2005, the Oregon Department of Transportation (ODOT) established a statewide traffic 

mobility program. This program has been extremely successful with coordination efforts, 

forecasting conflicts, and resolving freight related issues. ODOT has developed a close 

partnership with the trucking industry, holding regular meetings to discuss policy issues. Within 

Oregon, the Oregon Transportation Commission (OTC) makes decisions about investments on 

the highways and, to a lesser extent, for other freight-moving modes. The OTC uses several 

broad criteria for making investment decisions, which vary by funding program. For example, 

“projects that support freight mobility” was one of the prioritization factors established for the 

2008–2011 Statewide Transportation Improvement Program (McMullen et al. 2010). Because of 

this legislation, the OTC was directed to consider factors such as transportation cost reduction, 

multi-modal connections, system efficiency, project costs, and economic benefits in selecting 

projects.

Washington

The Washington State Department of Transportation (WSDOT) worked with three State Freight 

Plan technical teams (urban goods movement, rural economies, and the State’s global gateways) 

to identify and prioritize WSDOT’s truck freight performance goals. WSDOT coordinated with 

over 60 freight stakeholders to serve on these teams, including carriers, local governments, 

ports, air quality experts, laborers, and academic experts. These stakeholders helped determine 

performance goals that align with both State and Federal policies and are most important to 

shippers, carriers, and residents of Washington. These goals will track the performance of the 

State’s truck freight economic corridors.

WSDOT worked with MPOs, the technical teams, and its regional transportation planning 

organization to develop the truck performance measures and to develop criteria used to define 

State truck, rail, and waterway freight economic corridors. These criteria will be used to evaluate 

State truck freight economic corridors, such as slow speeds, resiliency, bottlenecks, truck 

collisions, and locations with poor state of freight repair.

I-95 Corridor Coalition

I-95 spans 1,917 miles along the eastern seaboard and carries an average of over 10,000 trucks 

per day, with peak daily truck traffic of over 31,000. I-95 carries approximately 35 percent of the 

Nation’s vehicle miles traveled and carries over 5.3 billion tons of freight each year.

The I-95 Corridor Coalition (I-95 CC) was formed in the early 1990s as a partnership of 

transportation agencies, toll authorities, public safety, and related organizations spanning the 
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Freight Performance Measure Primer

entire east coast from Maine to Florida. The purpose of the I-95CC is to provide a forum for 

decision-makers to address transportation and operations issues. The I-95CC focuses its efforts 

on the long-distance movement of freight, passengers, and travel that stretches through multiple 

jurisdictions and often across multiple modes.

The I-95 CC understands that with the volumes of traffic and the amount of freight being moved 

along I-95, multi-modal and multi-State performance measures are essential to promoting a 

safer and more efficient movement of goods and people. In 2016, the Coalition, in conjunction 

with FHWA, completed a study that evaluated the development of freight performance measures 

across multi-State supply chains. The study produced a framework for measuring performance, 

data and measurement techniques, identification of short-term solutions for defining critical trade 

lanes, and longer-term solutions for improved data and multi-modal approaches.

U.S. Department of Transportation, Federal Highway Administration

The FHWA is leading efforts in the United States to implement a Freight Fluidity System of 

performance measures and analysis. The Freight Fluidity System (USDOT 2015) spans across 

the freight modes and incorporates multi-modal freight movement data on key freight corridors 

and infrastructure to reveal congestion points and trends. This information has been used to 

identify where operational investments are most needed to improve the system and can help 

to realize economic and environmental improvement opportunities. State and local DOTs and 

MPOs can apply this tool both broadly at the total system level and specifically on congestion 

points.

RECOMMENDATIONS FOR A COMPREHENSIVE FREIGHT PERFORMANCE 

MEASUREMENT PROGRAM

Many options can be considered when investigating the development of performance measures. 

Statewide, system-wide, corridor-wide, or individual routes are just a few of the possibilities. 

The development of a Freight Advisory Committee (FAC) is recommended. Freight advisory 

groups can play a key role in selecting the most appropriate freight performance measures. These 

committees can be made up of any number of public and private freight stakeholders. A FAC 

should consist of stakeholders from State DOTs, motor truck associations, railroads, ports, air 

freight, economic development organizations, freight shippers and receivers, logistics companies, 

agricultural industry, and other relevant freight stakeholders as deemed necessary.

The Oregon DOT “Freight Performance Measures: Approach Analysis” report researched each 

State DOT’s long-range plans. Table 7 shows the most-cited goals and the number of States 

reporting those goals.
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Freight Performance Measure Primer

The aim of most DOTs and MPOs is to increase infrastructure efficiency through focused 

investments. Therefore, measures need to be developed that predict the impact of investment on 

transportation flows through the entire State and to project potential modal shifts. This, however, 

will likely require performance measures for multiple modes. To better evaluate the performance 

of the freight transportation system, data relating each of the major policy goals for freight 

by mode is required. Obtaining this data is a first step in developing metrics that are useful to 

decision-makers for policy analysis.

Most safety-related freight performance measures include the number of accidents or fatalities 

from incidents involving freight vehicles on a segment of roadway. Safety measures would 

quantify the amount of loss and damage because of the accident or fatality, or the total cost of 

freight loss and damage from crashes divided by the total freight vehicle miles traveled. This 

measure could also be considered as a system performance measure.

Table 8 below presents performance measures that States and MPOs could include in their 

freight planning efforts. Some of these performance measures will be identified by FHWA as the 

proposed rule making process is finalized. Other measures were derived from a research of States 

and MPOs currently measuring freight performance.

Major Policy Goal # States Citing 

as a Goal

Safety 42

Environmental Stewardship / Quality of Life 32

Protection / Maintenance of Transportation Investment 29

Mobility of People and/or Goods (Only 11 Cited Freight Specifically) 28

Accessibility 21

System Efficiency 18

Promotion of Interconnectedness / Multi-Modal Systems 16

Security 15

Economic Vitality 15

Economic Development 13

Revenue Enhancement 12

Congestion Management 8

Table 7. Stated goals for transportation policy.
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Freight Performance Measure Primer

Category Measure

Observed

Estimated

Data 

Available1

 Safety

Highway

Motor carrier crash rate X a

Motor carrier truck at-fault rate X a

Number of heavy truck-related fatalities X a

Capacity of weigh stations (number of trucks processed per 

hour) X a

National Highway System (NHS) pavement conditions X b

NHS bridge conditions X b

NHS intermodal connector condition X c

Total cost of freight loss and damage from accidents per Vehicle 

Miles Traveled (VMT) X b

Railway

Total loss and damage from accidents per route-mile X a

Total loss and damage from accidents per tons moved X a

Number of at-grade railroad crossings along freight-significant 

corridors such as freeways and interregional corridors X a

Number of rail fatalities X a

Train derailments per ton moved X b

Water

Value of cargo lost or damaged per tons or value of cargo 

moved X d

Containers damaged or lost per containers handled/total 

containers X d

Air

Total loss and damage from accidents divided by value of freight X d

Percent of study airports meeting Traffic Safety Administration 

(TSA) guidelines for general aviation security X a

Incidents per 1,000 operations at freight-significant airports X a

Maintenance and Preservation

Highway

Percent of pavement in good condition on freight-significant 

highways X b

Number of weight-restricted bridges per total number of bridges X a

Percent of bridges that meet good and poor structural condition 

thresholds X a

Service life remaining on highway pavement X b

Table 8. Recommended performance measures.

1 a) Data available. No manipulation needed.

b) Data available. Manipulation or analysis needed.

c) Data could be generated from simulation or model.

d) Data not readily available.
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Freight Performance Measure Primer

Category Measure

Observed

Estimated

Data 

Available2

Highway Benefit of truck weight enforcement on pavement service life X c

Railway

Miles of track in expected or Federal Railroad Administration 

(FRA) Class I divided by total miles of Class I track X a

Number of double-stack tunnel restrictions per number of 

tunnels X a

Water

Percent of tons of freight moving though constrained locks X b

Unscheduled lock closure time (hours) 

Channel depths at the port divided by depths at competitive 

ports 

X b

Air Percent of pavement in fair or poor condition at freightsignificant airports X a

Mobility, Reliability, and Congestion

Highway

Percent of interstate providing reliable travel times* X a

Percent of interstate where peak hour travel times meet 

expectations* X a

Percent of non-interstate NHS providing reliable travel times* X b

Percent of non-interstate NHS where peak hour travel times 

meet expectations* X b

Annual hours of excessive delay per capita* X X b

Urban: Average hours of delay per day for freight vehicles on 

freight-significant links X X b

Urban: Travel Time Index (TTI) on freight-significant links 

(ratio of the peak travel time to free-flow travel time) X b

Percent of interstate mileage allowing reliable truck travel 

times* X a

Percent of interstate mileage that is uncongested* X X a

Clearance time for incidents, crashes, or hazardous materials X b

Number of intersections and ramps with inadequate turning 

radii for large trailers on freight-significant corridors X X c

Table 8. Recommended performance measures (continuation).

* Performance measures to be determined in MAP-21 proposed rulemaking

Source: Minnesota Department of Transportation, Measurement sources for freight performance

measures and indicators, Oregon Department of Transportation, Freight Performance Measures: Approach Analysis.

2 a) Data available. No manipulation needed.

b) Data available. Manipulation or analysis needed.

c) Data could be generated from simulation or model.

d) Data not readily available.
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Freight Performance Measure Primer

Category Measure

Observed

Estimated

Data 

Available3

Highway

Urban: Buffer index on freight-significant links (ratio of the 

95th percentile travel time, the average travel time to average 

travel time) 

X X b

Rural: Average hours of delay per day for freight vehicles on 

freight-significant links X X b

Number of truck rest areas and their capacities X a

Rural: Average travel time on freight-significant links X c

Railway

Tons or ton-miles of freight over relevant period X b

Average terminal dwell time train-hours of delay X c

Percent of rail track-miles with 286,000-pound railcar capacity 

rating X a

Railroad corridor level of service X c

Water

Tons of traffic arriving at a port X a

Twenty-Foot Equivalent Units (TEUs) passing through port 

(port throughput) X a

Gate reliability or truck turn time X b

Ship unload rate (time per container) X d

Ship load rate (time per container) X d

Average delay per barge tow on river X a

Air

Flight frequency by airlines with cargo capacity (number per 

day) X b

Average time between flights by airlines with cargo capacity 

(minutes) X b

Percent of on-time departures at freight-significant airports X a

Percent of on-time arrivals at freight-significant airports X a

 Accessibility and Connectivity

Highway

Triple trailer VMT as a percent of total freight VMT X X a

Percent of major generators with appropriate roadway access to 

interregional corridors and major highways X a

Percent of shippers with access to triple network X c

Railway Class I: Ratio of unit train carloads (or tons) per total carloads 

(or tons) X d

Table 8. Recommended performance measures (continuation).

3 a) Data available. No manipulation needed.

b) Data available. Manipulation or analysis needed.

c) Data could be generated from simulation or model.

d) Data not readily available.
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Freight Performance Measure Primer

Category Measure

Observed

Estimated

Data 

Available4

Railway

Percent of shippers within 50 miles of intermodal trailer-onfreight-car (TOFC) facility X b

Percent of major freight generators with appropriate rail access X a

Number or capacity of intermodal facilities X a

Water

Shippers within 50 miles of river port (for barge accessibility) X d

Availability of container-handling capability and/or bulk 

transfer capability X b

Air

Flight frequency by airlines with cargo capacity (number per 

day) X b

Average time between flights by airlines with cargo capacity 

(minutes) X b

Average travel time delay for trucks on airport access roads X b

Number of docks or acres of cargo-handling facilities X a

Environmental

All

Total tons of emissions reduced from Congestion Mitigation 

and Air Quality Improvement Program (CMAQ) projects for 

applicable criteria pollutants and precursors* 

X d

All Pounds of greenhouse gas emissions X d

All Increase in energy consumed or costs related to energy 

consumption X d

All Increase in air pollution impacts/costs X d

Table 8. Recommended performance measures (continuation).

* Performance measures to be determined in MAP-21 proposed rulemaking

Source: Minnesota Department of Transportation, Measurement sources for freight performance

measures and indicators, Oregon Department of Transportation, Freight Performance Measures: Approach Analysis.

4 a) Data available. No manipulation needed.

b) Data available. Manipulation or analysis needed.

c) Data could be generated from simulation or model.

d) Data not readily available.
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Freight Performance Measure Primer

REFERENCES

AASHTO (American Association of State Highway and Transportation Officials).

—2012. “Summary of the Fixing America’s Surface Transportation Act,” 2012, Available at 

http://fast.transportation.org/Pages/default.aspx, As of December 16, 2015.

Association of American Railroads.

—2015. Website, https://www.aar.org/.

ATRI (American Transportation Research Institute).

—2016. Website, http://atri-online.org/.

—2016. Cost of Congestion to the Trucking Industry. Arlington, VA: ATRI. April.

—2015. An Analysis of the Operational Costs of Trucking: 2015 Update. Arlington, VA: 

ATRI. September. 

Cambridge Systematics, Inc. and SRF Consulting Group, Inc.

—2005. Minnesota Statewide Freight Plan. Minnesota Department of Transportation. 

Coalition for America’s Gateways and Trade Corridors.

—2015. Summary of freight provisions in FAST Act, December, 2015.

Drucker, P. 

—1995. “The information executives truly need,” Harvard Business Review, Jan./Feb. 

1995. In Harvard Business Review on Measuring Corporate Performance Boston, MA: 

Harvard Business School Press, 1998. (pp. 1-24).

Eccles, R.

—1991. “The performance measurement manifesto,” Harvard Business Review, Jan./Feb. 

1991. In Harvard Business Review on Measuring Corporate Performance Boston, MA: 

Harvard Business School Press, 1998. (pp. 25-45).

FDOT (Florida Department of Transportation).

—2014. “Freight System Performance Measures for the Tampa Bay Region,” Tampa Bay 

Regional Goods Movement Study. Florida Department of Transportation, District 7. June.

Frigo, M. 

—2002. Strategy-Focused Performance Measures, Strategic Finance, September.

Geurs, K. & van Wee, B.

—2004. “Accessibility evaluation of land-use and transport strategies: review and research 

directions,” Journal of Transport Geography 12. (pp. 127-140).

ICF & Leidos. 

—2015. Measuring the Impacts of Freight Transportation Improvements on the Economy 

and Competitiveness. Washington, D.C.: Federal Highway Administration (FHWA).
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Freight Performance Measure Primer

Kaplan, R. & Norton D. 

—1998. The Balanced Scorecard—Measures That Drive Performance, Harvard Business 

Review, Feb. 1992. In Harvard Business Review on Measuring Corporate Performance, 

Boston, MA: Harvard Business School Press, 1998.

Katsikides, N. 

—2015. FHWA’s Freight fluidity Program (presentation). Federal Highway Administration, 

Office of Freight Management and Operations. Washington, D.C., June 22.

McMullen. B., Monsere, C., Knudson, R., Fobi, L., & Eckstein, N.

—2010. Freight Performance Measures: Approach and Analysis. Portland, OR: Oregon 

Department of Transportation & Oregon Transportation Research and Education 

Consortium. May. 

Michael Baker Inc., CDM Smith, Inc., & Renaissance Planning Group.

—2012. OIPI Multimodal Technical Assistance: Applying Accessibility Measures; Task 1 

Technical Memorandum Measuring Accessibility of Intermodal Centers. Virginia Office of 

Intermodal Planning and Investment. 

Parsons Brinckerhoff.

—2013. Ohio Statewide Freight Study. Ohio Department of Transportation.

Supply Chain Quarterly

—2014 Special Edition. John Boyd, Jr.

Transport Canada. 

—2014. “Gateways and Corridors: Fluidity Indicator.” Presentation available at

https://www.tc.gc.ca/eng/policy/anre-menu-3023.htm. May. 

TRB (Transportation Research Board).

—2014. Development of Tools for Assessing Wider Economic Benefits of Transportation. 

Washington, D.C.: Transportation Research Board.

—2013. SHRP2 Project C11: Reliability Analysis Tool: Technical Documentation and 

User’s Guide, Washington, D.C.: Transportation Research Board, July.

—2013. Freight Demand Modeling and Data Improvement Strategic Plan, Strategic 

Highway Research Program. Washington, D.C.: Transportation Research Board.

—2011. NCFRP Report 10 – Performance Measures for Freight Transportation. 

Washington, D.C.: Transportation Research Board.

—2011. NCHRP Report 708, A Guidebook for Sustainability Performance Measurement for 

Transportation Agencies. Washington, D.C.: Transportation Research Board.
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Freight Performance Measure Primer

U.S. Army Corps of Engineers.

—2016. Website, http://www.usace.army.mil/.

U.S. Department of Labor, Bureau of Labor Statistics.

—2015. Website, http://www.bls.gov/. 

U.S. Department of Transportation, Federal Highway Administration. 

—2016. “The Economic Costs of Freight Transportation.” Available at

http://www.ops.fhwa.dot.gov/freight/freight_analysis/freight_story/costs.htm#content as of 

May 4, 2016. 

—2015. “Transportation Equity Act for the 21st Century – A summary,” November 4, 2015. 

Available at https://www.fhwa.dot.gov/tea21/sumtoc.htm, as of May 9, 2016.

—2014. “MAP-21 – Moving Ahead for Progress in the 21st Century. Section 1116: 

Prioritization of Projects to Improve Freight Movement Questions & Answers.”

http://www.fhwa.dot.gov/map21/qandas/qasec1116.cfm. Available as of May 5, 2016.

—2013. Introduction to the National Performance Management Research Data Set 

(NPMRDS), FHWA Office of Operations Webinar Series, Washington, D.C.: U.S. 

Department of Transportation. August 8.

—2013. Legislation, Regulation, and Guidance. Washington, D.C.: U.S. Department of 

Transportation. May 16.

—2012. A Summary of Highway Provisions in MAP-21. Washington, D.C.: U.S. 

Department of Transportation. July 17.

—2011. “Freight Facts and Figures 2011.” Available at

http://www.ops.fhwa.dot.gov/freight/freight_analysis/nat_freight_stats/docs/11factsfigures/

figure3_17.htm as of May 4, 2016.

—2005. A Summary of Highway Provisions in SAFETEA-LU. Washington, D.C.: U.S. 

Department of Transportation. August 25.

—1886. A Method for Estimating Posterior BAC Distributions for Persons Involved in Fatal 

Traffic Accidents, Washington, D.C.: U.S. Department of Transportation (USDOT), National 

Highway Traffic Safety Administration (NHTSA). July 1886.

U.S. Department of Transportation, Federal Railroad Administration.

—2016. Website, www.fra.dot.gov/. 

U.S. Energy Information Administration.

—2015. Independent Statistics and Analysis.

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Freight Performance Measure Primer

APPENDIX A. HISTORY OF FEDERAL AUTHORIZATION AND ITS

RELATIONSHIP TO FREIGHT PERFORMANCE MEASURES

FEDERAL AUTHORIZATIONS

Intermodal Surface Transportation Efficiency Act

In 1991, President George H.W. Bush signed the $155 billion Intermodal Surface Transportation 

Efficiency Act, or ISTEA, which set the tone for many highway, transit, bicycle, and pedestrian 

policies. The goal of the legislation was to develop a national intermodal transportation system 

that was economically efficient, environmentally sound, and provide the foundation for the 

Nation to compete in the global economy and move people and goods in an energy-efficient 

manner (U.S. Department of Transportation [USDOT] May 2013).

Transportation Equity Act for the 21st Century

The Transportation Equity Act for the 21st Century (TEA-21) was enacted June 9, 1998 as Public 

Law 105-178. TEA-21 authorized the Federal surface transportation programs for highways, 

highway safety, and transit for the six-year period from 1998 to 2003. TEA-21 was built on 

the initiatives established in ISTEA with the intention of improving safety as highway traffic 

continued to increase. Other important features of TEA-21 included protecting and enhancing 

communities and the natural environment and advancing America’s economic growth and 

competitiveness domestically and internationally through efficient and flexible transportation. 

TEA-21 expanded the role of the Bureau of Transportation Statistics (BTS) and opened the 

door for the development of a Transportation Database, a National Transportation Library, and a 

National Transportation Atlas Database (USDOT November 2015).

Major focus areas of TEA-21 included infrastructure and motor carrier safety. This included 

provisions for off-road and bicycle safety improvements. TEA-21 also provided grant funding 

to encourage States to improve upon their highway safety data collection, including timeliness, 

accuracy, completeness, uniformity, and accessibility. The National Motor Carrier Safety 

Assistance Program was restructured to promote performance-based activities intended to reduce 

crashes and develop innovative approaches to improving motor carrier compliance. Additionally, 

funding was increased to eliminate railway-highway crossings, railroad trespassing accidents, 

and railway fatalities and injuries.

TEA-21 also featured heavy investment in research to maximize the performance of the 

transportation system. Intelligent Transportation Systems was a focal point in an effort to 

improve operations and management of the transportation system and improve vehicle safety.

Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users

On August 10, 2005, President George W. Bush signed the $244.1 billion Safe, Accountable, 

Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU) into law. 

Inasmuch as ISTEA and TEA-21 began shaping the highway program to meet evolving 
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Freight Performance Measure Primer

transportation needs, SAFETEA-LU continued to build on their principles through the 

largest surface transportation bill in history and continued investments in our transportation 

infrastructure.

SAFETEA-LU addressed many infrastructure challenges, such as safety, congestion, intermodal 

connectivity, protecting the environment, and the efficient movement of freight. SAFETEALU also established the Highway Safety Improvement Program, which allowed States to target 

funds for their most critical safety needs, including increased funding for the Railway-Highway 

Crossing Program. For the first time ever, States were required to develop and implement a 

strategic highway safety plan and include annual reports containing a list of highway safety 

improvement projects, implementation strategies, and their effectiveness in decreasing the 

number of injuries and fatalities. (USDOT August 2005)

Moving Ahead for Progress in the 21st Century Act

In July 2012, President Barack Obama signed into law Public Law 112-141, the Moving Ahead 

for Progress in the 21st Century Act (MAP-21). This Surface Transportation Program represented 

the first long-term transportation bill since SAFETEA-LU in 2005 and provided funding, 

policy guidance, and a programmatic framework for investments that will guide the growth 

and development of the Nation’s transportation infrastructure. MAP-21 builds on many of the 

policies established under ISTEA, TEA-21, and SAFETEA-LU and directs the Federal Highway 

Administration (FHWA) to work closely with stakeholders to ensure that local communities are 

able to build multi-modal and sustainable projects (USDOT July 2012).

MAP-21 is a multi-modal program intended to address the multitude of challenges facing 

an aging and deteriorating transportation network. MAP-21 focused on improving safety, 

maintaining infrastructure conditions, reducing traffic congestion, improving the efficiency of 

the system and freight movement, protecting the environment, and reducing delays in project 

delivery. (USDOT November 2015)

Some of the key freight-related highlights of MAP-21 include the expansion of the National 

Highway System (NHS). The expansion will incorporate many principal arterial highways 

currently not on the NHS network. MAP-21 establishes a performance-based program that 

will allow more efficient investment of Federal transportation funds by focusing on national 

transportation goals, increasing the accountability and transparency of the Federal highway 

programs. (USDOT 2012)

MAP-21 authorized Federal funding for roads, bridges, bicycling, and walking improvements. 

Additionally, MAP-21 enhances innovative financing and encourages private sector investment 

through a substantial increase in funding for the Transportation Infrastructure Finance and 

Innovation Act (TIFIA) program. It also includes a number of provisions designed to improve 

freight movement in support of national goals.

Federal funding for the Highway Safety Improvement Program has doubled under MAP-21. 

This program is aimed at reducing highway fatalities, and in particular the FHWA’s efforts to 
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eliminate distracted driving. This program also pushes to improve transit and motor carrier 

safety.

In addition, due to the critical focus on infrastructure conditions, MAP-21 requires that each 

State maintain minimum standards for interstate pavement and National Highway System (NHS) 

bridge conditions. If a State falls below either standard, that State must spend a specified portion 

of its funds for that purpose until the minimum standard is exceeded (USDOT 2014).

Fixing America’s Surface Transportation Act

President Obama signed the Fixing America’s Surface Transportation (FAST) Act into law on 

December 4, 2015. The FAST Act is a five-year Federal authorization for Federal fiscal years 

2016 through 2020 and authorizes $305 billion from the Highway Trust Fund and the General 

Fund over the life of the legislation. The FAST Act created a new National Highway Freight 

Program (NHFP), a formula-based program that distributes roughly $1.2 billion per year for 

States to invest in freight projects on the National Highway Freight Network (NHFN). Up to 

10 percent of these funds may be used for intermodal projects (American Association of State 

Highway Transportation Officials [AASHTO] 2015).

The FAST Act also creates a new discretionary program: the Nationally Significant Freight and 

Highway Projects program. This program is funded at an average of $900 million per year. In 

addition, the FAST Act provides funding for Federal transit programs, as well as funding for the 

National Highway Traffic Safety Administration (NHTSA) and Federal Motor Carrier Safety 

Administration (FMCSA). Furthermore, this act provides funding over the five-year lifespan of 

the legislation for the Federal Railroad Administration (FRA) and Amtrak.

The FAST Act created a national multimodal freight network that includes the NHFN established 

under MAP-21(consisting of all interstate highways, 41,518 miles of primary highway freight 

network, critical rural freight corridors, and critical urban freight corridors), NHFN, Class I 

railroads, ports with annual foreign and domestic trade of at least two million short tons, inland 

and intra-coastal waterways, the Great Lakes and St. Lawrence Seaway, Maritime Administration 

marine highways, the 50 U.S. airports with the highest annual landed weight, and other assets as 

identified by the Undersecretary of Policy (i.e., short line railroads). States may seek additional 

assets on the NHFN with the input of freight stakeholders, including critical rural freight assets 

(Coalition for America’s Gateways and Trade Corridors 2015).

The FAST Act also requires all States using formula dollars to complete a State Freight Plan. 

While developing a State Freight Plan was optional as outlined in MAP-21, the FAST Act 

mandates the creation within two-years of the enactment of legislation and requires plan updates 

a minimum of every five-years. State Freight Plans may be independent of or included with 

the State’s long-range transportation plan. States are still encouraged to form a State Freight 

Advisory Committee as was the case under MAP-21 (Coalition for America’s Gateways and 

Trade Corridors 2015). Features of the NHFP include:
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• Highway freight projects must be on the NHFN or a highway or bridge project on the 

NHS, including adding interstate capacity.

• Other freight projects may also include rail, intermodal facilities, projects in scenic areas, 

and highway-rail grade separation; however, non-highway projects are limited to $500 

million over five-years, must improve freight on the NHFN, and must provide public 

benefits.

• Eligible projects and project costs must be equal to or exceed the lesser of $100 million, 

30 percent of a State’s apportionment if the project is in one State, or 50 percent of the 

largest State’s apportionment in a multi-State project.

• Ten percent of the program must be set aside annually for Small Projects (grants that are 

still at a minimum $5 million).

• Twenty five percent of annual eligible funding will be set aside for projects in rural areas 

(areas outside urbanized areas with populations over 200,000).

• Federal project shares may not exceed 60 percent, or 80 percent if other Federal resources 

are used to satisfy the State share.

• Freight projects will be treated as if they occur on a Federal-Aid Highway.

• Congress may disapprove funding for a project within 60 days of USDOT’s selection.

The FAST Act established the NHFP. This program is formula-based and authorizes $6.2 billion 

over the five-year legislation. State shares are based on the State’s overall share of the highway 

program apportionments. Formula funds may be used on the following activities and projects:

• Project development.

• Construction, reconstruction rehabilitation, and land acquisition.

• Intelligent Transportation System projects (including intermodal facilities and border 

crossings).

• Rail-highway grade separation.

• Geometric design improvements.

• Truck-only lanes.

• Truck climbing lanes.

• Runaway truck lanes.

• Shoulder widening.

• Truck parking.

• Electronic screening/credentialing.

• Real-time information systems.

• Traffic signals.

• Ramp metering.

• Work zone management.

• Capacity improvements for freight bottlenecks.

• Data collection and analysis.

• Performance target development.

The FAST Act established the Nationally Significant Freight and Highway Projects (NSFHP) 

program to provide financial assistance—competitive grants, known as FASTLANE grants, or 

credit assistance to nationally and regionally significant freight and highway projects that align 
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with the program goals. The following are project requirements for FASTLANE grants:

• Highway or freight projects carried out on the NHFN.

• Highway or bridge projects carried out on the NHS including those that add capacity on 

the interstate system that improve mobility or projects in a national scenic area.

• Railway-highway grade crossing or grade separation projects.

• Freight projects that are:

o Intermodal or rail projects.

o Within the boundaries of a public or private freight rail, water, or intermodal facility; is 

a surface transportation infrastructure project necessary to facilitate direct intermodal 

interchange, transfer, or access into or out of the facility; and will significantly 

improve freight movement on the NHFN.

The FAST Act legislation includes a number of additional freight provisions:

• Considers trucks carrying fluid milk products on the interstates’ as non-divisible loads.

• Allows interstate weight waivers for covered, heavy-duty tow and recovery vehicles.

• Allows up to 86,000 pounds of gross vehicle weight on interstates for emergency vehicles.

• Allows vehicle weight attributable to natural gas propulsion system to be exempt from 

total vehicle weight calculations.

• Provides specific interstate highway weight waivers in certain States.

• Sets aside $1.2 billion of Highway Safety Improvement Program funds over five-years for 

the Section 130 highway-rail grade crossing program.

• Includes ferries/terminals, truck parking facilities, rail-grade crossings, and intermodal 

transfers in ports as eligible projects in the new Surface Transportation Block Grant 

(STBG) program. The STBG provides flexible funding that may be used by States and 

localities for projects to preserve and improve the conditions and performance on any 

Federal-aid highway, bridge and tunnel projects on any public road, pedestrian and bicycle 

infrastructure, and transit capital projects, including intercity bus terminals.

• Calls for a report on delays in goods movement.

• Establishes a working group to expedite State approval for emergency routes for 

commercial vehicles, including examination of potential pre-approved routes.

• Creates a new port performance statistics program.

• Provides community safety grants for crude-by-rail.

• Creates a framework for emergency planning and response for crude-by-rail derailments.

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APPENDIX B. FREIGHT STAKEHOLDER INTERVIEWEES

AND INTERVIEW QUESTIONS

INTERVIEWEES

Bala Akundi – Baltimore Metropolitan Council.

Jody Binnix – Genesee Transportation Council, Metropolitan Planning Organization for the 

Genesee-Finger Lakes Region.

Teresa Brewer – City of Anchorage Metropolitan Planning Organization.

Ted Dahlburg – Delaware Valley Regional Planning Commission.

Terry Freeland – Baltimore Metropolitan Council.

Valorie LaCour – Baltimore City Department of Transportation.

David Lee – Florida Department of Transportation.

Lauren Lejeune – Florida Department of Transportation.

Caroline Mays – Texas Department of Transportation.

Marygrace Parker – I-95 Corridor Coalition.

Lynn Soporowski – Kentucky Transportation Cabinet.

Lori Tavasszy – The Netherlands Organization for Applied Scientific Research.

Alissa Torres – Orange County Government, Florida.

Wenjuan Zhao – Washington State Department of Transportation.

INTERVIEW QUESTIONS

1. Does your organization track performance measures?

2. If so, what are your organization’s top 5 freight performance measures (for example safety, 

environmental, congestion)?

3. What do YOU think should be the top 5 freight performance measures (if different than 

above) and why? (i.e., what other measures should be tracked that would benefit your 

organization’s goals)

4. Do existing performance measures reflect your customers’ needs/perspectives? (Who are 

your customers?)
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5. What does your organization do with the results of your performance measures? (e.g., 

Publish? Prioritize funding? Develop new projects? Put in government required reports? 

Organize action based stakeholder committees?)

6. Are your current performance measures derived from organizational strategic goals?

7. How frequently are performance measures evaluated/updated? (Monthly, annually, every 5 

years?)

8. Do you use your measures for forecasting into the future? If so, how accurate has this 

been?

9. What methods/tools/technologies does your organization use to measure freight 

performance?

10. What, if any, are the shortcomings of those tools?

11. What, if any, are the strengths of those tools?

12. How accurate, reliable, and timely are the data?

13. Are the data used for performance measures easy to collect with low investment of effort 

and time?

14. Who is responsible for data collection and developing performance measures? Were 

internal and external stakeholders involved?

15. What other organizations, if any, utilize your freight performance measures? How do they 

use them? (Publish them in their report/newsletter? Put in government required reports? 

Other?)

16. What modes are included in your freight performance measures?

17. Why are the other modes excluded? (Choose from marine, truck, rail, air freight, pipeline.)

18. Has MAP-21 or FAST Act altered your performance measures? If so, in what ways?
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APPENDIX C. FREQUENTLY USED FREIGHT DATA

TRUCK PROBE DATA

The Federal Highway Administration (FHWA) has been a leader in developing freight data and 

performance metrics. Since 2002, FHWA has worked with private sector trucking operations 

to collect data through a trusted third party, the American Transportation Research Institute 

(ATRI.) What began as the collection and analysis of Global Positioning System (GPS) signals 

or probes for a limited set of trucks on only a few highways has become a robust data source 

of probes collected from over 600,000 trucks all across the United States. This information has 

been used since its inception to form the FHWA Freight Performance Measurement program 

(FPM). Analysis of probes has been used to understand speeds and reliability on the Nation’s 

highways, to identify bottlenecks, and to support State- and regional-level analyses of truck 

movements with the goal of improving freight performance. FHWA has learned a great deal 

from evaluating the probes and is continuously finding new ways to develop the data and use 

it in innovative analytical methods. Use of these data has produced many successful analyses 

of freight movement, both in continuous monitoring and in incident analysis. The information 

revealed by the data has helped numerous decision-makers determine both operation and capital 

improvements for truck movements. FPM gathers information only on truck movements at this 

time. FHWA is working with other modal operating administrations to attempt to build probes 

for other modes and to evaluate freight movement from a multi-modal perspective.

While FHWA’s FPM probe data are available to FHWA, FHWA will provide access to the data to 

support innovative research conducted by States, Metropolitan Planning Organizations (MPOs), 

regional authorities, and academics. States and MPOs may also acquire the data directly for their 

own needs from the data provider. Additionally, it is important to note that these data provides 

unique identifiers only for the truck probe, latitude, longitude, speed, and heading. There is no 

information about the truck type, truck owner, fleet size, commodities, or other characteristics. 

FHWA is not aware of any data source that currently provides truck probes and commodity 

information at this time, and is working with public and private stakeholders to improve on the 

data to obtain more detail in the future that could support freight analysis. Analyses using the 

FHWA FPM probe data are publicly available on FHWA’s website. FHWA uses the probe data to 

routinely measure the following:

• Speed and reliability of the top 25 domestic freight corridors.

• Freight Efficiency Index, which consists of the following components:

o Measures of known bottlenecks.

o Metropolitan area speeds.

o Intermodal and port locations.

o Border crossings.

In addition to the routine measures, FHWA has supported a number of freight analyses with the 

probe data that are published on the FHWA website. Most of the resources are located at

http://ops.fhwa.dot.gov/Freight/freight_analysis/perform_meas/index.htm.
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NATIONAL PERFORMANCE MANAGEMENT RESEARCH DATA SET

Though FHWA does not provide external entities with access to this truck probe data, FHWA 

does make available a derivative of the data in the form of travel times to States and MPOs for 

free. The National Performance Management Research Data Set (NPMRDS), or vehicle probe 

data, is a historical archive of average travel times measured in five-minute increments. This data 

set covers the entire National Highway System (NHS) and is available on a monthly basis, with 

each month’s data usually available by the middle of the following month. NPMRDS data covers 

all 50 States, Washington, D.C., and Puerto Rico. It also covers crossings into both Canada and 

Mexico. The data set contains three distinct travel times: freight, passenger, and a combination 

of all traffic. NPMRDS data are divided into States and regions, making it easy for States to 

conduct corridor analyses.

There are a number of potential uses for the NPMRDS. States and MPOs are encouraged to 

use the NPMRDS data to produce system performance measures that cover operational and 

freight management strategies. NPMRDS can also be used to produce performance measures 

that will allow a more efficient highway system to improve the movement of freight. NPMRDS 

can be used to produce measures that will better inform States of where congestion is regularly 

occurring or how congestion occurs when there is an incident or work zone operation. This 

information helps them to identify where operational improvements or long-term investments 

can be made.

FHWA makes the NPMRDS available to State departments of transportation (DOTs), MPOs, and 

Federal agencies. States and MPOs are required to sign a licensing agreement with the NPMRDS 

data provider. More information on the NPMRDS, including webinars and reference documents, 

is located on FHWA’s website under the vehicle probe data section at

http://www.ops.fhwa.dot.gov/freight/freight_analysis/perform_meas/index.htm.

COMMODITY FLOW SURVEY

The Commodity Flow Survey (CFS) is the primary source of national and State-level data on 

domestic freight shipments by American establishments in mining, manufacturing, wholesale, 

auxiliaries, and selected retail and services trade industries. Data are provided on the types, 

origins and destinations, values, weights, modes of transport, distance shipped, and ton-miles 

of commodities shipped. The CFS is a shipper-based survey and is conducted every five-years 

as part of the economic census. It provides a modal picture of national freight flows and is the 

only publicly available source of commodity flow data for the highway mode. The CFS was 

conducted in 1993, 1997, 2002, 2007, and most recently in 2012.

FREIGHT ANALYSIS FRAMEWORK

The Freight Analysis Framework (FAF), produced through a partnership between the Bureau of 

Transportation Statistics (BTS) and FHWA, integrates data from a variety of sources to create a 

comprehensive picture of freight movement among States and major metropolitan areas by all 

modes of transportation. Starting with data from the 2012 CFS and international trade data from 
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the Census Bureau, FAF incorporates data from agriculture, extraction, utility, construction, 

service, and other sectors. The FAF version 4 (FAF4) baseline edition provides estimates for 

tonnage and value by regions of origin and destination, commodity type, and mode for 2012, 

the most recent CFS year. Data are available through the Data Extraction Tool, for download as 

a complete database, as well as summary files. Throughout 2016, releases of additional FAF4 

products have provided forecasts through 2045; State-to-State flows for 1997, 2002, and 2007; 

truck flows assigned to the highway network for 2012 and 2045; and domestic ton-miles and 

distance bands.

This compilation of data creates a comprehensive picture of freight flows, trends, and a baseline 

forecast to support analysis and performance for all modes of transportation among States and 

major metropolitan areas. FAF forecasts are a reasonable estimation of current trends; however, 

they do not reflect major shifts in the national economy, future capacity limitations, or changes 

in transportation costs or technology. FAF data would need to be supplemented with additional 

models to identify such trends.

FHWA has an online tabulation tool that is useful for States and MPOs who want to understand 

what freight, how much, and by what mode is occurring in their area. The tool is available at 

http://faf.ornl.gov/fafweb/Extraction0.aspx.

COMMODITY FLOW SURVEY MICRODATA

The first generation 2012 Commodity Flow Survey (CFS) Public Use Microdata (PUM) file is 

now available. This new, experimental data product contains information for approximately 4.5 

million shipments obtained from businesses included in the 2012 CFS. The PUM file provides 

access to shipment-level characteristics while continuing to protect the confidentiality of 

individual business information. PUM file users can create customized tables and build models to 

track and analyze the movement of commodities. (http://www.rita.dot.gov/bts/sites/rita.dot.gov.

bts/files/publications/commodity_flow_survey/index.html)

HIGHWAY PERFORMANCE MONITORING SYSTEM (HPMS)

The Highway Performance Monitoring System (HPMS) is a national level highway information 

system provided to FHWA by State DOTs. The HPMS data are used extensively in the analysis 

of highway system conditions, performance, and investment needs that make up the biennial 

Condition and Performance Reports to Congress. The HPMS contains administrative and system 

information on all public roads. Extensive data are available for interstates and principal arterials, 

while a mix of sample data is reported for minor arterial and collector functional systems. 

Limited information on travel and paved miles is included in summary form for the local 

functional systems.

The HPMS was developed in 1978 as a continuing database and has been modified several 

times since its inception. The system has been altered to reflect changes in the highway systems, 

legislation, national priorities, and new technology, and to consolidate or streamline reporting 

requirements.
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For freight, States and MPOs often use HPMS data to derive Annual Average Daily Truck 

Traffic (AADTT) counts, Percent Peak Combination Trucks, or other truck count information for 

roads included in the HPMS data. FAF uses HPMS in its network assignments for freight flows. 

(https://www.fhwa.dot.gov/policyinformation/hpms.cfm)

HIGHWAY STATISTICS

FHWA’s Highway Statistics Series consists of annual reports containing analyzed statistical 

information on motor fuel, motor vehicle registrations, driver licenses, highway user taxation, 

highway mileage, travel, and highway finance. This information is presented in tables and charts 

and has been published annually since 1945. Most highway data are submitted by the States 

directly to FHWA. Each State’s data are analyzed for completeness, reasonableness, consistency, 

and compliance with data reporting instructions contained in “A Guide to Reporting Highway 

Statistics.” While the FHWA Office of Highway Policy Information is responsible for preparing 

this publication, a number of the statistical summaries are prepared by other units within the 

FHWA. More information is available at

http://www.fhwa.dot.gov/policyinformation/statistics.cfm.

MARITIME DATA

The U.S. Maritime Administration (MARAD) provides a number of data resources through 

the MARAD Open Data Portal and link to Maritime Data and Statistics. Below is a list of the 

available resources.

Reports

• 2013 Vessel Calls in U.S. Ports and Terminals (Uploaded 6/17/2015).

• 2012 Vessel Calls in U.S. Ports, Terminals and Lightering Areas Report (Released 

10/8/2014).

• 2002–2012 Vessel Calls in U.S. Ports, Terminals, and Lightering Areas Report (Released 

10/8/2014).

• 2011 Coastal Tank Vessel Market Snapshot.

• 2011 U.S. Water Transportation Statistical Snapshot.

• 2011 Vessel Calls at U.S. Ports Snapshot.

• Guide Market Research for Marine Transportation Services.

Fleet Statistics

• U.S.-Flag Privately Owned Fleet (As of 4/1/2016).

• U.S.-Flag Integrated and Articulated Tug-Barge Units.

• Merchant Fleets of the World Report for 2015 (As of January 2015).

• Top 25 Flags of Registry.

• 2000–2014 U.S.-Flag Privately Owned Fleet Summary.

• 1946–2014 U.S.-Flag Privately Owned Fleet.
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MARAD U.S.-Flag Fleet Lists

• Listing of U.S.-Flag Carriers.

• Maritime Security Program List.

• Agricultural Preference Trade Fleet List.

Historic Fleet Reports and Fleet Lists

MARAD has electronically imaged some historic Vessel Inventory Reports on the U.S.-Flag 

Fleet. The Vessel Inventory Reports from July 1990 to January 2016 are available for research 

purposes.

Trade Statistics

• U.S. Waterborne Foreign Trade by U.S. Customs Ports 2000–2015.

• U.S. Waterborne Foreign Trade by U.S. Customs Ports 1997–1999.

• U.S. Waterborne Foreign Trade by U.S. Custom Districts.

• U.S. Waterborne Foreign Trade by Trading Partners.

• U.S. Waterborne Foreign Container Trade by U.S. Customs Ports 1997–2014 Report.

• U.S. Waterborne Foreign Container Trade by Trading Partners.

Historical Trade Statistics

• Containerized Cargo on Selected Trade Routes CY 1970.

• Containerized Cargo Statistics CY 1971 (unavailable).

• Containerized Cargo Statistics CY 1972–1983.

OPA-90 Related Statistics

• Coastal Tank Vessel Market Snapshot.

• Tanker Calls at U.S. Ports.

• U.S.-Flag Oil Pollution Act of 1990 (OPA-90) Phase-Out.

• U.S.-Flag Tank Vessels Removed.

• U.S. Tank Vessel Trades.

Survey Series

• Mainstream Container Services.

• Coastal Tank Barges.

• Deck Barges.

• Inland Tank Barges.

MARINE TRANSPORTATION SYSTEM DATA INVENTORY

• Marine Transportation System (MTS) Data Portal.

(http://www.marad.dot.gov/resources/data-statistics/)
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U.S. ARMY CORPS OF ENGINEERS NAVIGATION DATA CENTER

The U.S. Waterway Data is a collection related to the navigable waters in the United States, 

including inland waterways, off-shore waters, the Great Lakes, and the Saint Lawrence Seaway. 

Data on commerce, facilities, locks, dredging, imports and exports, and accidents are included, 

along with the geographic waterway network. The data were compiled by several agencies, 

including the U.S. Army Corps of Engineers Navigation Data Center, the U.S. Bureau of the 

Census, the U.S. Coast Guard, Oak Ridge National Laboratory, and Vanderbilt University. One 

of the objectives of this coordinated effort is to make waterway data more widely available and 

easily accessible. The data included are in standard file formats that can be easily imported into 

other software tools such as spreadsheets, databases, and Geographic Information Systems (GIS).

The Navigation Data Center had several objectives in developing the U.S. Waterway Data. These 

objectives support the concept of a National Spatial Data Infrastructure and will include:

• Providing public access to national waterway data.

• Fostering interagency and intra-agency cooperation through data sharing.

• Providing a mechanism to integrate waterway data (U.S. Army Corps of Engineers Port/

Facility and U.S. Coast Guard Accident Data, for example).

• Establishing a basis for intermodal analysis.

• Assisting standardization of waterway entity definitions (ports/facilities, locks).

• Supplying public access to the National Waterway Network, which can be used as a base 

map to support graphical overlays and analysis with other spatial data (waterway and 

modal network/facility databases, for example).

• Making available reliable data to support future waterway and intermodal applications

(http://www.navigationdatacenter.us/data/data1.htm)

PIPELINE DATA

Oil Pipeline Statistics

The U.S. Energy Information Administration (EIA) publishes statistics on crude oil, gasoline, 

diesel, propane, jet fuel, ethanol, and other liquid fuels. Information collected includes petroleum 

prices, crude reserves and production, refining and processing, imports/exports, stocks, and 

consumption/sales. (http://www.eia.gov/dnav/pet/pet_move_pipe_dc_R20-R10_mbbl_m.htm)

Pipeline Safety Statistics

The Pipeline and Hazardous Materials Safety Administration’s (PHMSA’s) Office of Pipeline 

Safety provides a variety of data about federally regulated and State-regulated natural gas 

pipelines, hazardous liquid pipelines, and liquefied natural gas plants. The operators of these 

pipeline facilities report this data in accordance with Part 191 and Part 195 of PHMSA’s pipeline 

safety regulations. PHMSA provides downloads of the raw data, yearly summaries, multi-year 

trends of safety performance metrics, and inventories tracking the removal of aging and other 

higher-risk infrastructures. (http://www.phmsa.dot.gov/pipeline/library/data-stats)
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RAILROAD DATA

Surface Transportation Board Rail Waybill Sample

The Rail Waybill Sample is a stratified example of carload waybills for all U.S. rail traffic 

submitted by rail carriers terminating 4,500 or more revenue carloads annually. Because the 

Waybill Sample contains sensitive shipping and revenue information, access to this information 

is restricted. Access is granted when the Waybill Sample is the only source of the data, obtaining 

the data from other sources is burdensome or costly, and/or the data are relevant to issues 

pending before the State Transportation Board (STB). Waybill data are used by transportation 

practitioners, consultants, and law firms with formal proceedings before the STB or other State 

boards. There is also a group designated as “other users.” To obtain the Waybill Sample, the 

requestor must submit a written request explaining the need for the data and how it will be used. 

There is a Public Use Waybill File. The Public Use Waybill File is created from the confidential 

Waybill Sample File and is a non-proprietary version of the Waybill Sample that can be used to 

support freight analysis. (http://www.stb.dot.gov/stb/industry/econ_waybill.html)

Association of American Railroads

The U.S. freight rail network consists of nearly 140,000 miles of rail and is operated by over 

560 different railroads. Freight railroads are predominantly privately owned and operate almost 

exclusively on tracks owned and maintained by the private owners.

The Association of American Railroads AAR compiles and distributes information on North 

American freight railroads, including finances, operations, performance, input cost indexes, 

traffic, U.S. carloads, intermodal traffic, Class 1 rail tons, and more. The AAR can supply data 

on safety statistics, hazardous materials, and grade crossings as well. Data is collected weekly, 

monthly, and annually depending on the data required (AAR website 2015).

(https://www.aar.org/pages/freight-rail-traffic-data.aspx)

Railroad Performance Measures

The Railroad Performance Measures website is where six major North American freight railroads 

have voluntarily reported three weekly performance measures—Cars On Line, Train Speed, and 

Terminal Dwell—since 1999, in a commitment to improve communications with their customers. 

The six Class I railroads are shown in Table 9.

Burlington Northern and Santa Fe (BNSF) Kansas City Southern

Canadian National (CN) Norfolk Southern

Chessie and Seaboard (CSX) Union Pacific

Table 9. Class 1 Railroads.
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All six railroads use the same definitions to calculate their performance data; however, because 

the level of one railroad’s performance relative to another’s may differ sharply because each 

railroad is unique, these measures are most useful for examining trends and relative changes, 

and least useful as absolutes. Differences between railroads include terrain, physical routes and 

network design, traffic mix and volume, the extent of passenger operations, and operational 

practices. External factors such as weather and port operations can also cause variations 

between railroads over time. In addition, individual differences in the collection and reporting of 

operational data may affect the absolute level of the measures to some degree.

(http://www.railroadpm.org/)

AVIATION DATA

Air Cargo Summary Data

This monthly freight summary includes both freight and mail carried by U.S. airlines in all 

service classes (scheduled and non-scheduled). Data in this report goes back to October 2002, 

when a change in reporting requirements increased the number of reporting carriers and 

collection of monthly all-cargo service data started. (http://www.transtats.bts.gov/freight.asp)

Landing Weights

The Bureau of Transportation Statistics provides information on landing weights for cargobearing airports throughout the United States. Data is available through the BTS Summary data. 

(http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/state_transportation_statistics/

summary/index.html)

BORDER CROSSING DATA

Transborder Freight Data

The North American Transborder Freight Database, available since April 1993, contains freight 

flow data by commodity type and by mode of transportation (rail, truck, pipeline, air, vessel, and 

other) for U.S. exports to, and imports from, Canada and Mexico. The database includes two sets 

of tables; one is commodity-based while the other provides geographic detail. The purpose of 

the database is to provide transportation information on North American trade flows. This type 

of information is being used to monitor freight flows and changes to these since the signing of 

the North American Free Trade Agreement (NAFTA) by the United States, Canada, and Mexico 

in December 1992, and its entry into force on January 1, 1994. The database is also being used 

for trade corridor studies, transportation infrastructure planning, marketing and logistics plans, 

and other purposes. It allows users to analyze movement of merchandise by all land modes, 

waterborne vessels, and by air carriers.

(http://transborder.bts.gov/programs/international/transborder/)
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Freight Performance Measurement Border Data

As previously mentioned, the Freight Performance Measurement (FPM) program collects 

information on select border crossings for North America using truck probes. The probe data, 

collected by FPM and the NPMRDS, can be used to analyze truck movements and travel times, 

respectively, for border crossings. The NPMRDS extends into the five-mile radius of arterials in 

Canada and Mexico.

Travel Time Index Border Data

The Texas A&M Transportation Institute maintains the Border Crossing Information System, 

which computes average travel times of U.S.-bound commercial vehicles for the selected 

segment over the selected time period, calculated at 10-minute intervals.

(http://bcis.tamu.edu/Commercial/en-US/queryArchivedData.aspx)

GENERAL AND ECONOMIC FREIGHT DATA RESOURCES

The following freight data resource links are provided as additional resources that can be 

consulted for freight data and analysis. While most analysts use the above data sources for freight 

performance measures and the Commodity Flow Survey or FAF for economic information, the 

following could supplement analysis, depending on the objective. Although not detailed in this 

primer, the interested reader may wish to consult these sources:

• Agricultural Trade (www.fas.usda.gov/data).

• Fedstats: Gateways to Federal Statistics and Statistical Agencies

(http://fedstats.sites.usa.gov/).

• National Transportation Statistics

(http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_

transportation_statistics/index.html).

• Statistical Abstract of the United States

(http://www.census.gov/library/publications/2011/compendia/statab/131ed.html).

• Transportation Energy Data Book (http://cta.ornl.gov/data/index.shtml).

• U.S. International Trade (http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/

publications/us_international_trade_and_freight_transportation_trends/2003/index.html).

Economics:

• County Business Patterns (http://www.census.gov/programs-surveys/cbp.html).

• Employment Statistics (http://www.bls.gov/bls/employment.htm).

• Industry Economic Accounts (http://www.bea.gov/Industry/Index.htm).

• Pay and Benefits (http://www.bls.gov/bls/wages.htm).

• Producer Price Index (http://www.bls.gov/pPI/).

• Productivity Statistics (http://www.bls.gov/bls/productivity.htm).

• Regional Economic Accounts (http://www.bea.gov/regional/index.htm).
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• Surface Transportation Board Economic Data

(http://www.stb.dot.gov/stb/industry/econ_reports.html).

• Transportation Satellite Account

(http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/transportation_

satellite_accounts/index.html).

• U.S. Department of Commerce Data Hub (https://data.commerce.gov/).

Classification Systems Used for Freight Data:

• Harmonized System (https://usatrade.census.gov/).

• North American Industrial Classification System

(http://www.census.gov/eos/www/naics/).

• North American Product Classification System

(http://www.census.gov/eos/www/napcs/index.html).

• Standard Occupational Classification System (http://www.bls.gov/soc/).
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APPENDIX D. ADDITIONAL AVAILABLE HIGHWAY DATA

MOTOR CARRIER FINANCIAL AND OPERATING STATISTICS

The collection of for-hire trucking company financial and operating statistics (F&OS) data is 

mandatory (Code of Federal Regulations Title 49 Part 369). The collection is managed by the 

Federal Motor Carrier Safety Administration (FMCSA) Motor Carrier F&OS Program. F&OS 

data can be downloaded by row (trucking company), groups of rows (e.g., segment of the 

industry), or as a whole, with each column of data representing F&OS characteristics of the 

trucking companies taken from the line items and schedules on the 1999 Form M or Form QFR 

(annual reports, depending on revenue). Consult the FMCSA website for more information and 

copies of Form M and Form QFR.

(http://www.transtats.bts.gov/DatabaseInfo.asp?DB_ID=170&Link=0)

VEHICLE INVENTORY AND USE SURVEY

The Vehicle Inventory and Use Survey (VIUS) provides data on the physical and operational 

characteristics of the Nation’s private and commercial truck population. Its primary goal is 

to produce national and State-level estimates of the total number of trucks. This survey was 

conducted every five-years, until 2002, as part of the economic census.

(http://www.census.gov/svsd/www/vius/products.html)

MOTOR FUEL TAX DATA

Receipts into the Federal Highway Trust Fund come from a variety of taxes on highway fuel, 

tires, heavy vehicle use tax, and truck/trailer sales taxes. The motor fuel excise tax, currently 

18.4 cents per gallon for gasoline/gasohol and 24.4 cents for special fuel (primarily diesel), raises 

the majority of the revenue. This revenue is then placed into the Highway Trust Fund by the U.S. 

Treasury Department, after collection by the Internal Revenue Service. These funds are then 

distributed to the States based on formulas provided in Federal legislation.

On a monthly basis, each State is required to report to the Federal Highway Administration 

(FHWA) the number of gallons taxed by that State. These data are analyzed and compiled by 

FHWA staff. The data on the amount of on-highway fuel use for each State is then used to 

attribute Federal revenue to each State. Every year, the FHWA Office of Policy provides data 

from the previous year for use in the attribution process. The previous year’s data are provided 

to States to review, allowing them to verify that the data report is correct and ready to be used in 

attribution. (https://www.fhwa.dot.gov/policyinformation/motorfueldata.cfm)

HIGHWAY FINANCE DATA COLLECTION

The FHWA’s comprehensive highway finance information collection effort involves extensive 

input on Federal, State, and local government financing of highways. The U.S. Congress 

recognized the need for highway finance information to support highway policy development, 

and as early as 1904, the Federal Government began inquiring about highway taxation, sources 
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of revenue for highways, and highway expenditures. The role of the Federal Government in 

highway transportation has changed greatly, but its role in assembling highway finance and 

related data has continued. As highway agencies and highway programs change and evolve, 

FHWA continues to revise and enhance its data collection program.

Highway finance information is used by FHWA in its analysis to support the Federal-Aid 

Highway Program’s reauthorization. It is also used in the development of the proposed 

legislation and reports to Congress; evaluating the extent, performance, condition, and use of the 

Nation’s transportation systems; analyzing existing and proposed Federal-aid funding methods 

and levels; assigning user-cost responsibility; and evaluating Federal, State, and local highway 

programs. (https://www.fhwa.dot.gov/policyinformation/hwyfinancedata.cfm)

NATIONAL HOUSEHOLD TRAVEL SURVEY

The National Household Travel Survey (NHTS) is a periodic national survey, to assist 

transportation planners and policy makers who need comprehensive data on travel and 

transportation patterns in the United States. The 2009 survey is the latest collected by FHWA, in 

coordination with a private firm who conducted the survey around the country.

Previous surveys included the 2001 NHTS, and the former Nationwide Personal Transportation 

Surveys (NPTS) of 1969, 1977, 1983, 1990, and 1995.

The NHTS/NPTS serves as the Nation’s inventory of daily travel. Data is collected on daily trips 

taken by households and individuals in those households over a 24-hour period, and includes:

• Purpose of the trip (work, shopping, social).

• Means of transportation (car, walk, bus, subway).

• Travel time of trip.

• Time of day/day of week.

These data are collected for all trips, modes, purposes, trip lengths, and all areas of the country, 

urban and rural.

Uses of Surveys:

• Quantify travel behavior.

• Analyze changes in travel characteristics over time.

• Related travel behavior to the demographics of the travel.

• Study the relationship of demographics and travel over time.

(http://nhts.ornl.gov/introduction.shtml)

HIGHWAY SAFETY DATA

Highway fatality data can be extracted from the Fatality Analysis Reporting System (FARS), 

which is compiled by the U.S. Department of Transportation (USDOT), National Highway 
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Traffic Safety Administration (NHTSA). Data are gathered from a census of police accident 

reports (PARs), State vehicle registration files, State drivers licensing files, State highway 

department data, vital statistics, death certificates, coroner/medical examiner reports, hospital 

medical reports, and emergency medical service reports. A separate form is completed for 

each fatal crash. Blood alcohol concentration (BAC) is estimated when not known. Statistical 

procedures used for unknown data in FARS can be found in the NHTSA report A Method for 

Estimating Posterior BAC Distributions for Persons Involved in Fatal Traffic Accidents, DOT 

HS 807 094 (Washington, D.C.: July 1986).

Data are collected from relevant State agencies and electronically submitted for inclusion in the 

FARS database on a continuous basis. Cross-verification of PARs with death certificates helps 

prevent undercounting. Moreover, when data are entered, they are checked automatically for 

acceptable range values and consistency, enabling quick corrections when necessary. Several 

programs continually monitor the data for completeness and accuracy. Periodically, sample cases 

are analyzed for accuracy and consistency. FARS data do not include motor vehicle fatalities 

on nonpublic roads, which account for about two percent or fewer of the total motor vehicle 

fatalities per year.

NHTSA’s General Estimates System (GES) data is a nationally representative sample of policereported crashes that contributed to an injury or fatality or resulted in property damage, and 

involved at least one motor vehicle traveling on a roadway. GES data collectors randomly sample 

PARs and forward copies to a central contractor for coding into a standard GES system format. 

Documents such as police diagrams or supporting text provided by the officers might be further 

reviewed to complete a data entry (http://www.nhtsa.gov/NCSA).

NATIONAL BRIDGE DATA

The National Bridge Inventory contains detailed technical and engineering information about 

hundreds of thousands of bridges in the United States, including year built, bridge design, 

condition, and many other fields. Nationalbridges.com is the website that makes this information 

available to the public. The National Bridge Inventory is a compilation of data supplied to FHWA 

from States for bridges located on public roads. Significant freight-related data such as Average 

Daily Truck Traffic, if the bridge is on a Strategic Highway Network route, clearance heights, 

and design loads are also available from the National Bridge Inventory.

(https://www.fhwa.dot.gov/bridge/britab.cfm)

PAVEMENT DATA

FHWA maintains highway pavement condition data. The Long-Term Pavement Performance 

(LTPP) program was established to collect pavement performance data as one of the major 

research areas of the Strategic Highway Research Program (SHRP). The first five-years of the 

LTPP program were funded and directed by the SHRP. Since 1991, FHWA has continued the 

management and funding of the program. The LTPP program is managed by the Long-Term 

Pavement Performance Team under the Office of Infrastructure Research and Development.
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The LTPP program is a large research project that includes two fundamental classes of studies 

and several smaller studies to investigate specific pavement-related details that are critical to 

pavement performance. The fundamental classes of study are the General Pavement Study (GPS) 

and the Specific Pavement Studies (SPS). The combined GPS and SPS programs consist of 

over 2,500 test sections located on in-service highways throughout North America. The LTPP 

program will monitor and collect pavement performance data on all active sites. The collected 

data include information on seven modules: Inventory, Maintenance, Monitoring (Deflection, 

Distress, and Profile), Rehabilitation, Materials Testing, Traffic, and Climatic.

The LTPP Information Management System was established in 1988 and is the central database 

where all data collected under the LTPP program are stored. This database is continuously being 

developed as more data are collected and processed. Four regional offices are established under 

the LTPP program to coordinate and communicate LTPP-related activities throughout the United 

States and Canada.

(http://www.fhwa.dot.gov/research/tfhrc/programs/infrastructure/pavements/ltpp/)
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APPENDIX E. TERMS AND DEFINITIONS

American Transportation Research Institute (ATRI) – Not-for-profit research organization 

that is a part of the American Trucking Associations Federation aimed at conducting 

transportation research with an emphasis on the trucking industry’s essential role in a safe, 

efficient, and viable transportation system.

Automobile – a travel mode that includes all motor vehicle traffic using a roadway except transit 

buses (includes such vehicles as trucks, recreational vehicles, motorcycles, and tour buses).

Average Annual Daily Truck Traffic (AADTT) – The total volume of truck traffic on a 

highway segment for one year, divided by the number of days in the year.

Bottleneck – A section of a highway network that experiences operational problems such as 

oversaturated congestion. Bottlenecks may result from factors such as reduced roadway width or 

steep freeway grades that can slow trucks.

Capacity – The maximum number of vehicles that can reasonably be expected to traverse a point 

or a uniform section of roadway during a given time period under prevailing conditions.

Commodity – An item that is traded in commerce. The term usually implies an undifferentiated 

product competing primarily on price and availability.

Container – A large, standard sized metal box into which cargo is packed for shipment.

Corridor – (1) A set of essentially interrelated, parallel transportation facilities for moving 

people and goods between two points; (2) a geographic area used for the movement of people 

and goods.

Delay – Additional travel time beyond some norm experienced by a traveler.

Demand – The number of persons or vehicles desiring to use a mode or facility.

Facility – A length of roadway composed of points and segments.

Fixing America’s Surface Transportation (FAST) Act – Fixing America’s Surface 

Transportation Act signed into law on December 4, 2015. The FAST Act is a five-year Federal 

authorization for Federal fiscal years 2016 through 2020 and authorizes $305 billion for freight 

projects.

Fluidity – Tracking a commodity across multiple modes or regions; measuring end-to-end travel 

times of key commodities.

Freight – Any commodity being transported.
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Freight Analysis Framework (FAF) – Integrates data from a variety of sources to create a 

comprehensive picture of freight movement among States and major metropolitan areas by all 

modes of transportation.

Freight Coalition – A regional organization that cooperates in the planning, operation, 

preservation, and improvement of freight transportation infrastructure and networks.

Freight Performance Measures – Freight-specific performance measures help to identify 

needed transportation improvements and monitor their effectiveness. They also serve as 

indicators of economic health and traffic congestion.

Governing data – More freight data are available each year. Recently, truck Global Positioning 

System (GPS) data have become available to planners. While the additional data create 

opportunities, they also create new challenges to managing and processing them. Additionally, 

the private sector controls a lot of valuable data. Establishing relationships and contractual 

arrangements for sharing these data are also obstacles.

Heavy congestion – A situation in which traffic demand is sufficient to cause the level of service 

to be below a specified Level of Service standard.

Heavy vehicle – A vehicle with more than four wheels touching the pavement during normal 

operation.

Highway – A general term for denoting a public way for purposes of vehicular, bicycle, and 

pedestrian travel, including the right-of-way.

Highway modes – Automobile, bicycle, bus, pedestrian.

Indicator – A mobility performance measure which primarily shows a trend over time and is not 

used to achieve a goal or objective nor is it used in a decision-making process.

Intermodal – Related to the connection between two or more modes of transportation.

Level of Service (LOS) – A qualitative assessment of a road’s operating conditions. For local 

government comprehensive planning purposes, LOS is an indicator of the extent or degree 

of service provided by, or proposed to be provided by, a facility based on the operational 

characteristics of the facility. LOS indicates the capacity per unit of demand for each public 

facility.

Logistics – All activities involved in the management of product movement; delivering the right 

product from the right origin to the right destination, with the right quality and quantity, at the 

right schedule and price.

Merging data – It is often necessary to combine data from different sources. This can be 

difficult because different agencies or local governments may use different definitions, data may 
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have been collected at different times of the year, and the technology for collection may differ. 

For example, the different technology used to count vehicles may use different classification 

schemes for trucks.

Metropolitan Planning Organization (MPO) – A federally funded and mandated organization, 

composed of representatives from local government and transportation authorities, charged with 

establishing transportation policy and decision-making for a metropolitan area.

Mobility – The movement of people and goods.

Mobility performance measure – (1) A metric that quantitatively tells us something about 

mobility; (2) a mobility metric directly tied to achieving a goal or objective or used in a decisionmaking process.

Mode – A means of moving people or goods.

Motor carrier – A firm engaged in providing commercial motor freight or long-distance 

trucking.

Moving Ahead for Progress in the 21st Century (MAP-21) Act – Moving Ahead for Progress 

in the 21st Century Act is the federally authorized surface transportation act signed into law on 

July 6, 2012. MAP-21 created a streamlined, performance-based, and multi-modal program to 

meet the many challenges facing the U.S. transportation system.

Multi-modal – More than one travel mode potentially including the four highway modes (auto, 

bicycle, bus, pedestrian), aviation, rail, seaports, and transit.

Non-recurring events – As it pertains to traffic, a delay caused by an unforeseen event; usually 

a traffic incident, the weather, a vehicle breakdown, a work zone, or other atypical event. Even if 

planned in many cases, like work zones and special events, they are irregular and not predictable 

in location and duration.

Passengers (aviation, rail, seaport) – People in a vehicle making use of a mode.

Performance measure – A metric composed of a number and a unit of measure.

Quality of freight-related data - The National Cooperative Freight Research Program’s 2011 

report on performance measures included this statement: “A primary finding is that freight 

performance measurement is challenged both by an abundance of data and by a lack of complete 

data for many important freight system performance functions.” While the amount of available 

data continues to grow, significant gaps remain, such as sufficient origin/destination data, 

estimates of value of cargo shipped, and network coverage. The quality of data is also affected 

by its age and how well it has been maintained. Data are ideally collected in a consistent manner 

year after year.
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Quantity of freight-related data – While there continues to be gaps in the available freight data, 

the quantity of data is growing. GPS data provided through a partnership of the Federal Highway 

Administration (FHWA) and ATRI create several new possibilities, particularly with regard 

to truck travel speed and reliability, but they also create new challenges for management and 

processing.

Recurring event – As it pertains to traffic, a recurring event is a traffic condition (i.e., a 

bottleneck or backup) that occurs in the same location and at the same time daily, albeit for 

weekday or weekend conditions. Examples would be peak hour slowdowns at junction points, 

intersections, and ramps. Drivers can plan for these events because they know by routine that 

such events will occur time and again in the same manner and place.

Reliability – The degree of certainty and predictability in travel times on the transportation 

system. Reliable transportation systems offer some assurance of attaining a given destination 

within a reasonable range of an expected time. An unreliable transportation system is subject to 

unexpected delays, increasing costs for system users.

Stakeholders – Public-sector agencies, community groups, and private-sector businesses that 

have a direct role in, or are affected by, transportation and land use planning and decisionmaking processes.

Supply Chain – Starts with unprocessed raw materials and ends with the final customer using 

the finished goods.

Sustainability – An objective calling for policies and strategies that meet society’s present needs 

without compromising the ability of future generations to meet their own needs.

System – A combination of facilities or services forming a network or being selected for 

analysis.

Throughput – The maximum number of people or vehicles that can reasonably be expected 

to traverse a point or a uniform transportation facility section during a given time period under 

prevailing conditions.

Transit – A travel mode in which vehicles (including buses, streetcars, and street-running light 

rail) stop at regular intervals along the roadway to pick up and drop off passengers.

Travel time – The total time spent getting from one point to another.

Travel time reliability – The consistency or dependability in travel times, as measured from 

day-to-day and/or across different times of the day.

Truck – A heavy vehicle engaged primarily in the transport of goods and materials.

Truck probe data – Data collected by commercial GPS fleet management devices mounted on 
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trucks. These data are processed in a Geographic Information System (GIS) and assigned to a 

roadway network to provide performance measures for individual segments.

Twenty-Foot Equivalent Unit (TEU) – The eight-foot by eight-foot by 20-foot intermodal 

container used as a basic measure used for container cargo.

Vehicle – A motorized mode of transportation.

Vehicle miles traveled (auto) – The total number of miles traveled by vehicles using a highway 

system.

Volume to capacity ratio – The ratio of demand to capacity.

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APPENDIX F. TRUCK PARKING METRICS

Table 10. Tier I truck parking metrics.

Table 11. Tier II truck parking metrics.

Category Metric Data Resources Readiness Scale

Demand Truck Travel on NHS HPMS, FAF Current Corridor

Supply

Number of Spaces, 

Public and Private

State inventories of 

public spaces and 

use of private truck 

stop resource for 

private data

Current; requires 

State data input and 

purchase of private 

data

Corridor

and 

Facility

Number of Spaces 

in Relation to NHS 

Mileage

HPMS mileage for 

the NHS Current Corridor

Number of Spaces in 

Relation to VMT HPMS VMT Current Corridor

Number of Spaces in 

Relation to GDP by 

State

Bureau of 

Economic Analysis 

GDP Data

Current Corridor

Category Metric Data Resources Readiness Scale

Demand, Driver 

Demographics 

and Needs

Utilization for Public 

and Private Facilities 

(hourly, weekly and 

monthly)

State DOT 

inventories and 

surveys; truck stop 

owner and operators

Data Collection 

Required Facility

Parking Needs by 

Driver Type Driver Surveys Anecdotal Facility

Parking Needs by 

Industry Represented Driver Surveys Anecdotal Facility

Demand, 

Economic

Origin and 

Destination 

Information

FAF/CFS/Use of 

Vehicle Probe Data

Current for FAF and 

CFS, use of FPM 

requires additional 

analysis

Corridor 

and 

Facility

Demand, Safety Inventory of Problem 

Locations

Interviews with 

State motor carrier 

safety staff

Current

Corridor 

and 

Facility 

FAF = Freight Analysis Framework • GDP = Gross Domestic Product • HPMS = Highway Performance 

Monitoring System • NHS = National Highway System • VMT = vehicle miles traveled

CFS = Commodity Flow Survey • DOT = Department of Transportation • FAF = Freight Analysis Framework

FPM = Freight Performance Measures
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Table 11. Tier II truck parking metrics (continuation).

Table 12. Tier III truck parking metrics.

Category Metric Data Resources Readiness Scale

Economic 

Valuation, 

Demand, 

Supply, 

Development

Proximity to Industry 

and Highway 

Facilities

GIS shape files for 

parking locations; 

industrial locations; 

travel time data

Current and Data 

Collection Required

Corridor 

and 

Facility

Safety

Hours of Service 

Violations

State DOT and 

Police Records Current

Corridor 

and 

Facility

Fatigue-Related 

Crashes

State DOT and 

Police Records Current Corridor

Supply

Amenities at Parking 

Facilities

State DOT and 

Truck Stop Owners 

and Operators 

Survey Data

Current and Data 

Collection Required Facility

Inventory of Driver 

Perceived Shortages, 

Parking Challenges

Driver Surveys Anecdotal

Corridor 

and 

Facility 

Category Metric Data Resources Readiness Scale

Demand

Impact of 

Congestion on Travel 

Time and Resulting 

Driving Distance, 

Need for Parking

Corridor and 

congestion studies; 

traffic monitoring 

sites

Current and 

Data Collection 

Required; Metric 

Approach Needed

Corridor 

and 

Facility

Driver 

Demographics 

and Needs, 

Demand

Average Haul 

Length/ Multi-Day 

versus Single-Day

Freight data 

summaries prepared 

by professional 

organizations

Anecdotal

Corridor 

and 

Facility

Use of Technology to 

Determine Parking 

Availability

Survey of truck stop 

operators Anecdotal

Corridor 

and 

Facility

CFS = Commodity Flow Survey • DOT = Department of Transportation • FAF = Freight Analysis Framework

GIS = Geographic Information System • FPM = Freight Performance Measures
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NHS = National Highway System

Table 12. Tier III truck parking metrics (continuation).

Category Metric Data Resources Readiness Scale

Economic 

Valuation, 

Development

Return on Investment 

for Parking 

Development

Survey of truck stop 

operators

Data Collection 

Required Facility

Optimization 

of Return on 

Investment

Survey of truck stop 

operators

Data Collection 

Required Facility

Business Locations, 

Industrial Land Uses

Local economic 

development 

agency reports

Data Collection 

Required

Corridor 

and 

Facility

Employment by 

Industry for Truck 

Facilities

Federal and State 

employment data

Data Collection 

Required

Corridor 

and 

Facility

Diesel Fuel Sales Federal and State 

taxing authorities

Current; Metric 

Approach Needed Facility

Economic 

Valuation, 

Development, 

Location 

Dynamics

Parcel Size and 

Zoning Local land use plans Data Collection Required Facility

Environment, 

Development

Environmental 

Impact Metrics (i.e., 

Air Quality/Idle 

Reduction, Parking 

Development)

State and local air 

quality monitoring 

agencies

Data Collection 

Required

Corridor 

and 

Facility

Safety

Crime Reports by 

Location

State and local law 

enforcement records

Data Collection 

Required Facility

Reported Parking 

Violations on NHS

State and local law 

enforcement records

Data Collection 

Required Corridor

Fixed-Object 

Crashes with 

Trucks on Highway 

Shoulders

State and local 

crash data bases

Data Collection 

Required Corridor

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APPENDIX G. INNOVATIVE RESEARCH: USING FUEL

TAXES TO TRACK TRUCK MOVEMENT

INTRODUCTION

The Moving Ahead for Progress in the 21st Century (MAP-21) legislation and the subsequent 

Fixing America’s Surface Transportation (FAST) Act legislation require the U.S. Department 

of Transportation (USDOT) to develop new tools and/or improve existing tools to support an 

outcome-oriented, performance-based approach to evaluating proposed freight-related and nonfreight-related transportation projects. These tools must include developing methodologies for 

analyzing benefits and costs of transportation projects and ensure that projects consider safety, 

economic competitiveness, environmental sustainability, and system condition.

ISSUE

USDOT utilizes a variety of resources to collect freight related data including Freight Analysis 

Framework (FAF), Commodity Flow Surveys (CFS), and vehicle probe data. But this data 

does not show the complete picture of how freight moves within the United States. Many data 

gaps still exist, such as the lack of local commodity flow data, origin/destination data, zip code 

data, trip frequency data, routing information, and better accident or crash data that will require 

additional multi-modal research and development.

POTENTIAL SOLUTIONS

The method of using fuel tax documentation to approximate truck movement across the United 

States is based on the concept that truck movements can be observed as a series of trips with 

each trip beginning at a fuel station. Each truck travels a limited distance until it runs out of fuel. 

Its next trip begins after refueling. This method collects the known location information of the 

origins of all truck trips: the addresses of the fueling stations that are reported on the monthly 

fuel tax reports. All trucks refueling at a given station can only travel within an area bounded 

by the distance they can travel after refueling. This results in a known area where truck travel 

associated with each fueling station occurred. The center of that area is the fuel station address. 

Regardless of whether a truck returns to the same station or travels to another fuel station, its 

miles traveled can always be associated with the station where it began its trip.

Using geospatial software, fuel consumption areas are mapped, yielding information such as:

• Macroscopic level: Plot the fuel consumption at all distributor addresses graphically to 

present the variations in density of travel across the United States. In addition, comparing 

the results across multiple timeframes and only displaying where changes occurred would 

result in trend maps that emphasize where change is happening.

• Microscopic level: Assign all truck mileage associated with each distributor’s area to 

specific truck route segments in that area. The truck mileage could be assigned in a 

logical manner such as using weighted average daily traffic (ADT) values. The geospatial 
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database could sum the segment mile totals from all overlapping fuel distributor areas to 

give a total of all truck traffic for each segment. The resulting map would then display 

truck travel densities per roadway segment. In addition, trend maps can be produced, 

comparing maps across multiple timeframes, displaying only the changes.

METHODOLOGY

Step 1 - Create a template for each fuel vendor (refer to Figure 12):

• The center represents the address of a fuel vendor such as a truck stop.

• The radius represents the maximum travel distance a typical truck can drive between a 

typical fill-up (calculation based on average fuel consumption rates and typical fill-up 

quantities).

• This template represents the territory where that fuel will be spent, which also equals the 

territory where all truck miles associated with that vendor will travel.

Step 2 - Place vendor template on a GIS map of truck routes (refer to Figure 13):

• The center of the template is placed at the address of the fuel vendor on the GIS map.

• The truck routes within the circle represent all possible routes for trucks to use in that area 

as they consume the fuel.

• The ADT (acquired from State GIS databases) is then captured for each roadway segment 

that falls within the circle.

• Each segment’s centerline mileage is multiplied times its ADT value to produce a vehicle 

miles traveled value for that segment. If a State distinguishes ADTs by vehicle type, that 

would be a more precise method.

• All segment vehicle miles traveled are then totaled to produce a grand total for the vendor 

area.

Source: AECOM.

Figure 12. Diagram. Fuel vendor location and average distance of truck travel.

Fuel Vendor Address

Avg. Miles per

Fill-up Distance
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• The contribution (percentage) of each segment’s vehicle miles traveled then becomes a 

multiplier. This multiplier is stable over time and represents to what extent trucks use that 

segment.

• The gallons of truck fuel reported 

by that vendor on their monthly tax 

statement is then converted to total 

truck miles by multiplying the total 

gallons times a typical truck’s fuel 

consumption rate.

• Each roadway segment is then 

assigned a portion of the total miles 

driven by multiplying the total mileage 

times the multiplier for that segment.

The resulting table approximates how the 

total miles driven by trucks fueling at that 

vendor can be assigned specific roadway 

segments each month.

Step 3 - Repeat Step 2 for all vendors:

• Repeat the mileage assignments in 

Step 2 for each vendor reporting fuel 

tax. The template circles will overlap.

• Sum the truck miles traveled on each 

individual roadway segment across all 

vendor templates.

The table of segments represents approximately where all the truck miles were driven during that 

month. Once the model is created (Steps 1 through 3), it can be updated by replacing the monthly 

fuel usage reported by all vendors.

Step 4 - Derive performance measures

The resulting table applied to a GIS database can be used for a variety of purposes including:

a) At the macroscopic level, a “heat map” could show roughly where the truck traffic is 

operating. Plotting semi-transparent circles to represent the fuel quantity dispensed at 

all distributor addresses will show the variations in density of travel across the United 

States.

b) At the microscopic level, truck route segments would provide a more detailed 

presentation of truck traffic volumes.

c) A table or map displaying changes can identify trends in behavior, such as comparing the 

15

15

19 15

13

13

19

30

6

Source: AECOM.

Figure 13. Diagram. Roadway segments 

attributed to fuel vendor.
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vendor node values or segment values across multiple timeframes. These changes could 

result from a number of causes such as seasonal, weather impacts, economic downturns, 

or commodity usage trends.

d) Historical fuel tax reports could be used in analysis. Any time range could be used, but 

the smallest unit would be in months (the typical vendor fuel tax reporting frequency).

ADVANTAGES

There are many advantages to this tool. One advantage is the ease of data acquisition. Data 

would be available for any area, and there are no privacy issues related to acquiring vehicle miles 

traveled minimizing trucking industry concerns.

This tool can also be used to:

• Enable the use of historical data to produce truck traffic volumes.

• Monitor freight movement trends over different time frames.

• Monitor when shipping contracts and manufacturing rates change because of economic 

trends.

• Enable methods for assessing changes in truck movements at different levels over different 

periods of time:

o Macroscopic: changes in regional wholesale fuel distribution would indicate changes 

in regional truck miles driven.

o Microscopic: changes in truck miles driven by route could be estimated.

The results of this tool can be used to:

• Identify trends in freight movements at local and regional levels beyond commodity 

analysis 

• Study locations for new commercial/industrial facilities that have high freight movement 

activity in their operations.

• Recommend highway capacity improvements where roadway capacity is being challenged 

due to increasing truck volumes.

• Focus truck motor carrier inspection resources by knowing where the truck movement 

trends are occurring.

• Adjust pavement life cycle maintenance per trending behavior 

• Determine if hydrocarbon emission measuring sites are reporting the same trends in 

pollution as the trends in vehicle miles traveled. 

• Overlay truck crash heat maps with truck traffic maps to identify the correlation between 

truck volumes and crashes involving trucks. This could then lead to targeted enforcement 

campaigns and prioritized highway safety improvement plans for routes that have 

excessive crashes per truck mile rates.
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CONSIDERATIONS

• There is error inherent in the mileage assignment system. However, this method leverages 

the knowledge of where trucks fueled. All travels emanated from these points of origin, 

enabling approximations of truck operations and distances. 

• Updates to the model could be accomplished by replacing the fuel quantities sub-table of 

the GIS database with fresh values reported by each vendor each month.

• Periodic updates to the list of vendors and updates to the ADT values would be necessary 

to recalibrate the model.

• The radius of each template is critical to this system, and calculations would have to be 

identified. Factors to consider include:

o A typical 18-wheeler has two 100 to 150 gallon tanks, and average about seven miles 

per gallon, so the truck has a long travel range between fill-ups. There are a number of 

factors influencing when a driver chooses to refuel. For example, drivers have hours 

of service limits, and 700 miles (the approximate range for one typical saddle tank) is 

roughly the miles they could drive during their allowable hours of service. It seems 

reasonable to assume that long-distance drivers would tend to refuel during their rest 

break time.

o Local and intrastate trips would be shorter, but would likely occur entirely within a 

smaller template area than the long-distance drivers. This would suggest a smaller 

radius for the vendor template for these types of vehicles.

o The typical mileage driven between refueling is also critical to the accuracy of the 

system. One way to get a realistic mileage per tank would be to conduct a statistical 

analysis of the number of gallons per fill-up using a sampling of vendors to get a 

realistic typical value for all trucks using these facilities. This could be derived from 

their fuel purchase receipts.

• States tax fuel in several ways, including:

o Wholesale level: Where the tanker trucks fill up 

o Retail level: Where the trucks refuel 

o State fuel sales tax 

o Weight-mile tax along with a fuel tax. (Only Oregon has a weight mile tax)

It would be necessary to develop State-specific processes for capturing the monthly 

gallons of fuel (or miles driven in some cases) reported by each fueling station.

• Validation of the model can occur using actual carrier data. For fuel tax apportionment 

purposes, motor carriers are required to keep detailed records for each trip, including fuel 

consumption and routes driven through each jurisdiction, as well as fuel purchase receipts. 

This information is then summarized and used to report to the International Fuel Tax 
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Association (IFTA) where their fuel was consumed.

Summaries are submitted when carriers report their taxes. The IFTA requires that detailed 

trip records be retained for four-years for auditing purposes. Many of the carriers contract 

this tax reporting process out to specialized freight accounting services. It may be possible 

to contract these accounting services to anonymize clients’ trip data of any carrier-specific 

information and provide route-specific reports of actual trip mileage. 

A sampling process can validate the computer model results. This approach uses reports 

of actual routes and miles driven per carrier. It can provide more accuracy than calculating 

mileage from fuel consumed. However, acquisition of this detailed data from carriers 

would be difficult. However, a sampling of the accounting services could be used for 

validation purposes in a few specific sampling areas.

CONCLUSION

There are several benefits to using fuel taxes to track truck movement. This method can help 

identify freight trends at the local and regional levels beyond the typical commodity flow 

analysis. The results can also help identify land use trends. Finally, enforcement agencies could 

use the data to properly locate inspection facilities and resources and State DOTs would be able 

to use this data in determining pavement wear and life cycles. 

U.S. Department of Transportation

Federal Highway Administration

Office of Operations

1200 New Jersey Avenue, SE

Washington, D.C. 20590

ops.fhwa.dot.gov

October 2017

FHWA-HOP-16-089