Guidance on Data Needs, 

Availability, and Opportunities for 

Work Zone Performance Measures

March 2013
Source: Cover images (clockwise from left): (1) Texas Transportation Institute, (2) Texas 

Transportation Institute, (3) Freeway and Arterial System for Transportation (FAST), 

Regional Transportation Commission of Southern Nevada, Las Vegas, Nevada, and 

(4) Federal Highway Administration Publication (FHWA-HRT-12-004)

Notice

The Federal Highway Administration provides high quality information to 

serve Government, industry, and the public in a manner that promotes 

public understanding. Standards and policies are used to ensure and 

maximize the quality, objectivity, utility, and integrity of its information. 

FHWA periodically reviews quality issues and adjusts its programs and 

processes to ensure continuous quality improvement.
Technical Report Documentation Page 

1. Report No.

FHWA-HOP-13-011

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

4. Title and Subtitle

Guidance on Data Needs, Availability, and Opportunities for Work Zone 

Performance Measures

5. Report Date

March 20, 2013

6. Performing Organization Code

7. Author(s)

Gerald L. Ullman and Geza Pesti – TTI

Rachel Klein – Battelle

8. Performing Organization Report No.

9. Performing Organization Name and Address

Battelle, 505 King Avenue, Columbus, Ohio 43201

and

Texas A&M Transportation Institute, The Texas A&M University System, 

College Station, Texas 77843-3135

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-06-D-00007

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

Office of Operations

1200 New Jersey Avenue, SE

Washington, DC 20590

13. Type of Report and Period Covered

14. Sponsoring Agency Code

15. Supplementary Notes

Project Leader: Jawad Paracha, FHWA

Project carried out under contract to Battelle for the FHWA Office of Operations.

16. Abstract

Current Federal Regulations (23 CFR 630 Subpart J) encourage States to collect and analyze both safety and mobility 

data to support the initiation and enhancement of agency-level processes and procedures addressing work zone 

impacts. The purpose of this guidance document is to provide practitioners with the skills to identify current data 

sources (both existing data and data collected specifically for the work zone) for use in work zone performance 

measurement, as well as potential data sources that could be useful to work zone performance measurement in the near 

future. This document is also intended to assist practitioners in determining how to select and compute useful work 

zone performance measures, given the data sources available to them. For both current and potential data sources, 

guidance is presented on the viability of each source for work zone performance measurement, as well as on possibly 

leveraging opportunities to maximize the value of data collection and extraction efforts. In addition to information 

about data sources and opportunities, guidance is provided regarding work zone performance measures that the various 

data sources can support. Where appropriate, examples are provided as to how data assessment, collection, 

application, and interpretation can be accomplished. In this way, document users can obtain an overall perspective.

17. Key Words

Work Zone Safety and Mobility, Traffic Impacts, 

Performance Measures

18. Distribution Statement

No restrictions. This document is available to the public 

through NTIS:

National Technical Information Service

Springfield, Virginia 22161

http://www.ntis.gov

19. Security Classif.(of this report)

Unclassified

20. Security Classif.(of this page)

Unclassified

21. No. of Pages

102

22. Price

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

The research team gratefully acknowledges the voluntary participation and contributions of the 

project review panel. The project panel greatly contributed toward the preparation of this 

document by providing valuable insights, review and feedback in identification and prioritization 

of work zone performance measures. The project review panel included:

Charles Riedel, New York State Department of Transportation

Daniel Smith, Missouri Department of Transportation

Doug Gettman, Kimley-Horn

Frank Newboles, Washington State Department of Transportation

Kevin Balke, Texas A&M Transportation Institute

Michael Fontaine, Virginia Center for Transportation Innovation and Research

Reynaldo Stargell, Ohio Department of Transportation

Rich Margiotta, Cambridge Systematics

Steve Kite, North Carolina Department of Transportation

Zoubir Ouadah, City of Poway, California
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EXECUTIVE SUMMARY

Performance measures quantify how a particular work zone or several work zones together are 

affecting traveler safety and mobility. In addition, performance measures can help agencies and 

contractors assess if and how their work zone safety and mobility policies, processes, and 

procedures are working well or should be improved. In the context of highway work zones, 

there are three major data needs for performance measurement:

• Performance data (“how much” a work zone affects travelers, the agency, and/or 

contractor);

• Exposure data (“who” or “what” are being affected); and

• Work zone indicator or stratification data (“when” or “where” the effects are occurring). 

With regards to how much work zones affect conditions, four categories of performance data are 

viewed as important:

• Mobility – data that characterize how trip duration for an individual or the number of 

trips being made are affected by a work zone;

• Safety – data that characterize how risks to travelers and highway workers are affected by 

a work zone;

• Customer satisfaction – data that characterize how travelers, residents, and business 

owners in or near a work zone perceives its effect on them; and

• Agency and contractor efficiency and productivity – data that can be used to assess 

contractor/agency efforts to minimize the duration and extent of travel impacts from a 

work progress perspective. 

Within the mobility category, performance data can be further subdivided into the following 

subcategories: 

• Throughput;

• Unit travel times or delay;

• Travel time reliability; and

• Traffic queues.

Likewise, the safety performance category can be further divided into the following: 

• Traffic crashes;

• Operational safety surrogates; and

• Worker accidents.
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Exposure data categories include:

• Counts or distances traveled through work zones by travelers or vehicles; 

• Project, phase, activity durations (such as hours of work activity), or worker-hours in the 

field; and

• Number of phases or activities that occur.

Meanwhile, indicator or stratification data categories include:

• General roadway design characteristics (type of road, normal number of travel lanes, 

shoulder presence and size, speed limit, etc.) before the project began, and design 

characteristics of interest within the work zone (lane and shoulder widths, long-term lane 

closures, design characteristics of crossovers and lane shifts, etc.);

• Locations and times of work activities of interest (temporary lane closures, full road 

closures, etc.) and dates of major phase changes; and

• Time and characteristics of events of interest (weather events, major incidents, special 

events, etc.).

There are several ways practitioners may obtain data for use in work zone performance 

measurement. One method for accessing data is to extract it from existing sources. Another 

method is to specifically collect the data of interest through a variety of methods. Lastly, data 

may be interpolated or estimated from existing or collected data sources. For exposure and 

indicator or stratification data (which are used to compute many of the specific performance 

measures presented later in this document), it typically requires digging into project-specific files 

and databases, culling the desired data from them (usually by hand), and then collating and 

correlating these data with the performance data. 

Overall, work zone performance measure selection and use involves five main steps:

• Step 1. Determine the categories of work zone performance that are of interest 

• Step 2. Decide which work zones will be measured 

• Step 3. Decide what work zone conditions are of most value to measure 

• Step 4. Determine what data sources are available for computing performance measures 

at each work zone of interest, and assess their potential usefulness and 

limitations 

• Step 5. Identify and compute specific performance measures of interest 

Several sources of data exist for most of the performance measure subcategories of interest. 

These are identified, along with the key considerations regarding their application for work zone 

performance measurement, throughout this guidance document. A set of recommended 

performance measures that could be computed with the available data, prioritized by the expert 

practitioner panel assembled for this project, are also presented throughout the document. 
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TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS .......................................................................................................... i

EXECUTIVE SUMMARY .........................................................................................................iii

LIST OF ACRONYMS ............................................................................................................... ix

1.0 INTRODUCTION.......................................................................................................... 1-1

1.1 Why Are Work Zone Performance Measures Needed?....................................... 1-1

1.2 What Data Is Needed? ......................................................................................... 1-2

1.3 Where Can Practitioners Obtain Data? ................................................................ 1-5

1.4 Guidance Document Objectives .......................................................................... 1-7

2.0 SELECTING USEFUL PERFORMANCE MEASURES.......................................... 2-1

2.1 Characteristics of Useful and Effective Work Zone Performance Measures...... 2-1

2.2 Users of Work Zone Performance Measures....................................................... 2-2

2.3 Determining Work Zone Performance Measures of Greatest Interest and 

Use ....................................................................................................................... 2-3

3.0 DATA SOURCES AND COMPUTATIONAL METHODS FOR 

MOBILITY-RELATED PERFORMANCE MEASURES ........................................ 3-1

3.1 Mobility-Related Data Sources............................................................................ 3-1

3.1.1 Throughput Data ...................................................................................... 3-1

3.1.1.1 Data Sources That Already Exist...........................................................3-2

3.1.1.2 Potential Sources in the Near Future .....................................................3-5

3.1.1.3 Key Considerations and Trade-offs of Throughput Data Sources.........3-5

3.1.2 Delay, Unit Travel Time, and Travel Time Reliability Data................... 3-6

3.1.2.1 Data Sources Currently Available .........................................................3-7

3.1.2.2 Potential Sources in the Near Future ...................................................3-11

3.1.2.3 Key Considerations and Trade-offs of Delay, Unit Travel Time, 

and Travel Time Reliability Data Sources...........................................3-12

3.1.3 Traffic Queue Data ................................................................................ 3-13

3.1.3.1 Sources Currently Available................................................................3-14

3.1.3.2 Potential Sources in the Near Future ...................................................3-18

3.1.3.3 Key Considerations and Trade-offs of Traffic Queue Data 

Sources.................................................................................................3-19

3.2 Identifying and Computing Specific Mobility Measures of Interest ................. 3-20

3.2.1 General Considerations.......................................................................... 3-20

3.2.2 Specific Throughput Measures.............................................................. 3-22

3.2.3 Specific Unit Travel Time and Delay Measures.................................... 3-22

3.2.4 Specific Travel Time Reliability Measures ........................................... 3-26

3.2.5 Specific Traffic Queue Measures........................................................... 3-26
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TABLE OF CONTENTS (CONTINUED)

Page

4.0 DATA SOURCES AND COMPUTATIONAL METHODS FOR SAFETYRELATED PERFORMANCE MEASURES .............................................................. 4-1

4.1 Safety-Related Data Sources................................................................................ 4-1

4.1.1 Crash Data Sources.................................................................................. 4-1

4.1.1.1 Sources That Already Exist ...................................................................4-2

4.1.1.2 Potential Sources in the Near Future .....................................................4-4

4.1.1.3 Key Considerations and Trade-offs of Work Zone Traffic Crash 

Data Sources..........................................................................................4-5

4.1.2 Safety Surrogate Data Sources................................................................. 4-6

4.1.2.1 Sources That Already Exist ...................................................................4-7

4.1.2.2 Potential Sources in the Near Future .....................................................4-9

4.1.2.3 Key Considerations and Trade-offs of Safety Surrogate Data 

Sources.................................................................................................4-10

4.1.3 Worker Accident Data Sources.............................................................. 4-11

4.1.3.1 Sources That Already Exist .................................................................4-12

4.1.3.2 Possible Sources in the Near Future ....................................................4-12

4.1.3.3 Key Considerations and Trade-offs of Worker Accident Data 

Sources.................................................................................................4-13

4.2 Identifying and Computing Specific Safety Measures of Interest..................... 4-13

4.2.1 General Considerations.......................................................................... 4-13

4.2.2 Specific Crash Measures........................................................................ 4-14

4.2.3 Specific Safety Surrogate Measures ...................................................... 4-18

4.2.4 Specific Worker Accident Measures ..................................................... 4-20

5.0 DATA SOURCES AND COMPUTATIONAL METHODS FOR 

CUSTOMER SATISFACTION-RELATED WORK ZONE 

PERFORMANCE MEASURES................................................................................... 5-1

5.1 Customer Satisfaction Data Sources.................................................................... 5-1

5.1.1 Sources that Currently Exist .................................................................... 5-1

5.1.2 Potential Sources in the Near Future ....................................................... 5-6

5.1.3 Key Considerations and Trade-Offs of Work Zone Customer 

Satisfaction Data Sources ........................................................................ 5-6

5.2 Identifying and Computing Specific Customer Satisfaction Performance 

Measures.............................................................................................................. 5-7

6.0 DATA SOURCES AND COMPUTATIONAL METHODS FOR AGENCY 

AND CONTRACTOR WORK ZONE EFFICIENCY 

AND PRODUCTIVITY PERFORMANCE MEASURES......................................... 6-1

6.1 Efficiency and Productivity Data Sources........................................................... 6-1

6.1.1 Sources That Already Exist ..................................................................... 6-1

6.1.2 Potential Sources in the Near Future ....................................................... 6-4

6.1.3 Key Considerations and Trade-Offs of Work Zone Efficiency and 

Productivity Data Sources........................................................................ 6-4

6.2 Identifying and Computing Specific Work Zone Efficiency and 

Productivity Measures ......................................................................................... 6-5

7.0 REFERENCES............................................................................................................... 7-1
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TABLE OF CONTENTS (CONTINUED)

Page

List of Appendices

Appendix A. Delphi Survey Methodology to Prioritize Work Zone Performance 

Measures................................................................................................................ A-1

List of Tables

Table 1-1. Overview of Data Types ........................................................................................ 1-2

Table 1-2. Work Zone Performance Data Categories.............................................................. 1-4

Table 1-3. Sources of Exposure and Indicator/Stratification Data .......................................... 1-6

Table 3-1. Considerations and Trade-offs of Throughput Data Sources................................. 3-6

Table 3-2. Considerations and Trade-offs of Delay, Unit Travel Time, and Travel Time 

Reliability Data Sources....................................................................................... 3-13

Table 3-3. Estimated Queue Length Each Hour.................................................................... 3-17

Table 3-4. Considerations and Trade-offs of Traffic Queue Data Sources........................... 3-20

Table 3-5. Examples of Specific Throughput Performance Measures.................................. 3-23

Table 3-6. Examples of Specific Unit Travel Time and Delay Measures............................. 3-24

Table 3-7. Examples of Specific Travel Time Reliability Performance Measures............... 3-27

Table 3-8. Examples of Specific Traffic Queue Measures.................................................... 3-28

Table 4-1. Considerations and Trade-offs of Traffic Crash Data Sources .............................. 4-6

Table 4-2. Considerations and Trade-offs of Traffic Safety Surrogate Data Sources........... 4-11

Table 4-3. Considerations and Trade-offs of Worker Accident Data Sources...................... 4-13

Table 4-4. Examples of Specific Work Zone Traffic Crash Performance Measures............ 4-15

Table 4-5. Examples of Specific Work Zone Safety Surrogate Crash Performance 

Measures............................................................................................................... 4-19

Table 4-6. Examples of Specific Worker Accident Performance Measures ......................... 4-21

Table 5-1. Considerations and Trade-Offs of Customer Satisfaction Data Sources ............... 5-7

Table 5-2. Examples of Specific Customer Satisfaction Performance Measures.................... 5-8

Table 6-1. Considerations and Trade-Offs of Construction Productivity and 

Efficiency Data Sources......................................................................................... 6-5

Table 6-2. Examples of Specific Agency or Contractor Efficiency and Productivity 

Performance Measures........................................................................................... 6-6
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TABLE OF CONTENTS (CONTINUED)

Page

List of Figures

Figure 2-1. Example Use of Performance Measures for Continuous Improvement of 

Agency Work Zone Policy..................................................................................... 2-4

Figure 3-1. Relationship between Traffic Demand and Throughput at Work Zones ............... 3-2

Figure 3-2. Some Traffic Signal Systems can be a Source of Traffic Volume Data ................ 3-3

Figure 3-3. Mechanical Counters can Sample Throughput and/or Demand Volumes at a 

Work Zone.............................................................................................................. 3-4

Figure 3-4. The Connected Vehicle Initiative involves Communication between 

Vehicles and between Vehicles and Roadside Infrastructure ................................ 3-5

Figure 3-5. Delays can be Estimated from a Series of Spot Speed Sensors along the 

Roadway Segment.................................................................................................. 3-7

Figure 3-6. Components of Work Zone Delay ....................................................................... 3-10

Figure 3-7. Bluetooth Detection Technology is a Low-cost Method of Collecting 

Anonymous Point-to-Point Travel Time Data ..................................................... 3-11

Figure 3-8. Steps to Estimate Queue Lengths from Spot Speed Sensor Data......................... 3-14

Figure 3-9. Example of Sensor Speed Analysis to Determine Duration and 

Length of Queue................................................................................................... 3-16

Figure 3-10. TOC Operators can Manually Record Upstream End of Queue Periodically 

for Use in Computing Queue Lengths and Durations.......................................... 3-17

Figure 3-11. Periodic Screen Captures of Private-Sector Traveler Information Maps can 

also be Used to Gather Work Zone Queuing Data............................................... 3-19

Figure 3-12. Example of a Work Zone Mobility Dashboard Under Development .................. 3-21

Figure 4-1. Crash Dashboard In Use by FAST Personnel in Las Vegas, NV........................... 4-4

Figure 4-2. Example of the Inspection Form Used by Oregon DOT........................................ 4-8

Figure 4-3. TTC is one Type of Safety Surrogate Data that can be Extracted from 

Traffic Simulation Model Output........................................................................... 4-9

Figure 4-4. Determination of Whether Crashes have Increased Significantly in a 

Work Zone............................................................................................................ 4-18

Figure 5-1. Focus Group Participants provide their Opinions about Particular Topics 

and can Offer Ideas on Possible Improvement Needs............................................ 5-2

Figure 5-2. Example of a Script Used during a Telephone Interview of South Dakota 

Motorists................................................................................................................. 5-3

Figure 5-3. Agency Websites are a Common Venue Used for Customer 

Surveys/Questionnaires.......................................................................................... 5-4

Figure 5-4. Illinois Tollway Actively Solicit Input about Work Zones on Its Facilities.......... 5-5

Figure 6-1. Various Construction Performance Metrics are Computed from an 

Agency’s Construction Management Database ..................................................... 6-2

Figure 6-2. Illinois Tollway Lane Closure Entry Screen .......................................................... 6-2

Figure 6-3. Illinois Tollway Temporary Lane/Shoulder Closure Database.............................. 6-3
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LIST OF ACRONYMS

AADT Annual average daily traffic

ATR Automatic traffic recording

AVI Automatic vehicle identification

AVL Automatic vehicle location

BLS Bureau of Labor Statistics

DMS Dynamic message signs

FAST Freeway and Arterial System for Transportation (Las Vegas)

FHWA Federal Highway Administration 

GIS Geographic information system

HCM Highway Capacity Manual

HOT High occupancy toll

HOV High occupancy vehicle

IRB Institutional Review Board

ITS Intelligent transportation system

MOT Maintenance-of-traffic

OSHA Occupational Safety and Health Administration

PDO Property-damage-only

SSAM Safety Surrogate Assessment Model

TMP Transportation management plan

TOC Traffic operations center

TT Travel time

TTC Time-to-collision

VMT Vehicle-miles-travelled
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1.0 INTRODUCTION

1.1 Why Are Work Zone Performance Measures Needed?

Public-sector agencies and private-sector companies need to understand how well they are 

designing and operating work zones in order to improve the efficiency and effectiveness of the 

work zones. They must determine whether any changes made to improve current and future 

work zones are having the desired effect. The selection, development, and use of performance 

measures are critical to this improvement effort.

The effect of highway work zones on traveler safety and mobility has become an increasingly 

important focus area for the Federal Highway Administration (FHWA) in recent years. Current 

Federal Regulations (23 CFR 630 Subpart J) encourage states to collect and analyze work zone

safety and mobility data. The data is to be used to support the initiation and enhancement of 

agency-level processes and procedures to avoid or mitigate work zone impacts. 

Performance measures quantify how a 

particular work zone or several work zones 

together are affecting traveler safety and 

mobility. In addition, performance measures 

can help agencies and contractors assess if and 

how their work zone safety and mobility 

policies, processes, and procedures are 

working well or should be improved.

The challenges related to measuring performance for many agencies and companies include:

1) Identifying which measures are most important;

2) Determining what data is needed for the measures;

3) Determining where and how to get the data; and 

4) Determining how to compute the desired measures from the collected data. 

Agencies and companies must also consider the availability, accessibility, and applicability of 

data when deciding which work zone performance measures to focus on and how the measures

will be computed and reported. These challenges and decisions can be somewhat daunting. This 

guidance document has been prepared to assist agencies and companies in developing useful and 

effective work zone performance measures.

The New York State Department of 

Transportation score the specific traffic control 

and safety attributes of the work zones it 

inspects each year, and uses those scores to 

identify key topics or issues to emphasize in 

future department work zone training efforts (1).
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1.2 What Data Is Needed?

In the context of highway work zones, there are three major data needs: 

• Performance data;

• Exposure data; and

• Work zone indicator or stratification data. 

An overview of the types of data needed is provided in Table 1-1. In essence, Performance data

describe “how much” a work zone or group of work zones affects travelers, the agency, and/or 

the contractor. Performance data includes travel times that motorists using the work zone 

experience relative to their normal travel times, traffic crashes or worker accidents, and public 

opinions gathered about driving conditions through a work zone. 

Table 1-1. Overview of Data Types

Type Description Examples

Performance 

data

(How much did 

the work zone 

affect things?)

Data that may be used by an agency, 

contractor, or other entity (i.e., the 

public) to evaluate something about work 

zone operations or its effects.

• Travel time changes

• Crash increases

• Driver opinions about travel 

conditions

Exposure data

(Who or what 

was affected by 

the work zone?)

Data used – in combination with 

performance data – to normalize to a 

rate of some type (i.e., per vehicle, per 

trip, per hour).

• Vehicles-miles passing through the 

work zone, for the entire day or for 

some subset of the day (i.e., during 

the peak periods)

• Hours of temporary lane closures

occurring during the project

• Total worker-hours occurring over a 

project or project phase

• Number of person-trips passing 

through the work zone, for the entire 

day or some subset of the day

Indicator or 

stratification 

data

(When or where 

did the effects 

occur?)

Data used to further stratify or focus the 

performance data to a particular subset 

of the work zone or work operation, such 

as:

• during peak travel hours, 

• during certain specific work tasks, 

• during particular project phases, or 

• for work zones with specific features 

of interest.

• Time periods when temporary lane 

closures occurred

• The specific hours selected as the 

AM or PM peak period

• Specific dates when a project was in 

a particular construction phase
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Meanwhile, exposure data count the “who” or “what” that are affected by the work zone or 

group of work zones of interest. Most commonly, exposure is measured in terms of number of 

vehicles, people, and/or miles of travel through the work zone. The number or total hours of lane 

closures or full roadway closures required during a project is another example of exposure data. 

In fact, the number of work zones that experience a particular condition (such as a traffic queue 

that exceeds a particular length) could also be considered a type of exposure data for assessing 

levels of compliance with an agency’s work zone mobility goals or policies.

Finally, work zone indicator or stratification data focus attention or otherwise target how 

performance was affected during specific activities, phases, time periods, or other events of the 

work zone. For example, an agency may be interested in determining whether its current lane 

closure restriction policy is keeping delays below a selected threshold. In this case, times and 

locations of lane closures would be indicator data needed to focus on those delays occurring 

when and where lane closures were in place. Indicator/stratification data might also be used in 

determining the appropriate exposure data of interest, such as the dates when a project was in a 

particular phase. Knowing those specific dates, an agency could then use crash data and vehiclemiles-traveled (VMT) through the work zone data to compute a crash rate per VMT during that 

particular phase. 

Overall, the three data types in Table 1-1 are extremely broad and general, and further 

categorizations within each type are necessary in order to fully assess and understand the current 

and potential sources for such data and the opportunities to use the data for work zone 

performance measurement. Key categories of work zone performance, exposure, and indicator

or stratification data addressed in this document are shown in Table 1-2. These categories were 

verified through Delphi survey techniques, using an expert panel of practitioners assembled for 

this document development, as being both important and useful to many different audiences with 

possible interest in work zone performance. 

With regards to how much work zones affect conditions, four categories of performance data are 

viewed as important:

• Mobility – data that characterize how trip duration for an individual or the number of 

trips being made are affected by a work zone;

• Safety – data that characterize how risks to travelers and highway workers are affected by 

a work zone;

• Customer satisfaction – data that characterize how travelers, residents, and business 

owners in or near a work zone perceives its effect on them; and

• Agency and contractor efficiency and productivity – data that can be used to assess

contractor/agency efforts to minimize the duration and extent of travel impacts from a 

work progress perspective.
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Within these main categories, subcategories also exist. As an example, within the mobility 

category, performance data can be further subdivided into the following subcategories: 

• Throughput;

• Unit travel times or delay;

• Travel time reliability; and

• Traffic queues.

Table 1-2. Work Zone Performance Data Categories

Type Categories Subcategories

Performance Data

Mobility

• Throughput

• Unit travel time

• Delays

• Travel time reliability

• Queues

Safety

• Crashes

• Safety surrogates

• Worker accidents

Customer satisfaction

• Project-specific satisfaction

• Agency or region-wide 

satisfaction with work zones

Agency and contractor productivity and 

efficiency

• Construction productivity

• Roadway durability

Exposure Data

Traveler/vehicle counts and travel 

distances

Durations of specific work tasks or N/A

conditions, worker-hours in the field

Work zone activities/conditions

Indicator or 

Stratification Data

Roadway classification/conditions

Work zone activity/conditions N/A

Events (incidents, special events, 

weather, etc.)

Likewise, the safety performance category can be further divided into the following: 

• Traffic crashes;

• Operational safety surrogates; and

• Worker accidents.
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Methods for Obtaining Data

1. Extract from existing sources

2. Collect using electronic or manual

methods

3. Interpolate or estimate from existing 

or collected data sources

As Table 1-2 illustrates, the exposure data and work zone indicator or stratification data include 

multiple categories. Exposure data categories include:

• Counts or distances traveled through work zones by travelers or vehicles;

• Project, phase, activity durations (such as hours of work activity), or worker-hours in the 

field; and

• Number of phases or activities that occur.

Meanwhile, indicator or stratification data categories include:

• General roadway design characteristics (type of road, normal number of travel lanes, 

shoulder presence and size, speed limit, etc.) before the project began, and design 

characteristics of interest within the work zone (lane and shoulder widths, long-term lane 

closures, design characteristics of crossovers and lane shifts, etc.);

• Locations and times of work activities of interest (temporary lane closures, full road 

closures, etc.) and dates of major phase changes; and

• Time and characteristics of events of interest (weather events, major incidents, special 

events, etc.).

The event data can be used to eliminate data with external influences (such as crashes occurring 

after a major incident created significant backups in the work zone), or may be used to further 

examine impacts that occurred during those events (such as how crashes in the work zone were 

affected when adverse weather conditions were present).

1.3 Where Can Practitioners Obtain Data?

There are several ways practitioners may obtain data 

for use in work zone performance measurement. One 

method for accessing data is to extract it from existing 

sources. For example, when evaluating queue lengths 

in work zones, practitioners may use data from 

existing Traffic Operations Center (TOC) spot speed 

traffic sensors. Another method is to specifically 

collect the data of interest through a variety of methods. In the case of queue lengths, this may 

entail manual on-site observations by field personnel, closed-circuit TV operated by TOC staff, 

or via temporary video cameras. Lastly, data may be interpolated or estimated from existing or 

collected data sources. For example, a practitioner may decide to estimate traveler delays from

manually-recorded queue length data, using basic macroscopic traffic flow relationships (an 

illustration of how to do this can be found in the chapter on mobility data and performance 

measures). 
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For Work Zone Performance Measures

The potential sources of performance data are described in detail in later chapters of this 

document. For exposure and indicator or stratification data (which are used to compute many of 

the specific performance measures presented later in this document), it typically requires digging 

into project-specific files and databases, culling the desired data from them (usually by hand), 

and then collating and correlating these data with the performance data. Table 1-3 presents the 

various exposure and indicator/stratification data categories listed in Table 1-2, and identifies the 

common source or sources where that data may be obtained. Vehicle and person-based exposure 

data can come from the same sources as throughput data (one of the mobility-related 

performance measure categories) and is discussed in more detail in chapter 3. For the other data 

categories, project-related files are the main source of information available. Presently, details 

about the status of each work zone on a day-by-day basis are commonly limited to whatever is 

captured in the daily diaries kept for each project. A few agencies transfer these diary entries 

into their overall construction management database, but still as a narrative summary of 

activities. Project engineers or inspectors are those most commonly assigned to this task, as are 

maintenance supervisors for crews performing various minor maintenance tasks with agency 

forces.

Table 1-3. Sources of Exposure and Indicator/Stratification Data

Exposure and Indicator/Stratification

Data Categories Potential Data Sources

Traveler/vehicle counts and travel distances • See discussion on work zone throughput in the

mobility-related performance measures chapter

(starting on page 3-1)

Durations of specific work tasks or conditions

• Project daily diary notes

• Electronic construction management database

• Agency maintenance activity records

• Lane closure notification/approval database 

Work zone activities/conditions

• Project daily diary notes

• Electronic construction management database

• Project plans (for maintenance-of-traffic 

conditions in each phase, temporary 

geometrics)

• Agency maintenance activity records

• Lane closure notification/approval database

Roadway classification/conditions • Roadway inventory file or database

Events (incidents, special events, weather, 

etc.)

• Transportation operations center (TOC) 

operator incident logs

• Project daily diary notes

• Electronic construction management database

entries

• National Climatic Data Center 

(www.ncdc.noaa.gov)
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For Work Zone Performance Measures

Document Objectives

Provide practitioners with the skills to:

1. Identify current data sources for work 

zone performance measurements 

2. Identify potential data sources for work 

zone performance measurement that 

may be available in the near future

3. Select and compute useful work zone 

performance measures, given the data 

sources that are available

Traditionally, there can be considerable variation in both the quality and quantity of information 

pertaining to work activities documented in the project diary or project correspondence files, 

depending on who does the documentation. As agencies and contractors become more involved 

in work zone performance measurement, emphasis on documentation of details that are of most 

interest will likely be required. Long-term lane and shoulder reductions will typically be 

documented in the maintenance-of-traffic (MOT) sheets of a set of project plans, but the actual 

dates that the reductions begin and end will not. Similarly, any changes in the MOT plan itself 

will need to be captured (both when and what) in the diary narrative. For short-term or shortduration lane or shoulder closures, documentation of the time, location, and extent of closures 

(how many lanes are closed or kept open) will often only be found in project diary information. 

A few agencies may have procedures in place to receive notification of these temporary lane 

closures (or perhaps to even approve them). In regions with TOCs, lane closure notifications to 

the TOC may also be an information source. For these agencies, this database can be a potential 

source of the desired exposure and 

indicator/stratification data, although the 

prevalence of “phantom” lane closures (those 

that have been requested and approved but 

which do not end up actually occurring) should 

be assessed before relying on this database for 

this purpose. 

Agencies will usually have a fairly good 

database of roadway conditions that exist 

normally on the facilities under its jurisdiction. 

These data can be easily accessed to establish 

baseline conditions regarding geometrics, access, etc. for comparison against conditions 

implemented through and around the work zone. For specific events that may compound with 

the effects of the work zone, common data sources include incident logs (for work zones located 

within the coverage of a transportation management center), unusual events documented in the 

project diary, and historical weather data from the National Climatic Data Center. 

1.4 Guidance Document Objectives

The purpose of this guidance document is to provide practitioners with the skills to identify 

current data sources (both existing data and data collected specifically for the work zone) for use 

in work zone performance measurement, as well as potential data sources that could be useful to 

work zone performance measurement in the near future. This document is also intended to assist 

practitioners in determining how to select and compute useful work zone performance measures, 

given the data sources available to them.
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For Work Zone Performance Measures

Work Zone Performance Measure 

Guidance Provided

1. Priorities identified by an expert 

practitioner panel

2. Basic computations required

3. Appropriate audience(s) for the measures

4. Impact of different data sources on 

measures that are computed

For both current and potential data sources, guidance is presented on the viability of each source 

for work zone performance measurement, as well as on possibly leveraging opportunities to 

maximize the value of data collection and extraction efforts. This leveraging can occur by 

identifying multiple ways in which a particular data source can be used for performance 

measures and for other purposes, such as for operating a work zone intelligent transportation 

system (ITS) for traveler information that can also provide data for work zone performance 

measures. Leveraging can also occur when certain data are used for multiple work zone 

performance measures. For example, an agency may choose to monitor work zone queues 

because of both work zone mobility and safety performance concerns. 

In addition to information about data sources and 

opportunities, guidance is provided regarding 

work zone performance measures that the various 

data sources can support. The measures have 

been prioritized by an expert practitioners panel 

using an on-line Delphi surveying technique

(details on how the measures were prioritized is 

included in Appendix A). Information is provided 

regarding the basic computations needed for each 

measure, its overall level of importance (as rated 

by the panel), and the audiences for which the measure would be most applicable. A discussion 

of how specific measures compare when computed using different data sources is also provided. 

Where appropriate, examples are provided as to how data assessment, collection, application, 

and interpretation can be accomplished. In this way, document users can obtain an overall 

perspective.
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For Work Zone Performance Measures

Points to Remember

• Measures should allow for monitoring of agency 

or company goals and targets

• Use only as many measures as needed

• Both project-level and aggregated measures can 

be important

Effective Performance Measure 

Characteristics

• Capable of showing progress in 

meeting goals

• Useful for making decisions on how to 

improve processes and procedures

• Can provide clear feedback on results 

of improvement actions

• Are clear and easy to understand

• Provide both accurate and precise 

indications of conditions

2.0 SELECTING USEFUL PERFORMANCE MEASURES

Performance measures can be valuable 

tools for both public agencies and 

private-sector companies. However, it 

is important to select and use 

measures that can actually help 

improve work zone processes and 

procedures, can be understood by 

others who might be shown the 

measures, and can be determined 

within the data availability constraints 

that exist. Collecting data and computing measures, by itself, does not automatically lead to 

better results; the measures must align with what the agency or company deems important. At 

the same time, performance measurement choices cannot be made without regard to the type and 

amount of data that can be obtained and used to compute the measures. Thus, the decision as to 

what performance measures will be monitored should not be made lightly.

2.1 Characteristics of Useful and Effective Work Zone Performance 

Measures

Ideally, work zone performance measures 

should have the following five 

characteristics:

Measures should show progress being 

made toward achieving desired results or 

goals – Measures that easily convey whether 

or not the agency or company is meeting or 

progressing towards its goals or targets (such 

as an agency’s work zone safety or mobility 

policy statements) will keep improvement 

efforts focused on those targets. 

Measures should aid in making 

improvement decisions – In addition to 

tracking progress towards ultimate goals or targets, measures that relate to or imply possible 

courses of action for improvement are desirable. Measures without a purpose are undesirable.

Measures should provide clear feedback on the results of improvements or courses of actions 

that have been implemented – Similarly, measures that are sensitive enough to track whether 

improvement decisions have or have not achieved their intent help agencies and companies 

adjust and revise those decisions more quickly and efficiently.
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For Work Zone Performance Measures

Measures should be clear and easy to understand – One of the important uses of performance

measures is to easily convey a “snapshot” of how an agency or company is doing and (hopefully) 

improving. Measures that are highly complex or which require significant technical background 

to understand, have more limited use than those which are inherently easy to understand. Also, 

most agencies will desire both project-level and program-level (aggregated across multiple 

projects) measures.

Measures need to be both accurate and precise – Performance measure accuracy defines how 

closely it can capture the “true” condition or improvement being measured; performance 

measure precision pertains to the level of confidence one can place on the performance measure

value itself. 

2.2 Users of Work Zone Performance Measures

Depending on how they are disseminated, work zone performance measures may be of interest 

to- and used by the following audiences:

• Practitioners responsible for designing the MOT plans for work zone projects, as well as 

other types of programmatic uses (such as establishing and revising work zone policies 

and procedures);

• Practitioners responsible for the day-to-day traffic safety and operations through the work 

zones; and

• The traveling public.

Work zone design practitioners are constantly tasked with striking a balance between providing 

space for traffic to use and providing space for work crews to complete the required project 

tasks. Often, certain less-than-optimum design features must be allowed on a temporary basis 

(e.g., narrowed lanes, temporary loss of shoulders, lane shifts) in order to accommodate the work 

tasks. The actual effects of these decisions are of interest to designers for consideration in future 

project designs. 

Once a work zone has been established, practitioners responsible for its day-to-day operation 

desire constant feedback on both the safety and mobility effects in order to detect and correct any 

problems that may exist. Many work zones are very dynamic, involving multiple phases with 

different traffic-handling schemes, and a range of work activities that can affect traffic. These 

dynamics make it particularly challenging to detect when problems exist.

Finally, work zone travelers themselves desire information about work zone conditions and 

performance, particularly mobility performance. The value of real-time traveler information to 

motorists has long been recognized. Measures that reflect expected changes to normal travel 

conditions and which can be used as a surrogate of future conditions can aid in both departure 

time and route choice decisions. Measures reflecting the safety record through a project 
Guidance on Data Needs, Availability, and Opportunities Page | 2-3

For Work Zone Performance Measures

(i.e., number of injury or fatal crashes occurring in a work zone) can be useful as a reminder to 

motorists to drive safely and defensively.

2.3 Determining Work Zone Performance Measures of Greatest Interest 

and Use

As suggested above, the value and usefulness of a particular work zone performance measure 

will depend in part on the audience(s) for which it is intended. 

• In general, the traveling public will be most interested in real-time (and forecasted) 

project-level, or if multiple projects exist along a particular route, corridor-level measures 

of the effects of work zones upon mobility and, perhaps, traffic and pedestrian safety. 

• Practitioners with day-to-day work zone operational responsibilities will also be 

interested in real-time safety and mobility performance as well as in customer 

satisfaction, or perhaps more accurately, lack of satisfaction. 

• Finally, practitioners who design work zones will be interested in how their design 

decisions ultimately affect safety, mobility, customer satisfaction, and the productivity 

and effectiveness of the work tasks that have to be accomplished.

Differences exist, within each audience group, of the level of detail required for various 

measures – While it is true that the types of measures of greatest interest will vary by audience, 

there will be differences in the level of detail desired in the various measures for certain subsets 

of those audiences. For example, travelers and higher-level manager practitioners responsible 

for overall operations in a given corridor may only be interested in the aggregated, corridor-level 

effect of all work zones upon peak-period travel times. Meanwhile, practitioners responsible for 

a given project may only be interested in how the project is impacting travel times during the 

peak periods, and may also be interested in how travel times are affected when lanes are 

temporarily closed for short-term work activities. 

The level of performance measure aggregation or disaggregation needed can affect the 

amount and type of data needed to generate those measures – For certain measures, such as the 

number of work zone fatalities occurring during a year, aggregation across all work zones is 

important to allow an agency to assess its level of compliance with, or progress towards, its 

overall work zone safety goal. In other cases, the effect of a single work zone may be of greatest 

importance, such as the increase in peak period travel times during a specific phase of a complex 

project. The data needs for both types of performance measures are obviously much different. 
Guidance on Data Needs, Availability, and Opportunities Page | 2-4

For Work Zone Performance Measures

Determining which work zone performance measures will be used also requires consideration 

of the data required to compute them. Ideally, an agency or company would simply identify the 

desired set of measures and the data necessary to develop those measures would be readily 

available. In reality, data availability must be considered as part of performance measure 

selection. As will be discussed later in this document, several data sources can be used for work 

zone performance measurement, if they are available to the practitioner, and more importantly, 

are appropriate for the conditions being monitored and measured. 

Ultimately, work zone performance measures should be an integral part of an agency’s or 

company’s continuous improvement process to reduce work zone impacts. An example of how 

this could occur is presented in Figure 2-1. An agency establishes a work zone mobility or safety 

policy goal (perhaps no more than a 20 percent increase in crashes in a work zone relative to prework zone conditions). The agency should periodically assess what percent of its projects are 

meeting that goal. Those projects that did not meet the goal could then be examined in greater 

detail to help the agency understand the possible causes for the failure to meet the goal. In turn, 

this better understanding of causes could lead to improvements in either the design or operational 

procedures that the agency establishes for future projects. 

Figure 2-1. Example Use of Performance Measures for Continuous

Improvement of Agency Work Zone Policy

Assess compliance 

with agency work 

zone policy goals

Identify 

projects failing 

to meet those 

goals

Assess possible causes 

for non-compliance at 

those projects

Revise/improve design and 

operational procedures for 

future projects

Revise agency 

policy goals if 

needed

Battelle
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For Work Zone Performance Measures

Overall, work zone performance measure selection and use involves five main steps:

Step 1. Determine the categories of work zone performance that are of interest – An agency or 

contractor must initially decide which of the basic categories of possible work zone performance 

data and measures are of interest. In determining these categories, practitioners should consider 

both the work zone mobility policies and goals or targets already established, and the possible 

data sources available for the agency or contractor to use. 

Step 2. Decide which work zones will be measured – Most agencies need not measure every 

work zone. A sampling of work zones, focusing on those of most interest to the agency, can 

actually be more useful from a continuous improvement process because it makes data 

collection, analysis, and interpretation more manageable. It should be noted that the same work 

zones need not be used for all performance measures. Rather, an agency or company may 

monitor some work zones because of concerns for their mobility impacts, monitor the safety 

impacts of several other work zones where certain crash countermeasures were implemented for 

testing, and monitor still other work zones located in a sensitive area in a downtown area for 

business and customer satisfaction, for example. 

Step 3. Decide what work zone conditions are of most value to measure – Work zones are often 

complex, involving multiple phases and a wide range of work activities at different locations that 

vary in terms of their effects on traffic. Identifying which phases, locations, and work activities 

are of interest is needed to determine what performance measures are most appropriate. 

Measures may be computed for the entire project, for certain phases, or for during work tasks. 

In addition, these decisions can influence what data is needed to compute those measures. 

In some cases, data availability (or lack thereof) may require a change in either which work 

zones will be measured, or possibly a change in the categories of performance data of interest.

Step 4. Determine what data sources are available for computing performance measures at 

each work zone of interest, and assess their potential usefulness and limitations – Depending 

on the location of the work zone and the types of measures of interest, data that can be used to 

generate the measures may already exist within the agency. If not, one or more of several 

possible data collection methods may be employed. Opportunities may exist to leverage data 

collection efforts to meet multiple performance measure needs. Trade-offs between data

accuracy/precision and costs to obtain must also be assessed.

Step 5. Identify and compute specific performance measures of interest – Once data that can be 

used for performance measurement have been located or collected, the final step is to identify the 

specific measures of interest, and then compute the measures. For most performance categories, 

measures can be defined to characterize the entire project (i.e., total number of hours when a 

traffic queue was present), specific times of interest (increase in average delay per vehicle during 

the peak hours), or for certain phases or work activities (average maximum queue length during 

hours when temporary lane closures were in place). Consideration must be given to the level of 

accuracy and precision attainable with the data when interpreting the results from a specific work 

zone as well as for an aggregated set of work zones.
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3.0 DATA SOURCES AND COMPUTATIONAL METHODS FOR 

MOBILITY-RELATED PERFORMANCE MEASURES

Considerable attention has been given in recent years to the mobility impacts of work zones upon 

travelers, business owners, and nearby residents. Many state work zone policies in place around 

the country have specific limitations on the allowable amount of delay that work zones may 

cause. Some agencies also limit the hours during which lanes can be closed on certain roadways 

so as to reduce or eliminate the potential for queues and delays to be created. 

Work zone performance has multiple mobility-related dimensions. Specifically, mobility 

impacts are commonly described and monitored in terms of the following categories: 

• Throughput;

• Delays;

• Unit travel times (i.e., minutes per mile traveled, which can also be expressed as an 

average travel speed over the length of the roadway segment);

• Travel time reliability; and

• Traffic queues.

A number of data sources are available to assess these mobility impacts. In some cases, data that 

already exists within an agency or is gathered as part of other agency requirements can be used; 

in other cases, data must be collected in and around the work zone in order to measure mobility 

impacts. A discussion of these various sources is presented in the following sections. 

3.1 Mobility-Related Data Sources

3.1.1 Throughput Data

Data pertaining to the number of vehicles (or, in some cases, people) that pass through a work 

zone, overall and as a rate per unit time, are useful to practitioners in assessing the adequacy of 

design decisions pertaining to the work zone MOT and the overall transportation management 

plan (TMP). Such data also represents vehicle/person exposure to the work zone that is then

used in normalizing other types of data and performance measures. Traffic throughput is 

dependent upon traffic demand attempting to pass through the work zone, as depicted in

Figure 3-1. If the work zone capacity is adequate to service traffic demands at all times 

throughout the day, then traffic demand volumes and throughput will be the same. However, if 

the work zone capacity is not sufficient to meet the traffic demand during certain times of the 

day, throughput per unit time will be equal to work zone capacity rather than traffic demand 

volumes. These conditions are characterized by congested traffic flows and traffic queues, and 

often result in significant traffic diversion away from the work zone during those congested 

periods. For these reasons, it is generally necessary to collect count data at work zones that 

experience overcapacity conditions if accurate throughput measures during the congested periods 

are desired. 
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For Work Zone Performance Measures

Throughput Data That May be Available from 

Existing Agency Sources

• TOC or traffic signal system vehicle count data

• Toll facility usage data

• Automatic traffic recording (ATR) station data

• Planning and programming AADT estimates

Battelle

Figure 3-1. Relationship between Traffic Demand and Throughput at Work Zones

Of course, if throughput is measured continuously over the entire duration of congestion build up 

and dissipation each day, cumulative throughput will be equal to cumulative demand volumes. It 

is important to keep in mind that traffic volumes during particular portions of a day (nighttime, 

AM or PM peak periods, etc.) can vary significantly from day to day, due to a variety of reasons. 

For purposes of work zone performance measurement, 

it is therefore beneficial to use an average from several 

days of data for throughput measures (normally, at 

least three days of data are desirable) to establish the 

“typical” volumes during the time period(s) of interest. 

3.1.1.1 Data Sources That Already Exist

Data already existing within an 

agency – A number of sources of 

work zone throughput data exist 

within most agencies. In many 

metropolitan areas, for example, 

tremendous amounts of detailed traffic 

volume data collected by TOCs can be 

accessed for work zone throughput. 

Work zones that occur within the 

limits of TOC coverage need only to 

Work zone throughput measures 

should be based on data from 

multiple days 

Throughput = Demand

Throughput = Capacity

Demand

Demand

Demand ≥ Capacity

Demand < Capacity
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For Work Zone Performance Measures

TTI

identify which traffic sensors are of interest for which dates and times, and request the data from 

the TOC operations personnel. Likewise, vehicle count data can be accessed from some arterial 

street signal systems for work zone throughput. 

Toll facility operators likewise have a ready source of 

throughput data available to them in the form of usage data. 

Because of the need or desire to continue to collect revenue, 

it is likely that interruption of the power supply will be less 

of a concern for toll facility work zone data. It should be 

noted, though, that project work may involve the loss of 

power within the work zone limits, rendering traffic 

sensors unusable unless steps are taken ahead of time to 

ensure power to them is maintained.

For those work zones located outside of the limits of TOC coverage or on toll facilities, agencies 

also collect significant amounts of traffic volume data to support their own programming and 

planning efforts, and to comply with federal reporting requirements about vehicle travel in its 

jurisdiction. Most agencies have deployed permanent automatic traffic recording (ATR) stations 

at strategic locations on key roadways. For work zones located immediately upstream or 

downstream of these stations, it is usually a simple manner to request count data for the dates of 

interest (during the work zone, and if significant 

diversion is expected, before and/or after the work 

zone as well). As mentioned above, it will be 

important to determine whether the data reflects 

capacity-constrained throughput or traffic demand 

volumes. More importantly, some agencies have 

algorithms built into their data systems to check for 

“irregularities” in the ATR data. A work zone that 

significantly reduces traffic flows over the ATR sensor could trigger an “irregularity” alarm and 

be replaced in the dataset with an expected value for that time period. Consequently, it is 

important to check to ensure that the volumes taken from an ATR location are indeed the actual 

counts during the days of interest. 

In many cases, even an ATR station is not 

conveniently located near the work zone of interest. If 

data cannot be specifically collected for the work zone 

(as discussed below), practitioners are limited to the 

availability of annual average daily traffic (AADT)

estimates that the planning and programming division 

may have available for the various roadway links 

within the system. While these are not actual counts, 

Figure 3-2. Some Traffic Signal 

Systems can be a Source of Traffic 

Volume Data

Verify that ATR volume data 

obtained for work zone performance 

measurement purposes are the 

“true” counts obtained and not an 

expected value generated by the 

system. 

AADT estimates on a roadway can 

be used to approximate throughput 

(and vehicle exposure) if it can 

reasonably be assumed that traffic 

demands never exceeded work zone 

capacity, and no other reasons 

existed to cause significant 

diversion away from the project. 
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For Work Zone Performance Measures

Throughput Data Collected Specifically 

For a Work Zone

• Work zone ITS deployment data

• Temporary mechanical data collection 

device data 

• Manual vehicle count data of work zone 

throughput at key times and locations

they can be useful for estimating work zone throughput when 1) it is believed that demand 

volumes will not exceed the capacity of the work zone, 2) it is believed that little or no diversion 

from the work zone will occur, and 3) estimates of throughput are desired for purposes of 

computing vehicular or traveler exposure levels through the work zone. If estimates other than 

24-hour exposure are desired, such as traffic exposure during peak periods or during short-term 

lane closure activities, it will be necessary to apply an estimated time-of-day volume distribution 

(usually obtained from ATR station data for similar roadway types) to extract the throughput 

values for the time periods of interest.

Data collected specifically for a work 

zone – If existing sources of work zone 

throughput are not sufficient for 

performance measurement purposes 

(because of large amounts of expected 

diversion or significant periods of 

congestion developing due to reduced 

work zone capacity, for example), 

practitioners will need to obtain data 

specifically collected for the work zone. 

If a work zone ITS has been deployed to 

help manage traffic and provide real-time driver information, data from that system can be used 

for performance measurement provided that arrangements have been made ahead of time for 

archiving the data and making it accessible for analysis in the desired format. Practitioners can 

also develop a sampling plan to collect throughput data during key phases or at key times of the 

day. Such a plan should include repeated measurements on separate days to increase the 

robustness of the values obtained (a minimum of three 

measurements should be targeted). 

Data samples can be obtained through various mechanical means 

(e.g., pneumatic tube counters, in-lane devices, portable video 

camera recordings) or can be done manually. It should be noted 

that mechanical counters and manual counters may not be very 

practical for data collection on high-volume, high-speed 

roadways.

Figure 3-3. Mechanical 

Counters can Sample 

Throughput and/or 

Demand Volumes at a 

Work Zone

TTI
Guidance on Data Needs, Availability, and Opportunities Page | 3-5

For Work Zone Performance Measures

Sources of Person Throughput Data

• Manual sampling of per-vehicle 

occupancy levels

• Manual or video collection of pedestrian 

throughput 

The New Jersey Department of 

Transportation identifies bicycle and 

pedestrian traffic as key evaluation 

measures (both within the project and 

on alternative routes) as part of its work 

zone traffic mitigation guidelines (2).

Person throughput data – Although vehicle 

throughput measures will normally be 

obtained, some work zones may be better 

measured on a per-person basis, if demand 

management strategies that encourage transit 

ridership, increased vehicle occupancy (i.e., 

high occupancy vehicle [HOV] or high 

occupancy toll [HOT] lanes), or green travel 

modes (walking, biking) are a component of a 

project’s TMP. Consequently, urban work zones that impact pedestrian travel may also 

necessitate person volume data collection and analysis. 

Typical person count sources include:

• Manual sampling of vehicle occupancy through 

the work zone; and 

• Counts (manual or electronic) of pedestrian 

throughput.

The vehicle occupancy rate determined through sampling is then multiplied by vehicle 

throughput to determine person throughput in the work zone. 

3.1.1.2 Potential Sources in the Near Future

Connected Vehicle Initiative – The national connected vehicle initiative now under 

development by multiple federal and state agencies as well as private-sector vehicle 

manufacturers and other partners may offer potential for gathering work zone throughput data. 

Work is underway to establish general architectures, communication protocols, and other 

standards upon which a connected vehicle system will operate. To be useful, sufficient market 

penetration of this technology will first need to occur so that reasonable estimates of throughput 

can be made based on the data 

provided by the various connected 

vehicles that pass through the work 

zone. 

3.1.1.3 Key Considerations and 

Trade-offs of Throughput 

Data Sources

Table 3-1 presents a tabular 

summary outlining the key 

considerations and trade-offs 

associated with the various 

throughput data sources currently 

available. As noted previously, the 

Figure 3-4. The Connected Vehicle Initiative 

involves Communication between Vehicles and 

between Vehicles and Roadside Infrastructure

TTI
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For Work Zone Performance Measures

location of the data relative to the work zone is a key consideration regardless of the data source 

used. Checking any ATR data to ensure that they are the “true” volumes and not modified 

because of an errant assumption about a sensor problem is another key consideration.

Table 3-1. Considerations and Trade-offs of Throughput Data Sources

Data Source Key Considerations and Trade-offs

All data types

• Location of data relative to the work zone dictates whether data is 

demand volumes or work zone throughput

• Data from multiple days are desirable to reduce effects of day-today variations in traffic flow

TOC or signal system

vehicle count data, toll 

facility usage data

• Important to verify that data will be available once work has 

started (no loss of power to sensors)

ATR station data • Need to verify that data are “true” counts and not adjusted 

Agency AADT estimates

• AADT values will provide reasonable estimates of throughput and 

exposure when work zone capacity is not exceeded at any time 

during the day

• If diversion occurs, AADT estimates will overestimate throughput 

and exposure

Work Zone ITS data • Important to verify that data will be archived and made available 

for performance measurement computations

Throughput data collection

via mechanical counters or 

manual counts

• Collection with certain types of mechanical counters (pneumatic 

tubes) are usually not practical for high-volume, high-speed 

roadways

• Manual counts are labor intensive

Manual collection of vehicle 

occupancy levels

• Useful at work zones where efforts to encourage shift to green 

travel and HOV travel are part of the work zone TMP

Manual or electronic 

collection of pedestrian 

throughput

• Useful at work zones where efforts to encourage shift to green 

travel and high-occupancy vehicle travel are part of the work zone 

transportation management plan

• Hours of peak pedestrian travel may not always coincide with 

hours of peak vehicle travel

Connected vehicle data • Date of availability still uncertain

3.1.2 Delay, Unit Travel Time, and Travel Time Reliability Data

Travel time delay, unit travel times, and travel time reliability can all describe work zone effects 

upon traveler mobility. Delay is perhaps the most common data category used in assessing work 

zone mobility impacts, and is defined as the difference between the actual travel time through the 

work zone and the travel time it normally takes to make the trip at that time of the day. In urban 

areas where trip origins and destinations are complex, some agencies find it preferable to discuss 

mobility effects in terms of unit travel times (i.e., minutes of travel per mile or other roadway 

length). Meanwhile, travel time reliability refers to the day-to-day fluctuations in trip times on a 

facility that a motorist must add to their expected trip time in order to ensure an on-time arrival. 
Guidance on Data Needs, Availability, and Opportunities Page | 3-7

For Work Zone Performance Measures

Delay, Unit Travel Time, and Travel Time 

Reliability Data That May be Available from 

Existing Agency Sources

• TOC spot speed sensor data

• TOC tracking of vehicles through use of 

cameras

• TOC point-to-point travel time data using AVI, 

AVL, or license-plate recognition technology

Shoulder closures and other temporary geometric restrictions in work zones can increase the 

effects of traffic incidents on travel times, thereby increasing the unreliability of travel times 

through the work zone. Similar to unit travel times, travel time reliability generally has more 

applicability to work zones on high-volume roadways in urban areas.

3.1.2.1 Data Sources Currently Available

Data from existing agency sources –

Sources that measure speeds at spot 

locations and interpolate between 

locations to approximate travel times 

over segments are common nationally. 

Most TOCs are now estimating travel 

times using data from their spot speed 

sensors and posting them on dynamic 

message signs (DMS). In general 

terms, use of spot speed sensor data to 

estimate travel times is less accurate 

than point-to-point travel time 

measures, especially when the roadway segment is congested. However, speed sensors will 

detect delays more quickly than point-to-point travel time measurement methods. 

TOC staff can also estimate travel times through the use of its camera surveillance system. 

Knowing the locations of the cameras and the distance between them, TOC operators can 

identify a unique vehicle in one camera view (a large truck, an automobile with a company logo 

on the side, etc.), record the time, pull up the next camera view downstream, wait until that same 

vehicle comes into view, and record that time. Subtracting the two times will then yield the 

elapsed travel time between the two cameras. This approach has been successfully used by 

personnel in the Freeway and Arterial System for Transportation (FAST) operations center in 

Las Vegas, NV.

Travel times and delays through a work zone are most 

directly determined from point-to-point travel time 

measurements. Historically, point-to-point travel time data 

was obtainable only through more costly technologies such 

as automatic vehicle identification (AVI), automatic vehicle 

location (AVL), or license plate recognition systems. 

Regions with significant toll road presence will generally 

have enough AVI transponder coverage to allow for 

reasonably accurate point-to-point travel time data to be 

obtained. In addition, it should be noted that data collected 

in this manner reflect recent, rather than, current conditions. 

Figure 3-5. Delays can be 

Estimated from a Series of Spot 

Speed Sensors along the 

Roadway Segment

TTI
Guidance on Data Needs, Availability, and Opportunities Page | 3-8

For Work Zone Performance Measures

Delay, Unit Travel Time, and Travel Time Reliability 

Data Collected Specifically for a Work Zone

• Data extracted from a work zone ITS deployment

• Deployment of portable point-to-point travel time 

data collection devices

• Manual spot speed sampling using radar or lidar 

devices

• Manual collection of travel times by driving 

through the work zone repeatedly

• Estimation of travel time delays from manuallycollected queue length data

For example, point-to-point travel time data that indicates delays of 30 minutes actually refers to 

trips that were initiated more than 30 minutes ago, not necessarily trips that are being initiated or 

entering a roadway segment of interest at the present time.

Data collected specifically for a 

work zone – Work zones located 

outside the boundaries of an 

operating TOC must rely on 

temporary data collection efforts. 

A variety of technical options are 

available. A work zone ITS 

deployment can often provide speed 

or travel time data, especially if the 

system is deployed to provide realtime traveler information. Care must 

be taken to ensure that such data is 

archived and made available for 

performance measurement analysis.

Conceptually, portable data collection systems 

could be developed for any type of point-to-point 

travel time data collection technology. Such a 

technology could be attached to any of a number 

of portable traffic data collection trailers in use 

as part of existing work zone ITS and easily 

integrated into the data streaming capabilities of 

those units. Currently, a number of work zone 

ITS vendors are working on the integration of Bluetooth antenna into their data collection and 

processing capabilities (see next section on potential sources). 

A number of lower-cost manual data collection approaches can also be used to obtain travel time 

data in work zones. Data collectors can utilize hand-held radar or lidar devices to obtain spot 

speeds at one or more locations along a work zone. These data may be sufficient for estimating 

travel time or delay if the location of work zone congestion is fairly short. Data collection 

personnel can also drive through the work zone multiple times and record the elapsed time it 

takes to complete each pass. This approach is most appropriate when conditions on the roadway 

were free flow prior to the work zone being deployed so that “normal” travel times can be 

assumed (free flow speed divided by travel distance). If not, travel time data prior to the 

implementation of the work zone will be needed as well. Because they are labor intensive, both 

of these techniques are more appropriate when they are used to target specific periods (i.e., peak 

The Arizona Department of Transportation 

used a license plate recognition system 

through a work zone on State Route 68 to 

monitor the contractor’s compliance with a 

travel time delay incentive/disincentive clause 

in the contract (4).
Guidance on Data Needs, Availability, and Opportunities Page | 3-9

For Work Zone Performance Measures

hour travel time) or certain work tasks. These techniques are also more feasible when used over 

shorter roadway segments. For situations where field personnel cannot obtain travel times by 

driving through the work zone, but can occasionally document how long a traffic queue extends 

upstream of the work zone, it is possible to estimate delay using basic traffic flow relationships. 

Again, for this approach to apply, conditions prior to the implementation of the work zone must 

be approximately free-flow (i.e., all delays that occur can be assigned to the presence of the work 

zone). 

To estimate how the documented queues result in travel time delays, it is assumed that both the 

queue itself and the work zone result in slower speeds for some travel distance. As depicted in 

Figure 3-6, if a queue has formed upstream of the work zone (at the lane closure bottleneck), it is 

realistic to assume that the flow rate through the work zone is at or near capacity, such that the 

speed at capacity flow can be assumed to govern through the work zone. For simplicity, an 

assumption of a linear relationship between speeds and density would suggest that the capacity 

flow speed would be one-half of the free-flow speed on the facility. Upstream of the work zone, 

the queue that develops would be flowing at a speed less than the capacity flow speed. Again 

using a simple linear speed-density relationship, the following equation (presented in Figure 3-6) 

produces an estimate of the average speed in queue as a function of the normal roadway capacity 

and the capacity expected through the work zone.

The capacity of the work zone can be estimated using procedures in the Highway Capacity 

Manual (HCM) (5). The HCM also provides procedures to estimate the normal traffic-carrying 

capacity of the roadway segment as a function of the free-flow speed on the facility. Assuming 

that these speeds are maintained, on average, through the entire length of the queue and work 

zone documented, estimates of average delays per vehicle through the queue can be computed as 

a function of the length of queue. Some threshold (most likely the desired speed or the posted 

work zone speed limit [UWZSL]) would serve as the basis against which the longer travel times 

through the queue would be computed. This queue delay would then be added to the delay that 

would be generated as vehicles pass through the remainder of the work zone at capacity flow 

speeds (30-35 mph):

Delay

Vehicle = Lq � 1

uq

− 1

UWZSL� + Lwz � 1

uf

2

− 1

UWZSL�

Once the average delay per vehicle is estimated for each time interval that a queue is noted on 

the documentation form, the total vehicle-hours of delay is computed simply by multiplying the 

normal hourly volume by these average delay values. If the begin and end times of the lane 

closure and queue do not occur exactly on the hour, extrapolation techniques should be used to 

estimate the delays during that portion of an hour. 
Guidance on Data Needs, Availability, and Opportunities

For Work Zone Performance Measures Page | 3-10

Figure 3-6. Components of Work Zone Delay

Performance Measurement. Report No. FHWA HOPUllman, G.L. T.J. Lomax, and T. Scriba. A Primer on Work Zone Safety and Mobility 

-11-033. FHWA, 

U.S. Department of Transportation, Washington, DC. September 2011. Accessible at http://ops.fhwa.dot.gov/wz/resources/publications/fhwahop11033/fhwahop11033.pdf.
Guidance on Data Needs, Availability, and Opportunities Page | 3-11

For Work Zone Performance Measures TTI

3.1.2.2 Potential Sources in the Near Future

Point-to-Point Travel Times by Bluetooth Address 

Matching – Most mobile electronics, and many vehicles 

themselves, are now enabled with Bluetooth technology to 

allow them to connect and communicate directly with other 

devices. Recent advances in Bluetooth signal detection 

technology and address matching capabilities has made it 

possible to track Bluetooth devices as a low cost way to obtain 

point-to-point travel time. Multiple address-matching protocols 

are now available, and ITS vendors are working on both 

permanent and portable deployment solutions of this 

technology. Advantages of Bluetooth technology are its 

privacy (the device wireless addresses are all that is tracked, so 

user information is kept private) and its relatively low cost. 

Like other point-to-point travel time collection technologies, 

however, the data represent recently completed trips rather than 

current trips underway. Thus, the delays being 

measured through the Bluetooth system will lag 

those actually being experienced by those currently 

entering the work zone. 

Private (3rd Party) Sources of Travel Time and 

Speed Data – Private-sector traffic information 

companies collect traffic speed and travel time data 

from various sources, fuse that data together, and 

then sell that information to various users to power 

various real-time travel information systems. Some 

agencies have now started to enter into agreements 

to obtain that data on routes they do not have 

instrumented in exchange for financial 

compensation and/or for data the agency already is 

collecting. These data will also be usable for the purposes of work zone performance 

measurement. In fact, some agencies have found that they can obtain such data after-the-fact for 

very little compensation (the main value of the data to these companies is in its real-time traveler 

information applications). To be useful, the data mapping protocols used by the private-sector 

companies (these are known as traffic message channel location codes) must be translated to 

mapping protocols used by the agency or contractor (such as mile point or reference markers). 

In addition, agencies and contractors must determine whether it will be possible to obtain data 

from individual days (currently, at least one private-sector company provides data averaged by 

time-of-day and day-of-week on a month-by-month basis).

Figure 3-7. Bluetooth Detection 

Technology is a Low-cost 

Method of Collecting 

Anonymous Point-to-Point 

Travel Time Data

The Virginia Department of Transportation 

examined the potential of obtaining

historical private-sector traffic data for the 

purposes of computing work zone 

performance metrics (7).

The Indiana Department of Transportation 

has used anonymous matching of Bluetooth 

devices in vehicles to track point-to-point 

travel times in work zones (6).
Guidance on Data Needs, Availability, and Opportunities Page | 3-12

For Work Zone Performance Measures

Connected Vehicle Initiative – Although connected vehicle data may eventually be useful for 

estimates of work zone throughput, it is likely to be more valuable sooner as a source of data for 

travel times and delays. As noted previously, work is underway to establish general 

architectures, communication protocols, and other standards upon which a connected vehicle 

system will operate. Presently, protocols and standards as to how vehicles will communicate 

with each other (i.e., vehicle-to-vehicle or V2V) is farther along than standards as to how 

vehicles will communicate with roadside devices (i.e., vehicle-to-infrastructure or V2I) that will 

be needed before agencies will be able to easily access data for work zone performance 

measurement purposes.

3.1.2.3 Key Considerations and Trade-offs of Delay, Unit Travel Time, and Travel Time 

Reliability Data Sources

Table 3-2 presents a tabular summary outlining the key considerations and trade-offs associated 

with the various throughput data sources currently available. Regardless of the technology or 

manual procedures used, it is important to have the limits of the travel time measurement extend 

upstream and downstream of the work zone to ensure all of the potential impacts are fully 

encapsulated. It is also important to keep in mind the differences between travel times measured 

through spot speed sensors and point-to-point travel time estimates. 
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For Work Zone Performance Measures

Table 3-2. Considerations and Trade-offs of Delay, Unit Travel Time, and

Travel Time Reliability Data Sources

Data Source Key Considerations and Trade-offs

TOC spot speed sensor data

• Travel time estimates tend to be less accurate when congestion is 

present 

• Important to verify that data will be available once work has 

started (no loss of power to sensors)

TOC point-to-point travel 

time data 

• Important to verify that data will be available once work has 

started (no loss of power to sensors)

• Accuracy depends on the level of market penetration of tracking 

technology used (AVI, AVL)

• Represents recently completed, rather than current, trip times. 

Work zone ITS data • Important to verify that data will be archived and made available 

for performance measurement computations

Portable point-to-point 

travel time data collection

• Accuracy depends on the level of market penetration of devices 

being detected and tracked

• Represents recently completed, rather than current, trip times. 

Manual spot-speed data 

• Labor intensive

• Most useful if work zone impacts occur in a fairly small section

• Most useful for assessing short time periods

Manual travel time data 

collection by driving 

through the work zone

• Labor intensive

• Most useful for long work zones

• Most useful for assessing short time periods

• Multiple trips through the work zone increases accuracy and 

precision of travel time estimates

3rd party (private-sector) 

travel time and speed data

• Level of detail available may vary by vendor

• Requires translation of data mapping protocols to 

agency/contractor system in order to assess work zone impacts

Bluetooth data

• Accuracy depends on the level of market penetration of Bluetooth 

devices in the traffic stream

• Represents recently completed, rather than current, trip times. 

Connected vehicle data • Date of availability still uncertain

3.1.3 Traffic Queue Data

The last type of work zone mobility-related performance data available to agencies and 

contractors are traffic queue data. Whereas some agencies have established work zone mobility 

policies and goals based on travel delays, others have based policies on the avoidance and 

limitation of queues. As would be expected, individual vehicle delays and queue lengths are 

highly correlated. However, traffic queues are also a safety concern, due to the high speed 

differentials that commonly exist between traffic approaching a queue and traffic already in 

queue. Consequently, emphasis on monitoring the occurrence of extent of queuing at a work 

zone is a higher priority for some agencies.
Guidance on Data Needs, Availability, and Opportunities Page | 3-14

For Work Zone Performance Measures

Queue Data Collected Specifically for a 

Work Zone

• Speed data extracted from a work zone ITS 

deployment

• Manual observation and documentation of 

queues from a permanent or work zone TOC

• Manual observation and documentation of 

queues by field personnel at the work zone

To fully characterize queues at a work zone, both the duration of queuing and its length over 

time need to be obtained. Traffic queues are the storage of vehicles when traffic demand 

exceeds the work zone capacity (maximum throughput). Queue lengths can change dramatically 

over time as demand volumes change and/or large numbers of drivers divert to alternative routes. 

3.1.3.1 Sources Currently Available

Data collected specifically for a work 

zone – Very few agency data sources 

exist or are normally collected which 

can also be used to measure and 

document traffic queues. If the work 

zone is located within the limits of 

TOC sensor coverage, spot speed 

traffic sensors can be used to 

approximate queue lengths. Thus, the 

accuracy of the estimation process 

depends on the spacing of the spot 

speed sensors, and the existence of 

detectors beyond the upstream end of the queue. The process to estimate queue lengths and 

durations from spot sensor data consists of four basic steps, as shown in Figure 3-8 (8):

Figure 3-8. Steps to Estimate Queue Lengths from Spot Speed Sensor Data

Step 1: Divide the Roadway into Regions of 

Assumed Uniform Speed

Step 2: Examine Speeds and Volumes 

Hour-by-Hour at each Sensor Location

Step 3: Compare Hourly Speed/Volume 

Profiles across Sensors to Identify Length 

of Queue

Step 4: Sum Region Lengths where 

Speeds are below Thresholds 

Ullman, G.L. T.J. Lomax, and T. Scriba. A Primer on Work Zone Safety and Mobility Performance Measurement. Report No. FHWA HOP-11-033. FHWA, U.S. Department of 

Transportation, Washington, DC. September 2011. Accessible at http://ops.fhwa.dot.gov/wz/resources/publications/fhwahop11033/fhwahop11033.pdf
Guidance on Data Needs, Availability, and Opportunities Page | 3-15

For Work Zone Performance Measures

Step 1: Divide the Roadway into Regions of Assumed Uniform Speed – In step 1, the 

roadway section where a work zone exists is divided into a series of segments of various lengths 

(L), with conditions in each segment assumed to be represented by its corresponding spot sensor 

data of volumes (V), average speeds (U), and detector occupancies (O) as illustrated below. 

Within each segment length, the travel time (TT) is estimated as the segment length divided by 

the average speed.

Step 2: Examine Speeds and Volumes Hour-by-Hour at each Sensor Location – To 

approximate queue lengths from spot sensors, the speeds at each sensor are examined in 

sequence and over time to identify the regions and times at each region in which speeds drop 

below a selected threshold. Speeds at successive sensor locations are examined together, and the 

length Li for each segment below the speed threshold is added together for each time interval of 

interest. In the example on the next page, spot traffic sensors are located 0.2 mile, 0.8 mile, and 

1.3 miles upstream of the temporary lane closure. Project diary information indicates that a lane 

closure began at 9:00 AM and ended at 3:30 PM. 

Step 3: Compare Hourly Speeds across Sensors to Identify Extent of Queue Propagation –

Starting with the downstream region, the average speeds over time at each region are examined 

in sequence moving upstream to estimate the upstream end of the queue each hour (as shown in

Figure 3-9). For each hour (or other analysis period preferred by the agency), the objective is to 

identify how far upstream the queue has propagated. To accomplish this, the agency should 

select a speed threshold it will use to define the difference between normal traffic flow and 

traffic flow in a queue. This threshold can be defined as part of the agency’s work zone policy or 

procedures in absolute terms (e.g., speeds below 10 miles per hour, or speeds less than one-half 

of the free-flow speed of traffic on a facility), or in terms of the amount of reduction in speed 

observed by traffic approaching the work zone. 

Li Li+1 Li+2 Lengths

Vij, Uij, Oij

TTij = Li / Uij

Vi+1j, Ui+1j, Oi+1j

TTi+1j = Li+1 / Ui+1j

Vi+2j, Ui+2j, Oi+2j

TTi+2j = Li+2 / Ui+2j Travel Times

Travel

Times
Guidance on Data Needs, Availability, and Opportunities Page | 3-16

For Work Zone Performance Measures

Figure 3-9. Example of Sensor Speed Analysis to Determine Duration and Length of Queue

G.L. Ullman, R.J. Porter, and G.J. Karkee. Monitoring Work Zone Safety and Mobility Impacts in Texas. Research Report FHWA/TX-09/0-5571-1. Texas Transportation Institute, College Station, TX, September 2008. 
Guidance on Data Needs, Availability, and Opportunities Page | 3-17

For Work Zone Performance Measures

Once a threshold is selected, it is a fairly simple task to determine the two regions in sequence 

that have a normal, high average speed at the upstream region and a low, congested speed 

indicative of the presence of queuing. The midpoint between the spot sensors of those two 

regions is where it is assumed that the upstream end of the queue is positioned during that hour. 

Performing this analysis hour-by-hour will result in a queue length profile over time at the work 

zone.

Step 4: Sum Region Lengths Where Speeds are Below the Threshold – To determine queue 

lengths for the above example, a 40 mph speed threshold was selected as indicating queued 

traffic conditions. Consequently, the analysis of speeds at the upstream sensor locations 

indicates that a queue began to develop at approximately 11:30 AM at the first sensor, which 

grew upstream and reduced speeds at the second sensor at about 12:30 PM. The queue did not 

extend back to the third sensor, since speeds never dropped below 40 mph at that location during 

the hours of work activity. The estimated queue length each hour is shown in Table 3-3 below:

Table 3-3. Estimated Queue Length Each Hour

Time Estimated Location of Upstream End of Queue Estimated Queue Length

11:00 am None 0

12:00 pm Between Sensors #1 and #2 0.2 + (0.6/2) = 0.5 mile

1:00 pm Between Sensors #2 and #3 0.2 + 0.6 + (0.5/2) = 1.05 mile

2:00 pm Between Sensors #2 and #3 1.05 mile

3:00 pm Between Sensors #2 and #3 1.05 mile

4:00 pm None 0

This approach will also work for data 

obtained from a work zone ITS 

deployment that uses spot speed sensors.

If TOC or work zone spot speed sensor 

data are not available for a work zone of 

interest, an agency or contractor must rely 

on manual techniques to obtain traffic 

queue data. Some TOCs and work zone 

ITS deployments, particularly those that 

exist primarily for incident identification 

and response purposes, do not offer full 

traffic sensor coverage of their system. 

Figure 3-10. TOC Operators can Manually 

Record Upstream End of Queue Periodically 

for Use in Computing Queue Lengths and 

Durations

TTI
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For Work Zone Performance Measures

The Michigan Department of Transportation has 

established a manual data collection and 

recording process delays and queues at projects 

selected for monitoring that do not have 

electronic surveillance in place (1).

Rather, as shown in Figure 3-10, operators in the control room constantly scan cameras located

throughout the system looking for stalled vehicles, crashes, or other operational problems. Such 

systems can be used to gather queue length data for work zones located within camera coverage. 

Operators must be trained to visually track the start of queue formation, identify key landmarks 

that can be used to estimate the upstream queue location on a regular basis over the duration of 

the queue, and document when the queue dissipates. This may require access to and use of 

multiple cameras if the queue propagates several miles upstream of the work zone. As an 

alternative, it may be possible for programmers at the TOC to write a script to capture snapshots 

of the camera views of interest at specific intervals, for post-event analysis of queues.

Another possible manual technique is for field 

personnel at the project site to record the 

queues observed. If the queue does not grow 

significantly and the upstream end can be seen 

from the work space, a field inspector or 

temporary traffic control technician can 

document it from the roadside. If the queue 

grows beyond view from the work space, staff may have to travel upstream in a vehicle to locate 

the upstream end. Continuous passes through the queue and the work zone could also be 

performed, each time identifying the upstream end of the queue and the beginning of the lane 

closure or work space that is serving as the bottleneck and thus represents the downstream end of 

the queue. However it is accomplished, the goal is still to document:

• The time when queuing begins;

• The length of the queue at regular intervals; and

• The time when queuing ends.

3.1.3.2 Potential Sources in the Near Future

Computer Screenshot Captures from 3rd Party Traveler Information Providers – Many 

private-sector traveler information providers provide website access to current travel conditions 

in a map format. Typically, color coding on the various routes is used to denote travel speeds on 

each roadway segment for which it has data. Colors generally range from green (for normal or 

good travel speeds) to red or black (for stopped or nearly stopped traffic). These maps most 

likely have a slight time lag to them (the amount of that lag is not known for certain and is not 

typically advertised by the providers themselves), but can provide a reasonable snapshot of travel 

conditions on a facility. As shown in Figure 3-11, there are software programs which can 

“capture” the image on a computer screen and store it in one of several possible file formats. 

Currently, someone with some computer science background could write a simple script to 

activate this screen capture software on a regular basis (such as every 15 minutes) and save the 

equivalent of a time-lapse series of queuing that occurs at a work zone over time. In the near 

future, it is possible that a time-lapse snapshot feature could be added to these software 
Guidance on Data Needs, Availability, and Opportunities Page | 3-19

For Work Zone Performance Measures

applications to accomplish 

the same task. Staff could

then use the screen capture 

images to estimate when 

the queuing started, how 

long the queue was at each 

point in time (scaling off 

the images), and when the 

queue dissipated. One 

challenge with using this 

approach will be in 

determining the appropriate 

map resolution level for the 

computer screen to capture 

the images. If the 

resolution is in too close to 

the work zone, the queue may grow out beyond the limits of the image. If the resolution is too 

far out, it will be difficult to accurately estimate the length of the queue at that snapshot in time. 

Private (3rd Party) Sources of Travel Time and Speed Data – Similar to the use of these data 

for work zone travel time and delay data, 3rd party sources of data can also be used for 

assessment of traffic queues at work zones. The overall approach can be identical to that 

outlined above for using spot sensors from a transportation management center or a work zone 

ITS deployment. Issues regarding the resolution between roadway segments as defined by the 

vendor and segments defined by the DOT will still exist, as will the need to get to individual day 

data for work zones where temporary lane closures cause only occasional queues.

Connected Vehicle Initiative – Detailed information about current vehicle speed is one of the 

key data elements planned for this initiative. Along with the current position of each vehicle, 

this data stream may eventually provide detailed queue location information by identifying 

specific vehicle locations at which speeds decrease below a defined threshold. Algorithms will 

be needed to collate the speed behavior of multiple vehicles to determine where the queue is said 

to “begin” at any point in time.

3.1.3.3 Key Considerations and Trade-offs of Traffic Queue Data Sources

Table 3-4 provides a summary of key considerations and trade-offs that exist with the existing 

and potential sources of traffic queue data for work zone performance measurement. Many of 

these considerations are similar to those documented under the travel time, delay, and travel time

reliability data sources. Manual data collection techniques (by TOC operators or by field 

personnel) will need the buy-in and support of agency or contractor administration, as it will 

impact staff ability to accomplish other assigned tasks.

Figure 3-11. Periodic Screen Captures of Private-Sector 

Traveler Information Maps can also be Used to Gather 

Work Zone Queuing Data

Google traffic map captured with the Snagit screen capture software tool
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For Work Zone Performance Measures

Table 3-4. Considerations and Trade-offs of Traffic Queue Data Sources

Data Source Key Considerations and Trade-offs

All data types

• Important to define what constitutes a queue to the agency or 

contractor (usually a minimum speed threshold)

• Both queue duration (start and end times) and length over time 

are important data

Transportation management 

center or work zone ITS 

deployment data using spot 

speed sensors

• Requires detailed analysis of speeds over time on a sensor by 

sensor basis

• Important to verify that data will be available once work has 

started (no loss of power to sensors)

Use of TOC cameras and 

operators to visually identify 

and document queues

• Requires adequate camera coverage upstream of work zone

• Useful to develop data collection protocols regarding landmarks 

to use to document upstream queue position, frequency of 

documentation, etc.

Collection of queue data by 

field personnel

• Training regarding data collection protocols to follow (how often, 

how accurate, etc.) is important

• May require personnel to travel upstream to locate queue

• Needs agency or contractor administration support to ensure field 

personnel understand its importance

Computer screenshot 

captures of real-time traffic 

condition maps

• Setting of appropriate screen resolution size required for each 

work zone (based on maximum queue lengths anticipated

• Time-lapse capabilities do not currently exist in most screen 

capture software. 

Use of 3rd party traveler 

information data

• Level of detail available may vary by vendor

• Requires translation of data mapping protocols to 

agency/contractor system to assess work zone impacts

Connected vehicle data • Date of availability still uncertain

3.2 Identifying and Computing Specific Mobility Measures of Interest

3.2.1 General Considerations

Once a determination of what work zone mobility-related data are or will be available, the final 

task is to determine what specific measures of performance are of interest to the agency, 

contractor, traveling public, etc. As noted previously, agency or contractor goals, policies, 

and/or concerns related to mobility will also influence the number and type of measures that will 

be of interest. The corresponding performance, exposure, and indicator/stratification data are 

then gathered and collated as necessary to compute those measures. 

To a large extent, the choice of appropriate measures depends on the MOT approach of each 

individual work zone, which in turn will depend on the roadway and traffic characteristics where 

the work is to be performed. Work zones where the MOT approach involves long-term closures 

of one or more travel lanes may suggest that measures pertaining to peak period traffic 

operations are of main importance. Conversely, work zones that use temporary lane closures 

during off-peak periods as the primary MOT approach would benefit more from a measure that 

targets mobility during the hours of those temporary lane closures than during peak hours. In 
Guidance on Data Needs, Availability, and Opportunities Page | 3-21

For Work Zone Performance Measures

many cases, the overall project will involve multiple phases in which different MOT approaches 

may be used in those different phases. 

Regardless of the specific MOT approach used, in almost all cases, the main interest in the 

mobility performance measures will be in terms of how conditions during the work zone changed 

relative to the pre-work zone (or normal) conditions on that roadway segment. In locations 

where volumes are relatively low and operating conditions are good most of the time, any 

mobility impacts that are computed (increased travel times, presence of traffic queues, etc.) will 

be easily attributed to the work zone. In locations where traffic conditions are degraded already 

during peak periods or other times of the day, the additional delays or queues above those 

normally occurring will be of main interest. This can further affect the choice of performance 

measures that are to be computed. In some jurisdictions with TOCs, efforts are underway to 

develop simple-to-use computer dashboards that can provide current traffic conditions in and 

around a work zone (see Figure 3-12). 

Figure 3-12. Example of a Work Zone Mobility Dashboard Under Development

Each jurisdiction will have to make its own decisions as to the performance measures it chooses 

to track. The following sections should be used as a guide as to the types of mobility-related 

work zone performance measures that should be considered by an agency or contractor, and not 

as a comprehensive list of all acceptable measures. Paracha, J. Work Zone Performance Measurement using Probe Data. Presentation of Maryland Work Zone Performance Measurement Project
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For Work Zone Performance Measures

3.2.2 Specific Throughput Measures

Table 3-5 presents a summary of possible throughput-related performance measures. 

Computational requirements of each measure, and its relative rating of importance by a panel of 

practitioners, are provided. Also presented in the table is an assessment of possible audiences or 

user groups for that measure. 

Two of the measures in the table pertain to changes in throughput that occur relative to the 

throughput occurring before the work zone was deployed. These reductions can reflect either a 

reduced capacity through the work zone (relative to pre-work zone conditions), reduced demand 

volumes due to driver diversion to other routes or modes, or a combination of both. Some 

agencies and contractors emphasize the minimization of mobility impacts during hours when a 

work zone is inactive (hence, the specification of that particular measure). 

3.2.3 Specific Unit Travel Time and Delay Measures

A number of possible performance measures pertaining to unit travel times and delays occurring 

in a work zone or series of work zones are provided in Table 3-6. Unit travel times, either along 

a corridor with multiple work zones, or through an individual work zone, can be computed fairly 

simply using any of the travel time data sources listed previously. These metrics are best suited 

for comparing to other similar facilities that do not have work zones in place, and can be a 

simple way of assessing the general effects of work zones on the corridor if data had not been 

collected prior to the deployment of the work zone(s). If data before the work zones were 

deployed is available, it is also possible to compute a change in a unit travel time measure. 

A fairly broad list of delay-based performance measures are also defined in Table 3-6. Average 

delays per vehicle are fairly well understood by the driving public, as are indications of the 

likelihood of being delayed (expressed as a percentage of vehicles delayed). Some agencies may 

choose to quantify any delays that occur at a work zone; others may choose to quantify only 

those that exceed a threshold defined in their safety and mobility policy. Given that most work 

zones will not experience travel delays at all times of the day or night, emphasis may be given to 

evaluating peak periods only or periods when work zone capacity is reduced (such as during 

temporary lane closures or full roadway closures). From an overall process assessment 

perspective, the number (or percentage) of work zones that experience delays that exceed an 

agency’s delay threshold can be a useful performance measure as well.

The majority of the measures listed pertain to vehicular travel (i.e., delay per vehicle, vehicles 

experiencing queues greater than some threshold), which are generally easier to collect or 

estimate than person travel. However, if a work zone is likely to alter modal splits on a facility 

due to demand management strategies encouraging carpooling or transit use, per-person 

measures may be more appropriate to use. 
Guidance on Data Needs, Availability, and Opportunities

For Work Zone Performance Measures Page | 

3-23

Table 3-5. Examples of Specific Throughput Performance Measures

Measure Computations Required

Value or 

Importance 

Ranking

Potential Audience(s)

Reduction in maximum 

throughput during 

specific work tasks

Maximum throughput flow rate before – Maximum throughput 

flow rate during work task High

MOT design practitioners,

Work zone operations 

practitioners

Reduction in maximum 

throughput when work 

zone is inactive

Maximum throughput flow rate before – Maximum throughput 

flow rate during work inactivity period High

MOT design practitioners,

Work zone operations 

practitioners

Maximum vehicle 

throughput per hour or 

other period

Maximum throughput flow rate during work zone High

MOT design practitioners,

Work zone operations 

practitioners

Maximum person 

throughput (per hour or 

other period)

Maximum throughput flow rate during work zone period of 

interest ∙ Average vehicle occupancy rate in the work zone Moderate

MOT design practitioners,

Work zone operations 

practitioners

Maximum throughput values described in this table should be based on a sustained volume (such as a continuous hour), and not on peak flow rates over short (i.e., 5- or 

15-minute) time periods. If desired, maximum throughput data collected on different days can be averaged together as long as the same basic condition (number of lanes open, 

presence of work vehicles, etc.) are the same on each day.
Guidance on Data Needs, Availability, and Opportunities

For Work Zone Performance Measures Page | 

3-24

Table 3-6. Examples of Specific Unit Travel Time and Delay Measures

Measure Computations Required

Value or 

Importance 

Ranking

Potential 

Audience(s)

Average unit travel time

for a given period or 

speed along a corridor 

with two or more work 

zones

Obtain an average total travel time through the corridor for the period of 

interest (peak period, during a particular phase or work activity, etc.) and 

divide by the length of the travel time route used.

High All

Average unit travel time 

or speed through the 

work zone during a 

given period

Obtain an average total travel time through the work zone for the period 

of interest (peak period, during a particular phase or work activity, etc.) 

and divide by the length of the travel time route through the work zone.

High All

Average per vehicle 

delays along a corridor

in a given period due to 

work zones

Obtain an average total travel time through the corridor under non-work 

zone conditions for the period of interest.a Determine the average total 

travel time for the same roadway segment and period when work zones 

are present. Subtract the two to determine the per vehicle delay due to 

the work zones during the period of interest

High

Traveling public,

Work zone 

operations 

practitioners

Delay per vehicle in a 

given time period 

through a work zone

Obtain an average travel time under non-work zone conditions for the 

period of interest and the roadway segment where the work zone will 

occur.

a

 Determine the average total travel time for the same roadway 

segment and period when the work zone is present. Subtract the two to 

determine the per vehicle delay due to the work zone during the period of 

interest

High All

Percent of time (for the 

period of interest) when 

delay per vehicle 

exceeds a threshold

Determine the total amount of time that a work zone is present for the 

periods of interest (e.g., the total hours that the work zone has a 

temporary lane closure in place). Determine the number of hours in 

those periods that the delay exceeded the threshold. Divide the 

“exceeds delay threshold” hours by the total hours of interest.

High

Work zone 

operations 

practitioners

Total vehicle-hours of 

delay per time period 

due to work zones 

located along a corridor

Determine average vehicle delays due to work zones along a corridor for 

the periods of interest. Determine total demand volumes in the corridor 

during the period. Multiply volume by the per vehicle delay.

Moderate

Work zone 

operations 

practitioners
Table 3-6. Examples of Specific Unit Travel Time and Delay Measures (Continued)

Guidance on Data Needs, Availability, and OpportunitiesFor Work Zone Performance Measures Page | 

3-25

Measure Computations Required

Value or 

Importance 

Ranking

Potential 

Audience(s)

Time required to 

convey delay 

information to drivers 

or pedestrians in 

corridor

Determine the average time it takes from the time a determination of the 

amount of delay present in the work zone is made to when it is posted on 

a sign, website, text message, etc.

Moderate

Work zone 

operations 

practitioners

Percent of projects 

along a corridor where 

delays exceed a 

threshold for a duration 

greater than a 

threshold

Determine each work zone in the corridor where delays exceeded the 

defined threshold at least once. Divide by the total number of work 

zones that occurred along that corridor.

Moderate

Work zone 

operations 

practitioners

Percent of travelers 

along a corridor that 

experience delay due to 

one or more work 

zones

Determine periods when work zones are creating delays along the 

corridor. Determine demand volumes during those periods, and for the 

entire day. Divide the volumes during periods of delay by the total daily 

volume.

Moderate

Work zone 

operations 

practitioners

Percent of travelers 

who experience a delay 

through a project

Determine the periods when the work zone creates delay. Determine 

demand volumes during those periods, and for the entire day. Divide the 

volumes during periods of delay by the total daily volume at the work 

zone.b

Moderate

Work zone 

operations 

practitioners

a For travel corridors that normally operate at near free-flow speeds during the time period, data collection before work zone implementation may not be needed. Rather, normal 

travel times may be estimated as the distance of the corridor divided by the normal speed of traffic. 

b If the work zone does not create a day on each of the periods (because volumes in a given period may change depending on the day of the week), this measure may be defined as 

the percentage of volumes travel through the work zone in the period of interest that experience delay.
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For Work Zone Performance Measures

3.2.4 Specific Travel Time Reliability Measures

Travel time reliability on a facility may be adversely affected by the deployment of a work zone 

by increasing the adverse effects of weather, incidents, or even peak period recurrent congestion. 

When conditions prior to the initiation of the work zone are good, the computation of travel time 

reliability measures may be somewhat redundant with 

the other types of mobility measures that can be 

computed more simply. However, when the roadway 

segment is already experiencing a significant amount 

of travel time delays and traffic congestion prior to the 

start of the work zone, it may be important to track 

travel time reliability during the work zone to more 

fully capture the effects of work upon mobility. The measures shown in Table 3-7 identify three 

of the major indices currently used for travel time reliability measurement. Presently, all three of 

these measures are somewhat difficult to convey to the traveling public, and so are useful 

primarily to practitioners with day-to-day operational responsibility of the work zones.

3.2.5 Specific Traffic Queue Measures

Table 3-8 presents a number of possible performance measures to characterize traffic queues that 

may develop in work zones. The measures reflect both project-level assessments (queue 

durations and lengths that occur at a given work zone) as well as broader region or jurisdictional 

assessments (percentage of projects experiencing queues that exceed time and/or length 

thresholds). 

Traffic queues tend to be highly variable and dynamic in work zones, as they are very sensitive 

to changes in both demand volumes and the capacity of the work zone. Furthermore, once a 

queue develops, the capacity of the work zone often dips as traffic moves from a uniform travel 

speed condition to one of stop-and-go behavior approaching and passing through the work zone 

bottleneck. Capturing the percentage of time that a queue is present thus requires almost 

continuous monitoring and collection or documentation when such queues develop. 

Travel time reliability can be a 

useful work zone mobility 

performance measure on roadways 

already experiencing recurrent and 

non-recurrent congestion.
Guidance on Data Needs, Availability, and Opportunities

For Work Zone Performance Measures Page | 

3-27

Table 3-7. Examples of Specific Travel Time Reliability Performance Measures

Measure Computations Required Value or Importance 

Ranking Potential Audience(s)

Change in xx-percentile 

travel time through the 

limits of a project, or 

along a route where 

one or more work 

zones are located

Collect travel time data across multiple days for the time 

period of interest prior to work zone deployment. Order 

travel times from lowest to highest. Determine the xxpercentile highest travel time in the sample. Repeat once 

work zone is deployed. Compute the difference between 

the two times.

High Work zone operations 

practitioners

Change in planning 

index through the limits 

of the work zone or 

along a route where 

one or more work 

zones are located

Collect travel time data across multiple days for the time 

period of interest prior to work zone deployment. Determine 

the average travel time during that period. Determine the 

free-flow travel time on that roadway segment and divide 

into the average travel time. Repeat once work zone is 

deployed. Compute the difference between the two indices.

Moderate Work zone operations 

practitioners

Change in buffer index 

through the limits of 

the work zone or along 

a route where one or 

more work zones are 

located

Collect travel time data across multiple days for the time 

period of interest prior to work zone deployment. Determine 

the average travel time during that period, and the xxpercentile travel time (the 95th percentile travel time is often 

used). Divide the xx-percentile travel time by the average 

travel time to obtain the buffer index. Repeat once the work 

zone is deployed. Compute the difference between these 

two indices.

Moderate low Work zone operations 

practitioners
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For Work Zone Performance Measures Page | 

3-28

Table 3-8. Examples of Specific Traffic Queue Measures

Measure Computations Required Value or Importance 

Ranking Potential Audience(s)

Average queue 

duration for a given 

time period, set of work 

activity periods, etc.

Obtain beginning and ending times of queues within the 

periods of interest over several days. Sum the amount of 

queuing time each day and divide by the number of days of 

data used. An agency may prefer to compute the average 

duration only for the periods when a queue actually 

occurred. In this case, the measure is the average queue 

duration when a queue is present in a given time period, set 

of work activity periods, etc.

High All

Percent of time when 

queues occur in a given 

time period, set of work 

activity periods, etc.

Obtain beginning and ending times of queues within the 

periods of interest over several days. Sum the queue time 

for those periods across the days sampled. Divide by the 

sum of total time in those periods for those same days.

High

MOT design practitioners,

Work zone operations 

practitioners

Percent of time when a 

queue exceeds a 

threshold in a given 

time period, set of work 

activity periods, etc.

Computed the same way as for the previous measure, with 

the caveat that queue times are not counted unless they 

exceed the threshold value.

High

MOT design practitioners,

Work zone operations 

practitioners

Maximum queue length 

for a given time period, 

set of work activity

periods, etc.

Obtain queue length data from the periods of interest over 

the duration of the work zone. Determine the maximum 

queue length recorded.

High All

Maximum total length 

of queue along a 

corridor experienced by 

a traveler due to one or 

more work zones

Obtain queue length data from each work zone along a 

corridor for the periods of interest over a series of days. For 

each day of data, combine queue lengths observed at 

similar times to estimate the total combined length of 

queues at those time intervals. Determine the maximum 

total queue length recorded.

High

Traveling public,

Work zone operations 

practitioners
Table 3-8. Examples of Specific Traffic Queue Measures (Continued)

Guidance on Data Needs, Availability, and OpportunitiesFor Work Zone Performance Measures Page | 

3-29

Measure Computations Required Value or Importance 

Ranking Potential Audience(s)

Average length of 

queue in a given time 

period, set of work 

activity periods, etc.

Obtain the beginning and ending times of queues, and 

queue length data at regular intervals over the period of 

interest on each of several days. For each sampling

interval, compute an average queue length for that interval. 

Multiply those average queue lengths by duration of that 

interval, and divide by the total duration of queue measured 

in the period of interest across the days that were sampled.

High

Traveling public,

Work zone operations 

practitioners

Percent of work zones 

with maximum queue 

length exceeding a 

threshold for a duration 

greater than a 

threshold

Obtain queue length data from each work zone being 

monitored. Identify times when queue lengths exceeded a 

defined length threshold for a period of time longer than a 

defined duration threshold. Divide the number of work 

zones exceeding these thresholds by the total number of 

work zones that were monitored.

Moderate Work zone operations 

practitioners

Percent of travelers 

who experience a 

queue in a given time 

period, set of work 

activity periods, etc.

Obtain beginning and ending times of queues in the period 

of interest on several days. Determine demand volumes 

occurring on each of those days during the period of 

interest, and during the times when a queue was 

determined to be present. Divide the total period demand 

volumes by the demand volumes when queues were 

present. If desired, the entire daily demand volume may be 

used instead of the demand volumes just during the period 

of interest.

Moderate

MOT design practitioners,

Work zone operations 

practitioners
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4.0 DATA SOURCES AND COMPUTATIONAL METHODS FOR

SAFETY-RELATED PERFORMANCE MEASURES

The safety effects of work zones have long been a major concern of both transportation agencies 

and highway contractors. However, the monitoring and systematic improvement of work zone 

safety remains a challenge. Work zones are highly variable, the combined result of decisions 

regarding specific work zone design elements to use, traffic handling strategies to employ, 

scheduling choices, signing, special traffic management strategies, and other aspects. Work 

zones also involve periodic changes to the overall layout and design as work progresses through 

various phases of a project. In some cases, multiple traffic handling schemes are overlaid on one 

another, such as during temporary lane closures performed within a large construction project 

that has already implemented long-term lane and shoulder closures, lane shifts, etc. 

Complicating matters further is the temporary nature of work zones themselves. Unlike 

traditional roadway design features that are implemented to last 30 to 50 years and for which 

significant amounts of data can be gathered over time to assess their safety implications, work 

zones can be in place for as little as a few minutes. Although some work zones are in place for

several years, one typically has much less data to use to assess if and how safety is being 

impacted at that site. 

Despite these challenges, work zone safety-related performance measures provide an opportunity 

for agencies to monitor performance in (near) real time, and to evaluate how decisions made 

regarding design and operations affected safety. Similar to mobility measures, work zones have 

several safety-related dimensions. Specifically, safety impacts are commonly described in terms 

of the following categories: 

• Traffic crashes;

• Safety surrogates; and

• Worker accidents.

Although less numerous than for mobility measures, multiple data sources are available to assess 

these safety impacts. In some cases, data that already exists within an agency or is gathered as 

part of other agency requirements can be used; in other cases, data must be collected in and 

around the work zone to measure safety impacts. A discussion of these various sources is 

presented in the following sections. 

4.1 Safety-Related Data Sources

4.1.1 Crash Data Sources

Agencies and contractors alike are concerned about the frequency and severity of traffic crashes 

that occur in a work zone. Crashes in work zones often become sources of litigation, which can 

take several years and hundreds of thousands of dollars to resolve. More importantly, crashes 

are the most direct indicator of work zone traffic safety. 
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Traffic Crash Data That May be Available from 

Existing Agency Sources

• Statewide traffic crash records database

entries

• Electronic or hard copies of crash report forms 

obtained directly from investing law 

enforcement officers

• TOC incident database entries

• Dispatch Logs (or Electronic Database) of 

Emergency Response or Service Patrols

Many agencies monitor the number of fatalities that occur in work zones each year. Due to the 

small (fortunately) sample size of such crashes each year, it is difficult to gain much insight into 

the problems and/or possible countermeasures related to these crashes. Consequently, it is 

preferable to expand the analyses to injury (and, possibly, even property-damage-only [PDO]) 

crashes occurring in work zones. Furthermore, collation of crash data with specific work zone 

locations and times allows exposure and indicator/stratification data to be used to focus the 

monitoring and assessment efforts on specific phases, work zone design features, and/or work 

activities if so desired. 

4.1.1.1 Sources That Already Exist

Data from existing agency sources –

Each state has a statewide crash 

records database that is used to 

systematically evaluate crash patterns 

and prioritize safety improvements to 

be made as part of each state’s

highway safety improvement program. 

These data come from the crash report 

form that is prepared by the 

investigating officer of a crash. The 

report and database contains a wide 

range of details regarding the roadway

location, the vehicles, and the 

drivers/passengers/pedestrians 

involved. Most states also have a field on the form that officers can code if the crash occurred in 

a work zone. Some of these states have additional fields to document the type of work zone 

(lane closure, lane shift, shoulder work, or moving work operation), the location of the crash in 

the work zone, and whether workers or law enforcement personnel were present. The accuracy 

of the data entered into these fields is believed to vary considerably from jurisdiction to 

jurisdiction, as a basic understanding of work zone traffic control concepts is required to 

properly code the fields.

There is typically a lag between when the crash occurs and a report is filled out, and the time that 

it shows up in the database. In some jurisdictions, the lag may be only a few weeks; in others, it 

may be several months. Especially long lag times hinder the ability of practitioners to monitor 

the ongoing safety record of a work zone while it is in place. In fact, the lag time may be so long 

that the project itself is completed before any crash data occurring during the project is made 

available. 

To mitigate the large lag times some agencies experience in obtaining crash information through 

their statewide database, some practitioners choose to manually collect copies of the actual crash 
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For Work Zone Performance Measures

The New Hampshire Department of 

Transportation examines freeway service patrol 

and fire dispatch calls throughout key work 

zones, with exceptionally high dispatch volume 

to a specific location serving as an indication of 

a potential safety problem in the work zone (1).

reports occurring at a select number of work 

zones directly from the law enforcement 

agencies with jurisdiction over each work 

zone of interest. Transportation agency staff 

typically meets with the local enforcement 

agency to describe the need for the data and 

set up a recurrent collection schedule of 

copies of the report form itself. The copy may be physical or electronic photocopies, depending 

on the available technology in the enforcement agency office. Practitioners who obtain these 

report copies must enter them physically into a spreadsheet or other database in order to perform 

the desired analyses. As part of that process, the practitioner may have to make judgments for 

certain database elements. Some agencies, for example, determine a manner of collision code 

after reviewing the crash report form and narrative itself rather than have the investigating officer 

make that decision. The practitioner may also need to contact more than one enforcement 

agency office if the work zone crosses jurisdictional boundaries or if crashes on the roadway can 

be investigated by several possible enforcement agencies (state highway patrol, sheriff, local 

police department, etc.). 

A third option that may exist for those work zones located within the limits of a TOC is to use 

incident logs recorded by system operators. Most centers document the incidents that occur 

within system boundaries, and can be readily extracted from the database by identifying the 

location and time periods of interest. An example of a safety dashboard that FAST personnel in 

Las Vegas have developed that can be used to evaluate work zone crash trends on a project is 

shown in Figure 4-1. Many of the recorded incidents may be minor PDO crashes that will not 

meet the agency’s reporting threshold for its statewide crash database. Consequently, it will be 

necessary to compare any incident data during a work zone to similar incident data collected at 

the same location and for a similar period of time prior to the initiation of the work zone. It 

should also be noted that most incident databases will include non-crash events (stalled vehicles, 

debris in road, etc.) that will need to be extracted prior to analysis. 

Similar to the use of a TOC incident database, 

some agencies may be able to access and use the 

dispatch logs or database of emergency response 

or service patrol personnel to a work zone. One 

would expect fairly good correlation between 

emergency response dispatches and crash reports 

that eventually end up in the statewide database. 

However, it will be necessary to pull out the nontraffic crash events that are likely to exist in both types of logs or databases. It is likely that 

some privacy concerns would need to be addressed as well before such data could be pulled and 

used for performance measurement purposes. 

The Ohio Department of Transportation personnel 

gather hard copies of police crash reports at major 

projects every two weeks and manually code that 

data into a database so that current crash statistics 

on those projects can be monitored (1).
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For Work Zone Performance Measures

Traffic Crash Data Potentially 

Available in the Future

• Work zone crash data collected directly by 

agency staff

• Connected vehicle initiative data

Figure 4-1. Crash Dashboard In Use by FAST Personnel in Las Vegas, NV

4.1.1.2 Potential Sources in the Near Future

Work zone crash and accident data 

collected by agency staff – As noted 

above, data collected as part of regular 

traffic crash investigations law 

enforcement officers typically has very 

limited information regarding specific 

features of the work zone itself. 

Project information, type of work 

being performed, type of traffic 

control devices in use, whether or not vehicles intruded into the work space, etc., will generally 

not be captured in a statewide database, and may or may not be documented in the narrative or 

diagram of a crash report form. Consequently, a few agencies rely on their own staff to collect 

specific work zone data present when a traffic crash occurs. New York State DOT staff does this 

for each work zone crash occurrence during active work hours, and for those it is notified about 

when the work zone is inactive. Agency staff then 

enters this information into an internal database for 

analysis (a few other states perform a less rigorous 

data collection effort, often limiting it to fatalities or 

very severe crashes). Analysis of these data can assist 

the agency in identifying traffic control set up errors 

Brian Hoeft, Freeway and Arterial System for Transportation (FAST), Regional Transportation Commission of Southern Nevada, Las Vegas, Nevada

Establishing and using an agencycollected work zone crash database 

requires the support of senior 

management.
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For Work Zone Performance Measures

or other issues that should be corrected. The process captures most, but not all traffic crashes. 

As a result, it is not possible to perform accurate before-during comparisons of these data to the 

statewide crash records database. Likewise, it is not feasible to combine these data with 

exposure or work zone indicator/stratification data to develop rate-based measures. However, 

the data can be useful in assessing the relative frequency of different work zone elements and 

element combinations involved in work zone crashes. A significant effort is required to establish 

protocols and documentation procedures to make this happen. Therefore, it requires the support 

and emphasis of an agency’s senior management in order to be successful. 

Connected vehicle initiative – As with most of the mobility-related work zone performance 

measure data sources, the connected vehicle initiative does offer potential as a future work zone 

crash performance measure data source as well. Work zones equipped with electronic 

infrastructure that can receive vehicle data about collisions in real time could offer agencies the 

opportunity to continuously monitor and update crash information at a work zone. Combined 

with other types of location-based electronic information about the roadway and the work zone 

itself, an agency could establish a variety of near-real-time dashboard metrics regarding work 

zone crashes overall and by type of roadway, work operations, etc. 

4.1.1.3 Key Considerations and Trade-offs of Work Zone Traffic Crash Data Sources

Table 4-1 provides a summary of the key considerations and trade-offs regarding the use of each 

of the work zone crash data sources described above. The appropriate data source will depend 

on the intended use. For monitoring of current work zones, the ability to obtain the data as 

quickly after a crash occurs as possible is critical. For comparisons across work zones or work 

zone operations, or for assessments of the relative effects of design or strategy decisions upon 

safety which require a more rigorous analysis and comparison to baseline or “expected” 

conditions, a complete and stable data source over multiple years is important.
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Table 4-1. Considerations and Trade-offs of Traffic Crash Data Sources

Data Source Key Considerations and Trade-offs

Statewide Crash Records 

Database

• Information regarding work zone features and activities is limited

• Significant time lags can exist in obtaining crash data for a given 

work zone

Electronic or hard copies of 

crash report forms from 

investigating law 

enforcement officers

• Information regarding work zone features and activities is limited

• Requires agency staff to obtain and then manually code the 

reports into a spreadsheet or database for analysis

• May require interaction with multiple law enforcement agencies to 

obtain all reports

TOC operator incident logs • Will include non-reported as well as reported crashes

• Will include non-crash events 

Dispatch Logs (or 

Electronic Database) of 

Emergency Response or 

Service Patrols

• Likely to include non-traffic crash events as well 

• Potential privacy concerns

Agency-collected crash and 

work zone database

• Significant agency effort will be required during initial phases, and 

for ongoing maintenance of the process

• Requires upper agency support and emphasis

Connected vehicle data • Date of availability still uncertain

4.1.2 Safety Surrogate Data Sources

One of the challenges in using actual crash data for performance monitoring and measurement is 

that crashes are rare events, and obtaining a large enough sample to draw reasonable conclusions 

about the safety of a work zone can take a considerable amount of time. Therefore, it can be 

beneficial to look at certain operational performance data that are believed to be correlated to 

traffic safety at the work zone. Traffic speeds, changes in speed, and speed variance are 

commonly-used safety surrogate measures. Erratic maneuvers and vehicle conflicts are other 

examples. The sections below identify the major sources of work zone safety surrogate data. 
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Safety Surrogate Data That May be Collected 

Specifically for the Work Zone

• Speed data collected by hand-held devices 

(radar, lidar)

• Speed data extracted from work zone ITS 

sensors

• Travel times through the work zone

• Videotaped traffic behaviors at key locations

• Work zone inspection scores

4.1.2.1 Sources That Already Exist

Data collected specifically for a work 

zone – Work zones located within the 

surveillance limits of a TOC may have 

spot speed sensor data available that 

could be employed for safety surrogate 

purposes, if the sensor is located where 

speed data would be beneficial. As 

described in the previous chapter on 

mobility-related data sources, it is 

important to verify the availability of 

TOC sensor data during the work zone 

prior to the project being initiated. 

Meanwhile, a number of data sources obtained specifically from a work zone can be used for 

safety surrogate purposes. Data collection personnel can use hand-held radar or lidar devices to 

easily obtain a sample of speeds at one or more locations before and within a work zone. 

Standard procedures for collecting this type of data exist in many traffic engineering manuals. 

Any spot speed sensors that are part of a work zone ITS deployment could also be used. Travel 

time data, collected as described in the previous chapter, can also be used to assess the speed 

being driven through a work zone relative to the posted speed limit.

Moving away from speed-related data as a surrogate of safety, the positioning of cameras at key 

trouble spots to videotape driving behaviors can be an effective source of safety surrogate data. 

Rates of erratic maneuvers and vehicle conflicts occurring at the location can be extracted 

manually from the video, and yield insights into the magnitude of possible safety problems at a 

location. The data can also indicate whether the implementation of a countermeasure to reduce 

such behavior has been successful. This approach relies heavily upon the skill of the analyst in 

defining specific movements and behaviors that constitute erratic or conflicting behavior, such as 

the severe braking maneuvers, last-minute lane changes, near miss sideswipes, etc. 

Finally, most agencies and contractors perform regular inspections of the temporary traffic 

control devices in place in a work zone. A few, however, formalize the inspection process by 

establishing specific levels of acceptability/effectiveness of devices and the overall time-tocollision (TTC) system and performing a detailed scored inspection of the work zone. These 

scores are then tallied to identify areas of needed improvement for the work zone. The scores 

can also be compared across work zones to identify patterns of deficiencies, establish baseline 

standards, and evaluate improvement efforts. An example of a form utilized by the Oregon 

Department of Transportation is illustrated in Figure 4-2. 
Guidance on Data Needs, Availability, and Opportunities Page | 4-8

For Work Zone Performance Measures Traffic Control Plans Unit. 2009 Construction Work Zone Tour. Oregon Department of Transportation, Salem, OR

Figure 4-2. Example of the Inspection Form Used by Oregon DOT
Guidance on Data Needs, Availability, and Opportunities Page | 4-9

For Work Zone Performance Measures Gettman, D., L. Pu, T. Sayed, and S. Shelby. Surrogate Safety Assessment Model and Validation: Final Report. Report No. FHWA-HRT-08-051. FHWA, U.S. Department of Transportation, Washington, DC. May 2008. Accessible at http://www.fhwa.dot.gov/publications/research/safety/0805 1/08051.pdf

4.1.2.2 Potential Sources in the Near Future

Microscopic traffic simulation – Traffic simulation is a powerful alternatives analysis tool used 

for a variety of operational assessments. Simulation has been used to compare and rank various 

work zone traffic control strategies such as speed and merge control, queue warning systems, 

ramp metering on freeways and signal timing changes on connected arterials, or any 

combinations of these.

Although traditionally used to assess the operational efficiency and mobility impacts of various 

alternatives, some users dig deeper into the simulation output to extract data that can, in theory, 

be used as safety surrogates. The FHWA has sponsored research into the development and 

testing of such a safety surrogate assessment model (SSAM) (11, 12). The tool takes the output 

from one of the traffic simulation models and processes the output to yield the following 

potential safety surrogate measures:

• Minimum TTC

• Minimum post-encroachment time 

• Initial deceleration rate

• Maximum deceleration rate

• Maximum speed 

• Maximum speed differential

• Classification as lane-change, rear-end, or path-crossing event type

• Vehicle velocity change had the event proceeded to a crash

Figure 4-3. TTC is one Type of Safety Surrogate Data that can be Extracted from 

Traffic Simulation Model Output 
Guidance on Data Needs, Availability, and Opportunities Page | 4-10

For Work Zone Performance Measures

Some safety surrogates (e.g. speed variance) can be relatively easily obtained using virtual 

detectors. Others, such as decelerations and TTC estimates, require some significant postprocessing of the simulation output (which is why the SSAM was developed). However, coding 

and calibrating a simulation model to use with SSAM does take considerable time and effort to 

do correctly. At the same time, it is not yet clear whether the underlying traffic behavior 

algorithms in the models accurately represent how the introduction of a work zone onto a 

roadway truly affects the safety surrogate outputs. 

The connected vehicle initiative already mentioned multiple times in this document is also 

expected to be a useful source of safety surrogate data at work zones in the future. The same 

types of measures listed above that are computed through traffic simulation (encroachments, 

deceleration rates, speed differentials between vehicles, etc.) will likely be available from actual 

vehicles through this initiative. Extraction and analysis of this data should yield valuable 

insights into work zone features and conditions that contribute to higher crash frequencies, and 

conversely to those features that promote safer vehicle operations.

4.1.2.3 Key Considerations and Trade-offs of Safety Surrogate Data Sources

Table 4-2 summarizes the key considerations and trade-offs associated with the various types of 

safety surrogate data that could be used for work zone performance measurement. One of the 

key considerations of all of the possible data sources described is the fact that the assumptions 

that such data is indeed correlated to work zone safety and crash experiences has not yet been 

verified. However, since essentially all of the data sources can be obtained from a work zone 

fairly quickly, they can help identify potential safety concerns and problem locations much faster 

than can be accomplished using actual crash data alone. As such, agencies and contractors 

should consider the collection and use of safety surrogate data as a good compliment to work 

zone crash monitoring and analyses.
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For Work Zone Performance Measures

Table 4-2. Considerations and Trade-offs of Traffic Safety Surrogate Data Sources

Data Source Key Considerations and Trade-offs

All data types

• Correlation of surrogate data to work zone crashes and safety 

have not yet been fully verified

• Most surrogate data can be obtained relatively quickly

TOC or work zone ITS speed 

sensor data

• Value of data depends on the locations of the sensors.

• Important to verify that data will be available once work has 

started (no loss of power to sensors), and will be archived and 

available for analysis

Speed data collected with 

hand-held radar or lidar

• Data collection is fairly easy to accomplish, as long as a suitable 

location for data collection can be found

• Useful for assessing speed behaviors at locations upstream or 

within the work zone of specific interest concern

• Care should be taken to be as inconspicuous as possible so as to 

not bias the behavior of approaching drivers

Travel times through the 

work zone to assess speed 

limit compliance

• Locations where significant speed changes are required can 

indicate potential problem locations

• Can be an easy way to assess the overall level of compliance 

with the work zone speed limit

Videotaped traffic behavior 

analyzed to determine 

erratic maneuver and 

vehicle conflict rates

• Can be difficult to find a unobtrusive vantage point from which to 

view and record traffic

• Analysis of data is very labor intensive

• Definition of specific behaviors of interest is needed to minimize 

biases of individuals performing data reduction

Work zone inspection 

scores

• Requires significant effort up front to establish scoring and rating 

system to be used by inspectors

• Correlation of scores to actual safety levels in work zone have not 

been verified

Traffic simulation output 

(analyzed with SSAM)

• Significant effort and technical skill are required to code and 

calibrate a traffic simulation model

• Correlation of safety surrogate measures to actual conditions that 

develop in a work zone have not been fully verified

• Correlation of safety surrogate results to work zone safety have 

also not been verified

Connected vehicle data • Date of availability still uncertain

4.1.3 Worker Accident Data Sources

The previous sections on crashes and safety surrogate data emphasize the traffic safety aspect of 

work zone performance. Although traffic safety is indeed of paramount importance to both 

agencies and contractors, the safety of their own employees is equally important. Accident 

statistics indicate that more highway workers are injured by work equipment and work vehicles 

within the work space than by traffic passing by or intruding into the work zone. 
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For Work Zone Performance Measures

Worker Accident Data Collected 

Specifically for the Work Zone

• Agency or contractor worker injury 

records

• State worker compensation commission 

accident statistics

• Bureau of Labor Statistics database

4.1.3.1 Sources That Already Exist

Data collected specifically for a work zone

– The Occupational Safety and Health 

Administration (OSHA) requires all 

employers with 11 or more employees to 

keep injury and illness records of its 

employees and to post them each year. 

Injuries and fatalities are then reported to 

OSHA, who may request the accident records

as well. OSHA and the Bureau of Labor 

Statistics (BLS) track accident statistics 

across the country for the various job 

classifications, including the construction industry. Therefore, agency or contractor injury 

records themselves are the main sources of worker accident data available. OSHA provides 

some guidance on the key elements to include in an accident report. 

Certainly, each agency or contractor will have 

ready access to its own worker accident data. 

For a smaller contractor, the amount of worker 

accident data available will be fairly small; for 

large agencies and contractors, the amount of 

worker injury data collected will be greater. 

However, interest may exist in how those data 

compare to other contractors in general, to other agencies, etc. Fortunately, state workers 

compensation commissions can provide basic accident trends at a fairly aggregate level (such as 

for highway construction employers). Likewise, accident statistics available through the BLS

can shed light on basic trends regarding injuries occurring in the highway and heavy construction 

field nationally. Although causal or contributory factors will not generally exist in these 

databases, relative frequencies of types of injuries, overall accident rates, etc. may be accessible 

for comparison purposes. OSHA also publishes some statistics on overall worker accidents (and 

accident rates as a function of hours worked) for comparison as well. 

4.1.3.2 Possible Sources in the Near Future

The New York State DOT construction accident record keeping program requires contractors to 

provide accident data on worker injuries occurring during highway work activities. Information 

on types of injuries, and narratives as to the events leading up to the accidents, have been useful 

in post-accident reviews of trends by the agency. In turn, the agency has enacted increased 

training, awareness programs, etc. to try and reduce some of the more common accidents. Other 

agencies may eventually choose to adopt a similar data collection and assessment approach. Of 

course, the same issues that existed for the collection of traffic crash data, such as the upfront 

The Bureau of Labor Statistics, United 

States Department of Labor tracks health 

statistics for occupations, including 

highway construction (13).
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For Work Zone Performance Measures

costs and efforts to establish a program and the allocation of resources to maintain it over time, 

will exist here as well.

4.1.3.3 Key Considerations and Trade-offs of Worker Accident Data Sources

A summary of the key considerations and trade-offs associated with the various sources of 

worker accident data are presented in Table 4-3. 

Table 4-3. Considerations and Trade-offs of Worker Accident Data Sources

Data Source Key Considerations and Trade-offs

Agency or contractor worker 

injury records

• Use of accident reports must be monitored carefully due to privacy 

concerns for the individual having the accident

• Sample size for many smaller companies will be fairly small, 

making it difficult to clearly identify any trends that can be mitigated

State worker compensation 

commission statistics

• Useful for comparisons to agency or contractor accident trends

• Level of detail will be limited

Bureau of Labor Statistics, 

OSHA worker accident 

statistics

• Useful for comparisons to agency or contractor accident trends

• Level of detail will be limited

Worker accident information 

in an agency-collected work 

zone crash and accident 

database

• Significant agency effort will be required during initial start up, and 

for ongoing maintenance of the process

• Requires upper agency support and emphasis

• Use of accident reports must be monitored carefully due to privacy 

concerns for the individual having the accident

4.2 Identifying and Computing Specific Safety Measures of Interest

4.2.1 General Considerations

Work zone safety performance measures can be useful to agencies and contractors in at least two 

ways:

1. To determine whether a safety problem exists at a particular work zone, so that an 

assessment can be initiated to determine if the issue or issues can be corrected while the 

work zone is still in place; 

2. To determine how a specific work zone design feature or combination of features, 

operating strategy, etc. affects the safety performance of a work zone, so that future 

decisions about the work zone design or maintenance of traffic plan are improved.

In both cases, one of the key challenges is in developing a reasonable level of confidence in the 

conclusions reached about the data. Hopefully, both crash and worker accident data at any given 

project are relatively rare events. However, their rarity does make it difficult to determine 

whether the number or rate of crashes or accidents that occur over time is greater than what 

should be expected, or is within the range expected given the normal randomness of these events. 
Guidance on Data Needs, Availability, and Opportunities Page | 4-14

For Work Zone Performance Measures

4.2.2 Specific Crash Measures

Table 4-4 illustrates four types of work zone traffic crash performance measures that were 

deemed to be of high or moderate importance by a panel of work zone practitioners. Although 

basic counts of crashes occurring at a project or over a region (many states track the total number 

of fatalities or fatal crashes that occur in their work zones each year) is commonly used as a 

performance measure, the real value in monitoring traffic crash counts is in assessing whether 

the number is greater than would be expected if the work zone were not present (and if so, by 

how much). Comparing the counts during the work zone period of interest to an average 

representing the same period prior to the work zone deployment is one way to assess if crashes 

have increased. Typically, a three-year average immediately prior to work zone deployment is 

used. However, this approach ignores the effect of any changes in traffic demand over time. In 

addition, this approach does not account for the stochastic nature of crashes themselves when 

comparing actual crash frequencies to what have been expected to occur if the work zone were 

not present.

A relatively simple procedure is available to assist in determining whether crashes have 

increased relative to what they would have been without the work zone (14). The following data 

are needed:

• the number of accidents observed during the work zone period of interest on the work 

zone segment of interest (L);

• the total number of accidents on the same segment and for the same period of interest in 

previous (pre-work zone) calendar years (K);

• the ratio of the work zone analysis period to the designated before period (rd);

• an estimate of the ratio of traffic in the work zone to traffic on the same segment and 

during the same calendar period for three years prior ( tf rˆ ); and

• the maximum percent safety reduction the district expects or is willing to accept 

(θ%tolerable).
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For Work Zone Performance Measures Page | 

4-15

Table 4-4. Examples of Specific Work Zone Traffic Crash Performance Measures

Measure Computations Required Value or Importance 

Ranking Potential Audience(s)

Number of crashes per 

time period of interest, 

change in crash 

frequency from the nowork zone condition

Use of simple crash counts does not require computations. 

Determination of how much the counts differ from what 

would have been “expected” had the work zone not been 

present is described in the text.

High

Traveling public

Work zone operations 

practitioners

Change in crash rate 

per vehicle-miletraveled per time period

Determine the number of crashes occurring during the 

period of interest (total project, certain phase, during peak 

periods, during certain work times). Compute the number of 

vehicles traveling through the work zone during the period 

and multiply by the length of the work zone to determine the 

VMT. Divide the number of crashes by the VMT. Compute 

for the same period of interest before the work zone was 

deployed. Determine the difference or percent change in 

rates.

High

Traveling public

Work zone operations 

practitioners

Distribution of crashes 

(or changes in the 

distributions) by 

severity, type of crash, 

contributing factor, etc.

Determine which crash factors are of interest. Determine 

levels for each factor, and number of crashes in each level. 

Divide each level by the total number of crashes across all 

levels. Compute the same for data obtained before the 

work zone was deployed. Compare the distributions.

Moderate

Work zone design 

practitioners

Work zone operations 

practitioners

Change in crash costs 

per time period of 

interest

Determine the number of crashes by severity over the work 

zone period of interest. Determine the expected number of 

crashes that would have occurred without the work zone 

present (described in the text). Determine the difference in 

crashes by severity. Multiply the difference in each severity 

level by the FHWA estimate of crash costs for that severity 

level. Sum across all severity levels.

Moderate Work zone design 

practitioners
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For Work Zone Performance Measures

Many agencies or contractors are interested in whether crashes have increased at all during the 

work zone period. However, if an agency chooses to acknowledge that crashes do tend to 

increase somewhat when a work zone is present, and only wishes to determine if crashes at a 

given work zone increase by more than what is typical, they would make use of the θ%tolerable in 

the computations below. 

Four computational steps are required:

Step 1. Estimate the safety of the work zone segment during the period of interest ( λ

ˆ ) and the 

variance of that estimate:

λ = L ˆ

VAˆR{λ

ˆ}= L

Step 2. Estimate what would have been the safety of the segment during the same time period 

had the work zone not been there (πˆ ) and that variance of the estimate:

Expected crash frequency (πˆ) = rd rˆ

tf K

VAR{ } rd rtf K 2 2 ˆ ˆ π =

The value for πˆ is estimated using an “average” of the accident frequency on the same segment 

and during the same calendar period while accounting for changes in traffic volumes. A threeyear average is recommended, reflecting a balance between the use of recent data and obtaining 

large enough sample sizes. If traffic has grown and is greater in the work zone than on the same 

segment for the three years prior, tf rˆ will be greater than 1. If traffic has decreased as a result of 

general trends or implementation of travel demand management strategies introduced as part of 

the temporary traffic management plan, then tf rˆ will be less than 1. If no information on traffic 

volumes is available, a value of 1.0 should be used for tf rˆ .
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For Work Zone Performance Measures

Step 3. Estimate the tolerable work zone safety given the maximum safety reduction the district 

expects or is willing to accept ( λtolerable

ˆ ) and the variance of that estimate:

π

θ λ 1 * ˆ 100%

ˆ % 



 



 = + tolerable

tolerable

{ } {π} θ λ ˆ ˆ 1 *

100%

ˆ ˆ % 2

VAR VAR tolerable

tolerable 



 



 = +

Step 4. Determine if the safety of the work zone segment during the period of interest (λ

ˆ ) is 

worse than the expected or tolerable work zone safety ( λtolerable

ˆ ) at the desired level of 

confidence (ε):

λ

ˆ > λˆ

tolerable + N(ε ,0,1) VAˆR{λ

ˆ}+VAˆR{λ

ˆ

tolerable} Safety of the work zone segment during 

the period of interest is worse than 

expected or tolerable.

λ

ˆ ≤ λˆ

tolerable + N(ε ,0,1) VAˆR{λ

ˆ}+VAˆR{λˆ

tolerable}

There is not enough evidence to conclude 

that safety of the work zone segment 

during the period of interest is worse than 

expected or tolerable (with caveat 

explained below).

The N(ε ,0,1) value corresponds to the standard normal statistic for the confidence level of 

interest. If the agency or contractor wants to be 90 percent confident that the number of crashes 

exceeds the threshold expected value, the N(ε ,0,1) value would be 1.282. If the agency or 

contractor were only interested in a 50/50 guess as to whether the crash number exceeds the 

threshold, the N(ε ,0,1) value would be 0. A simple way to apply step 4 is presented in 

Figure 4-4. The x-axis represents the computed expected number of crashes of the segment 

during the time period of interest if the work zone was not there (πˆ ). Values on the y-axis 

indicate the minimum number of work zone crashes observed during the period of interest that 

would indicate an increase in crashes had indeed occurred. Three levels of confidence are 

shown, 90 percent, 75 percent, and 50 percent. Note that the 50 percent “guess” is the same as 

the expected value without the work zone (as would be expected). Also note that crashes 

typically need to be 20 to 50 percent higher than expected to be 90 percent confident that crash 

frequency has indeed increased within the work zone.
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For Work Zone Performance Measures Battelle

Figure 4-4. Determination of Whether Crashes have Increased Significantly in a Work Zone

Although the use of expected crash frequencies is the preferred approach for analyses, and is the 

approach followed in the recently-released Highway Safety Manual (15), some practitioners are 

more comfortable with using crash rates as a function of VMT. Despite the statistical limitations 

of this approach when the number of crashes being compared is fairly low, it can also be used to 

compare pre-work zone crash conditions with those occurring during the work zone. 

4.2.3 Specific Safety Surrogate Measures

Table 4-5 provides a summary of work zone safety surrogate performance measures, how they 

were ranked by the practitioners, and their intended audience(s). Measures related to speeds are 

appealing to practitioners and understood by the traveling public, but have not been strongly 

correlated to safety. Several potential surrogate measures can be obtained through traffic 

simulation, but currently such measures are not highly rated by practitioners. Additional 

research and use of such measures will likely be needed before they are more readily adopted for 

work zone performance measurement. 

0

10

20

30

40

50

60

70

80

90

5 10 15 20 25 30 35 40 45 50

Minimum number of crashes in the work zone that would indicate 

that crashes have increased relative to what was expected

Number of crashes expected if the work zone had not occurred

90 percent chance that crashes have increased

75 percent chance that crashes have increased

50 percent chance that crashes have increased
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For Work Zone Performance Measures Page | 

4-19

Table 4-5. Examples of Specific Work Zone Safety Surrogate Crash Performance Measures

Measure Computations Required Value or Importance 

Ranking Potential Audience(s)

Speed limit compliance 

percentage

General indication of compliance can be obtained through 

work zone drive throughs. Formal computations can be 

done with a sample of at least 120 spot speed samples at a 

location

High

Traveling public

Work zone operations 

practitioners

Speed variance Standard computation of the variance of a sample of speeds High Work zone operations 

practitioners

Change in emergency 

response or service 

patrol dispatches 

within a work zone

Computations should be performed similarly to those 

outlined for crash analyses. Moderate Work zone operations practitioners

Frequency of low time 

to collision rates

Frequency or rate during the work zone can be obtained 

through video analysis of erratic maneuver and vehicle 

conflict data. More often estimated as part of traffic 

simulation analyses.

Moderately Low Work zone design 

practitioners

Work zone inspection 

scores

Inspection criteria can be scored numerically in various 

ways. Weightings and sums across various criteria is 

straightforward

Moderately Low Work zone operations 

practitioners

Frequency of high 

deceleration rates

Frequency or rate during the work zone can be obtained 

through video analysis of erratic maneuver and vehicle 

conflict data. More often estimated as part of traffic 

simulation analyses.

Moderately Low Work zone design 

practitioners

Frequency of forced 

merges per lane 

closure hour or number 

of vehicles

Frequency or rate during the work zone can be obtained 

through video analysis of erratic maneuver and vehicle 

conflict data. More often estimated as part of traffic 

simulation analyses.

Moderately Low Work zone design 

practitioners

Frequency of very 

small headways

Frequency or rate during the work zone can be obtained 

through video analysis of erratic maneuver and vehicle 

conflict data. More often estimated as part of traffic 

simulation analyses.

Moderately Low Work zone design 

practitioners
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For Work Zone Performance Measures

4.2.4 Specific Worker Accident Measures

Finally, Table 4-6 presents some work zone performance measures pertaining to worker 

accidents. Simple counts can be easily tracked by an agency or contractor, and are simple to 

interpret. If the target or expectation is low (i.e., zero occurrences for an agency or contractor in 

a given time period), the use of counts is meaningful. However, as the size of the agency or 

contractor increases, the frequencies of accidents would not be expected to be zero, and efforts to 

assess whether improvements are being made would necessitate rate-based metrics, commonly in 

terms of worker hours of exposure. For agencies and contractors with a substantial amount of 

worker accident data available, it can also be meaningful to look at accident trends in terms of 

types of injuries, contributing factors, etc. as percentages of the total dataset, and compare to 

statewide or national trends.
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For Work Zone Performance Measures Page | 

4-21

Table 4-6. Examples of Specific Worker Accident Performance Measures

Measure Computations Required Value or Importance 

Ranking Potential Audience(s)

Frequency of worker 

accidents No computations needed High

Traveling public

Work zone operations 

practitioners

Worker injury rates per 

person-hours of work

(i.e., 200,000)

Determine frequency of worker accidents recorded in a 

given period divided by the total number of agency or 

contractor worker hours. These can be developed for a 

single work zone (if large enough) or across multiple work 

zones as desired. 

High Work zone operations 

practitioners

Distribution of types of 

injuries, causes or 

contributing factors of 

accidents occurring

Determine frequency of each type of worker accident, divide 

by the total number of worker accidents in the dataset. High Work zone operationspractitioners
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Sources of Customer Satisfaction Data

• Focus group transcripts

• In-person or telephone interview responses

• Mail, email, or website surveys/questionnaire 

responses

• Customer complaint database entries

5.0 DATA SOURCES AND COMPUTATIONAL METHODS FOR

CUSTOMER SATISFACTION-RELATED WORK ZONE 

PERFORMANCE MEASURES

Most agencies and contractors recognize that the opinions and perceptions of travelers, residents, 

and businesses are an important component to the successful completion of a highway project. 

As stewards of significant amounts of public-owned infrastructure and funding, transportation 

agencies in particular are very sensitive to the opinions of its citizens, and strive to ensure that its 

customers experience a high degree of satisfaction with the agency. 

The delays, congestion, and overall inconveniences caused by work zones can be very 

challenging to an agency or contractor who desires to maintain good relationships with the 

travelers, residents, and business owners who are its customers. Therefore, monitoring and 

measuring customer satisfaction associated with work zones is a critical component to an 

agency’s or contractor’s set of useful work zone performance measures

5.1 Customer Satisfaction Data Sources

Agencies and contractors can assess customer satisfaction associated with a particular work 

zone, or with all work zones in general in a region, along a corridor, or even throughout a city or 

state. For the most part, methods available to obtain customer satisfaction data are similar for 

both types of assessments. However, the questions asked and amount of data ultimately needed 

to establish some level of confidence in the results obtained will differ. In most cases, an agency 

should work through an institutional review board (IRB) when preparing to obtain customer 

satisfaction data, as the collection of such data is construed as human subjects research.

5.1.1 Sources that Currently Exist

Several methods can be used to obtain 

either type of customer satisfaction 

data. The first method, focus group

studies, consist of a group of people 

(usually between 6 and 10) who are 

brought together and asked about their 

perceptions, opinions, beliefs, and 

attitudes towards a product, service, or 

concept. Focus groups can also help 

aid in establishing questions and 

anticipated answers for larger questionnaires/surveys. Group participants should be diverse in 

terms of socio-economic background, age, gender, and education. In some cases, it may be 

necessary or desirable to evaluate certain traveler groups (commuters, local residents, business 

owners, transit bus operators, etc.) if concerns exist about certain user group opinions
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For Work Zone Performance Measures

TTI

In a focus group study, the meeting 

facilitator engages the participants in 

open dialog, but keeps them on a script 

of issues and general questions during 

the one to two hours that the 

meetings typically last. Examples 

of discussion topics might include: 

problems encountered or noticed 

in a particular work zone or in 

work zones in general and 

thoughts on how a work zone 

could be improved. In some instances, 

it will be possible to obtain an overall 

consensus on a certain topic; in other 

cases, multiple viewpoints and positions on a topic can exist. Focus groups generally do not 

involve enough participants to provide statistically significant findings, so their primary value is

in the insights gained as participants provide their opinions, experiences, and suggestions to the 

various questions asked by the facilitator and subsequent discussion that may evolve within the 

group. Therefore, focus group results should be viewed as anecdotal findings and not as an 

assessment of the overall driving population within the work zone area.

The next customer satisfaction data available for use by agencies or contractors are one-on-one 

interviews. Such interviews can be done in person or over the telephone. Personal interviews 

are more appropriate when information pertaining to a particular project is of interest. For 

example, interviewers may approach potential participants in a public area within or just 

downstream of a work zone with the intent of gathering data while the driving experience 

through the work zone is still fresh on the participants mind. Conversely, when interest is on 

multiple work zones or work zones in general in a given region, such as is illustrated in 

Figure 5-2, a broader sample base is usually preferred and is more easily obtainable over the 

telephone.

When interviews focus on a single work zone, it is important to remember that the interview data 

is reflective of when the interview is conducted. Responses may vary at the same location over 

time if conditions change. Also, trained interviewers are usually needed to ask and record 

questions during the interview and assess findings post-discussion. For in-person interviews, the 

survey needs to be short (3 or 4 questions), as travelers will typically not be inclined to stay and 

participate for a long period of time. The administration of both in-person and telephone 

interviews is fairly labor intensive. 

Figure 5-1. Focus Group Participants provide their 

Opinions about Particular Topics and can Offer Ideas on 

Possible Improvement Needs
Guidance on Data Needs, Availability, and Opportunities Page | 5-3

For Work Zone Performance Measures Bender, D. and J. Schamber. SSDOT 2002 Statewide Customer Survey. Report No. SD2002-07-F. South Dakota Department of Transportation, Pierre, SD. December 2002. Accessible at http://sddot.com/business/research/projects/docs/SD2002_07_Final_Report.pdf.

Figure 5-2. Example of a Script Used during a Telephone Interview of

South Dakota Motorists
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For Work Zone Performance Measures

Figure 5-3. Agency Websites are a Common Venue Used for 

Customer Surveys/Questionnaires

MoDOT Work Zone Customer Survey. Missouri Department of Transportation. Accessible at http://www.modot.mo.gov/workzones/Comments.htm.

Another way that 

customer satisfaction 

data can be collected is 

through formal 

customer surveys/

questionnaires

administered through 

the mail, email, or from 

an agency or contractor 

website. 

Questionnaires/surveys 

may be designed for 

quantitative statistical 

analysis or for 

qualitative assessment. 

Generally, responses are 

limited to predetermined 

options although there 

may be an option to 

provide comments. 

Surveys can usually be 

developed to gather 

enough data to yield 

statistically significant 

findings. However, a 

trained survey designer 

should be used 

whenever possible to 

avoid developing 

questions and responses 

that lead or bias the 

results obtained. 

Surveys are often most 

useful when done 

periodically to compare 

responses over time. 

Survey costs can be high 

depending on the type 

of survey and the 

sample size. If 
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For Work Zone Performance Measures

administered via email or through an agency’s website, it is important to recognize that there is a 

potential for selection bias, as younger and more educated travelers tend to be more comfortable 

with using the internet. It is also important to realize that almost all types of questionnaires and 

surveys suffer from a slight negative bias. Customers who are unhappy with conditions and have 

a complaint are more likely to respond than those without problems or complaints.

A fourth source of customer satisfaction data for agencies and contractors is the unsolicited 

customer complaints. Most agencies have a formal process in place to respond to complaints 

(as do some contractors). However, the availability of the data for work zone performance 

monitoring and measurement purposes is dependent 

upon agency documentation practices. Some agencies 

maintain formal databases to track complaint arrivals 

and disposition, which can facilitate search and 

collation of the data for work zone related complaints. 

Some complaints will be easily associated with a work 

zone or work zones in a given corridor or region (i.e., complaints regarding the condition of a 

work zone, closure of a ramp or intersection, etc.). Other complaints, however, may be more 

indirect. For example, an increase in the number of complaints regarding poor signal timing at 

an intersection may be due to increased traffic that diverted from a nearby route where a work 

zone has degraded travel conditions. 

Customer complaints can be an effective data source for identifying specific operational or safety 

problems in work zones, and if tracked over time can aid agencies in assessing the effectiveness 

of their efforts to reduce such complaints. Some agencies (such as the Illinois Tollway) even 

solicit traveler comments and complaints in its work zones (see Figure 5-4). When taking the 

time to file a complaint, a driver may embellish the conditions they experienced. Complaints 

filed are not necessarily indicative of the overall level of driver satisfaction, as those who are not 

unhappy generally do not contact the agency to indicate their general satisfaction. It may be 

possible to establish general 

complaint rates and compare 

between projects or regions. 

However, statistical analyzes of 

such comparisons may not be 

possible due to small sample sizes.

Travelers, residents, or nearby 

businesses may embellish conditions 

somewhat when making a work 

zone-related complaint

Figure 5-4. Illinois Tollway Actively 

Solicit Input about Work Zones on Its 

Facilities

Best Practices in Work Zone Assessment, Data Collection, and Performance Evaluation. Scan Team Report, NCHRP Domestic Scan 08-04. October 2010. Accessible at http://onlinepubs.trb.org/onlinepubs/nchrp/docs/NCHRP20-68A_08-04.pdf.
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5.1.2 Potential Sources in the Near Future

Many agencies (and some contractors) have begun embracing the use of social media 

technologies, primarily Facebook and Twitter, to communicate with its customers. Examples 

already exist of efforts by various state DOTs to disseminate current travel conditions through 

these venues. A few agencies have begun using these sites as a way to collect customer opinion 

and satisfaction information. This approach does offer agencies the opportunity to easily “push” 

questions out to the more technology-savvy portion of its customer base. In addition, it does 

appear to be a popular way for that group of customers to interact with the agency to ask 

questions and obtain tailored responses. Concerns about selection biases and similar issues that 

surround the traditional customer survey techniques will continue to exist with these new 

approaches as well. Similar to customer complaint sources, social media responses may be 

skewed more negatively than a representative sample of the entire driving population would 

reveal. As long as they are used with consideration given to these biases, they can be an 

extremely useful method of obtaining information about perceived issues and problems.

Another emerging technology with potential use for obtaining customer satisfaction data is the 

use of web-based tools to conduct on-line focus group discussions. These tools are already 

being used for product testing and placement research (see, for example, http://www.efocusgroups.com/online.html). Depending on the tool used, capabilities exist to connect 

participants via web cameras to allow non-verbal interaction among participants. Various parts 

of the survey screen are assigned to various mechanisms for interacting with the group. For 

example, the system may allow the facilitator to “poll” the group on various questions, allow for 

private chat sessions between the facilitator and each of the participants, provide a “groupthink” 

area where participants can type in various thoughts they have on a topic and allow others to 

comment (similar to a Facebook post). These tools improve the ability of the facilitator to record 

the various ideas and statements provided during discussions. 

5.1.3 Key Considerations and Trade-Offs of Work Zone Customer Satisfaction 

Data Sources

A summary of the key considerations and trade-offs associated with customer satisfaction data 

sources for work zone performance measurement is presented in Table 5-1. As noted previously, 

most of these require attention to and compliance with federal regulations governing human 

subjects research.
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Table 5-1. Considerations and Trade-Offs of Customer Satisfaction Data Sources

Data Source Key Considerations and Trade-offs

Focus groups

• Best for gathering opinions, perceptions 

• A properly trained facilitator is critical

• Data from multiple groups may be needed to obtain adequate 

input from various types of customers

One-on-one interviews

• Best for obtaining responses and input from users during or right 

after they have passed through a work zone

• May need to do surveys multiple times as conditions a the work 

zone change

Surveys/Questionnaires

• Multiple dissemination mechanisms (mail, email, website) 

possible

• Potential to reach a larger sample size more efficiently

• Properly designed surveys with adequate sample sizes can yield 

statistically significant results

Complaints

• Work zone effects may trigger complaints directly or indirectly

• Customers may embellish the magnitude of the problem 

• Statistical analyses are usually not possible with the data

Social media uses

• Important to rely on trained survey designers for these 

applications as well

• Responses will be biased towards younger, more technologysavvy users

On-line focus groups

• Allows participants to remain at their computers to participate

• Although efforts to mimic the interactions that occur in face-toface focus groups are incorporated into the systems, their 

effectiveness in doing so is unknown

5.2 Identifying and Computing Specific Customer Satisfaction 

Performance Measures

Several possible measures to assess customer satisfaction in a work zone or across multiple work 

zones are presented in Table 5-2. Assessments of the quality and adequacy of specific aspects or 

features of a work zone or group of work zones was perceived by the expert panel as having a 

high level of importance or value to the agency or contractor, because it provided targeted 

guidance on issues to correct or improve. Interestingly, assessments of the quality or satisfaction 

associated with traveling through a work zone were viewed as only moderately important by the 

panel. It was noted that such ratings can be heavily influenced by media reports (positively or 

negatively) and will likely reflect most recent experiences that may or may not be totally 

attributable to the work zone itself (a major accident occurring in the work zone during the peak 

period, for example, could bias those customers regarding the overall quality of travel through 

the work zone in peak periods on most days). 
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5-8

Table 5-2. Examples of Specific Customer Satisfaction Performance Measures

Measure Computations Required Value or Importance 

Ranking

Potential Audience(s)

Ratings of the quality of various work 

zone features (signs, information 

provided regarding delays, queues, 

work activities) seen while driving 

through a work zone

No computations needed High

Work zone design 

practitioners

Work zone operations 

practitioners

Ratings of satisfaction with travel 

conditions through multiple work 

zones on a corridor or in a region or 

network

No computations needed Moderately High

Work zone design 

practitioners

Work zone operations 

practitioners

Frequency or rate of complaints No computations needed Moderately High

Work zone design 

practitioners

Work zone operations 

practitioners

Ratings of satisfaction while traveling 

through work zone No computations needed Moderate

Work zone design 

practitioners

Work zone operations 

practitioners
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Existing Sources of Work Zone Productivity and 

Efficiency Data

• Construction management system database

entries

• Lane closure request and approval database 

entries

• Daily project diary notes

6.0 DATA SOURCES AND COMPUTATIONAL METHODS FOR 

AGENCY AND CONTRACTOR WORK ZONE EFFICIENCY 

AND PRODUCTIVITY PERFORMANCE MEASURES

Methods of tracking work efforts and productivity in the highway construction industry have 

existed for many years, and have been well integrated into the institutional structures of most 

agencies and contractors. The primary focus of these efforts has been on ensuring that quality 

work is being performed efficiently, as well as on providing data that can be used for planning 

and estimation efforts on future projects. Recently, additional attention has been given to the 

influence of work productivity and quality upon work zone safety and mobility. Work 

completed as fast as possible minimizes the exposure of both travelers and work crews to the 

safety hazards of work zones, and can reduce the total amount of delay and congestion that are 

attributable to the presence of work zones on a facility. Likewise, construction and maintenance 

efforts that improve the overall durability of the roadway can reduce the frequency of all types of 

work zones on the roadway, which also reduces work zone exposure and impacts. Consequently, 

quantifying both productivity/efficiency and roadway durability and relating them to decisions 

on construction methods, contracting techniques, and materials used is another important 

dimension work zone safety and mobility performance measurement.

6.1 Efficiency and Productivity Data Sources

6.1.1 Sources That Already Exist

Data collected specifically for a 

work zone – All agencies maintain 

some type of electronic construction 

management system for the various 

projects being funded. Data from 

these databases are developed into 

productivity rates and quantities for 

various work tasks. These databases 

are also used to track such things as 

percent of project funds expended 

versus amount of work accomplished, 

percent of projects on time, and so on. Depending on the system, parsing of these data could 

yield measures that can assess how well a particular strategy to accelerate construction is 

working. It should be noted that although a construction management database provides an 

ongoing record of project activity on a section of roadway over time, the duration that such 

records are kept varies from agency to agency. As a result, it may be necessary to initiate 

additional data recordkeeping to track such efforts over the long term. 
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Figure 6-1. Various Construction 

Performance Metrics are Computed from an 

Agency’s Construction Management 

Database

Virginia Department of Transportation

Illinois Tollway Presentation to the NCHRP 20-68A Domestic Scan 08-04 Team. 

Columbus, Ohio. March 12, 2010

In addition, a construction management 

database will not include activities that are 

performed by agency maintenance forces 

or by utility departments. Some agencies 

have initiated formal lane closure request 

and approval processes for certain 

roadways, regardless of the entity 

performing the work. Also discussed 

previously as a source of lane closure 

exposure data, such data can also be used 

to assess time between work activities on a 

roadway segment over time, if all entities 

performing lane closures have to request 

permission. An example of such a system that is 

used by the Illinois Tollway Authority is shown 

in Figure 6-2 and Figure 6-3. 

Figure 6-2. Illinois Tollway Lane Closure Entry Screen
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Figure 6-3. Illinois Tollway Temporary Lane/Shoulder Closure Database

Information recorded in the daily diary of a project may be another important source of 

productivity and efficiency data. Hours of certain types of work activities (such as when 

temporary lane closures are occurring) have been previously discussed as a type of exposure data 

that can be used to compute other mobility or safety performance metrics (e.g., vehicle-hours of 

delay per hour of temporary lane closure). However, some of these data can also be used as 

performance to assess agency or contractor efforts to minimize work zone impacts (e.g., percent 

of work activity hours that required a temporary lane closure). Illinois Tollway Presentation to the NCHRP 20-68A Domestic Scan 08-04 Team. Columbus, Ohio. March 12, 2010
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6.1.2 Potential Sources in the Near Future

Mobile applications to gather work activity data in the field – Several companies are working 

on mobile applications to gather work activity directly from field personnel in real time. 

Although the primary intent of the technology is to improve accuracy of the data entered (many 

of the applications are based in part on a geographic information system [GIS] platform) and 

drastically reduce work load involved with manually entering data into a construction 

management system, additional development work could facilitate easier and more systematic

entry of data directly relevant to work zone safety and mobility performance measurement. For 

instance, field identification of work locations for each shift, any road or lane closure 

information (locations, number of lanes closed, start and end times) could be very easily 

recorded on a mobile device and stored in the database.

Electronic database of agency maintenance work – Many agencies do certain types of work 

with its own forces out of its area offices or maintenance yards. These activities generally are 

tracked by type of work task completed and location in some type of daily work diary to assist in 

assigning staff time to various accounts, but these data are not generally coded into any type of 

database that is tied to roadway locations. However, a few agencies are beginning to recognize 

the value of tracking these maintenance efforts, and have developed maintenance management 

systems that in many ways mimic those of the traditional construction management databases.

6.1.3 Key Considerations and Trade-Offs of Work Zone Efficiency and 

Productivity Data Sources

Table 6-1 provide a summary of some of the key considerations and trade-offs associated with 

the various construction productivity and efficiency data sources. Although mobile applications 

for field entry of project activities do currently exist, collection of MOT data of interest for work 

zone safety and mobility performance measurement can be recorded in these applications will 

depend on whether the underlying database being populated (i.e., the construction management 

database) has field structures and elements that allow the data to be input from the mobile 

device.
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Table 6-1. Considerations and Trade-Offs of Construction Productivity and

Efficiency Data Sources

Data Source Key Considerations and Trade-offs

Construction management 

system databases

• Focus of most systems is still mainly on contract-related data 

(quantities, task completion levels)

• Many of the MOT data elements of interest are entered via 

narratives into the system, with minimal consistency in entries 

across projects

Lane closure request and 

approval databases

• May include closures across all agencies and contractors in a 

region

• Will normally be limited to high-volume roadways only

• Depending on administrative processes established, the database 

may contain a large number of “phantom” closures that need to 

be removed prior to analyses

Daily project diaries • MOT data entered (if any) often varies significantly from project to 

project

Mobile applications for field 

entry of project activities

• Requires field personnel to carry and use mobile devices in the 

field (costs and durability of the devices may become an issue)

• An application of this type may not yet exist

Maintenance management 

system databases for 

agency staff 

• Requires detailed recordkeeping of activities by all maintenance 

crews and crew members

6.2 Identifying and Computing Specific Work Zone Efficiency and 

Productivity Measures 

Table 6-2 presents four types of construction productivity and efficiency-related performance 

measures that could be useful for work zone safety and mobility performance measurement. 

Level of activity, expressed as a percentage of possible days that could be worked, was cited as a 

high priority measures by the expert panel that reviewed and rated the measures. A measure of 

compliance with agency-established MOT criteria (i.e., the percent of lane closure hours 

occurring outside of accepted work windows) was also cited as a high-priority measure. Various 

types of traditional construction productivity measures that could be stratified based on roadway 

type, type of MOT used, construction methods used, etc. was considered a moderately-high 

priority. The panel also rated one specific type of construction method, the percentage of 

activities involving a temporary lane closure, as a moderate priority measure, due to its direct 

relevance to operating conditions and safety of the traveling public. Finally, a measure of 

average time between work efforts on a given roadway segment was not specifically rated by the 

panel, but was included in the table as a way to encourage the long term tracking of such work 

efforts by agencies and contractors in the future. 
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6-6

Table 6-2. Examples of Specific Agency or Contractor Efficiency and Productivity Performance Measures

Measure Computations Required Value or Importance 

Ranking

Potential Audience(s)

Percent of allowable or 

total days worked

Determine the actual number of days worked and divide by 

the total number of to date (either the allowable work days 

or calendar days)

High

Work zone design 

practitioners

Work zone operations 

practitioners

Percent of lane closure 

hours occurring 

outside established 

“work windows”

Determine total amount of lane closure hours occurring 

outside of accepted time work windows, divide by the total 

number of lane closure hours that have been worked (this 

should be a very low percentage)

High

Work zone design 

practitioners

Work zone operations 

practitioners

Typical work 

productivity measures 

stratified by type of 

project MOT, 

construction 

acceleration technique 

employed, etc.

Average productivity measures of interest across multiple 

projects having the same MOT in place, using the same 

type of construction acceleration technique, etc.

Moderately High

Work zone design 

practitioners

Work zone operations 

practitioners

Average hours worked 

during work activities 

involving temporary 

lane closures, possibly 

stratified by time of 

day, weekday versus 

weekend

Determine total hours of work activities that involve 

temporary lane closures, divide by the total hours of work 

activity overall or for specific time periods of interest

Moderate

Work zone design 

practitioners

Work zone operations 

practitioners

Average time between 

work zone activity, 

possibly by type of 

work being 

accomplished, 

construction methods 

used, etc.

The frequency of work efforts (construction, maintenance, 

or utility) over time on a roadway segment, divided by the 

total time that such activities were tracked

Not rated Work zone design 

practitioners
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7.0 REFERENCES

1. Best Practices in Work Zone Assessment, Data Collection, and Performance Evaluation. 

Scan Team Report, NCHRP Domestic Scan 08-04. October 2010. Accessible at 

http://onlinepubs.trb.org/onlinepubs/nchrp/docs/NCHRP20-68A_08-04.pdf.

2. Traffic Mitigation Guidelines for Work Zone Safety and Mobility. New Jersey Department 

of Transportation, Trenton, NJ. October 2007. Accessible at 

http://nj.gov/transportation/eng/documents/BDC/pdf/attachmentbdc07t07.pdf.

3. DOT Names Cities for first Connected Vehicle Technology Evaluations. Accessible at 

http://www.kbb.com/car-news/all-the-latest/dot-names-cities-for-first-connected-vehicletechnology-evaluations/.

4. Intelligent Transportations in Work Zones, A Case Study: Work Zone Travel Time System.

Report No. FHWA-OP-02-025. FHWA, U.S. Department of Transportation, Washington, 

DC. October 2004. Accessible at 

http://ops.fhwa.dot.gov/wz/technologies/arizona/arizona.pdf

5. Highway Capacity Manual. TRB, National Research Council, Washington, DC. 2010.

6. Haserman, R.J., J.S. Wasson, and D.M. Bullock. Real-Time Measurement of Travel Time 

Delay in Work Zones and Evaluation Metrics Using Bluetooth Probe Tracking. In Journal of 

the Transportation Research Board, No. 2169. Transportation Research Board of the 

National Academies, Washington, DC. 2010. pp. 40-53.

7. Edwards, M.B. and M.D. Fontaine. Investigation of Travel Time Reliability in Work Zones 

with Private-Sector Data. In Journal of the Transportation Research Board, No. 2272. 

Transportation Research Board of the National Academies, Washington, DC. 2012. pp. 9-

18.

8. Ullman, G.L. T.J. Lomax, and T. Scriba. A Primer on Work Zone Safety and Mobility

Performance Measurement. Report No. FHWA HOP-11-033. FHWA, U.S. Department of 

Transportation, Washington, DC. September 2011. Accessible at 

http://ops.fhwa.dot.gov/wz/resources/publications/fhwahop11033/fhwahop11033.pdf.

9. Hoeft, B. Performance Measures for Traffic Incident Management. Presentation at the 

Lifesavers 2012 Conference, Orlando, FL. June 15, 2012. 

10. Traffic Control Plans Unit. 2009 Construction Work Zone Tour. Oregon Department of 

Transportation, Salem, OR.

11. Pu, L. and R. Joshi. Surrogate Safety Assessment Model (SSAM): Software User Manual. 

Report No. FHWA-HRT-08-050. FHWA, U.S. Department of Transportation, Washington, 

DC. May 2008. Accessible at 

http://www.fhwa.dot.gov/publications/research/safety/08050/08050.pdf.
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For Work Zone Performance Measures

12. Gettman, D., L. Pu, T. Sayed, and S. Shelby. Surrogate Safety Assessment Model and 

Validation: Final Report. Report No. FHWA-HRT-08-051. FHWA, U.S. Department of 

Transportation, Washington, DC. May 2008. Accessible at 

http://www.fhwa.dot.gov/publications/research/safety/08051/08051.pdf

13. Injuries, Illnesses, and Fatalities. Bureau of Labor Statistics, United States Department of 

Labor. Accessible at http://www.bls.gov/iif/.

14. Ullman, G.L., R.J. Porter, and G.J. Karkee. Monitoring Work Zone Safety and Mobility 

Impacts in Texas. Report No. FHWA/TX-09/0-5771-1. Texas Transportation Institute, 

College Station, TX. May 2009.

15. Highway Safety Manual. American Association of State Highway and Transportation 

Officials (AASHTO). 2010.

16. Bender, D. and J. Schamber. SSDOT 2002 Statewide Customer Survey. Report No. SD2002-

07-F. South Dakota Department of Transportation, Pierre, SD. December 2002. Accessible 

at http://sddot.com/business/research/projects/docs/SD2002_07_Final_Report.pdf.

17. MoDOT Work Zone Customer Survey. Missouri Department of Transportation. Accessible 

at http://www.modot.mo.gov/workzones/Comments.htm.

18. Illinois Tollway Presentation to the NCHRP 20-68A Domestic Scan 08-04 Team. Columbus, 

Ohio. March 12, 2010.
Guidance on Data Needs, Availability, and Opportunities

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Appendix A.

Delphi Survey Methodology to 

Prioritize Work Zone 

Performance Measures

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DELPHI SURVEY METHODOLOGY TO PRIORITIZE WORK ZONE 

PERFORMANCE MEASURES

The Delphi Survey Method is a systems analysis technique designed to allow individuals to 

converge on opinions, ratings, priorities, etc. without having to be in face-to-face or 

video/teleconference communications. In fact, opinions of individuals are kept anonymous so 

as to avoid creating any biases in the data. The Delphi process itself works best with a limited 

number of participants, and so the research team, in consultation with FHWA, reached out to 

10 practitioners to participate as an advisory panel for the project and assist in the Delphi survey 

process. Five of the panel participants were state work zone traffic control engineers or 

equivalent, two were from research institutions and had both performance measurement and 

work zone safety and mobility backgrounds and expertise, two others were private-sector 

consultants with performance measurement backgrounds and expertise, and the final panelist was 

a city traffic engineer from a moderate-sized city on the west coast. For this project, an initial 

on-line survey instrument was created to obtain initial panel member ratings of the various work 

zone performance measures and measurement categories, and to provide any thoughts on 

additional measures or categories that could prove useful to agencies and other practitioners for 

evaluating and monitoring work zone safety and mobility impacts. The survey is included as 

Appendix A. Each participant was asked to consider each proposed performance measure in 

terms of its importance or value they would place upon it (assuming the measure accurately 

reflected the data upon which it was calculated). Panelists rated each measure on a scale from 

1 to 5, with a “1” indicating that they considered the measure to be of low importance, and a 

“5” indicating that they considered the measure to be of high importance. Any comments they 

wished to offer in terms of their thought processes were welcomed as well.

Once the initial ratings were obtained, researchers computed the median rating value, and both 

the 25th and 75th percentile rating values submitted. The median (50th percentile) value was 

where one-half (5) of the panelists rated the measure higher, and one-half rated it lower. The 

25th and 75th percentile rating values were calculated in a similar manner. If all panelists gave 

the measure the same rating value, the 25th, median, and 75th percentile value would be the same. 

All ratings that fell in this range were judged to indicate general agreement with the median 

rating value, whereas values outside of that range were considered to not be in general 

agreement. A second iteration of the survey was then developed that was tailored to each 

individual panel member. The panelist received a reminder of their initial rating value for that 

measure or category, the median value given by the overall panel, and a notice if their rating did 

not fall within the inter-quartile range for the group (i.e., the value was an outlier). The panel 

member then decided if they wanted to change their rating value to something within the interquartile range, or keep the rating outside the range and provide a justification for their rating 

value (i.e., why they rated the measure or category higher or lower than the group). This was 
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For Work Zone Performance Measures

repeated a third time so that everyone could see the justifications provided and offered a chance 

to change their ratings as well. 

Overall, the process worked extremely well, with the median rating values stabilizing after the 

second iteration, and the inter-quartile ranges for almost all of the measures and categories 

decreasing dramatically between the first and second iterations. Only a few final changes 

occurred during the third iteration of the survey, which had no effect on the median values and 

small reduction in inter-quartile ratings for a few of the measures and categories. 

U.S. Department of Transportation

Federal Highway Administration

Office of Operations (HOP)

1200 New Jersey Avenue, SE

Washington, DC 20590

FHWA-HOP-13-011

March 2013