Travel Time on Arterials and Rural Highways: 

State-of-the-Practice Synthesis on

Rural Data Collection Technology

Rural Data Collection 

Technology

April 2013
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.

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The Federal Highway Administration (FHWA) provides high quality information 

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

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Technical Report Documentation Page

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

FHWA-HOP-13-029

 4. Title and Subtitle 5. Report Date

Travel Time on Arterials and Rural Highways:

State-of-the-Practice Synthesis on Rural 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 rural highways. The current report focuses on rural highway travel 

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

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

emerging rural travel time (RTT) 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 RTT 

data collection are also discussed. In addition, two case studies are reviewed in detail (in Minnesota and Maine). 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 

RTT data collection is a relatively new and rapidly evolving area, RTT 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, rural travel time (RTT), 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 58 N/A

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.
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Rural 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Data Source Summaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.1 Bluetooth Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Toll Tag Reader. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.3 In-Pavement Magnetic Detectors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.4 Automatic License Plate Readers (ALPR). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.5 Machine Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.6 Connected Vehicle. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.7 Radar, Microwave, LIDAR (RML). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.8 Inductive Loops. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.9 Crowdsourcing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.10 Cell Phone Signal Monitoring. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3 Implementations of Rural Travel Time Data Collection. . . . . . . . . . . . . . . . . . . . . . 23

3.1 Minnesota DOT’s I-35 Temporary Travel Times System. . . . . . . . . . . . . . . . . . . . . . 23

3.1.1 Background and Planning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.1.2 Implementation and Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.1.3 Lessons Learned. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.2 Maine DOT’s Use of Variable Speed Limit (VSL) 

Signs to Provide Real-Time Traveler Information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

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

3.2.2 Implementation and Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.2.3 Future Considerations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.3 Various Statewide Routes, Wisconsin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.4 I-45 from Houston to Dallas, Texas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.5 Various Routes in Oregon, Frontier Travel Time Project. . . . . . . . . . . . . . . . . . . . . 31

3.6 State Route 520 in Orange County, FL. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.7 I-90, Snoqualmie Pass in Washington . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4 Best Practices for Rural Travel Time Data Collection. . . . . . . . . . . . . . . . . . . . . . . . 35

4.1 Need Assessment, Planning, and Specifications Development. . . . . . . . . . . . . . 35

4.2 Selecting and Acquiring Data Collection Technology. . . . . . . . . . . . . . . . . . . . . . . 38

4.3 Implementation, Management, and Evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
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Rural Data Collection Technology Federal Highway Administration

List of Figures

Figure 1 RTT signs in Washington State and Minnesota. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Figure 2 TrafficCast’s BlueTOAD Pole-mounted, Solar-powered Bluetooth Detector and Cabinet Interior . . . . . . . . . . . . . 8

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

Figure 4 Sensys Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Figure 5 Typical Sensys Implementation Showing Sensor Array, Repeater, and Access Point . . . . . . . . . . . . . . . . . . . . . . . . . 13

Figure 6 ALPR Illumination (foreground) and Camera (background) 

Used in a Work Zone on Arizona State Route 68 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Figure 7 Project Location and Minnesota State Route 23. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Figure 8 Travel Time Sign Located Near 40th Avenue on I-35 in Duluth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Figure 9 Travel Time Sign Locations and Messages on I-35. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Figure 10 State of Maine Variable Speed Sign. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Figure 11 Maine DOT’s 511 Traveler Information Map Showing the I-95 Corridor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Figure 12 Examples of Rural Freeway Travel Time Coverage in Wisconsin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Figure 13 Two Examples of AWAM Bluetooth Sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Figure 14 Houston TranStar I-45 Traffic Map. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Figure 15 Orange County Road Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Figure 16 Typical Display Format of OOCEA Travel Time Sign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Figure 17 Snoqualmie Pass Traffic Map. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Figure 18 Travel Time Sign Approaching Snoqualmie Pass. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

List of Tables

Table 1 Candidate RTT Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

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

Table 3 Candidate RTT Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
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Rural 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:

Rashmi Shirole Brewer

Minnesota Department of Transportation

Clifton Curtis

Maine Department of Transportation

Rick Gifford

Washington State Department

of Transportation

Leslie A. Griffin

Orlando-Orange County Expressway Authority

Jon Jackels

Minnesota Department of Transportation

Paul Keltner

Wisconsin Department of Transportation

Dave Mavec

Minnesota Department of Transportation

Ben McKeever

Federal Highway Administration

Donna Nelson

Traffax Inc.
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Rural Data Collection Technology Federal Highway Administration

List of Acronyms

ALPR Automatic License Plate Reader

AVI Automatic Vehicle Identification

CCTV Closed-Circuit Television

DMS Dynamic Message Signs

DOT Department of Transportation

DSRC Dedicated Short Range Communications

FHWA Federal Highway Administration

GPS Global Positioning System

IRIS Intelligent Roadway Information System

ITS Intelligent Transportation System

LIDAR Light Detection and Ranging

MAC Media Access Control

MaineDOT Maine Department of Transportation

MnDOT Minnesota Department of Transportation

ODOT Oregon Department of Transportation

OOCEA. .Orlando-Orange County

Expressway Authority

RTI Renaissance Technologies, Inc.

RFID Radio Frequency Identification

RFI Request for Information

RFP Request for Proposal

RML Radar, Microwave, and LIDAR

RTT Rural Travel Time

SAFETEA-LU

. .Safe, Accountable, Flexible,

Efficient Transportation Equity Act:

A Legacy for Users

TMC Transportation Management Center

TAC Technical Advisory Committee

TOIP Traffic Operations Implementation Plan

V2I Vehicle-to-Infrastructure

V2V Vehicle-to-Vehicle

VSL Variable Speed Limit

WisDOT Wisconsin Department of Transportation

WSDOT Washington State Department

of Transportation
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Rural 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, there is interest in travel time

messages now being communicated on rural highways.

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

rural highways.

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 rural highways,

• 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 rural highway travel time (RTT) 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 arterial

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 rural 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

rural highways.

For example:

• Travel times are not collected in isolation and

often their use is determined by the local goals

and communication needs—and these can be quite

different for and between rural roads.

• Rural roadways can vary greatly in their location

and use: some may be remote and carry low

traffic volumes, while others may be major

interurban thoroughfares.

• Low traffic volumes may create challenges in

acquiring sufficient data to be able to generate

accurate and timely travel time estimates.

• The focus is not only identifying and dealing with

congestion, but also tracking the occurrence of

major incidents and providing alternate route

information in the event of road closure.

• They can be hilly, rocky, curvy and mostly

unsuitable for deployment of reliable intelligent

transportation system (ITS) equipment or even

cell phone reception in some cases.

• Some do not have parallel alternate routes, so

it may be necessary to communicate issues to

drivers 60 miles or more away.

• There are numerous approaches being developed,

implemented, or experimented with nationally and

internationally to deal with some of these issues. 

The table on the next page briefly summarizes

candidate technologies.
vii

Rural Data Collection Technology Federal Highway Administration Table 1 Candidate RTT 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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Rural Data Collection Technology Federal Highway Administration

To further hone the opportunity of providing

useful and accurate travel time information

in rural 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 rural highways, how

feasible is deploying such technology?

The core of the report discusses available and

emerging RTT data sources as well as implementation

considerations, advantages, and limitations of 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 RTT

collection tech; open standard, allows for offthe-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; high detection rate; very

inexpensive, but invasive installation and

maintenance can increase costs; complete

privacy to drivers.

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.
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Rural Data Collection Technology Federal Highway Administration

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 RTT data collection are

also discussed:

Various State routes in Wisconsin

• I-45 from Houston to Dallas, TX

• Various routes in Oregon as part of the Frontier

Travel Time project

• State Route 520 in Orange County, FL

• I-90 Snoqualmie Pass in Washington.

• In addition, two case studies are reviewed in

detail: Minnesota DOT’s I-35 temporary travel

time system and Maine DOT’s use of variable

speed limit signs to provide real-time traveler

information.

In addition, two case studies are reviewed in

detail and lessons learned from the implementations

are summarized:

Minnesota

• Define terms and requirements

• Use current rather than historical data

for estimations

• Consider alternative methods for comparing

travel time

• Drivers appreciated information, especially

about alternative routes

• Costs less and more affordable than

permanent system

Maine

• Costs much less than a traditional system of

dynamic message signs (DMS)

• Posted variable speed information may imply

the need for alternative routes

• Considering mobile phone applications

The report synthesizes the prior information and

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

collection technologies and the state-of-the-practice

in RTT implementations to develop a set of best

practices that are based on systematic evaluation

(where possible) and real-world experiences. The best

practices relate to the data collection technology

only; a complete set of best practices for RTT

programs is beyond the scope of this effort. Best

practices were developed with the understanding

that every implementation of RTT 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

RTT 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.

These questions are focused on the following

general areas (including sample questions for each):

Needs Assessment, Planning, 

and Specifications Development

• What are the ultimate outcomes desired?

• What are the funding and scheduling constraints?

• What is the desired RTT 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?
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Rural Data Collection Technology Federal Highway Administration

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

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 RTT data collection is a relatively new

and rapidly evolving area, RTT 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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Rural Data Collection Technology Federal Highway Administration

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 rural 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 rural highways are relatively rare. 

Figure 1 shows examples of typical 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 rural highways 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 rural highway settings, such as the use

of Bluetooth technology (e.g., Click and Lloyd,

2012; Puckett & Vickich, 2010), toll tag readers

RTT Signs in Washington State and Minnesota

Source: wsdotblog.blogspot.com Source: Jackels, 2012

Figure 1
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Rural Data Collection Technology Federal Highway Administration

(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,

crowdsourcing (e.g., INRIX, 2012), connected vehicle,

cell phone signal monitoring (e.g., Avni, 2007),

and inductive loop detectors (e.g., Jeng, 2010). 

As this list demonstrates, there is a litany of data

collection technologies which have been used for

rural highway travel times, 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 rural highways has

unique challenges for the practitioner.

For example:

• Travel times are not collected in isolation, and

often their uses are determined by the goals and

communication needs at a location; these goals

and communication needs can be quite different

for rural versus urban roadways, and often vary

among individual rural roads.

• Rural roadways can vary greatly in their location

and use: some may be remote and carry low

traffic volumes, while others may be major

interurban thoroughfares.

• Low traffice volumes may create challenges in

acquiring sufficient data to be able to generate

accurate and timely travel time estimates.

• The focus is on not only identifying and dealing

with congestion, but also tracking the occurrence

of major incidents and the need to provide

alternate route information in the event of

road closure.

• Rural highways can be hilly, rocky, curvy and

mostly unsuitable for deployment of reliable ITS

equipment or even cell phone reception in

some cases.

• There can be a lack of necessary technological

backbone to support data collection and

information sharing. In Missouri, for example, a

fiber backbone doesn’t exist in many of the rural

localities, which forces transportation engineers

to resort to less reliable means of data transfer.

• Some rural roads do have parallel alternate

routes, so it may be necessary to communicate

issues to drivers 60 miles or more away.

• 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 rural highway

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 rural 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 real-time

travel time on rural highways. 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 rural highways, 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.
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Rural Data Collection Technology Federal Highway Administration

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 realworld 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 a rural

highway travel time 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.

The primary audience for this report is

transportation agencies who are either interested

in implementing a rural highway travel time

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 rural highway travel time 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 arterial 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 relatively

little information on RTT implementations and

evaluations. The search effort then expanded to

include targeted searches to explore the state of

the art technologies and practices used for RTT. 

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 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 RTT have not

been widely publicized and do not appear to be widely

known among transportation engineers.
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Rural 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 en-route RFID network

Journey time monitor* rural highway Connected Vehicle guidelines

Traveler 

information real-time remote Video/camera best practices

calculat* stand-alone satellite operations

estimat* intercity license plate reader implement(ation)

technology Bluetooth traffic

infrastructure GPS integrated corridor 

management

communicat* 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
5

Rural Data Collection Technology Federal Highway Administration

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

RTT 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 practice in RTT

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

scientific evaluation (where available) 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 RTT data

collection system.
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Rural 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.

These include:

• Bluetooth detection

• Toll tag reader

• In-pavement magnetic detectors

• Automatic license plate reader (ALPR)

• Machine vision

• Connected vehicle

• Loop detectors

• Radar, microwave, LIDAR (RML)

• 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 rural highways. 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 RTT 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 RTT 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

vehicle travel times and speeds by calculating the

time it takes for vehicles containing Bluetooth devices
7

Rural Data Collection Technology Federal Highway Administration Table 3 Candidate RTT 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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Rural Data Collection Technology Federal Highway Administration

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 a commonly used

technology for calculating travel times.

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, polemounted 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

that may be particularly beneficial on rural roads with

challenging topographies. An evaluation by Click and

Lloyd (2012) found that Bluetooth detectors placed

in the center of a wide rural median do not detect

vehicles as effectively as when they are placed on

the roadside, so for wide rural roads, two roadside

detectors may be more effective than one median

detector. 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

a fixed object 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. The relatively low power

consumption of Bluetooth detectors is a particular

benefit when infrastructure power is unavailable. 

Units can work with existing communications

infrastructure or record to local hard drives. When

given protection from precipitation and extreme

temperatures, device maintenance requirements

and failure rates can be very low.

Implementation Considerations

The flexibility of Bluetooth detection makes it a viable

option for a variety of implementation types, including

fixed, temporary, and portable (e.g., work zone, see

Haseman, Wasson, & Bullock (2010). Bluetooth

detection sensors detect any “discoverable”

Bluetooth-enabled devices within a radius of about

100 meters, if using a high-powered antenna. 

Measuring the travel time of an individual vehicle

along a road segment is as simple as comparing the

Source: trafficcast.com

Figure 2

TrafficCast’s BlueTOAD Polemounted, Solar-powered Bluetooth 

Detector and Cabinet Interior
9

Rural Data Collection Technology Federal Highway Administration

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 long dwell duration on a rural road

could indicate stopped or very slow moving traffic)

(Tsubota, Bhaskar, Chung, & Billot, 2011). Some

Bluetooth detection devices include global positioning

systems (GPS), which allow each device to be

precisely located and provides an automatic time

synchronization between devices, ensuring accurate

travel time calculations.

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 4.2.

Another potential limitation of some versions of

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

The system initially deployed in Houston, TX, 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

inaccuracy could lead to travel time inaccuracies,

especially over short segment lengths. Furthermore,

only 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), though this might not be an issue on

many lower-volume rural roads. 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 sensor-based 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 to $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. A MAC address is a unique and permanent

identifier 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 apps 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 as an added layer of

security against unauthorized access.
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Rural Data Collection Technology Federal Highway Administration

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 systems will either need to be replaced or

retrofitted for compatibility with the new technology,

if compatible. 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.

Toll Tag Reader (AVI) in Houston, TX

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

Figure 3

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 (RFID) 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

RFID device could also be used as an identifier, but

no viable plan exists to use such a system.
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Rural Data Collection Technology Federal Highway Administration

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. 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).

Implementation Considerations

Toll tag readers are only feasible for collecting travel

time data on routes where a sufficient percentage

of vehicles have toll tags. This may be a particular

concern in rural areas where few tolling facilities exist. 

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,

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

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, though vehicle fleet

penetration may not be sufficient in many rural

areas. 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
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• 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

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 travel time 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 Networks, so 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 (Greg 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 and 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 data that can be used

for various purposes. The high detection rate may

be especially valuable for maintaining the ability

to generate accurate travel time estimates on

rural roads with relatively low traffic volumes. A

comparison to ground truth video data conducted

by Sensys showed that in-pavement magnetometers

achieved a 70 percent 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

Sensys Sensor

Source: www.sensysnetworks.com

Figure 4
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Rural Data Collection Technology Federal Highway Administration

magnetometers are not likely to be practical for

short-term deployments.

Additional data such as vehicle class data can also

be derived from this technology, though additional

sensors may be required in an array. Research by

Medina, Hajbabaie, and Benekohal (2010) cautions

that magnetic detectors may overcount 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.

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 RTT data collection, as long

as communications are available, and is expected to

remain so.

Typical Sensys implementation showing sensor array, 

repeater, and access point

Source: www.sensysnetworks.com

Figure 5
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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

theoretically 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 (CCTV) 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 any 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

some 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

Institute estimated that ALPR would cost $25,000

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

own sensor (ITS International, 2010).
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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 highresolution 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”

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. 

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, however, 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. 

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 setup 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.

Costs

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.

Future Considerations

As cameras, software, data transmission, and

processors grow more capable and inexpensive, the

potential uses of machine vision systems are likely to
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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’s connected vehicles 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 ITS, including 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

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
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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 roads prone to high

speed variability, the relatively low cost and variety

of other uses and benefits of DSRC might make this

technology viable for RTT 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

Radar, microwave, and LIDAR are unique technologies

operate on the same principle: 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 is most prevalent on limited access roads

where speed variance along a road tends to be minimal.

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 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
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measurement, which can be relatively difficult

to use on roads with varying speeds due to

traffic control devices, speed limit changes, and

topographical features. Measurement locations

must be selected carefully to ensure applicability. 

The functional range of these technologies 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.

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.
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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 locationbased 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 to generate

accurate travel time estimates. GPS crowdsourcing

has only recently become a viable travel time 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 travel time data for non-freeway routes

come 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, which may be particularly advantageous

for rural areas where sensor instrumentations are

deemed infeasible. While it is theoretically possible

for a transportation agency to pursue its own

crowdsourced data (e.g., by providing a downloadable

data logging app to drivers), no examples of this

approach were identified in this review. If this
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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 app on their devices. Such apps 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 lower volume 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

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 then matches with a map database to

determine devices’ locations on roadways, and changes

in location over time can be used to calculate speed. 

This method of cell phone signal 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).

State of the Technology

Cell phone use in the United States is ubiquitous,

though cell phone coverage on rural roads may be

sporadic or nonexistent in some areas, depending

upon service carrier. In addition to improved

rural coverage, 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.
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Rural Data Collection Technology Federal Highway Administration

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

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 can be 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 rural roads,

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. Evaluation

of a different vendor’s cell phone signal monitoring

technology against ground truth data found that 95

percent of monitored limited access roads had an

absolute speed error within 10 miles per hour and

80 percent had a speed error bias within 5 miles per

hour. On rural arterials, only 75 percent of monitored

limited access roads had an absolute speed error

within 10 miles per hour and only 50 percent had a

speed error bias within 5 miles per hour. Accuracy

was even lower on urban arterials (Lattimer, 2010). 

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 that 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 rural 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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Rural Data Collection Technology Federal Highway Administration

3 Implementations of Rural Travel Time 

Data Collection

This section provides summaries of RTT data

collection implementations. 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 RTT implementations addressed in this section

(I-35 Minnesota, Duluth to Hinckley; and I-95 Maine,

Portland to Houlton) are described as detailed case

studies, whereas the following implementations are

described more briefly.

3.1 Minnesota DOT’s I-35 

Temporary Travel Times System

3.1.1 Background and Planning

Background

In 2012, Minnesota Department of Transportation

(MnDOT) initiated three separate roadway

improvement projects along I-35 from Hinckley

to Duluth that covered approximately 70 miles of

rural highway. The roadway is not a typical weekday

commuter route; I-35 is primarily a recreational

route with high traffic volumes over holidays and

weekends. The peak traffic times occur mostly on

weekends when motorists are traveling to and from

resort areas, and traffic would be most impacted

by construction from May 2012 to October 2012. 

A detailed account of the project and its results

is provided in the final report by Athey Creek

Consultants (2013).

Defined Need

MnDOT recognized the need to provide accurate

travel times and congestion information to motorists

along the freeway during construction with minimal

latency issues. The system was to be designed and

deployed to aid travelers in making decisions on

whether to travel I-35 or Minnesota State Route 23,

an approved alternate route during construction, as

shown in Figure 7. “The purpose of this project is to

inform the traveling public of the travel times through

three major reconstruction projects on I-35 this

summer,” said Dave Mavec, project engineer. “The

intent is to reduce driver frustration by displaying

the actual driving time it takes to drive through the

corridor. State Route 23 travel times are displayed to

encourage State Route as an alternate route.” It was

also important to MnDOT that the public be involved

in providing feedback on the travel time project.

Objectives

In addition to several internal criteria, MnDOT defined

the project’s intent and purpose as the following:

• Inform travelers of travel times on both I-35 and

the parallel State Route 23 using roadside static

signs with inserted changeable modules which

display real-time travel time information.

• Reduce driver frustration, enhance safety, and

increase traffic efficiency by sufficiently informing

the public about travel times on the alternate

route, thereby encouraging use of State Route 23

during peak traffic volume periods.

Project Location and Minnesota 

State Route 23

Figure 7

Source: Athey Creek Consultants, 2013
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Rural Data Collection Technology Federal Highway Administration

• Inform the traveling public, via a website link to

the MnDOT 511 website, about real-time travel

time information as displayed on the signs.

• Adhere to specific accuracy requirements for

each travel time display.

Specifications

The travel time system was to be automated and

used temporarily during the I-35 construction projects

MnDOT created a performance specification allowing

the contractors to pursue the travel time system that

met the project objectives so long as maintenance

and upkeep of the system were minimal. Among other

criteria, the specifications included the following:

• The system shall have sufficient traffic detection

devices to detect traffic speeds and by the use

of an algorithm compute and communicate

estimated travel time to the signs at the locations

in the plan.

• All traffic sensors for this project shall be nonintrusive to the pavement except as permitted by

the Engineer.

• The system message shall update at least

every 5 minutes. The system shall self-test

for communication or sensor failures. All

sensors shall be of a type whose accuracy and

latency are not degraded by inclement weather

or degraded visibility conditions including

precipitation, fog, darkness, excessive dust, and

road debris and have sufficient power capability

to run 24 hours per day/7days per week for

the duration of the project.

• The contractor shall create a website which is

accessible from a link on the MnDOT 511 website

which displays a map-based representation of the

project showing the sign locations and the near

real-time travel times as shown on the actual signs

(deployed in the field).

3.1.2 Implementation and Management

Contract Requirements

MnDOT hired a contractor through its Design-Build

process to implement a temporary standalone travel

time project throughout the I-35 work zones and on

an arterial highway, State Route 23. The system

included deployment of seven roadside static signs

along I-35 with inserted changeable modules that

displayed travel times to the motorists in real-time,

as shown in Figure 8.

The system was required to have sufficient traffic

detection devices (sensors) to detect traffic speeds

and, by the use of an algorithm, compute and

communicate estimated travel times. The quantity

and location of sensors were not specified in the

plans and were to be tailored to the site by the

contractor. Sensors were required to be relocated

when construction stages changed. The contractor,

Renaissance Technologies, Inc. (RTI), deployed 12

radar sensors to collect travel time information. The

traffic data collected was archived in an .zxml format

and ultimately transferred to MnDOT ownership (Athey

Creek Consultants, 2013). Updates to travel time

information were required to occur at least every

5 minutes. The accuracy and latency values were

determined by reviewing values used by MnDOT for

travel time displays in the Metro area, calculations

based on a percentage of travel times between signs

at normal speeds, and a maximum deviation thought to

be acceptable to the public. After this analysis, MnDOT

estimated that the public would not likely accept more

than a 15 minute deviation at any one sign. The final

accuracy and latency deviation from the displayed

travel time values were determined using engineering

judgment for each sign location.

Travel Time Sign Located Near 

40th Avenue on I-35 in Duluth

Figure 8

Source: Athey Creek Consultants, 2013
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Rural Data Collection Technology Federal Highway Administration

Dissemination

RTI’s flagship software, TrafAlertTM, was used to

communicate travel time information to the public

via eight DMS, seven roadside static signs, and an

online travel time map. Messages displayed on the

seven roadside static signs are depicted in Figure 9. 

The TrafAlertTM software enabled total control of

roadside signs and sensors from a central location. 

Because this work zone was outside of the installed

communications devices of the MnDOT TMC’s central

dispatch, the I-35 system operated independently

of the TMC and was not integrated into any other

MnDOT system.

Evaluation

Travel time accuracy was assessed by MnDOT using

floating car runs to compare travel time estimates

to actual drive times. The assessment found that

95 percent of travel times were within the allowable

accuracy range and that, among the 5 percent that

were out of range more than 85 percent of these

instances occurred during or shortly after periods

of congestion began. MnDOT also conducted online

surveys to assess travelers’ reactions to the travel

time system. The surveys found that nearly all

respondents who traveled the designated route noticed

the travel time information. Respondents felt that

the information helped them make route and other

planning decisions, set expectations, and reduce stress.

3.1.3 Lessons Learned

Overall, MnDOT deems the project as a success which

benefited travelers throughout the summer travel

season. Feedback obtained through a public survey

was generally positive, and respondents appreciated

having information that prepared them for congestion

in the work zone and assisted them in making

decisions about taking alternate routes.

Through comments provided by MnDOT staff and the

contractor, MnDOT has indicated they may provide

additional system specifications to contractors in the

future in order to enhance travel time data output

accuracy and minimize latency. MnDOT indicated that

project costs were much more affordable compared

to implementing a permenant system.

Travel Time Sign Locations and Messages on I-35

Figure 9

Source: Athey Creek Consultants, 2013
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Rural Data Collection Technology Federal Highway Administration

An extensive set of lessons learned by MnDOT and

the project contractor are provided in the project

final report (Athey Creek Consultants, 2013) and

select lessons learned are summarized below:

1 The “best-value procurement” method was

effective in helping MnDOT to select a qualified

contractor. MnDOT has a manual that describes

the best-value procurement method (MnDOT

Office of Construction and Innovative

Contracting, 2012).

2 Language used in bid documents should avoid

unnecessarily restrictive requirements and all

terms should be unambiguous.

3 MnDOT provided specifications to the contractor

that emphasized performance outcomes rather

than detailed design specifications. This gave the

contractor more ability to innovate and adapt to

create the best possible system. This approach also

transferred risk and liability to the contractor rather

than to MnDOT. However, more detailed design

specifications for some system aspects (e.g., sensor

spacing and detection capabilities) could have

potentially led to improved system performance. 

The project staff acknowledges that there are

tradeoffs between cost and system performance.

4 Contractors should be required to provide a

detailed quality control plan that outlines how

they will set up and test the system and monitor

and correct issues.

5 The methods and criteria that the transportation

agency plans to use to verify travel time data

accuracy should be specified to contractors

during the bid process so that they can assess

the validity of the method and the potential

risks of not meeting performance criteria. The

verification method should include multiple

assessments at different times and days.

6 The contract initially stated that monetary

deductions would be assessed against the

contractor if any part of the system was not

functional, but was later revised to state that

deductions would only occur if the nonfunctional

component adversely affected travel times. This

revised language provides contractors with an

incentive to design a robust system and avoid

penalization for inconsequential issues.

7 A project website with a construction map was

created by the contractor and was available via

a link on the MnDOT 511 website. The link was

difficult to find, however, and true integration of

the site within the MnDOT website could have

been more effective.

8 It would have been useful to provide ITS system

training to work zone supervisors prior to

developing bid documents so that they could

become more familiar with the candidate

technologies.

9 Public feedback about the travel time project

was generally positive, although northbound

travelers would have liked to receive the travel

time information before passing an exit to a

viable alternate route. This emphasizes the need

to provide motorists with travel time information

prior to decision points to allow them to make

effective routing choices.

3.2 Maine DOT’s Use of Variable 

Speed Limit (VSL) Signs to 

Provide Real-Time Traveler

Information

3.2.1 Background and Planning

Background

During 2008 and 2009, the State of Maine updated

its VSL signs system (see Figure 10) on Interstate 95,

Interstate 295, and surrounding arterial routes. The

State’s intention was not just to capture data related

to user delay and vary speed limits to make them

appropriate for conditions, but ultimately to provide

travel time information to motorists. The Maine

Department of Transportation (MaineDOT) currently

displays variable speed limits across the I-95 corridor

and provides situational alerts through the 511

system. The system measures and displays speeds to

enhance real-time information relayed to motorists.
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Rural Data Collection Technology Federal Highway Administration

Objectives

Initially, the State’s goal was to improve incident

notification information for all highway incidents

and weather events, control speeds, and improve

the State’s 511 System mapping capabilities for

the public. The Maine 511 Travel Information

Service is designed to help commuters and travelers

access information regarding weather-related road

conditions, construction zones, and congestion areas,

via either the 511 website (www.511maine.gov) as

shown in Figure 11 or phone, 24 hours a day and

7 days a week. The 511 website hosts a traveler

information map containing information on speeds

along the corridor. While motorists aren’t explicitly

given travel time information, in a rural setting the

posted variable speed limit may help to identify areas

where delays exist and implicitly advise motorists to

consider other routes.

Data Collection

The addition of the radar units on the back of the VSL

signs allows MaineDOT to measure average Interstate

segment speeds, which can be used to detect delays

State of Maine 

Variable Speed Sign

Figure 10

Source: Maine DOT, 2013

MaineDOT’s 511 Traveler Information Map Showing the I-95 Corridor

Figure 11

Source: www.511maine.gov
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Rural Data Collection Technology Federal Highway Administration

and estimate travel times to key junction points. The

agency uses this information to manage the corridor

and reduce impacts to travel caused by incidents

or weather, with information being updated to the

traveler information map.

3.2.2 Implementation and Management

Sensor Locations

The systems are installed at the end of interchange

on-ramps on both the northbound and southbound

side of I-95 and I-295 and controlled via cell modem

from MaineDOT’s central radio room. There are 75

signs installed from Portland to Houlton covering

a distance of approximate 260 miles. The distance

between the interchanges ranges from 2 miles to

10 miles apart. The VSL signs allow the Department

to measure average Interstate segment speeds, which

can be used to detect delays and allow motorists

to estimate travel durations to key junction points. 

MaineDOT is also using Bluetooth technology through

TrafficCast to monitor real-time travel data for

several other routes.

System Architecture and Processes

The VSL system is not part of an integrated system,

but it uses a tiered approach that involves the radar,

camera, and dispatcher information to verify incidents

on the corridor and reinforce decisions for the route. 

Analysis of the data is automated, but it involves direct

review, analysis, and decision making by MaineDOT

dispatchers. MaineDOT built this into their process

to give a sense of ownership to the dispatchers. The

dispatchers have three main roles: 1) monitor the twoway radio communications; 2) address public inquiries;

and 3) maintain the data entry of the information

posted on the 511 website and traveler information

map. The dispatchers oversee the communications to

the signs. This is accomplished through the use of a

cellular modem, which is sampled every 15 minutes. 

Speed and occupancy data are sent to a server and

processed through an algorithm analysis. A threshold

speed of 40 mph initiates an alert that is emailed to

a dispatcher. The dispatcher reviews the data, which

are displayed in a spreadsheet. Based on the data

presented, the dispatcher will program the VSL sign(s)

to reflect an appropriate speed for the current road

conditions. If available, the dispatcher will use a camera

feed to verify the roadway condition, and issue a

situational alert for public and operations posting.

3.2.3 Future Considerations

Mobile Applications

MaineDOT is considering evaluating mobile applications

used by smartphones to collect traveler information

that the user agrees to send. One example of a social

application is Waze, a community-based traffic and

navigation application for real-time traffic and road

information. The data from the Waze application

may be used in the future to build value on top

of data currently available to MaineDOT and can

supplement information being collected by the

agency. This information may eventually help to

explicitly communicate travel times to motorists who

are without access to mobile devices. To consider

integrating mobile applications into the traffic

management process, MaineDOT is working on a

request for information (RFI) to solicit other application

vendors to come forward and be evaluated.

Costs

Costs for this basic but informative way to share

travel information are much less than deployment

of DMS and associated hardware, software, and

technology packages. The total MaineDOT VSL

project cost was $776,849.54. The replacement

cost for a VSL sign is estimated at approximately

$10,000. To add a radar unit to each sign costs

approximately $500 and each camera costs

approximately $1,500. The combined cost for

cameras and radar units was approximately $66,000. 

In comparison, deploying this type of system costs

approximately one-tenth of traditional travel time

communication devices such as DMS.

3.3 Various Statewide Routes, 

Wisconsin

Wisconsin DOT (WisDOT) has implemented travel

time data collection systems on rural roads as part

of its Traffic Operations Implementation Plan (TOIP),

which guides efforts in transportation planning and

improvement and allows ITS to be implemented in

major roadway projects at a cost that is considered

incidental to the project (i.e., less than 10 percent of

project cost). The TOIP encourages ITS deployment

to be considered in the construction process to

mitigate construction-related congestion; it also
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Rural Data Collection Technology Federal Highway Administration

supports efforts to distribute ITS deployments where

they are most needed.

On rural freeways, WisDOT captures travel time data

using sidefire microwave detectors. RTT coverage

is provided in the vicinity of the I-90/I-94 split near

Tomah, WI (see Figure 12) on the I-94 corridor

between Milwaukee and Madison (see Figure 12), and

a portion of US-41 between Milwaukee and Green

Bay. WisDOT has been implementing these travel

time deployments for about 7 years. Prior to sidefire

radar, WisDOT used loop detectors as its primary

source of travel time data, then shifted to microwave

detection. Microwave detection was preferred over

loop detectors and eventually became standard

practice because it was easier to maintain and

replace, with the particular benefit of not requiring

lane closures and invasive pavement work.

WisDOT experience shows that rural travel times vary

infrequently unless there is a traffic incident. The RTT

system, however, is not intended specifically for use

as an incident detection system and does not include

any features for automatic incident detection; WisDOT

generally learns of incidents first from police reports.

WisDOT provides DMS showing travel times on

some rural routes. In a non-scientific survey,

about 80 percent of drivers stated a preference

for messages that show both travel time and

Examples of Rural Freeway Travel Time Coverage in Wisconsin

Figure 12

Source: 511wi.com
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Rural Data Collection Technology Federal Highway Administration

distances to destinations. In rural areas, travel time

sign destinations may be relatively far away and

destination names might not be meaningful in and of

themselves to drivers, so providing distance allows

drivers who are unfamiliar with the area to calculate

travel times for themselves.

In the future, WisDOT anticipates that travel time

data collection technologies may transition to

Bluetooth probe technologies due to the lower cost

of installation and maintenance, or the agency may

opt to purchase third-party travel time data. WisDOT

is also interested in the possibilities of connected

vehicle for travel time data. It is possible that the

future role of WisDOT and other DOTs will not be in

collecting travel time data themselves, but validating

data from third-party sources.

3.4 I-45 from Houston 

to Dallas, Texas

The I-45 corridor is a rural Interstate of approximately

230 miles that connects Houston and Dallas and serves

as a hurricane evacuation route. In recent years, major

hurricanes threatened the Gulf Coast and have brought

attention to the need for effective hurricane evacuation

management. Transportation officials needed the ability

to monitor traffic conditions along the evacuation route

and to determine whether to deploy contraflow (Texas

Transportation Researcher, June 2010).

In 2009 Bluetooth detectors were installed along

225 miles of I-45 to collect the necessary traffic

information. Two examples of pole-mounted Bluetooth

units are shown in Figure 13. Detector spacing ranges

from 5 miles to 20 miles, but sensors are typically

between 5 and 8 miles apart. Bluetooth was selected

for use primarily because it was significantly less

expensive than the toll tag reader technology that

has been used in earlier implementations. Before the

implementation began, Houston TranStar, a partnership

of government agencies responsible for providing

transportation and emergency management services

to the Greater Houston Region, conducted feasibility

testing to ensure that Bluetooth captured sufficient

data and reported accurate travel times. Testing

showed that Bluetooth performed comparably to

Houston’s more established toll tag reader technology

(Puckett, 2011)

In addition to aiding with evacuation planning, the

travel time system is intended to provide useful

information to motorists and TranStar staff regarding

current traffic conditions. Color coded real-time

travel times are displayed on a map on the Houston

TranStar website (see Figure 14).

Two Examples of AWAM 

Bluetooth Sensors

Figure 13

Source: tti.tamu.edu

Houston TranStar I-45 Traffic Map

Figure 14

Source: traffic.houstontranstar.org/layers/layers.

aspx?mapname=region_houston_dallas&inc=true&rc=true
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3.5 Various Routes in Oregon, 

Frontier Travel Time Project

In 2000, the Frontier Project Technical Advisory

Committee (TAC), which includes representatives

from eight State DOTs, selected an RTT estimation

system for demonstration on rural highways near the

Oregon coast (Wright, Shi, & Lee, 2005). Congestion

and incidents are common on this corridor, and

the TAC intended for the demonstration project to

be capable of providing travel times and incident

information to Oregon DOT (ODOT) and to motorists.

The TAC distributed system requirements to

interested vendors and vetted bids based on price,

previous success with travel time deployments,

and timeliness. The TAC chose an infrared ALPR

technology. The system read license plates,

encrypted the license plate numbers, and sent them

via telephone communications to a central server,

where vehicle matches were made and travel times

were calculated. The entire travel time system

consisted of a total of six license plate recognition

cameras mounted on cantilevers above the road. 

Each installation consists of two cameras, one for

each direction. One road segment was 3.15 miles

long and the other road segment was 22.25 miles

long. The TAC cooperated closely with the vendor to

ensure that the system was installed as specified.

The system operated as intended for the first

2 months in 2001, but then a series of software and

equipment failures ended the initial effort, and the

system remained dysfunctional until 2004. The most

significant problem was a faulty power supply module,

which was not replaced by the vendor for almost

1 year. Once the replacement part was available, it

took another year for ODOT staff to fully repair the

system because there was a significant backlog of

maintenance work elsewhere, and the travel time

system was considered non-critical to safe operation

of the roadway network.

Upon restoration of the system, the quality of the

system’s data was evaluated. Analyses showed that

when the system was functional, it provided accurate

travel times, though an insufficient amount of data

were available under congested conditions to evaluate

system accuracy when traffic is heavy. Segment

length had a significant effect on vehicle match rates:

for the long 22-mile segment, match rates were

under 5 percent of traffic, whereas for the short

3-mile segment, match rates were above 15 percent. 

The system also could be used for incident detection,

but ODOT found that cell phone calls from motorists

and other communication means were generally

faster and more reliable. Despite this success, the

system was discontinued due to ongoing operational

and maintenance issues; ODOT was not willing to rely

on the data or disseminate it given that the system

was prone to failure.

Lessons learned in this demonstration

project include:

• Maintenance of ITS devices is particularly

burdensome to rural transportation districts,

which may have less staff covering a wider area

than urban districts and fewer maintenance funds

• Travel time systems must be operationally

reliable to be used effectively

• Sufficient staff, equipment, and funding should

be available for system maintenance

• There are risks in deploying a first-of-its-kind

project

• Segment length and congested conditions have

an effect on data reliability

3.6 State Route 520 

in Orange County, FL

The Orlando-Orange County Expressway Authority

(OOCEA) manages one DMS, located in the eastern part

of Orange County, between Orlando and the Atlantic

coast. The sign was installed in 2008 on northbound

SR-520, about 0.5 miles before reaching SR-528. This

sign is in a largely undeveloped, rural area.

This DMS was originally intended to assist with coastal

evacuations: in such scenarios, the SR-520 northbound

to SR-528 westbound route would be heavily utilized

(see Figure 15). However, at this junction drivers

headed toward Orlando have the choice of either

remaining on SR-520 or switching to SR-528; both

routes are viable options to reach much of the Orlando

area. To aid drivers, travel times for westbound SR-528

are shown on this DMS. SR-528 is a toll road that is

heavily used by commuters to Orlando and travelers

headed to or from Orlando International Airport. 

Although travel times are not shown for SR-520,

drivers can use the SR-528 travel time to help

determine which route would be best for them. For
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Rural Data Collection Technology Federal Highway Administration

example, an unusually long travel time on SR-528

might suggest that SR-520 would be a faster route. 

As an added incentive, there is no toll on SR-520.

Travel times are calculated from vehicles’ on-board

toll transponders, used for toll collection on SR-528. 

Travel times are calculated each minute. However,

10 vehicles matched between 2 locations are needed

to develop an average. When there are fewer than

10 matches every minute, the sign displays the travel

time as calculated from the most recent 10 reads.

The sign is normally formatted as displayed in Figure

16. The destinations displayed are always Orlando

International Airport and SR-417. However, if the

travel time to either of these destinations is a certain

percentage (e.g., 50-100 percent) above the normal

or expected travel time, there is instead another

display. (Expected travel time is calculated from the

mean travel time at that time of day, as recorded

over the past 4 weeks.) The banner “TRAVEL TIME

VIA SR 528 TO” is then replaced by “TRAVEL

TIME ALERT.” The word “ALERT” flashes, as do the

destination(s) for which the travel time is elevated. 

The travel times also flash. In the case of a travel

time alert, the DMS is two-phased and alternates with

an incident-related message (i.e. CRASH AHEAD). 

Although travel times are not shown for the alternate

route (SR-520), drivers can use the SR-528 traffic

conditions as a basis for route choice. The sign

operates 24 hours a day.

Orange County Road Network

Figure 15

Source: Google Maps

Typical Display Format of OOCEA 

Travel Time Sign 

Figure 16

Source: www.fl511.com
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Rural Data Collection Technology Federal Highway Administration

 3.7 I-90, Snoqualmie Pass 

in Washington

I-90 is a major freeway connecting Seattle with inland

Washington State, though much of the route is situated

in a rural setting. About 50 miles from Seattle, I-90

traverses Snoqualmie Pass, a mountain pass at 3,000

feet elevation. According to Washington State DOT

(WSDOT), the Pass carries more than 10 million

travelers per year. Due to the pass’s unique climate,

it receives huge amounts of precipitation, including an

average of more than 400 inches of snow per year. 

The extreme weather conditions can make the Pass

unsafe to drive, occasionally required road closures

due to unsafe conditions or avalanche risk. In recent

winters, crash-related closure of the Pass has averaged

fewer than 20 hours per winter. Avalanche closures

are more varied, ranging from fewer than 10 hours

in 2009-2010 up to more than 100 hours in 2007-

2008 and 2008-2009. In addition to winter weather

issues, Snoqualmie Pass can experience congestion

on summer weekends and holidays, and a long-term

construction project is expected to add to delays (Rick

Gifford, personal communication, February 2013).

Snoqualmie Pass Traffic Map

Figure 17

www.wsdot.com/traffic/passes/snoqualmie/

Due to unpredictable mountain pass conditions, in

2011 WSDOT began providing information to drivers

on its website, smartphone app, and on travel time

signs on I-90. The website (www.wsdot.com/traffic/

passes/snoqualmie/) provides travel times for the

74-mile stretch of mountainous terrain as well as a

color coded traffic map with live traffic cameras

(see Figure 17), and additional information on current

weather and road conditions, forecast, and travel

restrictions.

Three travel time signs (one eastbound and two

westbound) provide drivers with travel times and

distances to the Pass (see Figure 18). The travel

times are currently calculated using data from the

third-party data provider INRIX. However, WSDOT

is currently installing nearly 30 radar detectors

along the 74 miles surrounding the Pass that will

ultimately be used to calculate travel times in place

of the INRIX data.

Advantages of radar over INRIX data for WSDOT

include data ownership, a more compatible

costing structure (i.e., easier to obtain funding for

purchases than ongoing payments), and overall
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Rural Data Collection Technology Federal Highway Administration

cost savings (Rick Gifford, personal communication,

February 2013). The combination of radar and

probe data provides WSDOT with more precise

speed data, and adds traffic volume data.

According to WSDOT, “This work was funded

by $800,000 of electrical preservation funds, as

well as $500,000 from the I-90 Snoqualmie Pass

East corridor project. The $1.3 million project

replaces a 20-year-old communications system and

includes the online [traffic] flow maps, travel time

signs that were activated earlier this summer, and

provides real-time information to drivers and our

Traffic Management Center.”

Travel Time Sign Approaching 

Snoqualmie Pass

Figure 18

Source: wsdotblog.blogspot.com
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Rural Data Collection Technology Federal Highway Administration

4 Best Practices for Rural Travel Time 

Data Collection

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

RTT data collection technologies and the state-ofthe-practice in RTT implementations to develop a set

of best practices that are based on evaluation and

real-world experiences. It focuses on best practices

related to the data collection technology; a complete

set of best practices for RTT programs is beyond the

scope of this effort. The best practices were developed

with the understanding that every implementation of

RTT 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

travel time 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 RTT implementation.

4.1 Need 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 RTT Program?

Agencies that have implemented their own RTT

programs have done so for a variety of reasons. 

Some intended to enhance their own abilities to

monitor regional traffic conditions. Some intended

to provide traveler information to improve drivers’

abilities to make efficient routing decisions during

construction projects. Some intended to improve

their ability to monitor and control evacuations.

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 RTT coverage?

While the primary objectives of an RTT 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, RTT

systems can provide data on traffic volumes, vehicle

class, 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 the objectives have been defined, the next step is

to determine the specifications for the RTT program. 

Development of specifications for an RTT 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 RTT

program will be capable of achieving its objectives.

How Can RTT Meet Mandates?

An RTT 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) (http://ops.fhwa.

dot.gov/safetea.htm). According to FHWA, “The RealTime System Management Information Program is
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Rural Data Collection Technology Federal Highway Administration

to provide the capability to monitor in real-time the

traffic and travel conditions of the major highways

across the United States and provide a 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 rural 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 RTT program is within the

scope of available funding. For example, depending

on how it is implemented, an RTT 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 RTT Coverage Area?

A clear definition of the project objectives should help

to identify the target coverage area. If the objective

is to provide travel times to motorists to help them

choose between multiple routes, sufficient coverage

of alternate routes is required, as well as a means to

disseminate the data in ways that allow drivers to

make timely route choices.

What Are the Needs for Scalability?

When first implementing an RTT 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 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 real-time, or for

an extended period of time, a battery-powered, selfcontained Bluetooth data collection device can be used

for a period of a few weeks.

What Secondary Benefits Can be Achieved?

Secondary benefits of an RTT 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 RTT 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 and loop detectors) 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 RTT 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 travel time system’s ability to generate travel

time estimates with high confidence, and may result
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Rural Data Collection Technology Federal Highway Administration

in delayed processing in order to acquire enough

matches to generate an estimate. These challenges

may be more significant in the rural environment

than the freeway environment due to the potential

for lower traffic volumes and greater travel time

variability.

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 on rural

roads where speeds are variable due to traffic control

devices, varying speed limits, and so forth. 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. Delay will increase if segments between

sensors are long, if travel times increase (e.g., due

to congestion), or if traffic volumes are low. 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 RTT program must fit into the broader traffic

management context. Often, this involves sharing

resources and ensuring compatibility with other

agencies, including contractors. For instance, a

major rural route may traverse multiple jurisdictional

boundaries. 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 alternate route travel

times for dissemination to the public.

What Are the Infrastructure Requirements?

Each RTT 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 has relatively high data transmission and

power requirements, Bluetooth detectors are

particularly flexible in terms of installation location

and power requirements, and crowdsourced data

using GPS sensors likely requires no infrastructure

elements at all. If the rural road 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 rural roads, new structures

such as mounting poles and cantilevers might be

undesirable or infeasible due to aesthetic concerns,

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

Some new infrastructure components may also

require environmental assessments. Rural roads

without available network lines may require cellular

communications for real-time data transmission. 

If this is the case, it is important to evaluate the

availability of cellular signal on available cellular

providers’ networks. An inexpensive sensor can

be purchased for this purpose. A strong signal is

preferred because a weak signal can lead to sporadic

data transmission and increased power drain on

detectors’ cellular modems.

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 and in a timely manner. Research suggests

that on routes with relatively low speed variability,

between four and eight vehicle probe data points are

sufficient to generate a travel time estimate (Click

& Lloyd, 2012). However, when traffic volumes are

especially 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 whether travel times are

required during low volume periods, given that travel
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Rural Data Collection Technology Federal Highway Administration

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 RTT 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, RTT 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

organizations can significantly increase the efficiency

with which data can be processed and used. If new

software is required to make optimal use of RTT data,

a potential solution is 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 RTT 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 (www.

itskrs.its.dot.gov/) 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 RTT.

As discussed in Chapter 2, the reported costs of RTT

data collection technologies 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 sensors 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 costs (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 RTT 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 thirdparty 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 RTT

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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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, 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.

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. Finally, should there

be any significant unanticipated problems with the

system, it should be clear who assumes these risk and

the financial and liability responsibilities for problems.

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?

Ideal sensor location is highly dependent upon the

features of the route. Technologies that measure spot

speed (e.g., loop detection, radar) are generally only

effective on roads where average travel speeds are

fairly constant along the route with minimal “friction,”

and therefore spot speed measurements can be

considered to be fairly representative of the road

segment. Technologies that measure the segment travel

times of vehicles can be used on any type of rural road. 

Sensor spacing should be based primarily on traffic

patterns. Routes with a high percentage of through

traffic that does not turn off or stop along the route

can have sensors relatively distant from one another

because little data loss is expected. In such cases,

sensor spacing may be similar to freeway spacing,

though low volume or low speed roads may benefit from

closer spacing to minimize data lag. Although there is no

general rule of thumb for sensor spacing on rural roads,

in current practice, sensor spacing on limited access

rural roads has generally ranged from about 3 to 8

miles. The technology vendor may 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 of the devices 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 RTT 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. 

However, especially on rural roads, it is important to

ensure that missing data is not indicative of a road

blockage due to a collision or other problem.

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.
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Rural Data Collection Technology Federal Highway Administration

• TMC software and operators have access to the

most data sources, and are therefore likely to be

the best sources of replacement data.

• 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 connection (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 and 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 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 staff should be available to deal with

potential startup problems. Finally, 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 RTT implementers have conducted formal

evaluations of data quality, but guidance can be found

in evaluations of other travel time implementations. 

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).

How Should Public and Media Relations 

be Handled?

It is important to proactively address potential public

concerns about an RTT program. If the RTT 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 environments, a new pole or

DMS might be seen as an eyesore.

How Can the Effectiveness of an RTT Program 

be Evaluated?

To date, there have been few formal evaluations of

the effectiveness of RTT programs in meeting their

goals. In part, this may be because many goals of

RTT programs can be difficult to assess affordably

and with statistical rigor. Establishing performance

measures for evaluating the RTT 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.
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Rural Data Collection Technology Federal Highway Administration

5 Conclusion

Real-time travel time information can be valuable to

both motorists and transportation agencies. While

travel time practices are relatively well-established

on major freeways, rural highways pose unique

challenges, and few practitioners have experience

collecting travel time data on rural roads. This report

provides practitioners with information on the current

and emerging technologies available for RTT data

collection, summarizes current practice, and provides

a set of key considerations and questions to ask when

planning and operating an RTT program.

Although RTT data collection is a relatively new

and rapidly evolving area, RTT 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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Rural Data Collection Technology Federal Highway Administration

References

Athey Creek Consultants (2009)

Minnesota Arterial Travel Time (MATT) project. Final report

prepared for Minnesota Department of Transportation.

Athey Creek Consultants (2013)

Evaluation of rural travel times during construction. Final

report prepared for Minnesota Department of Transportation.

Avni, O. (2007)

Performance and limitations of cellular-based traffic

monitoring technologies: Based on case study of

benchmarking Cellint’s TrafficSense with road sensors. 

In Institute of Transportation Engineers (ITE) Technical

Conference and Exhibit, 2007, San Diego, California, USA.

Blokpoel, R.J. (2009)

Vehicle reidentification using inductive loops in urban

areas. Presented at the 16th ITS World Congress and

Exhibition on Intelligent Transport Systems and Services,

Stockholm, Sweden.

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U.S. Department of Transportation

Federal Highway Administration

Office of Operations

1200 New Jersey Avenue, SE

Washington, DC 20590

Toll-Free “Help Line” 866-367-7487

www.ops.fhwa.dot.gov

April 2013

FHWA-HOP-13-029