Arterial Data Collection 

Technology

April 2013

Travel Time on Arterials and Rural Highways: 

 State-of-the-Practice Synthesis on 

Arterial Data Collection Technology
Notice

This document is disseminated under the sponsorship of the 

U.S. Department of Transportation in the interest of information exchange. 

The United States Government assumes no liability for use of the information

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

specification, or regulation.

The United States Government does not endorse products or manufacturers. 

Trademarks or manufacturers’ names appear herein only because they are 

considered essential to the objective of this document.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high quality information 

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

understanding. Standards and policies are used to ensure and maximize the quality, 

objectivity, utility, and integrity of its information. FHWA periodically reviews 

quality issues and adjusts its programs and processes to ensure continuous

quality improvement.
Technical Report Documentation Page

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

FHWA-HOP-13-028

 4. Title and Subtitle 5. Report Date

Travel Time on Arterials and Rural Highways:

State-of-the-Practice Synthesis on Arterial Data Collection Technology

April 2013

 6. Performing Organization Code

 7. Authors 8. Performing Organization Report No.

Jeremiah Singer (Westat), A. Emanuel Robinson (Westat), 

Jessica Krueger (Westat), Jennifer E. Atkinson (SAIC), Matthew C. Myers (SAIC)

 9. Performing Organization Name and Address 10. Work Unit No. (TRAIS)

Westat

1600 Research Blvd.

Rockville, MD 20852

SAIC

1710 SAIC Dr.

McLean, VA 22102

11. Contract or Grant No.

12. Sponsoring Agency Name and Address 13. Type of Report and Period Covered

U.S. Department of Transportation

Federal Highway Administration

1200 New Jersey Ave. S.E.

Washington, D.C. 20590

Synthesis Report

August 27, 2012 – April 30, 2013

14. Sponsoring Agency Code

HOTM-1, FHWA

15. Supplementary Notes

Government Task Monitor (GTM): Jimmy Chu

16. Abstract

Travel time to a destination is a key piece of information that motorists want and need, and is vital for good decision-making by travelers. 

Technology now makes it feasible to provide drivers with real-time information about how long it takes to reach a given destination. The collection 

of travel time data is a challenging problem that deserves a systematic review. The purpose of this project was to identify, review, and synthesize 

information on current and potential future efforts in real-time travel time on arterials. The current report focuses on arterial highway travel time 

data technology considerations and is not a primer for general travel time best practices. Also, a companion report on rural travel time data collection 

technology can be found in Singer, Robinson, Krueger, Atkinson, & Myers (2013). The core of the report discusses available and emerging arterial 

travel time (ATT) data sources as well as implementation considerations, advantages, and limitations of each. These technologies researched include 

Bluetooth detectors, toll tag readers, in-pavement magnetic detectors, automatic license plate readers (ALPR), machine vision, connected vehicle, 

radar/microwave/LIDAR, inductive loops, crowdsourcing, and cell phone signal monitoring. Several implementations of ATT data collection are 

also discussed. In addition, two case studies are reviewed in detail (Chandler, AZ and St. Louis, MO). The report then emphasizes key lessons 

learned based on questions for a practitioner to consider at each step of the planning, implementation, and management process. Although ATT data 

collection is a relatively new and rapidly evolving area, ATT can be successfully implemented when a project is properly planned and executed. 

Successful implementers have carefully considered project objectives and have provided detailed implementation plans. Regardless of the latest 

specific data collection technology, asking the right questions is paramount, beginning with planning, continuing to selection, and culminating with 

execution and evaluation. Practitioners who focus on asking the right questions and heed lessons learned by colleagues will greatly increase the 

chances of a successful implementation.

17. Key Words 18. Distribution Statement

Intelligent Transportation System (ITS), real-time travel time, arterial travel time (ATT), travel 

time data collection technology, Advanced Traveler Information System (ATIS), Transportation 

Management Center (TMC)

No restrictions

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No of Pages 22. Price

Unclassified Unclassified 60 N/A

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.
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Arterial Data Collection Technology Federal Highway Administration

Table of Contents

Executive Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Background and Objectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Organization of Synthesis Report. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Data Source Summaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1 Bluetooth Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Toll Tag Reader. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3 In-Pavement Magnetic Detectors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.4 Automatic License Plate Readers (ALPR). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.5 Machine Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.6 Connected Vehicle. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.7 Radar, Microwave, LIDAR (RML). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.8 Inductive Loops. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.9 Crowdsourcing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.10 Cell Phone Signal Monitoring. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3 Implementations of Arterial Travel Time Data Collection. . . . . . . . . . . . . . . . . . . 22

3.1 Case Study: Chandler, AZ. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.1.1 Background and Planning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.1.2 Implementation and Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.1.3 Lessons learned. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.2 Case Study: St. Louis, MO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.2.1 Background and Planning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.2.2 Implementation and Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.2.3 Lessons Learned. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.3 I-95 Corridor Coalition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.4 Utah County, UT, I-15. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.5 Houston, TX, Multiple Routes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.6 Flagstaff, AZ, US-180. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.7 Atlanta area, GA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.8 Essex County, United Kingdom. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4 Best Practices for Arterial Travel Time Data Collection. . . . . . . . . . . . . . . . . . . . 36

4.1 Needs Assessment, Planning, and Specifications Development. . . . . . . . . . . . 36

4.2 Selecting and Acquiring Data Collection Technology. . . . . . . . . . . . . . . . . . . . . . . . 39

4.3 Implementation, Management, and Evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
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Arterial Data Collection Technology Federal Highway Administration

List of Figures

Figure 1 ATT Signs in Chandler, Arizona. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Figure 2 TrafficCast’s BlueTOAD Pole-mounted, Solar-powered Bluetooth Detector and Cabinet Interior . . . . . . . . . . . . . 6

Figure 3 Toll Tag Reader (AVI) in Houston, TX. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Figure 4 Sensys Sensor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Figure 5 Typical Sensys Implementation Showing Sensor Array, Repeater, and Access Point. . . . . . . . . . . . . . . . . . . . . . . . . 12

Figure 6 ALPR Illumination (Foreground) and Camera (Background) Used in a Work Zone 

On Arizona State Route 68. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Figure 7 Bluetooth Detector on Light Pole that Taps into a Pedestrian Signal Head Using Power over Ethernet . . . . . 23

Figure 8 ATT Signs in Chandler, AZ. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Figure 9 Locations of DMS and Advised Routes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Figure 10 Arterial-based Display on Approach to I-55. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Figure 11 Arterial-based Display on Route 94. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Figure 12 Freeway and Arterial Coverage (as built) in Philadelphia, PA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Figure 13 Both Phases of a Trailblazer Sign. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Figure 14 Houston TranStar Arterial Traffic Map (detail). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Figure 15 ATT Detection Coverage (Shown as Color-coded Arterials) in Cobb County, Georgia: 

US-41/Cobb Pkwy, Chastain Road, Bells Ferry Road, and Barrett Pkwy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Figure 16 ATT Coverage (Shown as Green-colored Arterials) in Essex County, UK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

List of Tables

Table 1 Candidate ATT Technologies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

Table 2 Table of Search Terms and Categories. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Table 3 Candidate ATT Technologies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Table 4 Vehicle Probe Project Current Contracted Coverage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
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Arterial Data Collection Technology Federal Highway Administration

Acknowledgements

The research reported herein was performed under the Federal Highway Administration’s TMC Pooled Fund

Study under contract DTFH61-06-D-00007 by Westat and SAIC. SAIC was the prime contractor, with Westat

having the technical lead. Dr. Emanuel Robinson, Senior Research Scientist at Westat, was the Project Director

and Principal Investigator. The other authors of this report are Jeremiah Singer and Jessica Krueger, both

Research Associates at Westat, and Jennifer Atkinson and Matthew Myers, both Transportation Engineers at

SAIC. Mrs. Atkinson also served as overall Task Manager. We would like to give special thanks to Lisa Bedsole

who served as the Technical Editor, provided helpful comments throughout, and ensured 508 compliance of

the final reports. In addition, we would like to thank Rick Huey, Senior Research Engineer at Westat, for helpful

comments on the final reports. We would also like to thank Westat’s Graphics Services, especially Brandy Gruner

and Kenneth Von Alt, for producing the final layouts and formatting of the reports.

The authors acknowledge Jimmy Chu, who served

as the Federal Highway Administration Government

Task Monitor for this project, as well as the

Transportation Management Center Pooled Fund

Study panel members who provided advice and

review throughout the project:

Elizabeth Birriel

Florida Department of Transportation

Mark Demidovich

Georgia Department of Transportation

Jeff Galas

Illinois Department of Transportation

Mike Jenkinson

California Department of Transportation

Jim McGee

Nebraska Department of Transportation

Cathy McGhee

Virginia Department of Transportation

Chris Quesnell

Wisconsin Department of Transportation

Jason Sims

Missouri Department of Transportation

The authors also thank the experts and

practitioners who contributed to the case studies,

review of practice, and review of technologies:

Jeff Jenq

OZ Engineering

Ben McCawley

City of Chandler

Ben McKeever

Federal Highway Administration

Donna Nelson

Traffax Inc.

Gregory Owens

Missouri Department of Transportation

Edward Smaglik

Northern Arizona University
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Arterial Data Collection Technology Federal Highway Administration

List of Acronyms

ALPR Automatic License Plate Reader

AVI Automatic Vehicle Identification

ATMS Advanced Traffic Management System

ATT Arterial Travel Time

CCTV Closed-Circuit Television

CHATTS Chandler Automated Travel Time System

DMS Dynamic Message Signs

DOT Department of Transportation

DSRC Dedicated Short Range Communications

FHWA Federal Highway Administration

FMPO. .Flagstaff Metropolitan Planning

Organization

GDOT Georgia Department of Transportation

GPS Global Positioning System

IRIS Intelligent Roadway Information System

ITS Intelligent Transportation System

LIDAR Light Detection and Ranging

MAC Media Access Control

MCDOT. .Maricopa County Department

of Transportation

MoDOT Missouri Department of Transportation

RADS Regional Archived Data System

RFID Radio Frequency Identification

RFP Request for Proposal

RFQ Request for Quotation

RML Radar, Microwave, and LIDAR

TCC Traffic Control Center

TMC Transportation Management Center

V2I Vehicle-to-Infrastructure

V2V Vehicle-to-Vehicle
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Arterial Data Collection Technology Federal Highway Administration

Executive Summary

Travel time to a destination is a key piece of

information that motorists want and need. It is vital

for travelers to make good decisions about which

route to take and whether to divert from their

planned path. Technology now makes it feasible to

provide drivers with real-time information about

how long it will take to reach a given destination. 

While travel time information has traditionally been

provided by transportation agencies only on major

urban freeways, travel time messages are now being

communicated on arterial roadways.

The collection of travel time data and proper

dissemination is a challenging problem that deserves

a systematic review. The purpose of this project was

to identify, review, and synthesize information on

current and potential future efforts in real-time travel

time on arterials. 

There were four main objectives:

• Identifying, reviewing, and synthesizing available

and emerging technology (both nationally and

internationally) for obtaining data necessary for

calculating travel times on arterials,

• Collecting and summarizing agencies’ experiences

with using such technology,

• Providing guidance to agencies for making the

best use of available and emerging technologies

to meet future needs,

• Determining the feasibility of deploying such

technologies. 

It should be noted that the current report focuses

on arterial travel time (ATT) data technology

considerations. It is not a primer for general travel

time best practices. A good source of general travel

time guidelines can be found in Turner, Eisele, Benz,

& Douglas, 1998. Also, a separate report on rural

travel time data collection technology can be found in

Singer, Robinson, Krueger, Atkinson, & Myers (2013).

A more recent set of guidelines has been developed

based on the experiences of the I-95 Corridor Coalition

Vehicle Probe Project (University of Maryland Center

for Advanced Transportation Technology, 2011).

The Transportation Management Center (TMC)

Pooled Fund Study recognized the need to collect

travel time data on arterial roads, knowing that it

must first be determined if technologies are being

developed to obtain data necessary for calculating

travel times that address specific challenges. Due

to the challenges inherent in this environment and a

limited history of implementations, there was a need

to provide transportation agencies with information

that will help them to implement such systems in a

practical and cost-effective way. There are many

challenges and benefits in collecting and distributing

travel times on arterial highways.

For example:

• Travel times are not collected in isolation and

often their use are determined by the local goals

and communication needs—and these can be quite

different for and between arterials.

• Arterial roadways can vary greatly in their

location and use.

• Arterials present unique issues such as signal

timing disruptions and dropping out of the route

at various points such as parking lots, side

streets, alternate routes, etc.

• Urban arterials may be part of a dense and

complex roadway network that creates challenges

in distinguishing between “signal” and “noise”

(i.e., detected motorists/vehicles that should be

used to calculate travel times versus those that

should not).
vii

Arterial Data Collection Technology Federal Highway Administration Table 1 Candidate ATT Technologies Technology Spot Speed Segment Travel Time Real-time Tracking Sensor Location Coverage Per Sensor % Vehicles Detected/ Matched† Implementation Cost† Non- traffic-info Functions† Traffic Volumes Vehicle Class Bluetooth detection X Roadside/ above road All lanes Low Low Low Toll tag reader X Roadside/ above road One or more lanes** Low Med Med In-pavement magnetic detectors X In pavement One lane High Med Low X X Automatic license plate reader (ALPR) X Roadside/ above road One or more lanes** Med Med Med X X Machine vision X X* Roadside/ above road One or more lanes** High Med High X X Connected vehicle X X* Roadside/ above road & in vehicle All lanes ??? Low High Radar, microwave, LIDAR X Roadside/ above road One or more lanes** High Med Med X Inductive loops X X* In pavement One lane High Low Low X X* Crowdsourcing X None All lanes ??? Low Low Cell phone signal monitoring X None All lanes ??? Low Low

* Possible depending upon capabilities of technology

** Multiple lanes possible depending upon capabilities of technology and sensor placement

† Can vary substantially depending on a variety of factors; estimates are approximate based on user experience‡ Other functions can include tolling, traffic law enforcement, unregistered vehicle detection, cooperative safety, etc.

??? Unknown/no basis for assessment
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Arterial Data Collection Technology Federal Highway Administration

To further hone the opportunity of providing useful

and accurate travel time information in arterial

locations, it is important to ask the following

questions:

• What insights and experiences have agencies

developed with these technologies, and what are

the best uses of these technologies?

• Given challenges faced in calculating and providing

travel time information on arterial highways, how

feasible is deploying such technology?

The core of the report discusses available and

emerging ATT data sources as well as implementation

considerations, advantages, and limitations on each. 

The key highlights of each follow:

Bluetooth Detection

Wireless technology that allows electronic

devices to communicate directly with one

another; recently emerged as viable ATT

collection tech; open standard, allows for

off-the-shelf equipment; detection range

limited to about 328 feet (100 meters);

less expensive than many other options;

flexible; some potential privacy concerns;

detection technology relies on drivers’ use

of Bluetooth-enabled devices.

Toll Tag Reader

Detect radio frequency ID of automated

toll tags, mature technology, inconspicuous,

detection accuracy can decrease with

distance, limited to areas with adequate

toll tag fleet penetration, some potential

privacy concerns, electronic tolling becoming

increasingly common.

In-pavement Magnetic Detectors

Arrays of magnetometers installed in

pavement, can identify and match vehicles

based on each vehicle’s unique magnetic

signature, quick installation and self-calibrating,

wireless sensors require access points and

possibly repeaters, high vehicle detection

rate, device life span of about 10 years, no

privacy concerns.

Automatic License Plate Readers

Optical cameras capture images of license

plates and software “reads” the information;

mature technology (over 30 years); installed

above the roadway and requires direct lineof-sight; particularly sensitive to factors that

reduce visibility; privacy issues are a concern.

Machine Vision

Use of video cameras to monitor flow; installed

above the roadway or on poles on the roadside;

data bandwidth is a consideration; highly

customizable set of features; privacy can be a

concern for high-resolution systems; potential

uses are likely to expand with advances in

technology, processing power, and data

transmission capabilities.

Connected Vehicle

Short range radio communications between

vehicles and vehicles to infrastructure,

technology is in very early stages of

development, radio transceiver installed

in host device within a vehicle, privacy

protocols are being established, very

inexpensive cost on a per unit basis,

usefulness for travel time calculations

uncertain, depends on implementation

factors, potential for widespread use if

initiative continues to develops.

Radar, Microwave, and LIDAR

A sensor emits radio waves (radar), microwaves,

or a laser beam (LIDAR), which reflects off of

vehicles; mature and widely used technology,

many products available with a variety of

different implementation approaches, complete

privacy to drivers.

Inductive Loops

Magnetic loops in pavement detect vehicle

presence, and multiple loops can be used to

calculate travel times; mature and widely used

technology, though use for ATT data collection

is rare; high detection rate; very inexpensive,

but invasive installation and maintenance can

increase costs; complete privacy to drivers.
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Arterial Data Collection Technology Federal Highway Administration

Crowdsourcing

Drivers’ vehicles or mobile devices provide

information to a public or private entity,

and that information is used to generate

traffic/travel time, early stage technology,

critical mass of users are necessary for

success, vehicle/motorist must have device

capable of transmitting information, no

roadway infrastructure needed, privacy

issues are minimal or non-existent when data

transmitted to agencies who purchase data,

use likely to increase.

Cell Phone Signal Monitoring

Cell phone location information is automatically

and anonymously downloaded from cellular

network switching centers in real-time;

relatively mature technology and cell phone

use is almost ubiquitous; data provided by

vendors, and data are anonymous when

provided to agencies; shows adequately precise

measurements of travel time.

Several implementations of ATT data collection are

also discussed: a) I-95 Corridor Coalition; b) Utah

County, UT, I-15; c) multiple routes in Houston; TX, d)

Flagstaff, AZ, US 180; e) Atlanta area, GA; f) Essex

County, United Kingdom.

In addition, two case studies are reviewed in detail

and lessons learned from the implementations are

summarized:

Chandler, AZ

• There are advantages to using open-source

software

• Conduct preliminary tests

• Weigh options across multiple vendors

• Bluetooth detection-based travel time is an

inexpensive way to add valuable information to

existing dynamic message signs (DMS)

• Limit exposure to system failures

• Consider additional data uses

• Eliminate barriers to data sharing

St. Louis, MO

• Deploy system slowly

• Verify travel time data using other methods

• Keep accurate implementation and asset

management records

• Seamless integration between agencies is

beneficial

• Involve public in issues relevant to them

The report synthesizes the prior information and

brings together the state-of-the-art in ATT data

collection technologies and the state-of-the-practice

in ATT implementations to develop a set of best

practices that are based on systematic evaluation

(where possible) and real-world experiences. 

The best practices related to the data collection

technology only; a complete set of best practices

for ATT programs is beyond the scope of this effort. 

These were developed with the understanding that

every implementation of ATT involves a unique

set of objectives, challenges, constraints, and

environments. Therefore, rather than providing

prescriptive guidance, this chapter emphasizes the

key considerations at each step of the planning,

implementation, and management process. 

One of the most important lessons learned by

ATT program implementers is the importance

of asking the right questions during the planning

and implementation stages. Therefore, each

key consideration is phrased as a question and

is followed by discussion of related issues.
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Arterial Data Collection Technology Federal Highway Administration

These questions are focused on the following

general areas:

Needs Assessment, Planning, 

and Specifications Development

• What are the ultimate outcomes desired?

• What are the funding and scheduling constraints?

• What is the desired ATT coverage area?

• What are the needs for scalability and mobility?

• Are real-time data required?

• What secondary benefits can be achieved?

• What are the requirements for data accuracy

and timeliness?

• What partnerships are beneficial and necessary?

• What are the infrastructure requirements?

• Are data needed during low volume times?

Selecting and Acquiring Data Collection 

Technology

• What software, hardware, and other architectural

requirements exist?

• What are the initial and ongoing costs of each

technology?

• Should the technology be purchased or rented?

• How long is the data path?

• What system features can be automated?

• How will data security and privacy be protected?

• How can preliminary data collection technology

testing be conducted?

• How should a vendor be solicited?

• How much ongoing support is offered by

the vendor?

• What is the division of responsibilities and rights?

• Who owns the data?

Implementation, Management, and Evaluation

• How can sensor locations be selected?

• What technology documentation is available?

• How should the program operate when

missing data?

• How should field equipment be monitored

and maintained?

• How can data quality be verified?

• How should public and media relations

be handled?

• How can the effectiveness of the program

be evaluated?

Although ATT data collection is a relatively new

and rapidly evolving area, ATT can be successfully

implemented when a project is properly planned and

executed. The importance of proper planning cannot

be overstated. Successful implementers have carefully

considered project objectives and have provided

detailed implementation plans. Regardless of the latest

specific data collection technology released, asking the

right questions is paramount, beginning with planning,

continuing to the selection stage, and culminating with

execution and evaluation. Practitioners who focus on

asking the right questions and heed the lessons learned

by colleagues will greatly increase the chances of a

successful implementation.
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Arterial Data Collection Technology Federal Highway Administration

Figure 1

1 Introduction

1. 1 Background and Objectives

Travel time to a destination is a key piece of

information that motorists want and need. It is vital in

travelers making good decisions about which route to

take and whether to divert from their planned path. If

motorists were to be provided travel time information

on arterial highways, they may plan their trips

accordingly with this new information, decreasing

delays and the potential for congestion downstream. 

They may also be warned in advance of an incident,

allowing sufficient time to choose an alternate route

around congestion and delays.

Technology now makes it feasible to provide drivers

with real-time information about how long it will take

to reach a given destination. Many jurisdictions within

the United States collect information on freeways and

that information is generally provided to travelers

via DMS along freeways. In contrast, cases where the

information is collected and displayed on non-freeway

roads such as arterials are relatively rare. Figure 1

shows examples of typical arterial practice.

Travel time is also a key piece of information for

transportation agencies. Real-time travel time

information can allow agencies to monitor roadway

performance, identify problems, develop forecasts,

plan future projects, and evaluate the effects of new

projects. Travel time data can also help to meet

goals for integrated corridor management or meet

Federal information provision mandates such as the

Real-Time System Management Information Program,

which was included in Section 1201 of the Safe, 

Accountable, Flexible, Efficient Transportation 

Equity Act: A Legacy for Users (SAFETEA-LU).

Current use of travel times for arterials is still limited. 

However, interest is high and examples of successful

implementations are becoming increasingly common. 

Researchers are investigating a variety of data

collection methods that can be applied to arterial

settings, such as the use of Bluetooth dectection

technology (e.g., Hardigree, 2011; Puckett & Vickich,

2010), toll tag readers (e.g., Wright and Dahlgren,

2001), automatic license plate readers (e.g., Eberline,

2008), in-pavement magnetic detection (e.g., Klein,

Mills, & Gibson, 2006), machine vision, radar/

microwave/LIDAR (e.g., Jeng, 2010), crowdsourcing

(e.g., INRIX, 2012), connected vehicle, cell phone

signal monitoring (e.g., Avni, 2007), and inductive

loop detectors (e.g., Blokpoel, 2009). As this list

ATT Signs in Chandler, Arizona

Source: City of Chandler & Oz Engineering, 2011 Source: Video capture from Wimmer, 2010 on ksl.com
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Arterial Data Collection Technology Federal Highway Administration

demonstrates, there is a litany of data collection

technologies which have been used for ATT, but each

has its own advantages and disadvantages. At this

point there is no comprehensive guidance on data

collection technologies and procedures.

There are numerous reviews and syntheses of travel

time data collection in general. But, travel time data

collection and dissemination for arterial highways has

unique challenges for the practitioner.

For example:

• Travel times are not collected in isolation, and

often the uses are determined by the goals and

communication needs at a location—and these can

be quite different for and between arterials.

• Arterial roadways can vary greatly in their

location and use: urban arterials often parallel

major freeways in more densely populated areas

while rural arterials often act as major alternates

through less densely populated areas.

• Although the access to technologies may be

similar to freeways, arterials present unique

issues such as signal timing disruptions, dropping

out of the route at various points such as parking

lots, side streets, etc.

The TMC Pooled Fund Study has recognized the

need to determine if technologies are being developed

to obtain data necessary for calculating travel times

that address specific challenges. State and local

agencies face the challenge of providing real-time

travel time to motorists–which entails obtaining

information on arterials–in a manner that allows

drivers to take full advantage of it. 

To further hone the opportunity of providing useful and

accurate travel time information in arterial locations,

it is important to ask the following questions:

1 . .What insights and experiences have agencies

developed with these technologies, and what are

the best uses of these technologies?

2 . .Given challenges faced in calculating and providing

travel time information on arterial highways, how

feasible is deploying such technology?

The purpose of this project and the resulting report

was to identify, review, and synthesize information

on current and potential future efforts in realtime travel time on arterials. There were four main

objectives: a) identifying, reviewing, and synthesizing

available and emerging technology (both nationally

and internationally) for obtaining data necessary

for calculating travel times on arterials, b) collecting

and summarizing agencies’ experiences with using

such technology, c) providing guidance to agencies

for making the best use of available and emerging

technologies to meet future needs, and d) determining

the feasibility of deploying such technologies.

The first objective (reviewing) is dealt with

while being mindful of the ever-changing nature of

recent technological advances. Unlike synthesis

reports in non-technological domains that focus

on research publications, many of the sources for

this report were from vendors, State agencies, and

practitioners who are the most up-to-date on the

rapidly changing technological developments and

implementation approaches.

The second objective (experiences) is addressed

by incorporating lessons learned and advice

(including from unsuccessful projects) from agencies’

experiences using a given technological approach. 

These lessons give extremely helpful insights that

can be provided to the practitioner and allow the

current synthesis to go beyond a simple summary

of documents.

The third objective (guidance) is based on

a synthesis of the first two, taking information

gained from reviewing technologies and merging it

with real-world experiences of practitioners. This

led to the development of lists of considerations in

the form of questions (and high level guidance in

response) that a practitioner should use when going

through the phases of assessing, planning, selecting,

acquiring, implementing, managing, and evaluating an

ATT system.

The final objective (feasibility assessment) was

not a formal financial feasibility analysis. Instead,

feasibility is taken in a broader context and refers to

environmental constraints that a practitioner should

take into consideration when weighing what type of

system to implement. Financial information is given

where available, but only in the context of background

information to use when evaluating the entire

practicality of an implementation approach.
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Arterial Data Collection Technology Federal Highway Administration

The primary audience for this report is

transportation agencies who are either interested

in implementing an ATT data collection system, or

considering making changes to an existing system. 

It is important to obtain, synthesize, and distribute

information now so that objectively based

recommendations can be provided to practitioners

as they design and implement such systems.

It should be noted that the current report focuses

on ATT data technology considerations and is not a

primer for general travel time best practices. A good

source of general travel time guidelines can be found in

Turner, Eisele, Benz, & Douglas (1998). This report also

focuses on travel time data collection methods that

use vehicle speeds or link travel times as data sources. 

Efforts to estimate travel times using other data

sources (e.g., traffic volumes) such as the Minnesota

Arterial Travel Time Project (Athey Creek Consultants,

2009) are not addressed. In addition, there is a

separate report from this project that focuses on travel

time data collection technology used for rural highways

“(Singer, Robinson, Krueger, Atkinson, & Myers, 2013).

1.2 Methodology

The information search for this project involved

two main components: the review of data collection

technology and the review of practice. The search

effort began with an organized set of keyword

searches. Five search categories were created to

encompass the key project dimensions. Using these

categories, a list of search terms was compiled within

each category (see Table 2).

As an example, the search terms “Bluetooth” and

“GPS” were both placed in the Specific Technologies

category. The table below shows the initial set of search

terms used within each category. Additional search

terms were added for follow-up searches. Note that an

asterisk represents a “wildcard” character.

The keyword search effort revealed a substantial

body of literature on data collection technologies and

numerous evaluations of ATT systems, often comparing

novel ATT data collection approaches against other

data collection approaches or ground truth data. 

The search effort then expanded to include targeted

searches to explore the state of the art technologies

and practices used for ATT. This search revealed

a rapidly expanding world of data collection

technologies and practices.

Finally, contacts with experts and implementers were

made to gain a clearer understanding of current and

emerging practice and to acquire additional details and

direct experience reports. Two general approaches

were used: 1) contacts with heads of committees and

professional organizations, and 2) contacts made

directly with travel time system implementers to learn

about relevant implementation details. Individuals and

organizations were selected for contact based on

knowledge gaps that they were expected to be able

to fill and their involvement in travel time programs

of interest. Contacts with implementers provided a

basis for implementation summaries and case studies

with emphasis on explaining project logistics, decision

making processes, and lessons learned. Organizational

contacts provided little new information about current

practice; many implementations of ATT have not been

widely publicized and do not appear to be widely known

among transportation engineers.

1.3 Organization of Synthesis Report

The following chapters of this report are organized

around three main topics of interest to practitioners:

Chapter 2 discusses available and emerging

ATT data sources as well as implementation

considerations, advantages, and limitations of

each.

Chapter 3 provides two detailed case studies

and additional brief summaries of select

United States and international practices in

ATT data collection. These summaries reflect

a broad range of implementation objectives,

strategies, technologies, and constraints.

Chapter 4 brings together what we know

about data sources, technologies, and

current implementations to develop a set of

best practices that are based on systematic

evaluation, where possible, and real-world

experiences. Rather than prescriptive guidance,

this chapter emphasizes identifying options

and practices that can be adapted to the needs

of a particular situation. It is framed around

questions that a practitioner can ask as he or

she goes through the stages of developing and

implementing an ATT data collection system.
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Arterial Data Collection Technology Federal Highway Administration

Table 2 Table of Search Terms and Categories

Core Concept Data and Technology Location Specific 

Technologies Supplementary 

Travel time Data/data collection Arterial RFID Network

Journey time Monitor* Off-freeway Connected vehicle Guidelines

Traveler 

information Real-time En-route Video/camera Best practices

Calculat* Freeway approach Satellite Operations

Estimat* On-ramp License plate 

reader Implement(ation)

Technology Surface street Bluetooth Traffic

Infrastructure Remote GPS Integrated corridor 

management

Communicat* Stand-alone Cellular/cell phone Accuracy

Instrument(ation) In-pavement/loop 

detect* Cost

Hardware Radar

Intelligent Transportation 

System (ITS) LIDAR

Software

Anonymous 

Wireless Address 

Matching/AWAM

Install

Collect

Wireless

* Indicates wildcard character
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Arterial Data Collection Technology Federal Highway Administration

2 Data Source Summaries

A variety of technologies and data sources can be

used as the basis for travel time calculations. This

chapter summarizes technologies that can be used to

capture ATT data. Each of the following technologies

will be discussed in separate sections:

• Bluetooth detection

• Toll tag reader

• In-pavement magnetic detectors

• Automatic license plate reader (ALPR)

• Machine vision

• Connected vehicle

• Radar, microwave, LIDAR (RML)

• Inductive loops

• Crowdsourcing

• Cell phone signal monitoring

Some of these technologies are mature and widely

used, while others have emerged recently or are

still in the early stages of development. While the

set of technology options is largely the same for

urban freeways, arterials, and rural roads, not all

technologies are equally suited to all road types,

environments, and topographies. This review of

technologies is focused on the implementation

considerations for arterial roads. In addition to

documenting information about the technologies

themselves, the review effort addresses the

integration of these technologies with existing

systems and infrastructure.

This chapter summarizes technologies that can

be used to capture ATT data. The summaries

emphasize the tangible aspects of the data sources

(e.g., hardware and installation requirements,

implementation considerations, costs), but also

address capabilities and disadvantages of each

technology, potential privacy concerns, and other less

tangible issues. Table 3 briefly summarizes candidate

technologies, and then each technology is discussed

further in its own subsection.

Data transmission and processing are important

aspects of ATT implementations, but are not

addressed in detail in this section because they are

largely independent of data source. Similarly, although

data sources are described individually in this section,

an important trend in travel time monitoring is data

integration, or “data fusion,” which allows agencies

to harness data from multiple sources to improve

the quality, reliability, and comprehensiveness of

information. Data transmission, processing, and use

are discussed further in Chapter 4.

2.1 Bluetooth Detection

At a Glance

• Wireless technology that allows electronic

devices to communicate directly with one another

• Recently emerged as viable travel time collection

technology

• Open standard, allows for off-the-shelf equipment

• Detection range limited to about 328 feet

(100 meters)

• Less expensive than many other options

• Flexible installation

• Some potential privacy concerns

• Detection technology relies on drivers’ use of

Bluetooth-enabled devices

How It Works

Bluetooth is a non-proprietary wireless technology

standard that allows electronic devices to

communicate directly with one another over

relatively short distances using radio frequency

communication. Since its development in the 1990s,

Bluetooth has become a ubiquitous feature on a

variety of electronic devices, including mobile phones,

computers, hands-free headsets, and even vehicles

themselves. Bluetooth detection systems work by

actively searching for in-range Bluetooth devices and

capturing the unique media access control (MAC)

address of each device. For a Bluetooth detection

system to read the MAC address of a device, the

device must be turned on and be in “discoverable”

mode (i.e., broadcasting its MAC address). Because

each device has a unique and permanent MAC

address, Bluetooth detection systems can determine
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Arterial Data Collection Technology Federal Highway Administration

vehicle travel times and speeds by calculating the

time it takes for vehicles containing Bluetooth devices

to travel between two or more Bluetooth sensors that

are a known distance apart.

State of the Technology

Although the Bluetooth standard is nearly 20 years

old, Bluetooth detection for travel time data collection

has only emerged as a viable option in recent years,

largely due to the rapid growth in the adoption of

Bluetooth-enabled devices. Despite its relatively

recent emergence, Bluetooth detection is among the

most commonly used technologies for calculating ATT.

Hardware and Installation

Bluetooth detection offers a wide range of hardware

and installation options. Because Bluetooth is an

open standard, it is possible to assemble detector

systems using off-the-shelf components, though

complete systems can also be purchased or leased

from vendors. An example of a solar-powered,

pole-mounted detector is shown in Figure 2. 

Detector units can be placed in any location near the

roadway as long as there are no major line-of-sight

obstructions. Reliable detection distance can reach

up to 328 feet (100 meters), allowing for flexibility

in placement and the opportunity to detect two-way

traffic on broad arterial roads. Detection units can

be pole-mounted, fastened to existing infrastructure,

installed in a signal cabinet, or, if powered by battery,

briefcase-size units can be placed in any desired

location (preferably chained to roadside hardware for

security). However, detection rates are likely to be

higher when sensors are elevated rather than placed

on the ground (Puckett & Vickich, 2010). Fixed units

can be powered by existing power infrastructure

or solar panels, and battery-powered units can

typically operate between two to three weeks on a

single charge. Data can be logged through existing

communications infrastructure or recorded to local

hard drives. When given protection from precipitation

and extreme temperatures, device maintenance

requirements and failure rates can be very low.

Implementation Considerations

Bluetooth detection sensors detect any

“discoverable” Bluetooth-enabled devices within

a radius of about 328 feet (100 meters), if using

a high-powered antenna. Detection range can be

reduced by using a lower gain antenna. Measuring

the travel time of an individual vehicle along a road

segment is as simple as comparing the time when the

vehicle is detected at the beginning of the segment

to the time when the vehicle is detected at the end

of the segment. Dividing the length of the segment

by the vehicle’s travel time yields an average speed

for the segment. Additional data can be acquired by

observing the duration that a vehicle is within the

detection zone of a single Bluetooth sensor (e.g., a

sensor near a major intersection could potentially

provide data on signal queue). Some Bluetooth

detection devices include global positioning systems

(GPS), which allow each device to be precisely located

Source: trafficcast.com

Figure 2

TrafficCast’s BlueTOAD Polemounted, Solar-powered Bluetooth 

Detector and Cabinet Interior
7

Arterial Data Collection Technology Federal Highway Administration Table 3 Candidate ATT Technologies Technology Spot Speed Segment Travel Time Real-time Tracking Sensor Location Coverage Per Sensor % Vehicles Detected/ Matched† Implementation Cost† Non- traffic-info Functions† Traffic Volumes Vehicle Class Bluetooth detection X Roadside/ above road All lanes Low Low Low Toll tag reader X Roadside/ above road One or more lanes** Low Med Med In-pavement magnetic detectors X In pavement One lane High Med Low X X Automatic license plate reader (ALPR) X Roadside/ above road One or more lanes** Med Med Med X X Machine vision X X* Roadside/ above road One or more lanes** High Med High X X Connected vehicle X X* Roadside/ above road & in vehicle All lanes ??? Low High Radar, microwave, LIDAR X Roadside/ above road One or more lanes** High Med Med X Inductive loops X X* In pavement One lane High Low Low X X* Crowdsourcing X None All lanes ??? Low Low Cell phone signal monitoring X None All lanes ??? Low Low

* Possible depending upon capabilities of technology

** Multiple lanes possible depending upon capabilities of technology and sensor placement

† Can vary substantially depending on a variety of factors; estimates are approximate based on user experience‡ Other functions can include tolling, traffic law enforcement, unregistered vehicle detection, cooperative safety, etc.

??? Unknown/no basis for assessment
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Arterial Data Collection Technology Federal Highway Administration

and provides automatic time synchronization between

devices, ensuring accurate travel time calculations

(see Quayle, Koonce, DePencier, & Bullock, 2010).

As with other travel time data collection technologies,

accurate travel time estimates depend not only

on accurate data collection, but also on sufficient

detection rates over time as well as the use of

algorithms capable of discarding outlier data. These

issues are addressed in detail in Section 4.3.

Another potential limitation of some versions of

Bluetooth detection is the system’s “inquiry time.”

The system initially deployed in Houston, TX (in

various locations west of the city), only reported

detected vehicles within a 10-second inquiry window,

and therefore all vehicles detected within this

window will report the same detection time. This data

measurement could lead to travel time inaccuracies,

especially over short segment lengths. Furthermore,

a maximum of eight detections could be reported

within each inquiry window. This has implications

for the potential to match vehicles at multiple

detector locations during heavy traffic periods

(Puckett & Vickich, 2010). Newer generations of

Bluetooth detection systems, however, are capable of

asynchronous input/output, which allows data to be

output as soon as it is read.

While proper placement should ensure the detection

of discoverable devices on the intended roadways, it is

possible that the non-directional sensors will also detect

devices on nearby roadways, parking lots, and other

surrounding areas. While data processing algorithms

can identify and remove many of the “noise” detections

from the dataset, it is best to place Bluetooth detectors

where unintended detections will be minimized. 

Unintended detections can also be reduced by using

a lower powered antenna, but care must be taken to

ensure that intended detections are not reduced as well.

Costs

Bluetooth detection systems are significantly less

expensive than most alternative travel time detection

technologies. Bluetooth detectors themselves are

inexpensive and the complementary hardware

(e.g., modem) is also inexpensive. Prices per location

vary depending upon the type of installation. For

instance, installation in an existing traffic signal

cabinet is likely to be substantially cheaper than

a solar-powered stand-alone implementation. 

Depending upon the hardware and installation

requirements, Bluetooth detection systems may cost

as little as $1,000 and $8,000 per location when

purchased from a vendor. Given the relative newness

of Bluetooth technology for traffic detection, limited

information exists on life span and maintenance costs,

though experience in Chandler, AZ suggests that the

devices can function for a period of years without

maintenance or adjustment.

Privacy Issues

Bluetooth detection systems work by reading the MAC

addresses of in-range Bluetooth-enabled devices, which

are unique and permanent identifiers linked to a single

device. MAC addresses are not linked to device sales

records, so there is no direct way to match a MAC

address with an individual owner or user. This affords

device users a layer of privacy. However, there are

indirect methods that could hypothetically be used to

match MAC addresses to individuals. For instance, if

an electronic device were seized by police as evidence,

the MAC address could be determined and matched

against Bluetooth detection records.

Similarly, users who download and use certain mobile

device applications may make personal information,

including their MAC addresses, known to the apps’

publishers, who could then potentially mine, share,

or sell this information. While these cases are

hypothetical, implementers should consider the

issues they raise, and the additional steps that can

be taken to further safeguard the public’s privacy. 

These measures include truncating the MAC address

so that only a portion of the address is used to

make the match (e.g., using only four of the address’

twelve digits), randomly reassigning a different

unique identifier, and/or deleting or randomizing MAC

addresses after their immediate use is complete. 

Strong data encryption should also be used an added

layer of security against unauthorized access.

Future Considerations

The future viability of Bluetooth detection systems

is dependent upon the continued prevalence of

Bluetooth-enabled devices. Although there are

currently no technologies on the horizon that

appear to be in a position to replace Bluetooth,

the generational life span of mobile technologies is

often short, and if a new technology does emerge to

replace or obviate Bluetooth detection, the market

penetration of Bluetooth may decline very quickly. 

If this does occur, existing Bluetooth detection
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Arterial Data Collection Technology Federal Highway Administration

systems will either need to be replaced or retrofitted

for compatibility with the new technology. For

example, WiFi could eventually become a dominant

in-vehicle technology and Bluetooth detectors could

be replaced or retrofitted to detect WiFi devices

rather than, or in addition to, Bluetooth devices. 

Nonetheless, Bluetooth appears poised to remain

dominant for the foreseeable future. Furthermore,

an increasing number of vehicle models are

manufactured with built-in Bluetooth devices.The

average life span of a vehicle is significantly longer

than the average life span of a portable electronic

device, so it is possible that the existence of a fleet of

Bluetooth-enabled vehicles will extend the viability of

Bluetooth detection even longer into the future.

2.2 Toll Tag Reader

At a Glance

• Detects radio frequency ID of automated toll tags

• Mature technology

• Accuracy can decrease with distance, but also

has directional advantage

• Limited to areas with adequate toll tag fleet

penetration

• Some privacy issues, so extra measures are

warranted

• Electronic tolling is becoming more common

How It Works

Toll tag readers, also known as automatic vehicle

identification (AVI) systems, detect the unique radio

frequency IDs of motorists’ automated toll tags (e.g.,

E-ZPass) at multiple locations and calculate travel times

based on the arrival time at each location. A vehicle

must have a toll tag to be counted, so the technology is

only feasible where a sufficient percentage of vehicles

have toll tags. Alternative radio frequency identification

(RFID) devices could also be used as an identifier, but

no viable plan exists to use such a system.

State of the Technology

Toll tag readers are a mature technology. They have

been used at toll facilities for more than 25 years and

have been used to provide real-time travel time data

by Florida Department of Transportation (DOT) and

Houston TranStar.

Hardware and Installation

Toll tag readers can be inconspicuously placed directly

above the roadway or on the roadside. Figure 3

shows an example of a toll tag reader fastened to a

cantilever. Readers are most accurate when located

close to passing vehicles and when aimed directly

at a single travel lane (i.e., overhead on multi-lane

facilities). Detection accuracy can decrease when

placed farther from the road, when aimed at multiple

travel lanes, and when there are physical obstructions

between the sensor and the vehicle (Haas et al.,

2009). The directionality of toll tag readers can be an

advantage when detecting traffic in congested areas

such as urban arterials.

Implementation Considerations

Toll tag readers are only feasible for collecting travel

time data on routes where a sufficient percentage

of vehicles have toll tags. As noted above, toll tag

readers must be appropriately positioned to achieve

high detection rates. To achieve the highest detection

rates, multiple readers may be required to cover all

lanes of a multi-lane road. However, a single reader

may be sufficient at each location if traffic volumes

of detectable vehicles and match rates are high

enough to generate accurate travel times. Another

implementation consideration for toll tag readers

is the potential for reader failure. A major toll tag

reader deployment undertaken in the iFlorida Model

Deployment experienced a device failure rate of

about 50 percent during the course of the evaluation. 

At any given time, about 10 to 20 percent of toll tag

readers were not functioning properly (Haas et al.,

2009). Although device failure is highly dependent

upon many implementation factors (e.g., operating

temperature, power conditioning), the potential

for failure should be considered. Best practices

for minimizing and responding to device failure are

presented in Section 4.3.

Costs

Costs of toll tag reader implementations can vary

significantly depending upon implementation factors

such as number of readers per location, distance

between reader sites, and mounting location. 

According to Cambridge Systematics (2012), the

cost per installed reader is about $15,000. According

to Hardigree (2011), a multi-lane implementation

of toll tag readers can cost $75,000 or more per

location. Voigt (2011) estimates that toll tag readers
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Arterial Data Collection Technology Federal Highway Administration

can cost $75,000 to $125,000 per arterial site,

excluding the cost of communications. According

to Wright and Dahlgren (2000), “Capital costs

per reader site where such systems have been

implemented range from $18,000 -$38,000 [for

a six-lane roadway] and for the operations center

from $37,000 to $86,000. Annual operating costs

range from $4,000 to $6,000 per detector site and

$48,000 to $96,000 for the operations center.”

Privacy Issues

Toll tag readers read the unique IDs of each toll

tag. Although toll tag IDs do not directly identify

individuals, the ID can easily be used to identify the

tag owner by matching the ID against the tolling

authority’s database of owners. As with Bluetooth

detection, motorist privacy can be enhanced by

truncating or transforming the toll tag IDs before the

data are transmitted to the transportation agency.

Future Considerations

Toll tag reading is a mature technology that is likely

to remain viable for the foreseeable future. Electronic

tolling is increasingly common. Although the emerging

connected vehicle technology (see Section 2.6)

uses a very similar detection technology, its privacy

restrictions may make connected vehicle technology

an unsuitable replacement for segment travel time

data collection.

2.3 In-Pavement Magnetic Detectors

At a Glance

• Arrays of magnetometers are installed in

pavement at detection locations

• Can identify and match vehicles based on each

vehicle’s unique magnetic signature

• Quick installation involves drilling; devices

self-calibrate

• Wireless sensors require access points and

repeaters

• Close to 100 percent detection rate

• Relatively high initial cost, but typically low

ongoing costs

• High degree of motorist privacy

Toll Tag Reader (AVI) in Houston, TX

Source: ops.fhwa.dot.gov/publications/travel_time_study/houston/houston_ttm.htm

Figure 3
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Arterial Data Collection Technology Federal Highway Administration

How It Works

Arrays of magnetometers are installed in pavement

at detection locations. The magnetometers can

identify and match vehicles at multiple locations

based on each vehicle’s unique magnetic signature

as it interacts with Earth’s magnetic field. Additional

information on magnetometers can be found in the

Federal Highway Administration (FHWA) Traffic 

Detector Handbook (Klein, Mills, & Gibson, 2006;

also see Cheung, Coleri, Dundar, Ganesh, Tan, &

Varaiya, 2005).

State of the Technology

In-pavement magnetic detection has existed

for decades, and has been used to collect travel

time data via vehicle re-identification for about

10 years. It has been used in various locations for

vehicle counts and classifications, traffic signal

actuation, and travel time calculation. Agencies

using magnetic detectors to provide real-time ATT

data include Missouri DOT and Utah DOT. The only

commercially available magnetic detector capable of

real-time data transmission identified in this review is

marketed by Sensys, therefore, this technology review

focuses on the capabilities of the Sensys sensor.

Hardware and Installation

Each magnetic detector is slightly larger than a

hockey puck (see Figure 4). Detectors are installed

in pavement by drilling a core, inserting the detector

in the proper orientation, and filling the hole with

epoxy. Each detector can be installed in 15 minutes

and requires no calibration. To be capable of

matching vehicles at multiple locations to generate

travel time data, an array of five sensors is needed

in each monitored lane (Gregory Owens, personal

communication, October, 2012). Unlike traditional

loop detectors, magnetic detectors can function

wirelessly and can have a battery life of up to 10

years. Relative to wired inductive loops, magnetic

detectors are easier to install, have limited effect

on traffic disruption, and are less prone to failure. 

Detectors require an access point to be installed

within about 150 feet to establish a communication

link. Additional repeaters may be used to extend

wireless connection range. Figure 5 shows a typical

installation configuration.

Implementation Considerations

An advantage of magnetic detectors over some

other probe vehicle technologies is that the detection

rate for vehicles passing over the monitored lane

approaches 100 percent (though missed detections

and double-detections are possible), providing

transportation agencies with volume data that can be

used for various purposes. A comparison to ground

truth video data (“ideal” output of perfect detection/

tracking applied to the video stream) conducted by

Sensys showed that in-pavement magnetometers

achieved a 70 percent vehicle match rate (Volling,

n.d.). The high detection and match rates may be

particularly advantageous on low volume roads. If

traffic volumes are sufficient, cost savings can be

achieved by only installing sensors in one travel lane. 

Sensys generally recommends installing sensors in

only the left through lane of a road because this lane

carries the most through traffic and is least affected

by vehicles entering and exiting the roadway. Due to

the invasive nature of the installation, in-pavement

magnetometers are not likely to be practical for

short-term deployments. Additional data such as

vehicle class can also be derived from this technology,

though additional sensors may be required in an

Sensys Sensor 

Source: www.sensysnetworks.com

Figure 4
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Arterial Data Collection Technology Federal Highway Administration

array. However, research by Medina, Hajbabaie, and

Benekohal (2010) cautions that magnetic detectors

may over count vehicles at or near signalized

intersections by 16 to 22 percent, largely due to

detections of vehicles in adjacent lanes or vehicles

positioned between lanes. Day, Premachandra,

Brennan, Sturdevant, and Bullock (2009) also found

that magnetic detectors may miss detections of

motorcycles if the motorcycle does not pass directly

over the detector. Although installed in pavement,

Sensys sensors can perform diagnostic checks and

software upgrades remotely. Devices can be removed

from pavement and reused.

Costs

Costs for in-pavement magnetic detectors depend

upon a number of factors, including number of

sensors per array and the number of arrays per

location (i.e., number of lanes monitored). Costs may

also vary depending upon other services provided

by the technology vendor. Although in-pavement

magnetic detection is likely to cost significantly more

than Bluetooth detection, the devices are expected to

require little to no maintenance during their reported

10 year battery life. However, road work may require

the devices to be removed before the end of their

battery life.

Privacy Issues

Because magnetic detectors rely on a vehicle’s

magnetic signature, which can change depending

upon vehicle occupancy and other factors, they offer

a very high degree of inherent motorist privacy. An

anonymous identifier is assigned to a vehicle that

allows it to be re-identified at a downstream location.

Typical Sensys Implementation Showing Sensor Array, 

Repeater, and Access Point 

Source: www.sensysnetworks.com

Figure 5
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Future Considerations

There are no emerging trends that are expected to

influence the use of in-pavement magnetic detection. 

It is a viable solution for ATT data collection and is

expected to remain so.

2.4 Automatic License Plate 

Readers (ALPR)

At a Glance

• Optical cameras capture images of license

plates and software “reads” the information

• Mature technology (over 30 years)

• Installed above the roadway and require direct

line-of-sight

• Particularly sensitive to factors that reduce

visibility

• Potential privacy issues are a concern

How It Works

Optical cameras capture images of license plates of

oncoming or receding traffic and use video image

processing to “read” the license plates. License plate

numbers can then be matched at sensor locations

downstream to generate travel times. Camera

images can be stored, though this is not required

for travel time data generation. While ALPR is often

implemented as a stand-alone function, it can also

be considered as one potential function of a machine

vision system (see Section 2.5). One advantage of

ALPR is that nearly all vehicles have a license plate

that can potentially be observed.

State of the Technology

ALPR is a relatively mature technology that has been

used for more than 30 years, though technology has

improved and become substantially less expensive

since the earliest implementations. ALPR has been

used for tolling, law enforcement (e.g., detection of

unregistered/stolen/warranted vehicles, automated

speed enforcement), and real-time travel time.

Hardware and Installation

ALPR uses cameras that operate in the visible light

spectrum. Cameras require direct line of sight to

license plates, so they must be installed above the

roadway or on the roadside in locations that minimize

visual obstructions (e.g., from surrounding traffic) and

avoid off-axis angles that could reduce recognition

accuracy. To trigger image capture, cameras can use

video image processing to detect vehicle presence

in the frame, or a separate presence-detection

technology can be used (e.g., inductive loop). At

night, cameras may need additional visible or infrared

illumination for adequate license plate recognition. 

Figure 6 shows an ALPR camera and illuminator

used in Arizona. Current systems most often use one

camera per lane for license plate recognition.

Implementation Considerations

ALPR can be used for a variety of purposes

individually or simultaneously. For instance, it can

theorectically be used to detect unregistered vehicles

(i.e., cross-reference license plate readings against

vehicle registration database), conduct average

speed enforcement (i.e., detect an individual vehicle’s

excessive speed over a road segment), serve as a

closed circuit television feed, and serve as a travel

time data source all at the same time. Because it

relies on a clear view of license plates, however,

ALPR is particularly sensitive to many factors that

reduce visibility, such as precipitation, lens fog,

line-of-sight obstructions, low ambient light, off-axis

viewing, and license plates that are dirty, obstructed,

missing, or have low character contrast. In a work

zone implementation on a two-lane rural highway in

Arizona, ALPR achieved a 60 percent recognition

rate and a segment license plate matching rate of

11 percent, which was considered sufficient for this

implementation (FHWA, 2004). Not all States require

vehicles to have a front license plate, so ALPR may

have the highest recognition rates using rear plates. 

Depending on how the system is implemented, data

bandwidth may be especially high for ALPR (e.g., if

video or camera images are transmitted).

Costs

The FHWA Knowledge Resources database provides

cost examples for ALPR. Eberline (2008) provides

cost estimates for an ALPR system used on freeways

in Arizona to detect unregistered and uninsured

vehicles. He estimates that the cameras cost

$20,000 each with an approximate installation and

supporting hardware cost of $4,000 per camera. 

Given that one camera is required for each monitored

lane of traffic, actual costs will be higher if multilane coverage is needed. The Texas Transportation
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Institute estimated that ALPR would cost $25,000

for a four-lane installation, where each lane has its

own sensor (ITS International, 2010).

Privacy Issues

ALPR records license plate numbers, and potentially

camera images or video of vehicles, which can

be used to identify vehicle owners by crossreferencing motor vehicle records. Depending on the

implementation, drivers may be identified by video or

camera images. The American Civil Liberties Union

has expressed concerns over the potential for ALPR

to be used to track individuals without a warrant

and has sued Federal agencies for access to their

records on license plate tracking (Crockford, 2012). 

Agencies can minimize privacy concerns by limiting

their access to personally identifiable information

and adhering to clearly stated policies for what can

and cannot be done with ALPR data. For instance,

ALPR systems can be designed to prevent operator

access to license plate numbers or images by deleting

records immediately after use and providing only

randomly reassigned identifiers to agencies for record

keeping purposes.

Future Considerations

ALPR is a relatively mature technology with a broad

set of potential uses. Improvements in digital camera

and image processing technology are likely to lead

to continued enhancements of these systems as well

as reduced costs. Perhaps the greatest potential

limitation of ALPR in the future relates to privacy

issues, as discussed above. 

ALPR Illumination (Foreground) and Camera (Background) 

Used in a Work Zone On Arizona State Route 68 

Source: FHWA, 2004

Figure 6
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2.5 Machine Vision

At a Glance

• Use of video cameras to monitor flow

• Installed above the roadway or on poles

on the roadside

• Data bandwidth is a consideration

• Highly customizable set of features

• Privacy can potentially be a concern for

high-resolution systems

• Potential uses are likely to expand with

advances in technology, processing power,

and data transmission capabilities; costs are also

likely to decrease with expanded usage

How It Works

In machine vision, also known as video image

processing, video cameras monitor traffic flow in

the visible light spectrum. Software is used to set up

detection zones within the video field that can identify

vehicle presence, lane occupancy, speed, and vehicle

class. Speeds can be measured using “virtual loops”

along corridors that may be up to several hundred feet

in length, depending upon camera location. The length

of virtual loops may provide a more useful speed

measure than instantaneous spot speed, especially if

the site is prone to congestion or intersection control

fluctuations in traffic speed. More advanced machine

vision systems have the potential to provide segment

travel times by matching vehicles at multiple roadway

locations (e.g., using ALPR). Additional information

on machine vision can be found in the FHWA Traffic 

Detector Handbook (Klein, Mills, & Gibson, 2006).

State of the Technology

Machine vision has been in use for many years for

a wide variety of traffic management purposes. 

One common use is signal actuation (i.e., detecting

vehicles at stop bars). Advances in hardware and

software capabilities have improved the functionality

and reliability of systems.

Hardware and Installation

Video cameras can be installed above the roadway or

pole-mounted on the roadside. Depending upon site

configuration and camera location, a single camera

may be sufficient to monitor traffic in both directions

or in several lanes of a multi-lane road. A variety of

camera configurations are possible including multiple

relatively low-resolution cameras in place of a single,

high-resolution camera. Another consideration for

machine vision systems is data bandwidth: sufficient

bandwidth is required to transmit images, and systems

can vary substantially in bandwidth requirements. 

Bandwidth requirements can be influenced by whether

the processing is done within the camera, at an on-site

processor (e.g., in a traffic signal cabinet), or remotely

(e.g., at a TMC). Cameras should be placed to avoid any

direct exposure to sun glare. The accuracy of machine

vision systems can also be affected by shadows and

lighting variations. Exterior visible or infrared lighting

may be required for nighttime functionality. Setup

requires users to establish virtual loops or other

image-based triggers within the video image that

are activated when a vehicle passes through them. 

A variety of activation zones can be set up and

customized to the particular implementation.

Implementation Considerations

Video image detection offers a highly customizable

set of features that can be used for a variety of

traffic management purposes, including direct

video monitoring. Hardware and software should

be selected carefully to ensure that needed

capabilities are present. Before implementation, it

should be confirmed that sufficient communications

connectivity can be provided at the site.

Costs

License plate recognition costs reported in the FHWA

Knowledge Resources database vary, but unit prices

were most often reported to be between $16,000

and $18,000, and life span was expected to be about

8–10 years. Systems are likely to become cheaper

and/or more powerful as the technology improves.

Privacy Issues

Machine vision systems can be used for many

purposes. At the most basic level, they observe

traffic similarly to CCTV cameras and capture no

personally identifiable information, so there are no

notable privacy issues. However, it is possible that

high-resolution systems could be used to capture

personally identifiable information (e.g., automated

enforcement and license plate recognition), and these

uses could raise privacy concerns, especially as the

increasing capabilities of machine vision systems

make such uses more feasible.
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Future Considerations

As cameras, software, data transmission, and

processors grow more capable and inexpensive, the

potential uses of machine vision systems are likely to

expand. For instance, increased image resolution can

make machine vision systems operate more precisely

and make feasible other uses including enforcement

of a variety of traffic laws, ALPR, and security (e.g.,

trespass detection). While current systems generally

process data at the camera, recent developments in

cloud computing and WiFi data transmission could

offer increased image processing power with less

investment in onsite systems.

2.6 Connected Vehicle

At a Glance

• Short range radio communications vehicle-tovehicle (V2V) and vehicle-to-infrastructure (V2I)

• Technology in very early stages of development

• Radio transceiver installed in host device

within a vehicle

• Privacy protocols being established

• Very inexpensive cost on per-unit basis

• Usefulness for travel time calculations uncertain;

depends on implementation factors

• Potential for widespread use if initiative continues

to develops

How It Works

The U.S. DOT-led connected vehicle initiative is

designed to provide communications between

vehicles, and between vehicles and infrastructure,

using dedicated short range communications (DSRC). 

DSRC is a communication standard allowing reliable,

low-latency radio-frequency communications. Within

the connected vehicle program, DSRC may be capable

of data transmission over distances up to about

3280 feet (1 kilometer). DSRC is capable of two-way

communication, allowing devices to both send and

receive data. DSRC transceivers may be built into

vehicles or portable devices such as smartphones. 

In the United States, a section of DSRC bandwidth is

reserved for use by intelligent transportation systems

(ITS), including ncluding V2V and V2I applications

applications. DSRC technologies and applications are

currently in development. The U.S. DOT is pursuing a

program of research and development that is focused

on providing applications that enhance highway

safety and mobility and reduce pollution, within a

secure and reliable architecture.

In V2V communications, vehicles anonymously

exchange information about their position, speed,

and heading, allowing each vehicle to be “aware”

of surrounding vehicles and potential threats. 

Currently envisioned V2V applications primarily

involve cooperative safety features designed to

warn drivers of potential collisions and conflicts. In

V2I communications, vehicles and infrastructure

(instrumented with DSRC transceivers) can

communicate with one another. Infrastructure can

communicate location-specific or general messages

to vehicles, such as curve speed warning, road

condition warning, weather information, incident/

detour information, and so forth. Vehicles can indicate

their presence to infrastructure, enabling features

such as traffic signal actuation, automatic toll

payment, and incident detection, as well as providing

more general information such as traffic volumes and

travel times.

State of the Technology

The U.S. DOT and a wide range of research partners

are currently testing DSRC technologies and

developing protocols. While the capabilities of the

technology are relatively well understood, numerous

key implementation decisions have not been made

yet, so the ultimate capabilities of DSRC are not yet

known. According to current estimates, some DSRC

features are expected to become available to the

public in or around 2017.

Hardware and Installation

At a minimum, DSRC requires a small radio frequency

transceiver to be present in a host device (e.g.,

vehicle, smartphone, signal cabinet). For basic travel

time data collection purposes, the vehicle-based

transceiver only needs to send its current speed to an

infrastructure transceiver. 

Implementation Considerations

To protect driver privacy, current plans call for DSRC

transceiver IDs to be randomly reassigned every

5 minutes (Ben McKeever, personal communication,

January, 2013). Although this plan could be revised

before rollout, this randomization scheme means
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that the ability to track individuals between multiple

locations would be severely limited. To achieve a

match using this method, a vehicle would have to

pass two or more infrastructure transceivers during

the 5-minute span in which a single random ID is

active. Match rates would likely be very low unless

infrastructure transceivers are closely spaced. 

For example, if the average travel time between

transceivers is 3 minutes, a match rate of about

40 percent could be expected, because about 60

percent of DSRC IDs would have expired in that span

of time; this match rate is before accounting for

other potential sources of data loss. If congestion

increases average travel times, the match rate

between stations could drop to zero. However, DSRC

could theoretically provide a near-100 percent

detection rate of instrumented vehicles if the system

is designed to base calculations on vehicles’ spot

speed measurements instead of segment travel times. 

While travel time estimates using spot speed can be

difficult to make, especially on arterial roads prone

to high speed variability and traffic signal queues,

the relatively low cost and variety of other uses and

benefits of DSRC might make this technology viable

for ATT estimation, either alone or as a complement

to other technologies.

Costs

Although commercial products containing DSRC

are not yet available, the enabling technology is

expected to be very inexpensive on a per-unit basis. 

Supplementary technologies such as GPS could add to

unit costs, though in many products (e.g., smartphones

and vehicles), GPS may already be built in.

Privacy Issues

Given the broad range of ITS features expected to

make use of DSRC, security of DSRC communications

is of critical importance. A substantial piece of

the DSRC development effort has been dedicated

to ensuring data security and privacy. Current

implementation plans provide for driver anonymity

by automatically randomizing the ID of DSRC

transceivers every 5 minutes, though this approach

has not been finalized (Ben McKeever, personal

communication, January, 2013). In addition to

providing anonymity, it would also prevent the

tracking of vehicles/devices over time and space,

which has implications for using these data for travel

time calculations.

Future Considerations

Although there are many uncertainties regarding the

details of implementation, DSRC has the potential to

be used for travel time calculations. The feasibility

of use, however, depends on a number of unknown

implementation considerations.

2.7 Radar, Microwave, LIDAR (RML)

At a Glance

• An active sensor emits radio waves (radar),

microwaves, or a laser beam (LIDAR), which

reflects off of vehicles

• Mature and widely used technology

• Many products available with different

features and implementation methods

• Use limited to spot speed measurement

• Complete privacy to drivers

How It Works

An active sensor emits radio waves (radar),

microwaves, or a laser beam (LIDAR), which reflects

off of vehicles. The frequency shift observed in the

reflected energy or the return time of the reflection

can be used to determine vehicle speed. Some

products can also measure vehicle speeds when

aimed perpendicular to the flow of traffic. Additional

information on these technologies can be found in

the FHWA Traffic Detector Handbook (Klein, Mills, &

Gibson, 2006).

State of the Technology

RML are mature technologies that have been used

to calculate vehicle speed for decades. Use of these

technologies for ATT calculations, however, is

relatively uncommon. Sidefire radar is used for ATT

calculations in Auckland, New Zealand.

Hardware and Installation

There are numerous RML products available, each

with its own capabilities and requirements. Interested

agencies can seek products that are best suited to

their needs. To ensure accurate speed measurement,

sensors must face oncoming or receding traffic,

though in some cases, sensors can be aimed

perpendicular to the travel flow). This can be
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achieved with overhead or sidefire sensors. 

Functionally, one of the primary differences between

radar and LIDAR is their directionality. Radar emits

energy in a wide cone that can monitor a broad

roadway while LIDAR emits a narrow beam that can

target a specific lane at long range.

Implementation Considerations

RML cannot be used to match vehicles at multiple

locations, so their use is limited to spot speed

measurement, which can be relatively difficult to

use as a basis for ATT, given the speed variability

and signal queues that are usually inherent on this

functional class of roadway. Measurement locations

must be selected carefully to ensure applicability. The

functional range of radar can be reduced during heavy

precipitation.

Costs

Installation costs per radar site are estimated

at $8,000 according to Cambridge Systematics

(2012), but costs may vary depending upon type

of installation. North Carolina DOT reports that

microwave sensors cost approximately $48,600 per

mile of roadway on a major freeway, based on the

typical sensor spacing used.

Privacy Issues

RML measure spot speed without capturing any

identifying information about a vehicle or driver. 

Therefore, these technologies provide complete

privacy to drivers.

Future Considerations

While RML can be used to measure spot speeds for

travel time calculations and may be effective on roads

where travel speed variability is minimal, their use is

likely to diminish as probe technologies that provide

direct travel time estimates continue to become

increasingly viable and affordable.

2.8 Inductive Loops

At a Glance

• Magnetic loops in pavement detect vehicle

presence

• Mature and widely used technology, though often

not ideally suited to travel time data collection

• Paired loops can measure spot speed; special

processors can match vehicle signatures at

multiple locations using single loops

• High detection rate

• Inexpensive, but invasive installation and

maintenance can increase costs

• Complete privacy to drivers

How It Works

Inductive loops detect the magnetic presence of a

vehicle. Loop detectors can be used to measure spot

speed when installed in pairs a known distance apart. 

The time differential between presence detection at

each loop can be used to calculate spot speed. With

suitable processing hardware, single loop detectors

can also be used to measure vehicle signatures for

re-identification at a downstream location (e.g., Jeng,

2010; Ritchie, Park, Oh, Jeng, & Tok, 2005; Coifman

& Cassidy, 2002; Zhang, Kwon, Wu, Sommers, &

Habib, 1997). Additional information on inductive

loops can be found in the FHWA Traffic Detector 

Handbook (Klein, Mills, & Gibson, 2006).

State of the Technology

Inductive loop detection is a mature technology that

is ubiquitous on highways for a variety of detection

purposes. Dual loops are commonly used to measure

spot speeds. The use of single loops for vehicle

signature detection is a relatively recent development

that has been proven feasible by researchers, but

has not been widely deployed. One processor card

capable of vehicle re-identification (IST-222 from

Inductive Signature Technologies, Inc.) no longer

appears to be in production.
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Hardware and Installation

Electrically conductive wire is installed in the roadway

along with a lead-in cable to connect the loop to a

processor. Typical processors measure vehicle presence

and can be used to derive other variables such as

vehicle speed and class. Special processors are required

for vehicle signature detection and re-identification.

Implementation Considerations

Transportation agencies are generally quite familiar with

the implementation considerations of inductive loop

technology. To add vehicle re-identification capability

may require an upgraded processor card, though

current inductive loops are likely to be compatible with

the new card. In fact, research by Blokpoel (2009)

suggests that re-identification can be achieved at a

rate of nearly 90 percent even when upstream and

downstream loop detectors are different sizes.

Costs

The materials cost for loop detection is significantly

lower than for most other travel time monitoring

sensors, but the cost and inconvenience of invasive

pavement installation can add significantly to lifecycle

costs. To add vehicle re-identification capabilities

to existing loops may require only an upgraded

processor card, which is likely to be a relatively

inexpensive way to add segment travel time capability

to an existing loop detector deployment.

Privacy Issues

Loop detectors do not capture personally identifiable

information, and therefore there are no privacy issues.

Future Considerations

Loop detectors have been ubiquitous as traffic

detectors for decades due to their simplicity and

low cost. They can be used in pairs to collect spot

speed data. While there have been developmental

efforts to use loop detectors to measure segment

travel times, this review did not identify any current

commercialized products or accounts of agency use. 

However, this remains an area of active research

among companies including Berkeley Transportation

Systems, Inc. and CLR Analytics, Inc., and it is

possible that new systems will be introduced in

the future.

2.9 Crowdsourcing

At a Glance

• Drivers’ vehicles or mobile devices provide

information to a public or private entity, and that

information is used to generate traffic/travel time

• Early stage technology

• Critical mass of users are necessary for success

• Vehicle/motorist must have device capable

of transmitting information; no roadway

infrastructure needed

• Private sector currently dominates this market

• Privacy issues largely within purview of private

sector entities

• Use likely to increase as trackable devices and

applications proliferate

How It Works

Drivers’ vehicles or mobile devices provide

information on their location, speed, and possibly

additional information to a public or private entity,

and that information is used to generate traffic/

travel time information. The typical model for

crowdsourced data involves location-aware (GPS or

cellular network-based) devices running an application

that automatically sends information to a central

server using cellular transmission. One particular

advantage of location-based crowdsourcing is that

vehicles can be individually tracked in near real-time,

allowing more precise and timely speed and travel

time estimates than can be achieved by other data

collection technologies.

State of the Technology

For travel time crowdsourcing to work, a “critical

mass” of active data-providing devices is required

to achieve sufficient roadway coverage in order

to generate accurate travel time estimates. GPS

crowdsourcing has only recently become a viable

ATT data source because of the rapid growth

of smartphones and similar devices that allow

transmission of data on vehicle speed and position

to servers where data can be analyzed, compiled,

and used to provide travel time data. Currently, the

only known examples of crowdsourcing that produce

enough data to generate accurate ATT data come
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from private sector vendors. Crowdsourcing data

providers include INRIX, Navteq, Google, TomTom,

and Waze. As of October 2012, INRIX claims to

have about 100 million data-providing devices in its

network (INRIX, 2012). Some of these vendors merge

crowdsourced data with data from other sources

to generate travel times. ENTERPRISE provides a

detailed report on the use of third-party travel data

and information (ENTERPRISE, 2012).

Hardware and Installation

In the current implementation model of travel time

crowdsourcing, transportation agencies simply purchase

travel time data from a private sector provider. No

sensors or other hardware are required. While it is

theoretically possible for a transportation agency to

pursue its own crowdsourced data (e.g., by providing

a downloadable data-logging application to drivers), no

examples of this approach were identified in this review. 

If this approach were to be used by a transportation

agency, crowdsourced data would likely need to be

initially used in combination with other data sources.

Implementation Considerations

For crowdsourcing systems to function, at a minimum,

vehicles must have a device capable of transmitting

their position and speed to central servers in near

real-time. Some mobile devices or GPS monitoring

systems may inherently be capable of doing this, while

others require users to install a specific application

on their devices. Such applications are typically free

or inexpensive, and may provide users with mapping,

navigation, and real-time traffic information in return. 

Transportation agencies that wish to purchase travel

time data from a provider must make arrangements

with the company to determine what information

will be provided and under what specifications (e.g.,

network coverage, timeliness, accuracy). Some

providers may also offer features such as incident

information, predictive travel time algorithms, and

fusion of data from other sources (e.g., roadway

sensors). One challenge of using vendor-provided data

may be combining third-party data with data collected

directly by the agency (e.g., using roadway sensors). 

This issue is addressed in Section 4.2.

Costs

Costs can vary substantially depending upon the

particular billing arrangements with data providers. 

According to the FHWA Knowledge Resources

database, travel time data received from a private

sector provider in the I-95 Corridor Coalition Vehicle

Probe Project cost about 75 percent less per mile

than data from microwave or radar sensors. 

Privacy Issues

Travel time data from private sector providers are

provided to transportation agencies anonymously,

so no privacy issues should exist for transportation

agencies. However, agencies should verify that the

vendor’s privacy procedures are deemed acceptable.

Future Considerations

Crowdsourcing is likely to become increasingly viable

and precise as the number of users with compatible

devices and applications continues to grow. As data

providers increase the size of their networks of dataproviding devices, travel time coverage becomes

increasingly available on arterial roads.

2.10 Cell Phone Signal Monitoring

At a Glance

• Cell phone location information is automatically

and anonymously downloaded from cellular

network switching centers in real-time

• Relatively mature technology and cell phone

use is ubiquitous

• Data provided by vendors; data are anonymous

when provided to agencies

• Shows adequately precise measurements of

travel time, though vehicle localization errors

can occur on routes with multiple adjacent roads

How It Works

Cell phone location information is automatically and

anonymously downloaded from cellular network

switching centers in real-time. A variety of statistical

methods can be used to determine the location of a

cell phone based on cell phone network handoff or

signal tower triangulation. Different methods can

operate with varying degrees of accuracy. One system

updates location information about every 750 feet

(230 meters) and compares it with a map database to

determine devices’ locations on roadways. In addition,

another system uses changes in location over time

to calculate speed. This method of cell phone signal
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Arterial Data Collection Technology Federal Highway Administration

monitoring provides reported location accuracy within

90 to 120 feet (27 to 37 meters). Other cell phone

signal monitoring methods may be accurate only within

1,500 feet (460 meters) or more (Avni, 2007). The

location inaccuracy of cell phone handoff systems

makes such systems potentially infeasible for arterial

use in some areas.

State of the Technology

Cell phone use in the United States is ubiquitous and

cell phone network coverage along arterial roads is

very high, especially in urban areas. While the base

technology has virtually reached saturation, there

may still be some room for technology providers to

enhance the location accuracy of cell phone signal

monitoring using improved statistical methods and

algorithms. 

Hardware and Installation

No hardware is required of transportation agencies. 

Data are provided directly by the vendor.

Implementation Considerations

Cellint’s TrafficSense is a traffic information system

that continuously monitors traffic speeds, travel

times, and incidents in real-time. Cellint describes its

implementation process for 100 miles of roadway

as taking hours of installation at the cellular network,

days of offline mapping and signature preparation,

and weeks of calibration and tuning before the system

goes operational (Cellint, 2007). Cellint reports that

it uses anonymous cell phone location data, but it is

not clear what percentage of cell phones are

successfully located. Cell phone signal monitoring

can differentiate between two nearby roads down to

150 feet (46 meters), which should result in accurate

location data on most arterials, but errors could occur

with two nearby parallel roads.

Cellint reports that, overall, about 5 percent of data

cannot be reliably correlated to a road. However,

evaluation of a different vendor’s cell phone signal

monitoring technology found that the data were not

highly reliable on arterial roads. In a comparison with

ground truth data (or data collected on location),

only about two-thirds of monitored urban arterial

road segments had an absolute speed error within

10 miles per hour, and fewer than half of segments

had a maximum speed error bias within 5 miles per

hour (Lattimer, 2010). Data from rural arterials

and limited access roads had less error. Although

the generalizability of these results is not clear, it

suggests that the feasibility of cell phone signal

monitoring data collection should be evaluated

before committing to an implementation, especially

on arterials.

Costs

Costs may vary significantly depending upon the

data required and must be determined on a caseby-case basis. However, Cellint claims that costs are

significantly lower for cell phone signal data than for

comparable roadway sensor data.

Privacy Issues

Location and speed data are provided to the

transportation agency anonymously, and technology

vendors also claim that they receive anonymous

data. Therefore, there are no direct privacy issues. 

However, cell phone tracking has received recent

media attention because it has become a popular

tool for law enforcement and can often be conducted

without a warrant. As a result, there may be public

sensitivity to the concept, which should be addressed

through clear public relations statements indicating

how individual privacy is protected. 

Future Considerations

Cell phone signal monitoring has been shown to

provide adequately precise travel time estimates

for arterial roads depending on the data processing

method, but has only emerged as a viable option

relatively recently. Cell phone signal monitoring

may be considered as a competitive (or possibly

complementary) technology to GPS device tracking. 

While cell phone signal monitoring currently has the

advantage of high vehicle fleet penetration rates,

the presence of GPS in vehicles is increasing and

GPS offers benefits in terms of location accuracy. 

Interested agencies may explore vendor options for

both data types.
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Arterial Data Collection Technology Federal Highway Administration

3 Implementations of Arterial Travel Time 

Data Collection

This section provides summaries of ATT data

collection implementations in the United States and

abroad. The implementations described were selected

to represent a broad range of implementation

objectives, methods, and technologies. Emphasis is

placed on implementations that collect travel time

data that can be leveraged in real-time. Note that

the first two ATT implementations addressed in

this section (Chandler, AZ and St. Louis, MO) are

described as detailed case studies, whereas the

following implementations are described more briefly.

3.1 Case Study: Chandler, AZ

3.1.1 Background and Planning

Background

The City of Chandler, Arizona has a unique

implementation of ATT in which DMS on arterials

approaching freeways display travel times that

combine the ATT to the freeway and the freeway

travel time to one or more destinations. In other

words, each travel time estimate includes both the

travel time to get to the freeway plus the travel time

on the freeway to reach the destination. 

Defined Need

Chandler is a city in the southeast corner of the greater

Phoenix metropolitan area. Many Chandler residents

commute to the adjacent cities of Phoenix, Tempe,

and Mesa using a combination of arterial and freeway

routes. When the City of Chandler received Federal

funding to deploy DMS, it chose to develop a travel time

system that would allow the signs to provide useful

information to drivers at most times of the day rather

than leaving them blank most of the time. The travel

time information was expected to aid drivers’ en-route

decision making, especially during the morning and

evening rush hours. Congestion is rare at other times.

Technology Selection

Bluetooth detection was selected for ATT data

collection largely due to the relatively low price and

ease of deployment. The project was also undertaken

on a compressed schedule of three months to

develop and deploy equipment, and Bluetooth

was seen as more feasible to deploy than other

technologies, particularly given the time constraints. 

Before the bid process began, the city acquired one

Bluetooth detection unit for testing and specification

development. Preliminary testing showed that with

detectors about 0.5 to 1 mile apart, match rates of

about 5 to 10 percent of traffic could be achieved,

which was expected to be sufficient for accurate

travel time estimates during the daytime hours when

traffic congestion was most likely (Ben McCawley,

personal communication, January 2013). The city

put out a request for quotes for a Bluetooth detection

system and received three quotes. Chandler selected

TPA North America, a distributor of Traffax’s BluFAX

system. In addition to the system’s successful history

of use, a major factor in this choice was long-term

price: BluFAX had no recurring costs after purchase

other than as-needed equipment maintenance. The

system was completed and began operation in June

2011. The entire project had a budget of $400,000,

including $50,000 for seven Bluetooth detectors and

$40,000 for data integration.

3.1.2 Implementation and Management

Location

Seven Bluetooth detectors were acquired and

installed in traffic signal cabinets or on light poles

where power and communications were available

(see Figure 7), and two additional “floating” detectors

were acquired that could be dynamically deployed at

different locations. The Bluetooth detector locations

were selected to provide travel times for three

major arterial commute routes. Chandler installed

three DMS on those same routes to provide arterial/

freeway travel times, each located between about one

and two miles from a freeway entrance ramp. The

signs are positioned to allow drivers to make route

choices before entering the freeway. Each sign shows

travel times from the current location to one or two

destinations on the freeway. Figure 8 shows each

sign in context with its default travel time message. 

Figure 9 shows the route covered by each DMS as

well as the default message format for each sign. 
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Arterial Data Collection Technology Federal Highway Administration

Data Collection

The Bluetooth detectors capture raw MAC address

data, which are transmitted over City of Chandler’s

fiber communications network and over a secured

internet connection to the BluFAX server hosted

by the vendor. (The initial plan called for data to be

housed on the vendor’s server for the first six months

after deployment so that algorithms could be refined

to ideally suit the data. However, the data continue to

be housed by the vendor so that the City of Chandler

and TPA North America can continue to refine the

algorithms.) TPA North America processes the data

and raw travel times are generated and sent to the

City of Chandler, where the data are encrypted and

MAC addresses are replaced by random identifiers. 

The data are then combined with the freeway

travel time data (from Arizona Department of

Transportation). The travel time data are then used

to update the travel time signs in near real-time, and

the data are also archived by the City of Chandler in

support of planning and performance monitoring. 

Multi-Source Data and Data-Sharing 

Between Agencies

A particularly interesting feature of City of Chandler’s

travel time system is the use of data from multiple

sources. One of the three travel time signs receives

all of its arterial and freeway travel time data from

Bluetooth detectors. The other two signs, however,

receive ATT data from Bluetooth and freeway data

from loop detectors. The freeway speed and travel

time data are collected by Arizona DOT as part of the

Freeway Management System, whereas the arterial

data are collected by the City of Chandler. Interagency

cooperation has allowed Arizona DOT and the City

of Chandler to share data using the Regional Archive

Data System (RADS) developed by OZ Engineering. 

RADS is a regional ITS database system created

by AZTech, an ITS consortium involving all regional

agencies led by the Maricopa County Department of

Bluetooth Detector on Light Pole 

that Taps into a Pedestrian Signal 

Head Using Power over Ethernet

Figure 7

Source: City of Chandler & Oz Engineering, 2011

ATT Signs in Chandler, AZ 

Figure 8

Source: City of Chandler & Oz Engineering, 2011
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Arterial Data Collection Technology Federal Highway Administration

Transportation (MCDOT). The data can be accessed via

FTP connection by a variety of regional transportation

agencies and private sector companies. For this

project, a system named Chandler Automated Travel

Time System (CHATTS) was developed to process and

fuse Bluetooth travel time with RADS freeway travel

time for posting on DMS.

Travel Time Calculations and Messaging

The data are then merged in the Intelligent Roadway

Information System (IRIS), which was also used

to control DMS message display. IRIS is an opensource advanced traffic management system

software package originally developed by Minnesota

Department of Transportation, and is made available

to other organizations through the Open Source

Consortium. It is available for general public use and

open-source development, making it an inexpensive

and customizable tool for transportation agency use. 

Once the arterial and freeway travel times are merged

in IRIS, travel time calculations basically involve simple

addition of ATT and freeway travel time to create a

combined travel time. The calculations and message

generation are performed automatically, though

operators have the ability to override default travel

time messages with custom messages.

ATT messages are shown on the signs from 6:00

am to 7:00 pm, Monday through Saturday. The signs

typically show only travel times, but in the case of an

incident, the signs can be phased to alternate between

travel times and incident information. When traffic

volumes are especially light, the signs will default

to show a free-flow travel time value representing

segment travel time at the speed limit. Engineers

initially planned for each sign to be able to show

travel time information for multiple routes, but pilot

testing showed that travel information for multiple

freeway routes could not be easily communicated in

an understandable way on DMS.

Public Involvement

The City of Chandler released a press release when

sign construction began and another when the signs

were activated. A web-based survey found that

public reaction to the signs has been largely positive. 

Seventy-six percent of respondents agreed that the

travel time information is helpful, 86% found the

information easy to understand, and 88% found the

travel time estimates to be accurate (City of Chandler

& Oz Engineering, 2011). The survey findings suggest

that even though the ATT signs do not specifically

state what route the travel times are calculated

for, respondents generally understood them. The

accuracy of travel time estimates provided by the

system has been validated only anecdotally to date.

System Maintenance

The City of Chandler reported that the Bluetooth

detection system has been virtually maintenance

free for 2 years. No Bluetooth units have failed or

required maintenance, and as a result, there has

been no need to implement a maintenance plan. 

The system is also capable of automatically alerting

operators by email or text message if there is a

failure or if other triggers are activated (e.g., travel

time for a segment exceeds a threshold value,

indicating the possibility of an incident).

Public Relations

When it was first unveiled, the Bluetooth detection

system was referred to by some media outlets as a

motorist tracking system. This description initially

caused some public concern, but these issues were

short-lived.

Source: City of Chandler & Oz Engineering, 2011

Locations of DMS and 

Recommended Routes

Figure 9
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Arterial Data Collection Technology Federal Highway Administration

Future Plans

The City of Chandler is seeking funding to add

additional Bluetooth detection coverage. The adjacent

City of Mesa is in the process of procuring Bluetooth

detection for a citywide implementation that will

include the use of six DMS. Pending the completion of

the Mesa project, Chandler may consider its own citywide installation, which could provide seamless travel

time data for both cities.

3.1.3 Lessons learned

Advantages of Open-Source Software

The City of Chandler opted to use the open source

IRIS software as part of the system for automatic

posting of travel times on the DMS. The project

team found that this approach provided many

benefits. First, it saved two to three months of

software development time, which led to significant

cost savings. Second, features can be customized

without the need for advanced coding skills, allowing

implementers to adapt the software to their needs

rather than being tied to a software package provided

by a product vendor. Finally, in the case of IRIS, the

software is well documented and was developed and

maintained by a reputable agency (Guerra, 2012).

Conduct Preliminary Tests with the 

Preferred Technology

For the City of Chandler, preliminary testing

was invaluable in understanding how Bluetooth

detection works and what the key implementation

considerations are. This testing helped Chandler to

better understand the capabilities and limitations of

the technology and allowed the City to clearly specify

functional requirements in its request for quote (RFQ)

and vet responses to the RFQ.

Weigh Options Provided by 

Multiple Product Vendors

In the case of Bluetooth detection, there are multiple

vendors whose detector packages provide similar

capabilities. However, there are notable differences in

aspects of services offered and pricing. Some vendors

charge ongoing fees for data provision while others

charge only an initial outlay for equipment. Vendors

also offer different software for data processing

and visualization as well as different degrees of

customization and cooperation.

Bluetooth Travel Time is an Inexpensive Way 

to Add Value to Existing DMS

The City of Chandler estimates that the cost of a

Bluetooth detector is about 5 percent of the cost

of a typical arterial DMS. Therefore, Bluetooth

travel time systems can be a relatively inexpensive

way to generate useful information to present to

drivers on DMS.

Limit Exposure to System Failures

Chandler’s travel time data are currently processed

by the product vendor on servers far from the

Chandler region. If the vendor’s servers were to go

down due to power loss or other failures, Chandler’s

travel time system would not be able to function

properly. Ben McCawley at the City of Chandler

notes that it would be hard to explain to the public in

Chandler that the travel time system is down because

of a major storm on the east coast. By keeping

data processing local or by having backup servers,

agencies can limit their exposure to system failure.

Consider Additional Uses of Data

Bluetooth detectors provide data that can be used for

many purposes other than generating travel times. 

Chandler intends to use historical data to understand

travel trends, including the effects of seasonal

residency in the region. Chandler also intends to use

the data for origin-destination study, and may expand

its Bluetooth coverage in the city to provide a more

complete picture of traffic patterns.

Make Data Easily Available to Partners

Chandler makes its travel time data available to public

and private entities that want to use it. By eliminating

barriers to sharing, Chandler’s travel time data can

be made available to travelers via a wider variety of

outlets.

Engage Media Outlets

When the Bluetooth detection system was unveiled

in Chandler, some media outlets referred to it as

a “tracking” system, which caused some public

concern. By engaging with media outlets early and

describing the system and privacy controls, an

agency can minimize the likelihood of misleading

media stories.
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Arterial Data Collection Technology Federal Highway Administration

3.2 Case Study: St. Louis, MO

3.2.1 Background and Planning

Defined Need

By 2009, Missouri Department of Transportation

(MoDOT) had completed instrumenting urban freeways

in the St. Louis area for travel time using sidefire radar

and felt that the next logical step was to expand travel

time coverage to arterial roads. Travel time technology

in the area serves two primary purposes: travel time

dissemination to the public and data collection for

internal purposes (e.g., volume, speed, and occupancy).

Technology Selection

MoDOT contracted Sensys Networks to provide its

travel time data using its in-pavement magnetometer

technology (see Section 2.3). At the time, Bluetooth

device saturation rates were not considered to be

sufficiently high and MoDOT was not convinced

of its efficacy for an arterial network under these

conditions. The Sensys system, however, provided

nearly 100 percent detection rates and a high vehicle

match rate of about 30 to 40 percent. MoDOT’s

investigation of available technologies convinced

them that the magnetometer system was the best

technology for their needs, so the project was solesourced to Sensys without soliciting other bids.

The Sensys system was more expensive than

alternatives such as Bluetooth, but the system was

considered a “fairly deluxe solution” that offered

benefits in terms of high vehicle detection and match

rates that allowed the system to work well with in the

arterial environment, along with the ability to collect

a full range of data (volume, spot speed, occupancy,

and classification). The relatively high cost of the

Sensys system is attributed to the technology itself

(i.e., five sensors per travel time array, sensors for

data collection, repeaters, and access points) system

integration costs, and the ongoing services provided

by Sensys.

3.2.2 Implementation and Management

Location

The MoDOT ATT system provides coverage on 15

arterial corridors, with a total of more than 130

instrumented miles of roadway. MoDOT considers its

Sensys deployment virtually complete because the

only uninstrumented arterial corridors are too minor

to justify sensor deployment.

Installation

Sensys sensors are installed in the pavement in

arrays of five “pucks” aligned perpendicular to the

direction of travel. Although installation requires lane

closures and a truck-mounted core drill, installation

is relatively quick; an array can be installed in

30 minutes. Sensys initially expected that each

array would require only three sensors per array,

but revised their practice when a new processing

algorithm was implemented. This change came late in

the design phase and nearly derailed the project due

to the extra cost and labor involved, though MoDOT

ultimately was able to accommodate the change by

cutting back on other costs. Because each array of

five sensors is tasked towards the sole purpose of

vehicle identification, MoDOT uses additional sensors

placed near the travel time arrays to collect other

data (volume, speed, occupancy).

This approach provides a more complete picture of

traffic conditions to MoDOT than travel time alone,

and allows data to be more effectively used to study

travel patterns for a variety of purposes. Raw data

from the wireless sensors are collected by an “access

point.” At sites where the access point cannot be

installed within wireless range, additional repeaters

are required. From the access point, data are sent

to the Sensys server at the MoDOT TMC, which uses

vehicle matches to generate travel times. The refined

data are then streamed to MoDOT’s ATMS for live

travel time display and data collection/reporting. 

Sensor Locations

Sensor arrays were installed approximately every 1

to 1.25 miles at most arterial locations, though some

were placed closer together near busy interchanges

where complicated traffic patterns could potentially

reduce match rates. Arrays were only installed in

the far left-hand lane because the left lane carries

the highest proportion of through traffic and is least

influenced by the effects of vehicles entering and

exiting the road. Installation locations were selected

by Sensys based on their experience in setting up

travel time systems.
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Arterial Data Collection Technology Federal Highway Administration

Data Integration

MoDOT currently uses sidefire radar as its primary

means of collecting freeway travel time data, with

Bluetooth sensors soon to enhance travel time

reporting through freeway interchanges. Because

MoDOT is responsible for both freeways and most

major arterials in the St. Louis area, the process of data

integration is simplified due to a lack of institutional

barriers, and all travel time data can be integrated in

MoDOT’s advanced traffic management system (ATMS). 

Data Visualization and Dissemination

With the refined data, MoDOT is able to visualize

travel time data as a layer on a Google Map and send

the data out for display on an arterial DMS. Although

the travel time data provided by Sensys are sufficient

for dissemination, the usefulness of the data in its

basic output format requires additional manipulation

to serve other purposes. For example, to compare

one arterial corridor to another, MoDOT uses its own

ATMS to sort through the Sensys data and reformat

it to better suit their needs.

Arterial DMS Practice

ATT displays are positioned before key diversion

routes and are intended to help drivers decide

whether to travel on the freeway or take an arterial

route. To avoid any problems that might arise from

drivers trying to read the DMS rather than focusing

on a traffic light, signs were installed at least 500

feet from signalized intersections. They were also

positioned to allow a 750 foot sight distance. Most of

the ATT signs display only arterial travel times. Some

mix freeway and ATT information. Others display

only freeway travel times because, near the terminus

of some arterial roads, few destinations besides the

freeway are available. Most signs are capable of

displaying three lines of text and are formatted as

in Figure 10. Figure 11 shows an ATT sign along an

arterial road. During peak hours, travel time is updated

approximately once every minute. When there is less

traffic during off-peak hours, travel time is updated

less frequently, with a maximum of once every

5 minutes. The signs are operated from 5 am to

10 pm. The signs are turned off overnight to prevent

drivers from becoming desensitized to the information

due to exposure at times when congestion does not

occur. By not disseminating travel times during the

overnight hours, MoDOT also limits concerns about

insufficient traffic volumes leading to incomplete

travel time data. Because some signs are within view

of residential areas, it was also seen as a courtesy to

residents to turn the signs off outside of peak hours.

Responsibilities of Vendor

MoDOT left most of the travel time system design

decisions up to Sensys because they had the

expertise and experience. Once the system was

Arterial-based Display 

on Approach to I-55

Figure 10

Source: King, 2011

Arterial-based Display on Route 94 

Figure 11

Source: King, 2011
28

Arterial Data Collection Technology Federal Highway Administration

online and producing data, MoDOT still had many

questions for Sensys and requested changes to the

data processing algorithms as unusual outputs were

detected. MoDOT had to continuously work with

Sensys to modify and adjust algorithms to account

for lower than expected speeds.

System Maintenance

This installation does not lend itself to extensive

maintenance. In the words of one MoDOT

representative, “There is no field work to be done

other than plopping the sensors into the pavement.”

The wireless sensors, however, do need to be replaced

every 7-8 years when the batteries run out of power. 

The system also presents issues if the roadway needs

to be repaved. If any of the arterial corridors were

simply repaved (without removing the sensors first),

MoDOT would lose the ability to find the sensors later. 

If repaving is necessary, MoDOT recommends first

removing the wireless sensors and filling the resulting

holes with epoxy. To re-install the wireless sensors,

the same process would need to be repeated: the holes

would be re-drilled and the sensors placed in those

holes and again secured with epoxy.

Future Plans

MoDOT has provided nearly complete coverage on

its urban freeways and major arterial roads, and

does not foresee any significant changes to this

implementation in the near future. The current focus

is on expanding freeway coverage on Interstates. 

MoDOT does not yet have any plans for when the

Sensys sensors reach the end of their battery life,

which could begin to occur in about 6 years.

3.2.3 Lessons learned

If possible, deploy the travel time system slowly. 

Although it took years for MoDOT to deploy its

entire ATT system, portions of the system were

often instrumented and brought online quickly,

as funding became available. In retrospect,

MoDOT suggests deploying the travel time more

incrementally to allow time to identify issues and

“fine-tune” the system (Gregory Owens, personal

communication, October 2012).

Use Observation and Experience 

to Verify the Travel Times Provided

As mentioned above, MoDOT noticed some oddities

and below-average speeds in its arterial data. MoDOT

has had to work with Sensys to tweak the processing

algorithms on an ongoing basis.

Keep Accurate, Up-To-Date Records

MoDOT did not keep detailed records of sensor

deployments, in part because the ITS project expanded

so quickly. These records are especially important so

that MoDOT does not lose sensors to repaving or other

construction efforts. Although Sensys Networks does

have a log of the exact location of each sensor, these

data have to be made available to MoDOT.

Seamless Integration Offers Significant Benefits

MoDOT is responsible for “major roadways” in

Missouri, including freeways and most of the major

arterials. For this reason, the integration of arterial and

freeway travel time deployments and other systems

was seamless, without any institutional barriers.

Involve the Public in Issues Relevant to Them

MoDOT received some complaints from the public due

to dissatisfaction with DMS located near residential

neighborhoods being seen as eyesores, especially

due to their height and bright lights after dark (Tyson

King, personal communication, October 2011). Public

involvement early in the process could have helped to

place DMS in more acceptable locations.

3.3 I-95 Corridor Coalition

The I-95 Corridor Coalition is a multi-State and

multi-jurisdictional organization that functions to

improve transportation system performance

through coordinated effort. The Coalition serves

I-95, which runs from Maine to Florida, passing

through 16 States along the east coast. Its members

include State and local transportation authorities,

toll authorities, and various other organizations

(e.g., Amtrak).
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Arterial Data Collection Technology Federal Highway Administration

In 2008, the Coalition launched its Vehicle Probe

Project, a public-private partnership with the University

of Maryland and INRIX. The purpose of the project was

to create a traffic monitoring system that could be

shared amongst members, seamlessly, and at low cost. 

Using third-party data from INRIX, the project provides

travel time and speed data on a network of freeways

and arterials along the I-95 corridor. INRIX “fuses”

data from a variety of travel information sources

including GPS probe data and traffic sensors using their

proprietary software and algorithms to generate travel

time data. Arterial coverage was included in the project

primarily for support purposes. The covered arterials

link major freeways and metropolitan areas, and they

may serve as alternate routes in the case of significant

freeway congestion.

Although the original contract detailed plans for data

on 1500 centerline miles of freeways and roughly

1000 miles of arterials, the project has since been

expanded. Table 4 shows the project’s current

coverage by State. These data have been used for

the dissemination of travel information on 511 phone

lines and websites, as well as on DMS (in Maryland,

Virginia, and South Carolina only). According to

information on the project’s webpage, “The network

includes full coverage of freeways and major arterials

in North Carolina and the Tidewater area of Virginia,

full or nearly full coverage of limited access roads

in New Jersey, Maryland and South Carolina and the

northern and eastern portions of Florida.” Figure 12

shows the freeway and arterial coverage (as built) in

Philadelphia, PA.

Early evaluations of the INRIX data revealed the

following distinct challenges of collecting travel

time data on arterial roads (Young, 2010):

Defining and Observing Congestion

Whereas freeway conditions can be fairly easily

determined by travel speed, each arterial segment is

likely to have a different “free flow” speed depending

Freeway and Arterial Coverage (as Built) in Philadelphia, PA

Figure 12

Source: King, 2011
30

Arterial Data Collection Technology Federal Highway Administration

on signal timing and intersection operations, which

can also vary through the day. As a result, congestion

is relatively difficult to identify.

Variations In Flow Within Arterials

As a result of traffic signal operations, arterial

roads are likely to exhibit high variation in travel

times between vehicles. These variations often

express themselves as bi-modal travel time

distributions, where two different travel time clusters

appear as a result of vehicle platoons spending

different amounts of time stopped at traffic signals.

Temporal Reporting Requirements

The highly variable travel times on arterials, in

addition to relatively low traffic volumes, makes

average travel time more difficult to characterize

over a short period of time. Longer time periods of

10 to 15 minutes may be needed to characterize

average travel time on arterials, whereas 5-minute

periods are sufficient for freeway travel times.

Table 4 Vehicle Probe Project Current Contracted Coverage

State Freeway Miles Other Miles Total Miles

Maine 66 0 66

New Hampshire 16 0 16

Massachusetts 96 0 96

Rhode Island 162 597 759

Connecticut 111 0 111

New Jersey 895 231 958

Pennsylvania 637 517 755

Maryland 781 3779 4,560

Washington, D.C. 31 233 264

Virginia 1,411 7,213 8,624

North Carolina 1,553 12,996 14,549

South Carolina 934 7,187 8,121

Georgia 357 191 548

Florida 718 0 718

Total 7,768 32,944 40,145

3.4 Utah County, UT, I-15

I-15 is the major north-south interstate in Utah,

passing through Salt Lake City and Provo. As Utah

DOT prepared to begin a major 2-year expansion

of 24 miles of I-15 (the I-15 CORE project) in Utah

County, solutions were sought to help ease the

congestion expected as a result of the road work. 

The construction contractor partnered with

TransCore to develop a system to provide travel time

signs on arterial roads informing drivers of the travel

times on I-15 as well as the travel times on a parallel

arterial road, allowing drivers to choose the route

that is best for them. 

TransCore developed a system using in-pavement

magnetic sensors from Sensys (see Section 2.3). 

Sensors were installed in Utah County on State

Street, a major arterial road that runs parallel to I-15. 

The sensors provide updated travel times every

2 minutes to TransCore’s data analysis software, and

updated travel times are displayed every 6 minutes
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Arterial Data Collection Technology Federal Highway Administration

on arterial-located travel time signs. Data are also

automatically routed to the Utah CommuterLink

website so that travelers can use the information

for trip planning (TransCore, 2011). Although the

system was developed to ease construction-related

traffic, it was also intended to remain in place after

construction was completed.

Eight ATT signs were installed on westbound

approaches to State Street so drivers can see the

travel time information, then choose whether continue

straight toward I-15 or turn right to drive north on

State Street instead. The signs, known as trailblazers,

are static signs with a dynamic component on the

left side of the sign. The static portion of the sign

states the destination (i.e., the city of Lehi, which is a

northbound destination). The dynamic portion of the

sign is a two phased element that shows travel times

to the destination via two routes: Remain on current

westbound route toward I-15 North (arrow points up)

or turn right to take State Street north (arrow points

right). Each phase of the sign is shown for 3 seconds. 

An example of trailblazer sign operation is shown in

Figure 13. According to Sensys, the design,

construction, and software development for the

system took 6 months and its implementation led

to a significant decrease in delays, congestion,

confusion, and collisions (Sensys, n.d.).

Both Phases of a Trailblazer Sign

Source: video capture from Wimmer, 2010 on ksl.com

Figure 13

3.5 Houston, TX, Multiple Routes

Houston TranStar had used toll tag readers as

a source of freeway travel time for a number of

years, but switched to Bluetooth detectors for a

2011 arterial implementation due to the relative

cost savings: Bluetooth detection was estimated to

cost nearly half the price of toll tag readers (City of

Houston, 2011). Initial investigations found that, on

a freeway, Bluetooth detectors and toll tag readers

achieved comparable detection and matching rates

(Texas Transportation Institute, n.d.). 

The City of Houston initially installed six Bluetooth

detectors at major intersections using funding for

demonstration of ITS technology. This arrangement

allowed travel times to be calculated on seven

roadway segments spread across five roads, covering

a total of 16 directional miles (Puckett & Vickich,

2010). This demonstration showed that ATT could

be successfully captured on an urban arterial

network. Following this successful deployment,

additional Bluetooth detectors were deployed in

the area, bringing the total to approximately 50

Bluetooth detectors in a 62-square mile section of

West Houston that carries an estimated 12.7 million

vehicle miles per day. Detectors were placed at major

intersections with distances between sensors ranging

from about 0.5 mile to 1 mile (0.8 to 1.6 kilometers).
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Arterial Data Collection Technology Federal Highway Administration

Houston TransStar communicates traffic

information via an online map, which can be viewed

at: traffic.houstontranstar.org/layers/layers.

aspx?mapname=houston_west&inc=true&rc=true.

A screenshot of the section of the map showing the

arterial traffic conditions is shown in Figure 14. In

addition to seeing color coded traffic speeds, users

can click on a road segment to see the current

average speed for that segment, view incident

reports, and view live traffic cameras. The City of

Houston expects to expand the Bluetooth detection

system to include pan-tilt-zoom cameras and DMS,

and to include other areas of Houston.

3.6 Flagstaff, AZ, US-180

US-180 near Flagstaff, AZ, is a major route to area ski

resorts and was experiencing significant congestion

from traffic going to and from the resort areas. The

Flagstaff Metropolitan Planning Organization (FMPO)

considered a variety of approaches to ease congestion. 

One of the potential approaches was developing a

traveler information system that could provide travel

times and other information (e.g., alternate route

guidance) to motorists.

To explore the feasibility of this approach, 16

Bluetooth detection units were deployed along a

six-mile length of US-180 and alternate routes. 

The units used for this evaluation were portable

suitcase-type devices powered by internal batteries

provided by Traffax, which was selected by Flagstaff

in a noncompetitive process. Each sensor included

a cellular modem to upload data to the vendor. Data

were processed by Traffax servers and then made

available to the project team in Arizona via a Traffax

web portal. Travel time data were not made available

to the public during the feasibility study.

The study found that travel times were generated

successfully for most segments at most times, but

that for any given 15-minute period of time, data for

the entire instrumented length of road had at least

one segment for which data were missing or spurious. 

Low traffic volumes and low match rates led to an

additional difficulty related to filtering outlier data. 

With few matches in each 15-minute period, the

system could not establish a statistically adequate

median travel time to be capable of effectively

identifying and filtering outlier data. This problem is

especially significant in an arterial environment where

many vehicles may leave the flow of traffic (e.g., stop

at a shopping center) between Bluetooth detection

locations. When the system was not effectively

filtering outliers, it also made it more difficult to

use to travel time data for incident detection,

because spuriously long travel time estimates were

occasionally recorded.

Houston TranStar Arterial Traffic Map (detail)

Source: traffic.houstontranstar.org/layers/layers.aspx?mapname=region_houston_dallas&inc=true&rc=true

Figure 14
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Arterial Data Collection Technology Federal Highway Administration

The results suggested a need to select Bluetooth

sensor locations that would allow high match rates

and implement travel time algorithms that can

accommodate low match rates (e.g., by applying more

site-specific filtering and using free-flow speed as a

plug value when data are inadequate). In retrospect,

the implementing agency felt that more thorough

planning before deployment, testing detectors in

situ in advance to ensure adequate match rates,

and more direct control over travel time algorithms

(including outlier filtering) could have led to a more

successful implementation (Edward Smaglik, personal

communication, October, 2012). 

Another lesson learned was related to the sensors

themselves. Because the sensors were portable,

they relied on cellular communications for data

upload. Cellular signal for the selected carrier

was unreliable in the study area, so the sensors’

cellular modems were often searching for signal,

and as a result, sensor battery life was significantly

shorter than anticipated (Edward Smaglik, personal

communication, October, 2012).

3.7 Atlanta Area, GA

Cobb County is a suburban county northwest of

Atlanta. I-75 is a major Interstate passing through

Cobb County to Atlanta. The I-75 corridor often

experiences congestion as a result of commuter

traffic going to and from the Atlanta area. When I-75

experiences heavy congestion, surrounding arterials

often experience congestion as drivers seek alternate

routes. Since 2003, Georgia DOT (GDOT) has

provided I-75 travel times to the public on freeway

DMS and its traffic website (www.511ga.org). There

are also four arterial DMS showing I-75 travel times

on approaches to I-75 to allow drivers to determine

whether to take I-75 or an arterial route. 

As part of an integrated corridor management initiative,

GDOT has recently expanded its travel time system to

include travel times for arterial routes themselves. As

of July 2012, Bluetooth detection was implemented

on select routes in Cobb County (pictured in Figure

15) and Fulton County. Using TrafficCast’s BlueTOAD

system, 19 sensors were deployed on arterials in

Cobb County; 12 sensors were deployed on arterials

in Fulton County, and an additional 4 sensors were

deployed in the City of Alpharetta (ARCADIS, 2012). 

GDOT uses the ATT data to monitor the roadway

network for incidents and to determine the effects

of heavy I-75 congestion on surrounding arterials. 

Historical data from the sensors has also helped

GDOT to evaluate the performance of its adaptive

signal system (TrafficCast, 2012). The arterial travel

conditions are shown on a color coded map on GDOT’s

website, but not on DMS, though GDOT has considered

developing DMS signs to present ATT to motorists.

3.8 Essex County, United Kingdom

The county of Essex is located to the northeast of

London and is within the “London Commuter Belt.”

Within Essex, travel time data are collected on two

interurban arterial routes: from Chelmsford to Harlow

and the M11 motorway (A414) and from Basildon

and Southend-on-Sea (A127) to the M25 motorway

(see Figure 16). Both motorways are used to

commute to London.

To collect travel time data on these two inter-urban

routes, the Essex Traffic Control Centre (TCC) uses

ALPR, as part of the Comet system provided by

Siemens. ALPR is a very well known technology in

the area; a huge implementation of ALPR is used in

London to conduct automatic tolling and perform

security tasks. In Essex, more than 80 ALPR cameras

are installed on the arterial roads. One of the most

significant challenges reported by the Essex TCC

was balancing the need to position cameras to

achieve high point-to-point vehicle match rates,

while also minimizing overall camera deployment to

keep program costs down. Experience showed that

achieving high match rates was relatively easy on

the interurban arterials because most traffic stays

on the road for the full interurban segment. However,

matches were much harder to achieve on urban

arterials due to the more complex traffic patterns. 

Where traffic patterns are predictable and match

rates are high, ALPR generally only monitors one lane

of traffic. Where traffic patterns are less predictable

or match rates are low, cameras may observe

multiple lanes. The single-lane monitoring approach,

however, tended to bias travel time estimates on

interurban arterials because heavy trucks were

overrepresented in the monitored lane, causing travel

times to reflect relatively slow truck speeds. The

Essex TCC notes that even with this bias, incidents

and congestion can still be easily detected.
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As noted on the previous page, travel times were

more difficult to calculate on urban arterials than on

interurban arterials due to complex traffic patterns. 

These challenges were compounded by the sites

selected for ALPR camera installation. Sites were

selected largely on the basis of convenience (e.g.,

where space and power were available), but were not

compatible with drivers’ actual traffic patterns. As a

result, about one-third of segments provided accurate

travel times, one-third provided acceptable data only

at peak traffic volume times, and one-third provided

no useable data.

Travel times from the Essex deployment are available

to the public through the Essex Traveler Information

website (emerge.essexcc.gov.uk), in both map

and text format, and on area DMS. Authorities are

considering expanding the travel time coverage to

other roadways and providing comparative travel

times via multiple routes.

Lessons learned in this implementation include:

• The capabilities and limitations of data collection

technology must be well understood to ensure

that they are used most effectively. In particular,

ALPR cameras require a precise location and aim

to read license plates at a high rate of accuracy.

• Detection technologies must be placed with

consideration of the origin-destination flow of

traffic to ensure that sufficient matches can be

achieved to calculate travel times.

• While data collection technologies can be

configured to report faults to authorities, not all

problems are identified as faults. For example,

if an ALPR camera is bumped so that its field of

view changes, it may not function appropriately,

but no fault condition will be reported. Only

operator vigilance will detect such issues.

• Vandalism of ALPR cameras was an issue, likely

due to incorrect assumptions that the cameras

were used for speed enforcement, or due to other

concerns about government monitoring.

ATT Detection Coverage (Shown as Color-coded Arterials) in 

Cobb County, Georgia: US-41/Cobb Pkwy, Chastain Road, 

Bells Ferry Road, and Barrett Pkwy

Source: www.511ga.org

Figure 15
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Arterial Data Collection Technology Federal Highway Administration

ATT Coverage (Shown as Green-colored Arterials) 

in Essex County, UK

Source: emerge.essexcc.gov.uk

Figure 16
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Arterial Data Collection Technology Federal Highway Administration

4 Best Practices for Arterial Travel Time 

Data Collection

This chapter brings together the state-of-the-art in

ATT data collection technologies and the state-of-thepractice in ATT implementations to develop a set of

best practices that are based on systematic evaluation

(where possible) and real-world experiences, focusing

on best practices related to the data collection

technology; a complete set of best practices for ATT

programs is beyond the scope of this effort. The best

practices were developed with the understanding that

every implementation of ATT involves a unique set of

objectives, challenges, constraints, and environments. 

Therefore, rather than providing prescriptive guidance,

this chapter emphasizes the key considerations

at each step of the planning, implementation, and

management process. 

One of the most important lessons learned by ATT

program implementers is the importance of asking the

right questions during the planning and implementation

stages. Therefore, each key consideration is phrased

as a question and is followed by discussion of related

issues. While the key considerations are divided across

three high-level topics, the issues addressed in this

chapter should be considered across all stages of an

ATT implementation.

4.1 Needs Assessment, Planning, 

and Specifications Development

The following questions may help practitioners assess

and plan their travel time needs as well as lay out the

framework for specification development. 

What Are the Ultimate Outcomes Desired 

from the ATT Program?

Agencies that have implemented their own ATT

programs have done so for a variety of reasons. 

Some intended to enhance their own abilities to

monitor regional traffic conditions. Some intended to

ease traffic over a congested corridor by providing

traveler information in order to improve drivers’

abilities to make efficient routing decisions. Some

decided that it was a cost effective way to provide

desirable information to motorists, or to make use of

underutilized DMS. 

Exploring high-level objectives should help to define

some basic parameters of the program:

• Are data needed in real-time?

• Who will have access to the data, and how will it

be disseminated?

• What geographic area requires ATT coverage?

While the primary objectives of an ATT program may

be initially defined according to high-level needs, it is

also useful to consider potential secondary benefits of

travel time data. Depending upon the data collection

technology selected and how it is implemented, ATT

systems can provide data on traffic volumes and

vehicle classification, perform enforcement tasks,

function as CCTV cameras, and more. Travel time data

can also be used for a variety of purposes, including

statewide planning model calibration, program/project

evaluation, integrated corridor management, and so

forth. Clearly defining “needs” and “wants” will aid in

the development of project specifications.

Once objectives have been defined, the next step is

to determine the specifications for the ATT program. 

Development of specifications for an ATT program is

likely to be the most critical stage in determining the

success of the program. Clearly stated specifications

will help to guide the technology acquisition and

implementation process and ensure that the ATT

program will be capable of achieving its objectives.

How can ATT Meet Mandates?

An ATT implementation could potentially help an

agency meet certain requirements for network

performance or information dissemination. One

important mandate is the Real-Time System

Management Information Program, which was

included in Section 1201 of the Safe, Accountable, 

Flexible, Efficient Transportation Equity Act: 

A Legacy for Users (SAFETEA-LU) (www.ops.fhwa.

dot.gov/1201). According to FHWA, “The Real-Time

System Management Information Program

is to provide the capability to monitor in realtime the traffic and travel conditions of the major

highways across the United States and provide a
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Arterial Data Collection Technology Federal Highway Administration

means of sharing these data with state and local

governments and with the traveling public. [...]” The

program is to be established on all Interstate routes

within 4 years (November 8, 2014) and on other

significant roadways as identified by the States and

local agencies within 6 years (November 8, 2016).”

Travel time systems on major arterial routes may be

important in meeting these requirements.

What are the Funding and Schedule Constraints?

Project funding mechanisms often place constraints

on how funds can be used, or for what purposes. It is

essential to ensure that an ATT program is within the

scope of available funding. For example, depending

on how it is implemented, an ATT program could

potentially be funded as a congestion or emissions

mitigation program, or as part of an integrated

corridor management program. A project may also

face schedule constraints. If a project faces a tight

implementation schedule, the scope of the project

and the data collection technologies selected should

consider these constraints. For example, purchasing

data from a private sector vendor may be the fastest

approach to program implementation, but sensor

technologies such as Bluetooth detectors can also

be installed and calibrated relatively quickly. Finally,

consider any requirements to demonstrate system

benefits, and the data that would be required to do so.

What is the Desired ATT Coverage Area?

A clear definition of the project objectives should

help to identify the target ATT coverage area. If the

objective is to provide travel times to commuters

to help them choose between multiple commute

routes, instrumenting one or two primary arterials

may be sufficient. If the objective is to monitor traffic

conditions on an urban grid, a network of arterial

roads may need to be monitored. The coverage area

has implications for the choice of data collection

technology. If ATT data are only desired for one or

two arterial roads, sensors placed on those roads

may be the most efficient solution. If ATT data are

desired throughout an arterial network, private-sector

providers that use GPS probe data might offer the

most appropriate solution. 

What are the Needs for Scalability?

When first implementing an ATT program, it is

often simplest to begin with a relatively small-scale

implementation. Once system functionality has been

verified, coverage can be expanded. If using this

approach, agencies should ensure that the system

architecture can be scaled up in the future. 

What are the Needs for Mobility?

It is also important to consider whether the system

should be semi-permanent or mobile. For example, if

intended to provide travel time data in a work zone,

mobility may be an important factor. A mobile system

should be capable of quick installation, calibration,

and removal. Mobility also has implications for data

and power requirements: a mobile travel time system

may require solar and/or battery power, as well as

wireless data transmission.

Are Real-time Data Required?

Real-time data require sensors that can transmit

data shortly after capture via wired or wireless

transmission methods. If data are not needed in realtime, or for an extended period of time, a batterypowered, self-contained Bluetooth data collection

device can be used for a period of a few weeks. 

What Secondary Benefits Can be Achieved?

Secondary benefits of an ATT program can include

use of travel time data for non-travel-time purposes

and the use of other types of data that the data

collection technology can obtain. Secondary uses

of travel time data could include origin-destination

studies and other evaluations of travel patterns. 

However, ideal sensor locations for these secondary

purposes might not be the same as for the primary

travel time purpose. Different ATT data collection

technologies have varying capabilities in terms

of non-travel-time data. Technologies with very

high vehicle detection rates (e.g., in-pavement

magnetometers) can be used to collect vehicle count

data that could be used for a variety of planning

and evaluation purposes. Some technologies

can determine vehicle class. Others can perform

automated enforcement functions such as detection

of unregistered vehicles (e.g., ALPR). Additional

benefits of ATT data should be considered early in the

planning process. 

What are the Requirements for 

Data Accuracy and Timeliness?

While perfectly accurate and timely travel time data

may be ideal, there are a variety of limiting factors. 

Low vehicle detection and match rates can limit a
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Arterial Data Collection Technology Federal Highway Administration

travel time system’s ability to generate travel time

estimates with high confidence, and may result

in delayed processing in order to acquire enough

matches to generate an estimate. These challenges

are likely to be much more significant in the arterial

environment than the freeway environment due to

the potential for lower traffic volumes, greater travel

time variability, and additional “noise” in the data

from vehicles entering and exiting the roadway.

Data accuracy and timeliness can be influenced by

the data collection technology selected. Technologies

with relatively high detection and match rates (e.g.,

in-pavement magnetometers) may be least prone

to delays and inaccuracies caused by low traffic

volumes. Travel time measurement methods that

use spot speed measurements rely on a limited set

of data points to generate travel time estimates, and

are therefore prone to error, especially in arterial

environments where travel speeds are likely to be

highly variable due to traffic control devices. 

Probe vehicle technologies that calculate vehicles’

actual travel times for a road segment can be much

more accurate than spot speed measurements, but

these estimates are delayed by the time it takes

each vehicle to traverse a segment between two

sensor locations. Data will be delayed to a greater

degree if segments between sensors are long or if

travel times increase (e.g., due to congestion). Probe

vehicle technologies that track vehicles in real-time

(e.g., using GPS transponders) have the highest

potential for accuracy and timeliness, but still rely on

appropriate processing algorithms to achieve high

accuracy. Across many data collection technologies,

predictive algorithms can be applied to factor

historical trends and current network conditions into

travel time estimates to account for some of the

inaccuracies inherent in travel time estimates.

What Partnerships May be Necessary 

or Beneficial?

An ATT program must fit into the broader traffic

management context. Often, this involves sharing

resources with other agencies. For instance, the

agency implementing ATT might not be the same

agency that is implementing a freeway travel time

program or an integrated corridor management

program. However, data-sharing and fusion of

data from multiple sources and agencies could be

particularly valuable and provide a more complete

picture than data from a single source alone. 

One particular case in which partnerships may

be useful involves the combination of freeway

and arterial travel times for dissemination to the

public. Often, different agencies are responsible for

freeway and arterial management. In Chandler, AZ,

implementers found success in establishing a data

sharing architecture in which State DOT data are

readily accessible to the city, allowing arterial and

freeway travel time data to be merged and presented

cohesively to drivers on DMS. 

What are the Infrastructure Requirements?

Each ATT data collection technology has its own

set of requirements and options for infrastructure

compatibility. For instance:

• Magnetometers must be installed in pavement

and have a wireless access point or repeater

within 150 feet (50 meters)

• Machine vision benefits from a high vantage point

and may have relatively high data transmission

and power requirements

• Bluetooth detectors are particularly flexible

in terms of installation location and power

requirements

• Crowdsourced data using GPS or cellular tracking

likely requires no infrastructure elements at all

If the arterial lacks power and data infrastructure,

wireless and solar powered technologies might be

most desirable. If additional infrastructure is required

for a technology, it is important to consider the costs

and other implications. On many arterial roads, new

structures such as mounting poles and cantilevers

might be undesirable due to aesthetic concerns,

safety impacts, or lack of available right-of-way. Some

hardware may require environmental assessments.

Are Travel Time Data Needed During Periods of 

Low Traffic Volume?

Travel time data collection technologies require

some minimum number of vehicle detections and

matches to be capable of calculating travel times

reliably. When traffic volumes are low (e.g., overnight

hours), travel time data collection technologies

may not function reliably, and technologies with

relatively low detection and matching rates are likely

to be especially hindered. Agencies should consider
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Arterial Data Collection Technology Federal Highway Administration

whether travel times are required during low volume

periods, given that travel time delays are unlikely to

occur when traffic volumes are low. If travel time data

are required during low volume periods, technologies

and data processing algorithms that work well during

low volume periods should be considered.

4.2 Selecting and Acquiring 

Data Collection Technology

The following questions may help practitioners

identify key factors in determining which travel time

technology is right for their specific implementation.

What Software, Hardware, and 

Other Architectural Requirements Exist?

When selecting ATT technology, it is important to

consider compatibility with existing systems and data

sources. Some data collection technology vendors

provide proprietary software for data visualization

and analysis. While effective for some purposes, some

agencies have found that vendor-provided software

does not provide all of the features and flexibility

that they desire. Ideally, however, ATT data and other

features (e.g., device diagnostics) should be capable

of integration with existing traffic management

software, hardware, power, and transmission

systems. Compatibility between different data

sources and cooperating agencies can significantly

increase the efficiency with which data can be

processed and used.

If new software is required to make use of ATT data,

a potential solution is to use open source software

such as IRIS (Intelligent Roadway Information System,

iris.dot.state.mn.us/). Open source software from a

reliable source can provide a customizable platform

that saves significant development time and effort

compared to creating a new platform from scratch

(Guerra, 2012). For any ATT program, it is critical to

ensure that the system is capable of providing the data

integration, visualization, analysis, and dissemination

tools required, as well as remaining flexible enough to

be able to adapt the system to future needs.

What are the Initial and Ongoing Costs 

Associated With Each Candidate Technology?

The FHWA Knowledge Resources database is a

valuable resource for exploring the costs, benefits,

and lessons learned related to a variety of technologies

and programs, though the database currently provides

few resources directly related to ATT.

As discussed in Chapter 2, the costs of ATT data

collection technologies can vary widely. In addition to

cost differences between technologies, reported costs

for any single technology have also differed. Initial

material costs can be considered as a function of the

price of each sensor unit and associated hardware

(including power and data connections), the number

of sensor units required per site, and the number of

sites required per implementation. Additional initial

costs can include installation and calibration. 

Ongoing costs can include hardware maintenance,

electricity and data transmission (usually negligible),

and data processing labor. Over the long term, life

cycle cost is a useful measure of the expected costs

over the life of the technology. The expected life span

of many travel time data collection devices is up to

about 10 years, though actual life span of individual

units can vary. To ensure a long technology life span,

it is useful to also consider the future viability of the

technology and its associated systems. 

A less predictable cost is the fee for vendor services. 

Different data collection technology vendors offer

different options for system services and support. 

Options may range from purchase of data collection

technology with no additional support to complete

turnkey solutions initiated and managed by a vendor. 

While vendor services can add significantly to the

cost of a system, an effective vendor can save the

agency significant labor costs and offer a degree

of protection from unanticipated costs. Interested

agencies should explore vendor support options to

find the most appropriate model.

Should the Data Collection Technology 

be Purchased or Rented?

Some data collection technology vendors offer the

option to rent or purchase equipment. The best option

may depend on a variety of factors. Renting may be an

appropriate choice for a temporary application (e.g.,

work zone project) or proof of concept study. Renting

may also be more compatible with certain contracting

requirements, and may make it easier for an agency

to change or upgrade equipment in the short term. 

Purchasing may prove to be more affordable in the

long term, and may provide an agency with more

control over how it uses the equipment.
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How Long is the Data Path?

It is not uncommon for travel time data to pass through

numerous physical locations and processing steps. 

For instance, data may travel from a sensor to a field

processing unit to a vendor’s server to the agency’s

server. Each location and transmission process

introduces additional risk of a failure somewhere along

the path. Furthermore, should a failure occur, a long

data path may make it more difficult to determine

exactly where the problem occurred.

What System Features can be Automated?

Raw travel time data generally goes through many

stages of processing before information is provided

in its final form to users. Automating many of these

processes can save labor hours, reduce processing

lag, and reduce the likelihood of errors. Examples

of effective uses of automation include processing

and generating of travel time messages for DMS and

websites, generation of reports and summary data,

and provision of alerts to system operators (e.g., via

text message or email) when certain conditions are

met (e.g., travel time in excess of threshold value or

sensor failure/error).

How will Data Security and 

Motorist Privacy be Protected?

Some data collection technologies capture

information that could be used directly or indirectly

to identify individuals or vehicles. Data of this

sort should be encrypted at the capture point to

prevent unauthorized access. To the extent possible,

personally identifiable information should not be

recorded or provided to staff, vendors, or other

partners, in its original form. Information such as

license plate numbers or Bluetooth MAC addresses

should be truncated, randomized, or otherwise

obscured to protect individuals’ privacy.

How can Preliminary Data Collection Technology 

Testing be Conducted?

The introduction of a new ATT program can come

with many uncertainties and unexpected challenges. 

Preliminary testing of a technology can help to narrow

specifications (e.g., determining the feasibility of a

particular technology or the proper spacing of detector

units) and reveal potential issues during the planning

process, allowing them to be accounted for in the

planning and contracting process. For preliminary

testing, a small number of devices can be purchased or

rented, and collaboration with a vendor or other third

party with experience using the technology can help

substantially in learning how to use the technology.

How Should a Technology Vendor/Provider 

be Solicited?

Depending on the preferred approach of the agency,

vendors can be approached for direct talks, or a

request for proposal (RFP) could be issued. Many

vendors are willing to work closely with agencies to

understand and accommodate requirements. A key

element of this process is effectively communicating

needs to potential vendors and discussing approaches

to meet those needs. Although vendors may

advertise a discrete set of options and features,

some can develop custom solutions to meet agency

needs. In general, it is advisable to solicit bids from

multiple vendors to fully explore options. Although

requirements should be clearly stated to potential

vendors, they should not be unnecessarily specific

to the point of disqualifying or deterring vendors

who might be able to offer a solution. Emphasizing

performance criteria over design criteria can give

potential bidders the flexibility to design the system

as they see fit while still ensuring that the system is

designed to meet agency needs.

Through ENTERPRISE, Washington State Department

of Transportation (2011) has made available for

public access its own RFP for contracted traffic

data, which may serve as a starting point for other

agencies interested in developing their own RFPs. 

ENTERPRISE also provides a detailed report on the

use of third-party travel data and information. The

report describes how agencies in the United States

are currently using third-party data, including their

experiences with these relationships, and provides

information on the data providers themselves

(ENTERPRISE, 2012).

How Much Ongoing Support 

is Offered by the Vendor?

Agency experience shows that issues and questions

are likely to continue to arise long after the ATT

program is up and running. Agencies should ensure

that the vendor will be responsive to such questions,

especially where changes to the system may be

required. The terms of system support should be

documented in a contract.
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Arterial Data Collection Technology Federal Highway Administration

What is the Division of Responsibilities and 

Rights Between the Agency and the Vendor?

The responsibilities of the agency and the vendor

must be clearly delineated. For example, who is

responsible for installation, calibration, maintenance,

repairs, status monitoring and replacement of

failed equipment, data housing, and so forth?

For any responsibilities assigned to the vendor,

performance requirements should be clearly stated

as well. For example, agencies can develop minimum

performance criteria for travel time estimates and

time requirements to replace failed equipment. A

problem experienced in multiple implementations is

equipment failure being compounded by unavailability

of replacement parts or maintenance staff. Finally,

should there be any significant unanticipated

problems with the system, it should be clear who

assumes these risks and the financial and liability

responsibilities for problems.

Who Owns the Data?

If a vendor processes the data and maintains data

on its server, it should be clearly stated who owns

the data and who has access to it for what purposes. 

Similarly, an agency should be aware of what aspects

of a vendor’s system are proprietary and therefore

inaccessible to the agency (e.g., if a vendor uses

proprietary travel time calculation algorithms, the

agency might not be permitted to explore how the

travel times are being calculated).

4.3 Implementation, Management,

and Evaluation

The following questions may assist practitioners in

determining answers to implementation dilemmas,

establishing management principles and goals, and

evaluating the functioning travel time system.

How Should Sensor Locations be Selected?

The arterial environment creates a number

of challenges when locating ATT sensors. For

technologies that calculate individual vehicles’ travel

times through a segment, vehicles entering and

exiting the traffic stream can cause data loss and

provide inaccurate data (e.g., if a vehicle makes a

stop or detour, then continues along the route). A

general rule of thumb used for many arterial probe

data technologies is to locate sensors 0.5 to 1 mile

apart, though circumstances may require alterative

spacings. For example, on roads with a substantial

amount of traffic entering and exiting the traffic

stream (e.g., near a major shopping center), or on

roads with relatively low traffic volumes, closer

spacing may be required to achieve an adequate

number of matches. Preliminary testing with a data

collection technology may help to determine how

traffic patterns may influence device placement. 

The technology vendor should also be a source for

guidance on sensor placement.

What documentation is available 

for the technology?

To the extent that an agency is implementing or

managing the data collection technology, complete

documentation should be available describing the

functionality and capabilities in detail. An example of

the value of documentation is offered by the iFlorida

initiative. Florida DOT experienced a high toll tag

reader failure rate and devised a relatively labor

intensive ad hoc system for checking each device

before learning of an undocumented feature that

allowed devices to perform a self-diagnostic check

using a web interface (Haas et al., 2009). 

How should the program operate in the 

case of missing data?

To the extent possible, an ATT program should be

capable of maintaining functionality when some

data are missing due to insufficient data quantity,

equipment failures, or other problems. In the case

of insufficient data quantity, a travel time value

representing free flow speed can be used under the

assumption that low traffic volumes are indicative of

a no-delay condition. 

The iFlorida deployment experienced significant

arterial data collection sensor outages. An

assessment of that deployment (Haas et al, 2009)

offers the following guidance:

• Missing data should be replaced with the bestavailable alternative data, such as historical

averages or alternative real-time sources such as

traffic cameras.

• TMC software and operators have access to the

most data sources, and are therefore likely to be

the best sources of replacement data.
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Arterial Data Collection Technology Federal Highway Administration

• Estimated data should be flagged as such so that

downstream software can distinguish measured

data versus plug data.

• Estimated data should be inserted as early in the

data flow as possible so that the missing data do

not cause problems with software.

How Should Field Equipment be 

Monitored and Maintained?

Appropriate monitoring of data collection technologies

can help to ensure that problems are detected and fixed

quickly. Some systems may be capable of automatically

reporting a malfunctioning device to the agency, while

others may only be detected by a review of data. 

Common reasons for device failure include exposure

to extreme heat (e.g., in a signal cabinet), power

surges, and loss of data transmission (including

fiber line cuts) or power source. These risks can

be minimized by ensuring that devices are installed

in locations that will not exceed their temperature

ratings or installing power conditioning devices and

surge protection. However, in the event that a device

does fail, down time can be significantly reduced by

ensuring that replacement parts are immediately

available. It can also be helpful to perform a root

cause analysis to determine why a device failed,

so that the same problem can be avoided in the

future (Haas et al., 2009). If a large amount of new

equipment is being installed and brought online in

a short period of time, adequate maintenance staff

should be available to deal with potential startup

problems. From a physical monitoring standpoint, it is

important to maintain asset management procedures

to clearly document where each device is deployed. 

How can Data Quality be Verified?

Few existing ATT implementers have conducted

formal evaluations of ATT data quality. Some have

conducted floating car runs as a basic verification,

and others consider a lack of negative public feedback

an indication that the travel times are accurate. A

relatively small number of studies have been conducted

to compare the data quality of multiple travel data

sources, but these types of studies may be beyond the

means of most agencies. One such study conducted in

Toronto compared data from four different sources,

and provides details of the statistical methodologies

used for this effort (Ministry of Transportation Ontario,

2012). Given that the arterial environment may have

a high prevalence of “noise” data from vehicles exiting

and entering the traffic stream, one way to explore

data quality is to view a plot of individual vehicles’

travel times to verify that data filters are appropriately

removing outlier data.

How Should Public and 

Media Relations be Handled?

It is important to proactively address potential public

concerns about an ATT program. If the ATT system

collects any type of data that could potentially be seen

as personal or sensitive (e.g., ALPR, MAC address),

the uses of the data and protections in place to ensure

privacy should be clearly stated. In the United Kingdom,

experience suggests that when the public perceive

a technology as an invasion of privacy or as a law

enforcement device, it may be more prone to vandalism. 

Furthermore, public opinion should be taken into

account when installing new infrastructure. For instance,

in some arterial environments, a new pole might be seen

as an eyesore.

How can the Effectiveness of an 

ATT Program be Evaluated?

To date, there have been few formal evaluations

of the effectiveness of ATT programs in meeting

their goals. In part, this may be because many

goals of ATT programs can be difficult to assess

affordably and with statistical rigor. Evaluations can

investigate effects in terms of network performance,

driver behavior, or more qualitative outcomes (e.g.,

driver satisfaction, comprehension). Establishing

performance measures for evaluating the ATT system

can help to ascertain whether the system is meeting

its primary reasons for installation. Developing

segment-, corridor-, or agency-wide success

indicators can help the implementers fine-tune their

systems and can even make the case for potential

future expansion. It is important to align performance

measures with whether or not the system is meeting

the needs of the implementation. Robinson, Jacobs,

Frankle, Serulle, and Pack (2012) investigated

travelers’ use of various types of traveler information

including travel time using surveys and focus groups. 

The researchers found that, across many areas of the

United States, travelers overwhelmingly like having

en-route travel time information and highly prioritize

accuracy, timeliness, and clarity of the information.
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Arterial Data Collection Technology Federal Highway Administration

5 Conclusion

Real-time travel time information can be valuable to

both motorists and transportation agencies. While

freeway travel time practices are relatively wellestablished, the arterial environment poses unique

challenges, and few practitioners have experience

collecting travel time data on arterial roads. This

report provides practitioners with information on the

current and emerging technologies available for ATT

data collection, summarizes current practice, and

provides a set of key considerations and questions to

ask when planning and operating an ATT program.

Although ATT data collection is a relatively new

and rapidly evolving area, ATT can be successfully

implemented when a project is properly planned

and executed. The importance of proper planning

cannot be overstated. Successful implementers have

carefully considered project objectives and have

provided detailed implementation plans. Regardless

of the latest specific data collection technology

released, asking the right questions is paramount,

beginning with planning, continuing to the selection

stage, and culminating with execution and evaluation. 

Practitioners who focus on asking the right questions

and heed the lessons learned by colleagues will

greatly increase the chances of a successful

implementation.
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Arterial Data Collection Technology Federal Highway Administration

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April 2013

FHWA-HOP-13-028