Applying Archived Operations Data 

in Transportation Planning

A PRIMER
Notice

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

Transportation in the interest of information exchange. The U.S. Government 

assumes no liability for the use of information contained in this document.

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or manufacturers’ names appear in this report only because they are considered 

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the facts and accuracy of the data presented herein. The contents do not necessarily 

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This report does not constitute a standard, specification, or regulation.

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Cover image: Thinkstock/zhudfeng
Technical Report Documentation Page

1. Report No. 

FHWA-HOP-16-082

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

4. Title and Subtitle 

Applying Archived Operations Data in Transportation 

Planning – A Primer

5. Report Date

December 2016

6. Performing Organizations Code

7. Authors

 Jocelyn Bauer, Richard Margiotta, Michael Pack 

(alphabetical order) 

8. Performing Organization 

Report No.

9. Performing Organization Name and Address

Leidos, Reston, Virginia

Cambridge Systematics, Inc., Knoxville, Tennessee

Michael Pack, LLC, College Park, Maryland

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

 Contract No. 

 DTFH61-12-D-00020

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

1200 New Jersey Avenue, SE

Washington, DC 20590

13. Type of Report and Period 

Covered

Primer, July 2014–May 2016

14. Sponsoring Agency Code

HOP

15. Supplementary Notes

Wayne Berman, Government Task Manager, Federal Highway Administration

16. Abstract

The purpose of this primer is to assist transportation planners in effectively using archived 

operations data for developing, analyzing, and evaluating transportation plans and 

programs. This primer raises planners’ awareness of the opportunities afforded through 

archived operations data and provides guidance on how to take advantage of that data 

to expand and improve planning practices. It also identifies innovative applications for 

archived operations data in planning. This primer aims to help planners and their operations 

data partners overcome the barriers to obtaining and using data, regardless of whether they 

are just getting started with using the data or have substantive experience.

17. Key Words

Archived operations data, planning, transportation, 

travel time, volume, speed, performance measures, 

analytic procedures.

18. Distribution Statement 

 No restrictions. 

19. Security Classif. (of this 

report) 

Unclassified

20. Security Classif. 

(of this page) Unclassified

21. No of Pages

 124

22. Price 

 N/A

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

ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

APPROXIMATE CONVERSIONS TO SI UNITS

SYMBOL WHEN YOU KNOW MULTIPLY BY TO FIND SYMBOL

LENGTH

In. inches 25.4 millimeters mm

ft feet 0.305 meters m

yd yards 0.914 meters m

mi miles 1.61 kilometers km

AREA

in.2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha

mi2 square miles 2.59 square kilometers km2

VOLUME

fl oz fluid ounces 29.57 milliliters mL

gal gallons 3.785 liters L

ft3 cubic feet 0.028 cubic meters m3

yd3 cubic yards 0.765 cubic meters m3

NOTE: volumes greater than 1,000 L shall be shown in m3

MASS

oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t")

TEMPERATURE (exact degrees)

°F Fahrenheit 5 (F-32)/9 or 

(F-32)/1.8 

Celsius °C

ILLUMINATION

fc foot-candles 10.76 lux lx

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS

lbf poundforce 4.45 newtons N

lbf/in.2 poundforce 

per square inch

6.89 kilopascals kPa

SI* (MODERN METRIC) CONVERSION FACTORS

*SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003).
iii

ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

APPROXIMATE CONVERSIONS TO SI UNITS

SYMBOL WHEN YOU KNOW MULTIPLY BY TO FIND SYMBOL

LENGTH

mm millimeters 0.039 inches in.

m meters 3.28 feet ft

m meters 1.09 yards yd

km kilometers 0.621 miles mi

AREA

mm2 square millimeters 0.0016 square inches in.2

m2 square meters 10.764 square feet ft2

m2 square meters 1.195 square yards yd2

ha hectares 2.47 acres ac

km2 square kilometers 0.386 square miles mi2

VOLUME

mL milliliters 0.034 fluid ounces fl oz

L liters 0.264 gallons gal

m3 cubic meters 35.314 cubic feet ft3

m3 cubic meters 1.307 cubic yards yd3

MASS

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or "t") megagrams (or "metric ton")1.103 short tons (2000 lb) T

TEMPERATURE (exact degrees)

°C Celsius 1.8C+32 Fahrenheit °F

ILLUMINATION

lx lux 0.0929 foot-candles fc

cd/m2 candela/m2 0.2919 foot-lamberts fl

FORCE and PRESSURE or STRESS

N newtons 0.225 poundforce lbf

kPa kilopascals 0.145 poundforce 

per square inch 

lbf/in2

SI* (MODERN METRIC) CONVERSION FACTORS

*SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003).

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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

Table of Contents

1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Making the Case – Using Archived Operations Data for Transportation Planning. . . . . . . . . . . 1

Benefits of Archived Data to Transportation Planners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Primer Purpose and Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2. MEETING A RANGE OF PLANNING NEEDS WITH ARCHIVED 

OPERATIONS DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Performance-Based Planning and Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Planning Model Development: Analyzing Alternatives and Identifying Future 

Performance Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3. CONQUERING THE CHALLENGES OF USING ARCHIVED OPERATIONS DATA . 15

Institutional Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Challenges in Changing Planning Methods and Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4. OBTAINING ARCHIVED DATA THAT PLANNERS NEED . . . . . . . . . . . . . . . . . . . . . . . 23

Locating and Accessing Archived Operations Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Obtaining Third-party Data and Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Data Elements Useful for Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Data Sources and Collection Techniques to Ensure Usefulness in Planning. . . . . . . . . . . . . . . 38

Data Archives: Implementation Options and Common Mistakes Associated with Each . . . . . 42

5. PLANNING OPPORTUNITIES FOR ARCHIVED OPERATIONS DATA – 

BASIC TO INNOVATIVE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Problem Identification and Confirmation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Development and Reporting of Mobility Performance Measures . . . . . . . . . . . . . . . . . . . . . . . 56

Use of Archived Operations Data for Input and Calibration of Reliability Prediction Tools . . . 67

Performance of Before and After Studies to Assess Projects and Program Impacts . . . . . . . . . 77

Identification of Causes of Congestion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

6. GETTING STARTED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

List of Figures

Figure 1. Map. New Jersey Department of Transportation management system integration 

of candidate project areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Figure 2. Diagram. Flow chart of performance-based planning and programming. . . . . . . . . . . . . . 9

Figure 3. Graphic. Timeline displaying time taken for each activity during the incident. . . . . . . . . 10

Figure 4. Diagram. Components of an effective archived operations data archive. . . . . . . . . . . . . . 15

Figure 5. Photo. Technician . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Figure 6. Map. Outline of New York State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Figure 7. Map. Outline of Arizona. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Figure 8. Map. Outline of Maryland. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Figure 9. Map. Outline of Oregon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Figure 10. Photo. Interstate highway shield for I-95. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Figure 11. Diagram. Incident data model for performance monitoring. . . . . . . . . . . . . . . . . . . . . . . 31

Figure 12. Photo. Incident clearance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Figure 13. Diagram. Work zone data for planning applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Figure 14. Photo. Traffic signal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Figure 15. Image. An illustration of interconnected infrastructure. . . . . . . . . . . . . . . . . . . . . . . . . . 37

Figure 16. Diagram. Computing facility travel times from intelligent transportation 

system roadway detectors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Figure 17. Diagram. Operations data archives built in-house or hosted by a third party. . . . . . . . . . 42

Figure 18. Photo. Congestion on freeway. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Figure 19. Screenshot. Ranked bottleneck locations for all of New Jersey during March 2014. . . . .48

Figure 20. Chart. Time spiral showing when a bottleneck occurred and length during that time. . 49

Figure 21. Graph. Congestion scan graphic for I-95. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Figure 22. Screenshot. User delay costs for a 17-mile stretch of I-295 in New Jersey 

(both directions of travel). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Figure 23. Screenshot. View of I-295 showing detailed statistics in a mouse tooltip for a 

particular hour of the day. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Figure 24. Screenshot. Project confirmation presentation graphic to confirm a “high-need” 

signalized intersection in New Jersey. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Figure 25. Screenshot. Example of problem identification and project confirmation at 

New Jersey Department of Transportation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Figure 26. Screenshot. Example analysis from New Jersey Department of Transportation 

using the Vehicle Probe Project Suite, 511 New Jersey cameras, and the New Jersey 

Consortium for Middle Schools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
List of Figures (continued)

Figure 27. Screenshot. Concept graphics explaining where high accident locations contribute to 

bottlenecks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Figure 28. Diagram. Performance measures provide a quantifiable means of implementing 

goals and objectives from the transportation planning process. . . . . . . . . . . . . . . . . . . . . . . . . . 56

Figure 29. Diagram. Performance measures should be carried across planning applications 

throughout the entire time horizon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Figure 30. Diagram. Program logic model adapted for incident management. . . . . . . . . . . . . . . . . 58

Figure 31. Graph. Travel time distribution is the basis for defining reliability metrics . . . . . . . . . . 59

Figure 32. Graph. Mean travel times under rain, crash, or non-crash traffic incident conditions 

for I-5 southbound, North Seattle Corridor, Tuesdays through Thursdays, 2006. . . . . . . . . . . . 62

Figure 33. Graph. Reliability trends reported by the Metropolitan Washington Council of 

Governments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Figure 34. Charts. Incident timeline trends reported by the Georgia Department of 

Transportation for the Atlanta region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Figure 35. Screenshot. Heat map of congestion on a corridor using the Performance 

Measurement System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .63

Figure 36. Image. Delaware Valley Regional Planning Commission’s outreach document is 

based on archived data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Figure 37. Image. Inside view of Delaware Valley Regional Planning Commission’s 

outreach document. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Figure 38. Graph. Plot of speed and vehicles per hour per lane on I-4 at Kaley Avenue, 

westbound. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Figure 39. Graph. Plot of speed and vehicles per hour per lane on I-4 at Michigan Avenue, 

westbound. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Figure 40. Graph. Plot of speed and vehicles per hour per lane on I-4 at Wynmore, eastbound. . . . 72

Figure 41. Graph. Plot of speed and vehicles per hour per lane on I-4 at Wynmore, westbound . . . 72

Figure 42. Graph. Plot of speed and vehicles per hour per lane on I-4 east of Wynmore, 

eastbound. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Figure 43. Graph. Plot of speed and vehicles per hour per lane on I-4 east of Wynmore, 

westbound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Figure 44. Graph. Speed contours from archived intelligent transportation system detector 

data are useful for identifying bottleneck locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Figure 45. Graph. Hypothetical before and after case: mean travel time index 

and vehicle-miles of travel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Figure 46. Graph. Hypothetical before and after case: planning time index and vehicle miles 

of travel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

vii
List of Figures (continued) 

Figure 47. Map. The Port of Miami tunnel project. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Figure 48. Map. Truck flow and speed impacts of the Port of Miami tunnel project. . . . . . . . . . . . 83

Figure 49. Screenshot. The bottleneck ranking tool. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Figure 50. Screenshot. Congestion time spiral graphic with incidents and events overlaid . . . . . . . 88

Figure 51. Screenshot. Congestion scan graphic of outlier congestion showing both northand southbound traffic on I-270 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Figure 52. Screenshot. Congestion scan graphic including Saturdays. . . . . . . . . . . . . . . . . . . . . . . 89

Figure 53. Screenshot. Heat maps function within incidents clustering explorer. . . . . . . . . . . . . . . 90

Figure 54. Screenshot. Correlation of coefficients ranking function in 

incidents clustering explorer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Figure 55. Graph. Plot of travel time by hour of day for I-66, created via Regional Integrated 

Transportation Information Systems Vehicle Probe Project Suite . . . . . . . . . . . . . . . . . . . . . . . 93

Figure 56. Screenshot. Historical dates and times to visualize weather and 

incident details during that date and time. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Figure 57. Screenshot. The road weather information systems history explorer . . . . . . . . . . . . . . . 95

Figure 58. Screenshot. Visual representation of the cost of delay for both passenger 

and commercial vehicles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

Figure 59. Screenshot. An interactive animated map showing conditions during a winter 

weather event (left) compared to conditions during normal days of the week (right). . . . . . . . . 96

ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

viii
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

List of Tables

Table 1. Types of archived operations data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Table 2. Granularity of Strategic Highway Research Program 2 reliability-oriented 

analysis tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Table 3. Highlights of operations data available to Capital District Transportation Committee. . . . 24

Table 4. Highlights of operations data available to Maricopa Association of Governments. . . . . . . 25

Table 5. Highlights of operations data available to Maryland State Highway Administration. . . . . 27

Table 6. Highlights of operations data available to Oregon Department of Transportation. . . . . . . 29

Table 7. Recommended set of reliability performance measures from Strategic Highway 

Research Program 2 Project L08. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Table 8. Upper end of speed-flow distribution, I-4, Orlando, Florida. . . . . . . . . . . . . . . . . . . . . . . . 71

Table 9. Facility free-flow speed calculations, Atlanta, Georgia, area freeways. . . . . . . . . . . . . . . . 75

Table 10. Incident characteristics for I-66, Virginia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Table 11. Incident blockage characteristics for case study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Table 12. Checklist for getting started. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Table 13. The National Performance Measurement Research Data Set (NPMRDS). . . . . . . . . . . . 99

Table 14. The National Weather Service Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Table 15. Google/Waze speed deviation and incident data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

Table 16. Third party probe-based speed data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

Table 17. Third party origin-destination data (O-D). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Table 18. Partner agency data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Table 19. Fatality Analysis Reporting System (FARS) data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

Table 20. Options for agencies with limited amounts and types of data. . . . . . . . . . . . . . . . . . . . . 102

Table 21. Options for agencies with extensive amounts and types of data. . . . . . . . . . . . . . . . . . . . 104
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

List of Acronyms

AADT annual average daily traffic

ABS antilock braking system 

ALPR automatic license plate recognition

ATDM active transportation and demand management

ATMS advanced transportation management system

AVL automated vehicle location

ADOT Arizona Department of Transportation

CAD computer-aided dispatch

CCTV closed circuit television

CDTC Capital District Transportation Committee

CHART Coordinated Highways Action Response Team

CMP congestion management process

DMS dynamic message sign

DOT department of transportation

DVRPC Delaware Valley Regional Planning Commission

FAST Fixing America’s Surface Transportation

FDOT Florida Department of Transportation

FFS free flow speed

FHWA Federal Highway Administration

FMS freeway management system

GDOT Georgia Department of Transportation

GPS Global Positioning System

HAR highway advisory radio

HCM Highway Capacity Manual

HPMS Highway Performance Management System

HOV high-occupancy vehicle

I-TMS Internet-Traffic Monitoring System

ICM integrated corridor management

iPEMS Iteris Performance Measurement System

IT information technology

ITS intelligent transportation systems

LRTP long-range transportation plan

MAG Maricopa Association of Governments

MATOC Metropolitan Area Transportation Operations Coordination

MIST Management Information System for Transportation
xi

ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

MPH miles per hour

MPO metropolitan planning organization

MSTM Maryland Statewide Transportation Model

MWCOG Metropolitan Washington Council of Governments

NHS National Highway System

NJDOT New Jersey Department of Transportation

NPMRDS National Performance Measures Research Data Set

O-D origin-destination

ODOT Oregon Department of Transportation

PCPHPL passenger cars per hour per lane

PeMS Performance Measurement System

PHV peak-hour volumes

POMT Port of Miami Tunnel

PORTAL Portland Oregon Regional Transportation Archive Listing

PSU Portland State University

PTI planning time index

QC quality control

RFP request for proposal

RITIS Regional Integrated Transportation Information Systems

ROI return on investment

RTMS remote traffic microwave sensors

RWIS road weather information systems

SHA State Highway Administration

SHRP 2 Strategic Highway Research Program 2

TDF travel demand forecasting

TMC transportation management center

TTI travel time index

UMD University of Maryland

VMT vehicle-miles of travel

VPP Vehicle Probe Project

WSDOT Washington State Department of Transportation

1

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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

Introduction

1. INTRODUCTION

Making the Case – Using Archived Operations Data for 

Transportation Planning

Effective transportation planning and investment decision making depends on timely, 

comprehensive, and accurate data. Traditionally, data for planning has come from manual collection 

techniques and approximations from model output. While these sources are still used, transportation 

planners at the State, metropolitan, and local level are beginning to leverage an increasingly 

prevalent kind of transportation data—archived operations data.

Archived operations data is information collected and stored to support the monitoring and 

management of the transportation system. Enormous amounts of data on the performance of the 

transportation system are generated daily. For example, there are over 4 billion probe-based road 

segment speeds generated in the United States each day. California alone produces nearly 30 

million sensor measurements for speed and volume daily.1 Archived operations data can include 

traffic, transit, bike, pedestrian, construction, and weather information that is usually collected in 

real-time by intelligent transportation system (ITS) infrastructure, such as in pavement inductive 

loop detectors, radar detectors, remote traffic microwave sensors (RTMS), Bluetooth, and E-ZPass 

or other unique identifier tag readers. It also includes incident or event information entered into 

electronic logs by transportation or public safety personnel. Although not exhaustive, Table 1 

contains many of the common types of archived operations data that are collected.

Historically, operations data was used almost exclusively “in the moment” to help manage the system in 

real-time, and much of the data generated from these 

systems was either not archived or was archived in 

ways that made it extremely difficult to access and 

work with. Most operations data archives were only 

used for auditing purposes and were only accessible 

by one or two individuals within the agency’s operations or information technology (IT) group. 

However, with shrinking budgets; a focus on performance-based planning; a greater commitment to 

data quality; and advances in sensors, collection techniques, and archiving technologies, agencies are 

realizing the value of these data collection efforts beyond day-to-day operations and are tapping into 

opportunities to make better use of existing operations data. This has led them to enhance operations 

data archiving in ways that make it more readily available to larger and more diverse user groups, 

such as planners. Transportation planners at the State, metropolitan, and local level are finding they 

are able to rely less on assumptions and modeled data, solve a broader range of problems, and make 

more effective decisions.

1 Pack, Michael, University of Maryland Center for Advanced Transportation Technology Laboratory, Personal Communication, 2015.

Transportation planners at the State, 

metropolitan, and local level are finding 

they are able to rely less on assumptions 

and modeled data, solve a broader range 

of problems, and make more effective 

decisions with archived operations data. 
2

ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

1Introduction

Table 1. Types of archived operations data.2

Archived 

Operations Data 

Type Description

Traffic volume, 

speed, class, and 

occupancy from 

point and probe 

data sources

Information collected by agencies and third parties from roadway sensors 

that could include inductive loops, side-fired sensors (acoustic, microwave, 

etc.), radar, and video. Also includes data from probe-based systems—

either agency-owned or third-party supplied.

Event, work zone, 

and incident 

information

Information entered by each agency into its own incident management 

system. Data typically includes incident location, type, severity, 

information about the vehicles involved and their status, to whom are 

notifications made and which responders are on-scene, lane closures, 

response plans or detours, and messages on dynamic message signs (DMS) 

or highway advisory radio (HAR).

Weather data

Weather alerts, temperature, precipitation types and rates, wind speeds, radar 

data, and other information from the National Weather Service, third parties, 

the media, etc. Also includes weather and pavement surface conditions that 

agencies gather from their roadway weather information systems.

Device 

operational 

status

Data on the operational status of roadway devices from each agency, including 

traffic detectors, DMS, traffic signals, HAR, roadway weather information 

systems, and closed circuit television (CCTV) cameras, where available.

Managed lane 

status

Data on when high-occupancy vehicle (HOV) restrictions are in effect, 

direction of reversible lanes by time of day, and price of high-occupancy 

toll lanes by time.

Surveillance 

video Live CCTV feeds focused on roadways, assets, or pedestrians.

Transit alerts Transit alerts, service disruptions, and other information transmitted by 

transit providers—both public and private.

Automated 

vehicle locations 

(AVLs)

The locations and status of freeway service patrols, transit vehicles, or 

other assets equipped with AVL hardware.

Signal status Operational status of signals at intersections or ramp meters, such as 

operational, maintenance mode, flashing, or offline.

Signal timing 

plans Signal timing plans, current or future timing schemes.

Computer-aided 

dispatch (CAD) 

information

Data from public safety computer-aided dispatch (CAD) systems, such as 

fire, emergency medical services, and law enforcement. Can include dispatch 

requests, incident types, severity, responder requests, or even lane status.

2 Pack, Michael and Ivanov, Nikola, National Cooperative Highway Research Program Synthesis 460, Sharing Operations Data Among 

Agencies (Washington, DC: 2014). Available at: http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_syn_460.pdf.
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

Introduction

Archived 

Operations Data 

Type Description

Static, descriptive 

information

Data from public safety CAD systems, such as fire, emergency medical 

services, and law enforcement. Can include dispatch requests, incident types, 

severity, responder requests, or even lane status.

Decision-support 

response plans

The various actions that the departments of transportation (DOTs) are 

likely to take to help minimize congestion impacts and clear roads more 

quickly. Could include pre-programmed DMS messages, signal timing 

plans, traveler information strategies, detours, etc., that are grouped 

together into a single, cohesive “plan of action” ready to implement. The 

sharing of these response plans can help agencies to better coordinate so 

that one agency’s response plan is not in conflict with another’s.

Parking data

Location of parking facilities, number of spaces occupied and available, 

time and duration of parking space utilization, current fees, restrictions, 

and data on how to reserve a space.

Travel time

Often a derivative of speed data, travel time data represents the number 

of minutes it takes a person to travel from one location to another. Travel 

times are often divided into road segments where the start and end points 

of the segments are intersections or key features, such as bridges or 

tunnels. Vehicle travel time data can be derived from point sensor speed 

data. It also can be directly measured by probes, such as license plate 

recognition, toll tag transponders, Global Positioning Systems, and cell 

phone tracking. Alternatively, it can be estimated and predicted from other 

data sources.

Freight 

movements

Mixture of data related to the origin-destination (O-D) of various 

shipments or types of shipments, statistics on the type of goods being 

shipped, the mode by which the goods are shipped, value of the goods, 

quantity of goods, type of shipping container, and safety records.

 O-D data 

Tells operations personnel and planners where trips begin; where they 

end; and, sometimes, the routes that are taken. This data can be valuable 

for planning purposes and is useful for real-time operations when trying 

to measure the impact of various traveler information strategies and the 

impact of incidents on arterials and other secondary roads. Private data 

aggregator services routinely make O-D data anonymous.

Routing data

Data that can be used by both emergency first responders and the traveling 

public to determine the fastest route, shortest path, etc., from one point to 

another. Comprised of road network data, turning restrictions, speed limits, 

and other information related to route types and distances.

Table 1. Types of archived operations data. (Continued)
4

ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

1Introduction

Benefits of Archived Data to Transportation Planners

Archived operations data provides numerous benefits to planners. Archived operations data 

is more representative of existing conditions than modeled data, can serve multiple purposes 

within a transportation agency, and enables new types of analyses to support better planning and 

investment decisions. Key benefits of using archived operations data in planning are discussed 

below.

 Fosters Performance-based Planning and Programming

The use of archived operations data enables performance-based planning and programming at 

metropolitan planning organizations (MPOs) and State departments of transportation (DOTs) 

through the use of system performance data to guide decision making. As explained further in 

Chapter 5. Planning Opportunities for Archived Operations Data – Basic to Innovative, archived 

operations data are a critical part of many performance-based planning and programming 

activities, including:

■ Setting outcome-based objectives.

■ Developing and tracking performance measures.

■ Identifying system performance needs and problems.

■ Analyzing and evaluating scenarios, programs, projects, or strategies.

■ Prioritizing and selecting programs and projects.

■ Monitoring and evaluating the impacts of implemented programs and projects.

 Provides a More Complete Picture of System Performance

Comprehensive real-world data can supplant synthetic data generated by a model and improve 

decision making in some applications. Because archived data is continuously collected, it 

overcomes the sampling error inherent in using small samples of data collected manually, which 

provides planners with a better reflection of reality. Large sets of operations data that are archived 

across long time periods are valuable in assessing the true variability and range of values. They 

can be used to evaluate more useful performance measures and provide verification that models 

are not unintentionally emulating unusual conditions.

Visualization of archived operations data inspires important insights into the performance 

characteristics of the transportation system, which supports more effective decisions on where to 

spend limited transportation dollars. 

 Opens Up New Types of Analyses to Support the Planning Process

The highly detailed nature of archived operations data allows for many types of analyses 

previously unavailable to planners, such that the planning process is able to address a wider 

variety of issues. For example, archived data facilitates the inclusion of reliability into long-range 

transportation plans (LRTPs). Travel time reliability could not be adequately measured or modeled 

without archived data, because it requires continuous data collection to see patterns over time. 
5

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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

Introduction

Another benefit of using archived data over traditional 

data is that it allows the impacts of relatively 

infrequent events to be documented and analyzed. 

Such events may include a major winter storm, 

full closure of a roadway due to a severe incident, 

evacuations prior to hurricanes, and days when air 

quality standards are exceeded. This also enables 

planners to identify the likely causes of performance 

problems by correlating multiple data sets over time.

 Enables More Sophisticated Modeling

Archived data can improve existing analysis tools 

and enable new ones by providing more complete 

input data than have been traditionally used, as well 

as more extensive data for calibration and validation. 

Planners also are able to calibrate more detailed 

models, such as activity-based and dynamic traffic 

assignment models, that benefit from archived 

operations data. The use of archived operations data 

at agencies around the United States enables the use 

of tools developed under various Strategic Highway 

Research Program 2 (SHRP 2) projects that present 

new reliability and capacity modeling tools and 

analysis methods.

Definitions of Reliability 

Travel time reliability is typically 

defined in one of two valid ways. 

■ The variability in travel times 

that occur on a facility or for 

a trip over the course of time. 

■ The number of times (trips) 

that either “fail” or “succeed” 

in accordance with a 

predetermined performance 

standard or schedule.

In both cases, reliability (more 

appropriately, unreliability) is 

caused by the interaction of the 

factors that influence travel times: 

■ Fluctuations in demand 

(which may be due to daily or 

seasonal variation or by 

special events). 

■ Traffic control devices.

■ Traffic incidents.

■ Inclement weather. 

■ Work zones.

■ Physical capacity (based on 

prevailing geometrics and 

traffic patterns). 

These factors produce travel times 

that are different from day to day 

for the same trip.
6

ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

1Introduction

Examples of Planning Use of Archived Operations Data 

Washington State Department of Transportation:

The Washington State DOT (WSDOT) has multiple applications for archived operations 

data to support their planning and overall performance management processes. WSDOT 

conducts before and after studies of congestion mitigation projects or programs as funds 

allow, typically with loop detector data in the Puget Sound region or automatic license plate 

recognition technology if funded by the project, and then provides the results as part of 

the Gray Notebook or in the Annual Congestion Reports. The agency also uses data from 

the NPMRDS. WSDOT recently reported on the effects of tolling operations and its traffic 

incident management program.

Houston-Galveston Area Council:

The Houston-Galveston Area Council used archived operations data to investigate how 

the timing of evacuation decisions affects traffic patterns. Archived operations data was 

used to identify potential improvements during the Hurricane Katrina and Hurricane Rita 

evacuations and to advance future plans for elements, such as the timing of government 

evacuation announcements and employee dismissals. 

New Jersey Department of Transportation:

The New Jersey DOT (NJDOT) uses archived operations and safety data to visualize and 

identify candidate project areas (Figure 1).

Figure 1. Map. New Jersey Department of Transportation management system 

integration of candidate project areas.

Source: New Jersey Department of Transportation.
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

Introduction

Examples of Planning Use of Archived Operations Data (Continued) 

Maryland DOT State Highway Administration:

Archived operations data is used to support travel demand forecasting and simulation model 

calibration (estimating model parameters) and validation (confirming model performance). 

The Maryland DOT State Highway Administration (SHA) uses probe-based speed data 

from a third party data vendor and the I-95 Corridor Coalition’s Vehicle Probe Project (VPP) 

Suite to calibrate its traffic simulation models, which serve as the basis for analyzing future 

scenarios for long-term planning. SHA also uses speed and traffic count data to calibrate 

existing conditions for travel demand and mesoscopic models, including the Maryland 

Statewide Transportation Model (MSTM).

Primer Purpose and Overview

The purpose of this primer is to assist transportation planners in effectively using archived 

operations data for developing, analyzing, and evaluating transportation plans and programs. This 

primer addresses the needs of planners from organizations with very little archived operations 

data, as well as organizations with an overwhelming amount of data. It raises planners’ awareness 

of the opportunities afforded through archived operations data and provides guidance on how to 

take advantage of that data to expand and improve planning practices. It also identifies new and 

innovative applications for archived operations data in planning. This primer helps planners and 

their operations data partners overcome the barriers to obtaining and using data, regardless of 

whether they are just getting started with using the data or have substantive experience.

This primer helps address the numerous reasons why archived operations data has not been fully 

adopted for transportation planning and programming. For example, planners may not be aware 

that the data exists or may have difficulty accessing it. They also may not know how to mine, 

manipulate, manage, or use the data to support their work. In addition, data collected for real-time 

system monitoring can be extremely large. While distilling the data to inform investment decisions 

is achievable, it may require new expertise or tools.

The remainder of this document is organized as 

follows. Readers are encouraged to jump to the 

section of this primer that speaks to their specific 

need at any given time. 

Chapter 1: Introduction – introduces the reader 

to the application of archived operations data for 

planning, including the benefits, a range of uses 

in planning, and real-world examples.

The purpose of this primer is to assist 

transportation planners in effectively 

using archived operations data for 

developing, analyzing, and evaluating 

transportation plans and programs. 
8

ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

1Introduction

Chapter 2: Meeting a Range of Planning Needs with Archived Operations Data – provides 

readers with an overview of a spectrum of planning activities that are enabled or enhanced with 

archived operations data. The case studies in Chapter 5 give an in-depth look at how archived 

operations data can be used for individual planning activities.

Chapter 3: Conquering the Challenges of Using Archived Operations Data – provides 

concrete guidance on handling institutional and technical challenges associated with obtaining, 

archiving, analyzing, and incorporating operations data into transportation planning activities.

Chapter 4: Obtaining Archived Data that Planners Need – informs readers about data sources, 

collection techniques, format, quality, and archiving.

Chapter 5: Planning Opportunities for Archived Operations Data – Basic to Innovative – 

offers rich case studies of archived operations data used to support planning and programming, 

which are often drawn from real-world experiences of planning organizations across the United 

States. 

Chapter 6: Getting Started – presents readers with tips and a checklist on how to get started 

using archived operations data, whether they have a little or a lot of data.
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

Meeting a Range of Planning Needs With Archived Operations Data

2. MEETING A RANGE OF PLANNING NEEDS 

WITH ARCHIVED OPERATIONS DATA

Archived operations data can be used to support existing planning methods, as well as enable 

new ones. This chapter introduces the planning activities that can be improved through the use of 

archived operations data. Many of these planning applications will be revisited using detailed case 

studies in Chapter 5, Planning Opportunities for Archived Operations Data – Basic to Innovative.

Performance-Based Planning and Programming

Performance-based planning 

has received a lot of attention 

from transportation agencies 

in the past 5 years and is a 

basic underpinning of the 

Fixing America’s Surface 

Transportation (FAST) 

Act requirements for State 

departments of transportation 

(DOTs) and metropolitan 

planning organizations 

(MPOs). Figure 2 shows the 

basic process of performancebased planning and 

programming.3 Conceptually, 

performance-based planning 

is similar to the congestion 

management process (CMP) in 

that agencies identify specific 

objectives and needs, take 

actions, and continuously 

monitor conditions with 

performance measures. 

3 Federal Highway Administration, Performance Based Planning and Programming Guidebook, Report Number: FHWA-HEP-13-041 

(Washington, DC: 2013). Available at: http://www.fhwa.dot.gov/planning/performance_based_planning/pbpp_guidebook/. 

Source: Federal Highway Administration.

Figure 2. Diagram. Flow chart of performance-based 

planning and programming.
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

2Meeting a Range of Planning Needs With Archived Operations Data

For the most part, undertaking performance-based planning would be impossible without archived 

operations data. Below are examples of how archived operations data enables key elements of 

performance-based planning: 

■ Monitoring and evaluating system performance to identify performance issues, 

prioritize projects, and develop reports. Archived travel time data forms the basis for 

computing a wide variety of congestion, reliability, and freight performance measures. 

The data is usually available at high spatial and temporal resolution (e.g., 1 minute measurements by individual link of roadway location—where segments can be as small as just a 

few hundred meters) and can be aggregated to many other levels (e.g., peak period by route). 

For example, Texas Agricultural and Mechanical University’s annual Urban Mobility 

Report now uses archived operations data to produce congestion and reliability metrics by 

urban area.4 Many agencies also produce mobility performance reports using this data.5

Travel time data is not the only useful type of archived data for performance-based 

planning. Analysis of travel times reveals trends and identifies problems, but it does not 

indicate what is causing them. Additional operations data on factors that may be causing 

congestion can be used to understand travel time trends. This data includes incident and 

work zone characteristics (especially duration by amount of blockage), weather patterns, 

and demand fluctuations. Figure 3 shows an example of how incident data can be used to 

“drill down” into the details of an event to explain outcomes—in this case, travel time 

reliability. Likewise, other contributors to congestion, such as work zones, can be broken 

down into smaller components to examine in more detail and compare against archived 

data. This not only helps to explain changes in the outcomes of an event, but also can lead 

to changes in response activities. In the case shown in Figure 3, by examining an incident 

timeline from an agency’s advanced transportation management system (ATMS), it is 

possible to identify which incident response actions take excessive time to complete. 

Several incident data aggregation and visualization tools have been developed that helps 

planners identify trends in operations, environmental conditions, geographies, and road 

usage, which can affect safety. As one example among many, see Indiana’s Mobility 

Performance Report (available at: http://docs.lib.purdue.edu/imr/).

 

Figure 3. Graphic. Timeline displaying time taken for each activity during the incident.

4 Texas Agricultural and Mechanical University, 2012, Annual Urban Mobility Report (College Station, Texas: 2010). 

Available at: http://mobility.tamu.edu/ums/.

5 As one example among many, see Indiana’s Mobility Reports, available at: http://docs.lib.purdue.edu/imr/.

INCIDENT TIMELINE

Incident 

Occurs

Incident 

Recorded into 

CAD (Detection)

Incident 

Verified

Personnel 

Dispatched and 

Actions 

Initiated

Responders 

Arrive to 

Scene

Incident Cleared 

and Actions 

Canceled

Return to 

Normal 

Conditions

6:35 A.M. 6:42 A.M. 6:47 A.M. 6:49 A.M. 6:50-7:00 A.M. 7:15 A.M. 8:26 A.M.

1 2 3 4 5 6

Detection 

Time

Verification 

Time

Dispatch 

Time

Response 

Time

Clearance 

Time

Time to 

Normal 

Conditions
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

Meeting a Range of Planning Needs With Archived Operations Data

■ Setting performance targets. The FAST Act requires agencies to set performance targets 

and to monitor progress toward those targets. Archived data provides information on past 

trends that are useful in establishing targets (i.e., “it is good to know where one has been 

before deciding where to go.”). When combined with forecasts, trend data provides a 

picture of what targets are attainable. Archived operations data also can be used to 

understand why targets were or were not achieved.

■ Evaluation of completed projects. A key component of performance-based planning is the 

ability to conduct evaluations of completed projects and to use the results to make more 

informed investment decisions in the future. State DOTs and MPOs routinely invest a great 

effort in forecasting the impact of capital, operating, and regulatory improvements on 

general and truck traffic trips, but they seldom have the opportunity to evaluate the 

outcomes of their investments, except in isolated cases where special studies are conducted. 

The availability of archived data is changing this situation—special data-collection efforts 

no longer have to be undertaken. Archived data enables the establishment of an ongoing 

project and policy evaluation program.

There are multifold advantages to instituting a formal evaluation program. Successes can be 

highlighted and failures can be enlightening. When a project does not perform as planned, 

the evaluation may indicate remedial action that can be taken. In addition, by documenting 

the measured impacts of a project or strategy, that information can be used by analogy 

when estimating the potential benefits of similar projects in the future. 

■ Trip-based mobility monitoring and accessibility. Although archived origin-destination 

(O-D) travel time data is just becoming available, it is expected to become more prevalent 

as technology evolves. O-D data makes it possible to monitor the performance of trips 

taken by travelers. By contrast, the current state-of-the-practice is to monitor performance 

from the facility’s perspective, because measurements are taken at the facility level. A 

comprehensive mobility measurement program will involve using both trip- and facility 

based measures because they each inform analysts about the nature of mobility in a region. 

Many transportation investments, as well as land use and development policies, are oriented 

to the entire trip-making process; therefore, understanding the full trip performance 

experienced by travelers is important. The use of emerging operations strategies, such as 

active transportation and demand management (ATDM) and integrated corridor 

management (ICM), also require information on the entire trip-making process. 

Additionally, measuring accessibility of employment, recreation, shopping, and other 

destinations is a logical extension of trip-based mobility performance.
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

2Meeting a Range of Planning Needs With Archived Operations Data

Planning Model Development: Analyzing Alternatives and Identifying 

Future Performance Issues

Archived operations data enables planners to significantly improve the performance of their 

existing planning models and expand the range of models they can use. Long-range transportation 

plans (LRTPs), as well as most of the studies conducted by planners, utilize models to identify 

deficiencies and compare the impacts of alternative investments. The travel demand forecasting 

(TDF) and traffic simulation models used by planners for these purposes are growing increasingly 

sophisticated in both their formulation and data requirements. Archived operations data provides 

a convenient source of data for these planning models. A major advantage of archived operations 

data is its high degree of temporal and spatial coverage. Traditionally, data to operate, calibrate, 

and validate models was based on relatively small samples. Archived operations data provides 

much greater detail in terms of coverage. The following list describes opportunities for archived 

operations data to support planning models. 

■ Model inputs. Several inputs to TDF models and transportation system performance 

monitoring can be developed from archived data:

◦ Peak-hour volumes (PHV), annual average daily traffic (AADT), peak-hour factor 

(K-factor), and directional factor (D-factor) can be derived from continuously collected 

volume data. Link capacities can be empirically derived. Improved vehicle-miles of 

travel (VMT) data for TDF and air quality models, as well as for safety analysis, also 

can be developed with archived data.

◦ From continuously collected speed and travel time data, free flow and peak-hour 

speeds and travel time distributions can be established for individual facilities. For air 

quality models, hourly speeds by link for the base condition can be developed with 

archived data. 

■ Model calibration and validation. The major outputs from TDF models are volumes and 

average speeds. These can be checked against archived data. Also, interzonal travel times 

can be developed from archived data; this can be used to calibrate the trip distribution 

phase of TDF modeling.

■ Emerging modeling frameworks. As TDF and traffic simulation models grow 

increasingly sophisticated, so do the data requirements to run the models. This is especially 

true for reliability analyses. Recently, traffic simulation models have been applied to 

reliability studies by making multiple runs, where each run (or scenario) represents various 

combinations of conditions that influence reliability. For example, demand levels may be set 

at low, medium, or high, and the presence of an incident may be defined by different 

blockage and duration levels. In the scenario approach, the probability of each occurrence 

for each combination must be known so that the results can be properly combined via 

weighting. Archived data for the facilities being studied is analyzed to determine the 

probabilities.
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

Meeting a Range of Planning Needs With Archived Operations Data

The Strategic Highway Research Program 2 (SHRP 2) was a national research program managed 

by the Transportation Research Board from 2006 to 2015 to find solutions for critical national 

transportation challenges: highway safety, congestion, and road and bridge renewal. SHRP 

2 research produced several analytical procedures for estimating reliability (Table 2). These 

procedures are beginning to be put into practice by transportation agencies across the United 

States. The products from all of these research efforts require inputs derived from archived 

operations data. As noted in Table 2, several SHRP 2 Reliability products require link capacity 

values as input. Modelers tend to use calculated rather than empirical link capacities; however, 

during the pilot testing of the SHRP 2 Reliability products, it was found that using empirical 

capacities is critical to obtaining reasonable results.

While the use of archived operations data for planning is uncharted territory to some agencies, 

more and more agencies are using archived operations data to enhance and expand their planning 

capabilities. The next chapter will help agencies that have a range of experience to overcome the 

institutional and technical challenges commonly encountered.

Table 2. Granularity of Strategic Highway Research Program 2 

reliability-oriented analysis tools.6

Strategic Highway Research 

Program 2 Project

Analysis Scale (in order 

of increasing complexity)

Input Requirements 

from Archived Data

Development of Improved 

Economic Analysis Tools 

(Project Number C11)

Sketch planning; system or 

project level.

Incident duration and 

frequency.

Evaluation of Cost-Effectiveness 

of Highway Design Features 

(Project Number L07)

Detailed sketch planning; 

mainly project level.

Incident and work zone 

lane-hours lost; hours 

of year with rainfall and 

snowfall above thresholds. 

Establishing Monitoring 

Programs for Mobility and 

Travel Time Reliability 

(Project Number L02)

Performance monitoring and 

project evaluations using 

empirical data.

Demand variability; 

presence of incidents, work 

zones, and weather tagged 

to individual facilities.

Incorporation of Travel Time 

Reliability into the Highway 

Capacity Manual (HCM) 

(Project Number L08)

Project planning using HCM 

scale of analysis.

Volumes on links and 

ramps, demand variability, 

incident duration by 

blockage type, and link 

capacities and free flow 

speeds.

6 Additional information on any of the SHRP 2 solutions listed in Table 2 can be found at the Transportation Research Board’s Strategic 

Highway Research Program 2 Web page: http://www.trb.org/StrategicHighwayResearchProgram2SHRP2/Blank2.aspx.
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

2Meeting a Range of Planning Needs With Archived Operations Data

Strategic Highway Research 

Program 2 Project

Analysis Scale (in order 

of increasing complexity)

Input Requirements 

from Archived Data

Understanding the Contribution 

of Operations, Technology, and 

Design to Meeting Highway 

Capacity Needs 

(Project Number C05)

Project planning using 

mesoscopic simulation scale 

of analysis.

Volumes on links and 

ramps, demand variability, 

and link capacities and free 

flow speeds.

Partnership to Develop an 

Integrated Advanced Travel 

Demand Model with Mode 

Choice Capability and FineGrained, Time-Sensitive 

Networks 

(Project Number C10)

Regional planning using 

linked travel demand and 

mesoscopic simulation 

analysis.

Incident duration and 

frequency.

Incorporating Reliability 

Performance Measures in 

Operations and Planning 

Modeling Tools 

(Project Number L04)

Regional planning using 

linked travel demand and 

mesoscopic or microscopic 

simulation analysis.

Volumes on links and 

ramps, demand variability, 

incident duration by 

blockage type, and link 

capacities and free flow 

speeds.

Table 2. Granularity of Strategic Highway Research Program 2 reliability-oriented 

analysis tools. (Continued)
15

3

ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

Conquering the Challenges of Using Archived Operations Data

3. CONQUERING THE CHALLENGES OF USING 

ARCHIVED OPERATIONS DATA

Data, tools, and domain expertise are the three components required to effectively use archived 

operations data in planning. Without these three basic components, users of archived data will be 

unable to derive the meaningful insights that help to make informed decisions that intelligently 

move an agency forward (Figure 4).

Figure 4. Diagram. Components of an effective archived operations data archive.

Data. Chapter 1 provided an overview of the various types of archived operations data. Data 

captured by an agency or third-party may include speeds, volumes, accidents, freight movements, 

transit schedule and ridership, agency assets, and others. However, simply knowing that this data 

exists and is in an archive someplace is not sufficient; stakeholder buy-in and understanding how 

to access the data also are needed.

Tools. Often overlooked, the tools that are available to agency staff are the most critical element 

in making use of archived operations data. These can include tools that fuse “siloed” data 

from disparate sources, the tools that fill in gaps (“missing data”), and those that identify or 

screen outliers. Other important tools support analytics and visualization that help the agencies 

“see” into the data—asking questions, identifying issues, deriving meaning from the data, and 

communicating those insights to others.

Source: University of Maryland CATT Laboratory.
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

3Conquering the Challenges of Using Archived Operations Data

Domain expertise. With the right tools and the right data, anyone can review a data set and start 

to ask questions, but to ask the right questions, to interpret the resulting analysis, and to otherwise 

act on the information in the archive, domain expertise is needed. Domain experts include 

transportation professionals with specific technical skills in areas such as traffic operations, 

transportation planning, or statistical analysis.

There is a mixture of technical and political challenges within an institution associated with 

creating an effective archive. All can be overcome with varying degrees of effort. The remainder 

of this chapter focuses on some of the more prevalent institutional challenges associated with the 

data, tools, and domain expertise of an operations archive.

Institutional Challenges

A common misconception about using archived operations data is that the technical challenges 

associated with the collection, fusion, and management of the data, along with the development 

of tools, are the greatest hurdles. In reality, these technical challenges are almost always easily 

solvable by qualified technical personnel. However, the institutional challenges (e.g., political, 

ideological, etc.) can create significant issues that delay and/or prevent operations data from 

being leveraged fully. The extent to which each of these issues may exist in each agency will vary 

greatly.

Technology policy. Many agencies have strict guidelines on what technologies can and cannot 

be used within its own enterprise. For example, some agencies use only Microsoft products and 

platforms, such as .NET and MS SQL; whereas, other agencies forbid open-source software for 

fear of being unable to find available support for a product or that misuse of the open-source 

technologies could lead to litigation.

Resource constraints. An agency may want to build and maintain their archive internally, but 

quickly find that they lack the capacity to do so. This may not necessarily be a lack of skills, but 

rather, a lack of skilled individuals. An agency who takes on a significant development effort with 

only one or two superior coders or minimal information technology (IT) support may find its 

program at a standstill if the specialized personnel leave the agency or are otherwise committed to 

other agency projects.

Storage and processing capacity. Technical barriers are common to agencies that are building 

archives in-house. Certain archives, especially those from connected vehicles or probe-based 

data sets, can grow at a surprising rate. The Florida Department of Transportation (FDOT), for 

example, collects data from approximately 8,200 speed/volume sensors. This sensor network 

produces the equivalent of 23.6 million data points every day and over 8.6 billion records each 

year. Sensor networks like the one FDOT uses produce only a fraction of the data generated by 

probe data sources. Using FDOT again as an example, the data the agency purchases from a thirdparty probe data provider produces nearly 34,000 records every single minute. That translates to 

48.9 million records per day or 17.8 billion records per year. Due to State procurement and hiring 

policies, very few departments of transportation (DOTs) have dedicated and responsive IT teams 

that can plan out, install, and configure expensive hardware or cloud services to accommodate 

these massive archives for long periods of time.
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Tools and accessibility. Even when hardware can be purchased and installed, developing the 

appropriate analytics software and databases that make the data easily accessible to end users 

can be a significant hurdle for agencies. For an agency to build successful tools independently, 

a healthy mixture of software engineers, architects, user interface and user experience design 

specialists, developers, and project managers is necessary. The tools will need to be maintained 

over time; therefore, complete documentation and staff that can be called upon over the course 

of many years to keep the tools up-to-date are needed. Because of the high barrier to entry and 

upkeep costs, many agencies are now choosing to either purchase existing tools, or leverage tools 

that other agencies or universities have already paid to develop. This effectively creates a pooledfund approach to software development and maintenance. This approach is becoming easier to 

use for those agencies who are unaccustomed to purchasing services and for those who have 

historically not adopted tools and products that have not been invented in-house or even in-State.

Networking bandwidth. Though less common, agencies may have issues with networking and 

bandwidth capacity. Large data sets that stream from various sources will need to be compiled and 

streamed to others. The ability to send large sets of data from field devices or to and from thirdparty providers can be difficult with existing infrastructure.

Security concerns. Working with agency security boards or committees can be another challenge 

that often seems insurmountable. Getting the appropriate permission to set up online data access 

or to deploy specialized software, for example, can range from 1 month to several years, or even 

be denied if there is not an advocate. 

If present, most technical issues are solvable 

if there is an internal advocate. Network 

capacity can be expanded at relatively low 

costs. Other IT and hardware procurement 

issues can be bypassed by outsourcing 

to contractors or through the purchase of 

software-as-a-service or IT-as-a-service. 

Internal resistance. Internal resistance may 

develop when: (1) trying to convince the 

owner of the data or archive to share it with 

others, (2) trying to convince those who have 

not traditionally relied on operations data to 

trust its quality, and (3) trying to convince 

potential users of the value of the operations 

data compared to traditional (or currently 

used) data sets.

Figure 5. Photo. Technician.

Source: Thinkstock/Dynamic rank.
18

ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

3Conquering the Challenges of Using Archived Operations Data

■ Sharing—internal conflict over the ownership and control of data, information, and the 

capability that comes with it is not uncommon. It is important to understand the various 

motivations for wanting data versus the motivations for providing data. It is the responsibility 

of the owner and administrator of a particular data set to keep that data safe and secure (i.e., 

to be the steward). What motivation is there for releasing that data to larger audiences? At 

best, the data steward may be recognized for his or her efforts to maintain the data or get a 

verbal “thanks.” More likely, however, the data steward is fearful that, in releasing the data to 

others, the data may become scrutinized excessively (or inappropriately) by outside groups. 

Thus, the data steward becomes bombarded with questions. If the data is incorrect, or even 

misinterpreted, or if there is a security breach or something is damaged, his or her job could 

be jeopardized. It can seem like a lose-lose situation for the steward. For the majority of 

agencies, there is little to no apparent benefit to the steward to make the data more widely 

available.

■ Trusting—for those who have yet to make use of archived operations data or those who 

have biases towards other types of data, deciding to look at operations data can be difficult. 

There is an old school of thought that goes: “Data collected for planners is of higher quality 

than that for the operations folks. Those operations people only care about in-the-moment 

conditions, and do not take the same quality control initiatives that planners take on their 

data.” This is essentially a lack of trust in the quality of someone else’s data, which is not 

perceived as being collected for the same purposes or need as someone else. This lack-oftrust issue is so pervasive that it is not uncommon to find more than one set of detectors at a 

single location: one for the planners, one for operations personnel, and a third for the 

private sector or other agency. Each group thinks the other group is not doing a good 

enough job in quality control (QC) or maintenance on their device. However, getting past 

the trust issue can be easier than many other challenges. A quick verification and validation 

process performed on the data can show where issues exist. Many third-party archive 

applications (e.g., Regional Integrated Transportation Information System (RITIS) and 

Iteris Performance Measurement System (iPeMS) have data quality checking modules that 

can quickly identify issues, patterns, and causality in just a few minutes. These tools can 

verify or deny the existence of an issue, and help to encourage fixing the problem. From an 

economic standpoint, it is often much more cost-effective to pool the resources of several 

departments to fix the quality of the operations data set rather than to continue to expend 

resources to collect data multiple times at the same location.

■ Valuing—when existing processes are in place for collecting, processing, and cleansing 

non-operations data, proving that the operations data has enough value to warrant 

switching to use it (or augmenting existing data with the operations data) can seem 

daunting. If a business unit in an organization already has a well-formed process for 

collecting, processing, and managing data from another source, then what is the impetus for 

trying something different? Often, it is simply a matter of better marketing and 

demonstrating the benefits of enhanced or augmented data by the operations groups. More 

and more, operations data sets, especially those from probes, are proving to be highly 

reliable, of finer granularity, and to have greater spatial coverage than traditional planning 
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data sets. The fact that they are continually collected also is a positive aspect of these data 

sets—even though that does present additional data management burdens. Ultimately, an 

internal advocate is needed to clarify the benefits and quality of the data and to make the 

case for either switching to it or augmenting existing data with it.

Additional arguments have been used to overcome issues with sharing, trusting, and valuing 

operations data with varying degrees of success. There is the sensible argument, which debates 

the philosophical and ethical arguments of sharing data and pooling resources. There is the legal 

justification, which attempts to leverage interagency agreements or executive orders to change 

behavior. There is the funding argument, which attempts to pay an agency or business unit to 

switch their data collection and sharing practices in an effort to get past the “the cost is too 

great” excuse. There is the “shame” argument, which attempts to show how everyone else in the 

organization is sharing data and leveraging resources, thus isolating those individuals who are 

resisting. The problem with these tactics is that they do not address the “fear factor” that many 

individuals have about changing their behavior and sharing their data with others.

There is the “make a friend” tactic, which leverages 

personal connections to try to instill change. This 

tactic often is successful as it is communicationsbased. However, if only the leadership within an 

organization is convinced, but the individuals 

who daily work with the data are unconvinced, 

then as soon as the ally within the organization 

gets promoted, retires, or otherwise leaves the 

organization, the resistance returns.

Ultimately, every manager and analyst in every agency has questions that need answers and 

is looking for insights into their own data. If these managers and data stewards can be shown 

the power of certain visualization and analytics tools that they will gain access to as part of the 

archive, they usually will understand the value of the data, realize that it is worth the supposed risk 

of using the data, and make it available to others through sharing. This is the “build it and they 

will come” approach. By showcasing the tools and capabilities that will be made available when 

the operations data is archived and accessible, others will want to make use of the data and share it 

with others. Tools can be built in-house with some risk, as described later. However, existing tools 

and applications developed by third parties or other agencies also can be showcased. Leveraging 

existing tools to showcase the power of operations data often is the quickest, cheapest, and leastrisky approach to demonstrating capabilities, securing stakeholder buy-in, and getting a process up 

and running in as little time as possible.

By showcasing the tools and 

capabilities that will be made 

available when the operations data is 

archived and accessible, others will 

want to make use of the data and 

share it as well.
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3Conquering the Challenges of Using Archived Operations Data

Challenges in Changing Planning Methods and Products 

The use of archived operations data may require planners to adjust their technical methods and 

modify their products. Historically, travel demand forecasting (TDF) models have been used not 

only to forecast future performance but also to estimate current performance where performance 

measurement data were lacking. With archived data, the use of models to “measure” current 

performance is no longer necessary. Therefore, products that formerly relied on models to derive 

current conditions now have to be converted to use measured data. Switching from models to data 

for these applications requires planners to develop a strategy. Some of the issues that need to be 

addressed include:

■ Coverage. Will the archived data cover the roadways of interest? If not, how will the 

non-covered roadways be handled?

 Example: Congestion management process (CMP). An agency only has archived 

operations data of speeds and volumes for area freeways, yet the current CMP 

monitors signalized arterials and collectors. The agency may decide to purchase travel 

time data from a commercial vendor.

■ Reporting period. How frequently does the archived data need to be obtained: monthly, 

quarterly, or annually? 

■ Maintaining continuity in trends. If a trend line exists from the models, how will the 

switch to archived data affect it?

 Example: CMP. An agency has been producing a biennial mobility performance report 

that is based on applying its TDF model. It is changing to vendor-supplied travel time 

data. A test revealed that, for the current year, the performance measures developed 

with the data are significantly different from the modeled measures, resulting in a 

discontinuity in trends. A possible solution is to use the results from the current year 

tests to adjust the previous years’ performance. This can be done by noting the percent 

difference between modeled and measured data for the current year to develop 

adjustment factors by time of day and highway type.

■ Definitions of performance measures. Will the measures developed with archived data 

represent the same underlying phenomenon as the modeled measures?

 Example: Delay estimates using TDF models. Delay computations require three inputs: 

a reference (usually free flow) speed, actual speed, and the volume of vehicles exposed 

to a speed. TDF models are used to develop these estimates; all three delay inputs are 

available at the link level; therefore, delay is computed for each link and summed. If 

vendor supplied travel times are used, there are numerous sources of incompatibility: 

the vendor and TDF networks usually will not align; reference speeds may be 

calculated differently; and, most significantly, companion volumes to the travel time 

estimates do not exist. Likewise, the estimates of speeds for air quality models would 

benefit from using both travel time and volume data so that the proper weighting can 

be applied. 
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The most desirable solution is to conflate the TDF and vendor travel time networks. 

This ensures that delay estimates are made on the same spatial basis and makes the 

volumes from the TDF model available for use with the travel time measurements. Note 

that conflation is a complicated process that requires both networks to use a common 

geolocation referencing system (e.g., latitude/longitude). If the TDF network does not 

have “points of contact” (either attributes or shared line work), the vendor network may 

be conflated to other traffic count databases that do provide that information, such as 

the Highway Performance Monitoring System (HPMS).

■ Acceptance of new performance measures. How will the introduction of new 

performance measures enabled by archived data fit into current products?

 Example: Including travel time reliability in the long-range transportation plan 

(LRTP). Improving travel time reliability is increasingly appearing as a stated goal of 

LRTPs. When this is the case, methods for monitoring and forecasting should be in 

place. Reliability performance can be measured directly with archived data, but what 

role does archived data play in forecasting reliability? Recent experience suggests 

there are at least two ways to use archived data. First, some metropolitan planning 

organizations (MPOs) have used archived data to reformulate the volume-delay 

functions used in their TDF models. By using continuously collected travel time data, 

the effects of the factors that cause unreliable travel (e.g., weather, work zones, 

incidents, or demand fluctuations) are accounted for in the revised functions. Second, 

archived data can be used to develop relationships between model-produced average 

travel time or speed values and reliability metrics. Both of these applications are 

discussed in more detail in Chapter 5.

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Obtaining Archived Data That Planners Need

4. OBTAINING ARCHIVED DATA THAT 

PLANNERS NEED

This chapter provides information aimed to help planners understand where they can obtain archived 

operations data, which data elements are particularly useful for planning, and basic information 

on how data can be processed to become useful information. This chapter also identifies critical 

considerations for agencies looking to implement a data archive to support planning.

Locating and Accessing Archived Operations Data

Planners who are interested in obtaining archived operations data have several options. Depending 

on the type of operations data desired, planners may find it within their organization, other public 

agencies (e.g., State departments of transportation (DOTs), metropolitan planning organizations 

(MPOs), local traffic departments), university research laboratories, or from the private sector. If 

critical data is not available, planners may be able to get the resources needed to collect the data; 

although, this primer does not provide guidance in data collection. Chapter 6 provides a detailed 

list of specific sources for archived operations data, including some that are available for no cost. 

For example, the Federal Highway Administration (FHWA) provides the National Performance 

Measures Research Data Set (NPMRDS), which contains archived speed and travel time data for 

the National Highway System, to MPOs and State DOTs for free. The four cases below provide 

examples of where planners at MPOs and State DOTs are obtaining archived operations data to 

advance their planning activities.

 Capital District Transportation Committee 

The Capital District Transportation Committee 

(CDTC) is the MPO for New York’s Capital Region 

operating out of Albany, New York. CDTC maintains 

a summary of traffic volume data for Capital District 

roadways in its Traffic Volume Report. The Traffic 

Volume Report includes data for all State and nonState roadways with functional classifications greater 

than local. CDTC collects data for this report through 

different mechanisms, including obtaining traffic 

counts from the MPO’s member agencies and the 

New York State DOT’s Management Information 

System for Transportation (MIST) data set.7 Table 3 

contains highlights of the operations data obtained by 

CDTC and its use in planning. 

7 Capital District Transportation Committee (CDTC), CDTC Traffic Volume Report Data, September 2011. Available at: http://www.cdtcmpo.

org/tvr/tvrdes.htm. 

Figure 6. Map. Outline of 

New York State.

Capital District 

Transportation Committee
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

4Obtaining Archived Data That Planners Need

Table 3. Highlights of operations data available to Capital District Transportation Committee.

Data Type Data Source

Who Collects the 

Data

Planning Activity 

Supported 

Volume

Traffic counts, loop detectors. Local agencies. Performance 

measurement.

Management Information 

System for Transportation 

(MIST), loop detectors.

New York State 

Department of 

Transportation 

(DOT).

Performance 

measurement, model 

improvement.

Speed

MIST, National Performance 

Management Research Data Set 

(NPMRDS), Global Positioning 

System vehicle probes.

Private sector. Performance 

measurement.

Incidents MIST, crash. New York State 

DOT. 

Identifying highincident locations.

CDTC has found value in using archived operations data 

for identifying transportation needs and problems. Specifically, it has access to the MIST data set that provides 

volume, speed, and incident data along I-87 and I-90. The 

committee has used its travel demand model to identify 

and quantify congestion problems. The model is focused 

on AM and PM peak-hour traffic on a typical weekday. 

The MIST data provides a more comprehensive understanding of weekday congestion patterns on the Interstate 

system. For example, MIST data demonstrated that average operating speeds on the Interstate system, in the absence of incidents, were generally higher than 

predicted by the model. The Northway (I-87) had a similar amount of delay as I-90, but the MIST 

data indicated that reliability was worse on the Northway, consistent with a popular perception that 

the Northway has worse congestion. The MIST data also highlighted that congestion was especially 

severe on Fridays in the summer, when vacation travel mixes with commuter travel. This perspective 

was overlooked by focusing on typical PM peak-hour data. MIST data allows the MPO to understand 

and weigh the costs and benefits of investments to address non-recurring and recurring delay. It contributed to CDTC establishing a strong priority for operations and incident management and a strong 

policy in the congestion management process and regional transportation plan that expressways in the 

region will not be widened to address congestion except in the context of managed lanes. 

The MIST archived data including 

volumes, speed, and incident data 

allow the CDTC to understand 

and weigh the costs and benefits 

of investments to address nonrecurring and recurring delay. 
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CDTC has used archived operations data to better understand its travel demand model. Comparing 

the outputs from their model with the archived data, CDTC learned that its models were not as 

effective in predicting delay as they had believed. CDTC intends to use archived data to improve 

these models.

CDTC contracted with a consultant to develop a tool to assist in the analysis of archived 

operations data. A previous challenge to analyzing the data was that the MIST data set was large 

and consisted of many unlinked tables within the database. Prior to the development of the tool, 

conducting analysis was a labor-intensive process requiring coordination between many of these 

different tables to understand the data.

 Maricopa Association of Governments 

The Maricopa Association of Governments (MAG) 

is the MPO for the metropolitan Phoenix area. The 

primary sources of archived data used at MAG come 

from the Arizona Department of Transportation’s 

(ADOT’s) Freeway Management System (FMS) 

and private sector probe data. The ADOT FMS data 

provides volume, speed, and vehicle classification, but 

is limited to approximately 69 percent of the urban 

freeway system. The data purchased from the private 

sector and NPMRDS data provides speeds for the for the 

entire freeway and arterial system, but does not provide 

volumes. MAG has purchased probe data for the last 

4 years. MAG has been using the NPMRDS data 

for travel model validation as well as for calculating 

congestion-related performance metrics. Table 4 identifies the operations data types obtained by 

MAG and how MAG uses the data to support its planning activities. 

Table 4. Highlights of operations data available to Maricopa Association of Governments.

Data Type Data Source

Who Collects 

the Data

Issues with Data 

Format or Quality

Planning Activity 

Supported

Classification

Arizona 

Department of 

Transportation 

(ADOT) Freeway 

Management 

System (FMS) 

loop detectors.

ADOT

Covers only 

69 percent of 

freeway lane miles.

Model calibration, 

validation, baseline 

for activity-based 

model, scenario 

planning, program 

rebalancing, project 

evaluation and 

ranking.

Figure 7. Map. Outline of Arizona.

Maricopa Association 

of Governments
26

ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

4Obtaining Archived Data That Planners Need

Data Type Data Source

Who Collects 

the Data

Issues with Data 

Format or Quality

Planning Activity 

Supported

Volume

ADOT FMS 

counts. ADOT

Covers only 

69 percent of 

freeway lane miles.

Performance 

measurement, model 

improvement.

Counts. Local agencies None identified. Same as above.

Speed

ADOT FMS. ADOT

Covers only 

69 percent of 

freeway lane miles.

Same as above.

Private-sectorpurchased 

probe data 

and National 

Performance 

Measures 

Research Data 

Set (NPMRDS). 

Private sector.

Supplements data 

on freeways from 

ADOT FMS and 

covers arterials. 

Only provides speed 

and not volume.

Same as above.

In 2012, MAG developed in-house an interactive performance dashboard that allows all audiences 

to access the various multimodal performance outcomes at the system and corridor levels. The 

dashboard, called MAGnitude, is supported through a partnership between ADOT, MAG, and 

Valley Metro. It provides reliability, delay, congestion, volume, speed, and other measures on a 

map-based interface that can be tailored to specific corridors or system wide. Users can look at 

data each year from 2009 to 2015 on maps, bar charts, and infographics. Archived ADOT FMS 

data, MAG counts, safety, and private-sector probe data fuel the website.

Maryland DOT State Highway Administration 

The Maryland DOT State Highway Administration (SHA) is a transportation business unit of 

the Maryland DOT that owns and operates the State’s the State’s highways outside of Baltimore 

City. SHA’s data collection program is comprised of both private-sector and public-sector data 

to evaluate existing projects and programs and identify short-term and long-term improvements. 

It applies multiple analysis and visualization tools to inform transportation decision-making and 

“tell a story” with archived operations data that clearly communicates the need for projects and 

outcomes of implementation in high-priority locations. 

Table 4. Highlights of operations data available to Maricopa Association of Governments. (Continued)
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Maryland DOT SHA gathers archived operations data from multiple sources:

■ The State’s advanced transportation management system (ATMS) platform, called 

Coordinated Highways Action Response Team (CHART), collects detailed incident and 

event data in real-time. This same system also collects speed and volume data from 

deployed intelligent transportation systems (ITS) sensors, road weather information 

systems (RWIS) data, construction and lane closure information, virtual weigh station data, 

and other ITS-related information. 

■ In addition to ATMS-collected data, SHA collects other vehicle classification, volume, and 

speed data from traffic counts, floating car runs, and automatic traffic recorders. This data 

is housed in the agency servers and publically accessible through the SHA Internet-Traffic 

Monitoring System (I-TMS) website. Data housed in the traffic count databases are 

collected through a uniform process for the whole network for FHWA Highway 

Performance Management System (HPMS) requirements. Traffic data also is collected on a 

project-by-project basis.

■ Probe-based speed and travel time data are 

purchased from the private sector and accessed 

through the Vehicle Probe Project (VPP) Suite.

■ For arterials and lower-functional-class 

roadways, SHA obtains volume data and truck 

classification data.

■ There is also Bluetooth coverage for speed data 

along certain segments of the SHA network, 

including I-270, which has high-occupancy vehicle (HOV) lanes. 

All of the above-mentioned incident, volume, speed, and ITS device data are fused and archived 

through the Regional Integrated Transportation Information System (RITIS) and SHA systems. 

A list of archived operations data types and sources available to SHA are described in Table 5. 

Table 5. Highlights of operations data available to Maryland Department of Transportation (DOT) 

State Highway Administration (SHA).

Data Type Data Source

Who Collects the 

Data

Planning Activity 

Supported 

Classification

Internet-Traffic Monitoring 

System (I-TMS), traffic 

counts, automatic traffic 

recorders.

Maryland DOT SHA.

Feasibility studies, project 

planning, highway design, 

performance monitoring.

Volume

Coordinated Highways 

Action Response Team, 

traffic counts, automatic 

traffic recorders.

Maryland DOT SHA.

Monitor/report congestion 

on arterials, model 

development.

Figure 8. Map. Outline of Maryland.

Maryland State 

Highway Administration
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

4Obtaining Archived Data That Planners Need

Data Type Data Source

Who Collects the 

Data

Planning Activity 

Supported 

Speed

Private sector vehicle probe 

speed data.

University of 

Maryland (UMD) 

- Center for 

Advanced 

Transportation 

Technology 

Laboratory.

Calibrate models, evaluate 

programs or projects, 

performance reporting.

Floating car runs. Maryland DOT SHA. Calibrate models.

Bluetooth. Maryland DOT SHA. Performance reporting, 

origin-destination patterns.

I-TMS, traffic counts, 

automatic traffic recorders. Maryland DOT SHA. Evaluate programs or projects.

Incidents I-TMS, traffic counts, 

automatic traffic recorders. Maryland DOT SHA.

Safety and reliability 

analysis for project 

planning.

 Oregon Department of Transportation

The Oregon DOT (ODOT) manages over 19,000 lane-miles of highway. Among other ITS 

equipment, ODOT has deployed over 384 highway cameras, 150 freeway ramp meters, 

126 portable message signs, 117 road and weather information stations, and 220 permanent 

variable message signs.8 Along with the corresponding sensors and support systems, many of 

these deployments assist ODOT in collecting and using archived operations data. 

Portland State University (PSU) archives data from ODOT’s freeways, including volume, speed, 

occupancy per lane, and vehicle classification. ODOT 

recently upgraded its ramp meter firmware and software 

to collect vehicle classification data by length and speed 

(four length categories with average speed per category) 

for all ramp meter locations. In addition, PSU archives 

all available traffic signal data, including Bluetooth and 

bus information from the arterials in Oregon, under 

the Portland Oregon Regional Transportation Archive 

Listing (PORTAL) effort. ODOT is actively adding new 

locations to install Bluetooth sensors on arterials and 

freeways to get a better understanding of travel times 

on the arterials. Once available, the intent is to archive 

this data in PORTAL. ODOT has also purchased private sector probe speed data. Table 6 lists the 

types of archived operations data available for ODOT planning and its sources.

8 Oregon Department of Transportation, Key Facts 2012. Available at: http://www.oregon.gov/ODOT/docs/keyfacts_2012.pdf.

Table 5. Highlights of operations data available to Maryland Department of Transportation (DOT) 

State Highway Administration (SHA). (Continued)

Figure 9. Map. Outline of Oregon.

Oregon Department 

of Transportation
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PSU manages and conducts all quality control (QC) and quality assurance of the archived data. 

ODOT has a steering committee to provide oversight for the PORTAL effort. While some of 

the ODOT data systems have built-in processes that alert ODOT to potential issues (e.g., sensor 

damaged or not functioning), PSU monitors the quality of the PORTAL data and sends alerts to 

ODOT when it detects data that may not be accurate (e.g., unrealistic travel speeds).

Table 6. Highlights of operations data available to Oregon Department of Transportation.

Data Type Data Source Who Collects the Data

Planning Activity 

Supported 

Volume Loop detectors. Oregon Department of 

Transportation (ODOT). None reported.

Speed Loop detectors. ODOT. None reported.

Vehicle 

Classification Ramp meters. ODOT. None reported.

Incidents Crash data system. ODOT.

Oregon Driver and Motor 

Vehicle Services crash 

reporting forms.

Obtaining Third-party Data and Tools

Third-party data is becoming increasingly popular among DOTs and MPOs as a way to more 

economically collect data on a wide geographic scale. Examples of third-party data can include:

■ Probe-based speed/travel time data from companies like HERE, INRIX, TomTom, etc.

■ Origin-destination (O-D) data.

■ Incident data.

■ Weather data.

The procurement of these data sets can dramatically improve capabilities within an agency, 

making it easier to measure system-wide performance, identify problems, prioritize projects, and 

have greater situational awareness.

Whenever an agency is considering the procurement of third-party data—whether that be probebased speed or travel time data, incident and event data, weather data, etc.—the most critical 

component of any procurement will be contract language controlling acceptable use. Far too often, 

agencies procure data without specifying terms of use in the request for proposal (RFP). If terms 

of use are not specified, then the data provider will specify their own terms of use. When this 

happens, the following can occur:

■ Acceptable use can be too restrictive to be of value to the agency (e.g., by limiting use to 

internal, real-time applications).

■ Agencies may be restricted from archiving the data at all.
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

4Obtaining Archived Data That Planners Need

■ Agencies may be restricted from using third parties to archive the data.

■ Agencies may not be able to share the third-party data with any researchers (internal or 

external) or with consultants working on behalf of the agency on specific projects.

■ Each respondent to an RFP could have widely varying acceptable use terms that could 

drastically change the cost models of each respondent, which can make comparing 

responses and offerings in a reasonable way difficult.

When developing RFPs, agencies frequently overlook their position of power. If the agency 

proposes its own desired acceptable use terms, the private-sector data providers will respond and 

price the data accordingly.

The I-95 Corridor Coalition has set a standard for 

acceptable use when acquiring third-party probe data, 

and their language could be considered in other State 

procurements of probe and other data (weather, incident, 

etc.). A copy of the coalition’s data use agreement can 

be found online (http://i95coalition.org/wp-content/

uploads/2015/02/VPPII_DUAv9_signed.pdf?dd650d ) 

and as can a copy of the actual RFP (http://i95coalition.

org/wp-content/uploads/2015/02/RFP-83794N-FINAL-2.

pdf?dd650d ).

As archived operations data sets continue to increase in 

size, quality, and availability, planners are realizing that is 

data alone are not sufficient to spark innovation and use in 

the planning process. Sophisticated analytics technologies, 

decision support tools, accessibility mechanisms, or other 

visualization tools are needed to leverage these archives, 

understand systems performance, make informed investment choices, and realize the full benefits 

of these archives. Visualization has far-reaching potential for communicating transportation needs 

to leaders who must prioritize budgets, enhance the ability of transportation organizations to 

deliver timely and ever-more complex programs within those budgets, educate the engineers who 

will make it all happen, and communicate to the traveling public the implications of transportation 

investments.

Numerous archived operations data analytics tools are available today. These tools can be 

purchased as-is, with existing data already incorporated into them from around the world. They 

also can be purchased and integrated within an agency’s existing archived data sets and can be 

highly customized to meet agency-specific goals and criteria. Examples of these products include 

the Iteris Performance Measurement System (iPeMS) suite of products and the University of 

Maryland Center for Advanced Transportation Technology (CATT) Laboratory’s RITIS suite 

of products.

Figure 10. Photo. Interstate 

highway shield for I-95.

Source: Thinkstock/Rex Wholster.
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Data Elements Useful for Planning

 Intelligent Transportation System Roadway (Point) Detectors: Volume, Speed, and 

Lane Occupancy

ITS roadway detectors have been deployed on freeways for several decades. Detectors provide 

lane-specific measurements of volume, speed, and lane occupancy. The data is transmitted from 

the field as 20- to 30-second summaries and is archived at levels up to 15 minutes (5-minute 

aggregations are very common). Detectors are spaced at roughly1/2-mile intervals in dense 

urban areas and include mainline lanes, as well as HOV and ramp lanes in some systems. A 

variety of technologies are used to collect the data, including inductance loops, radar, and video 

image processing, but they all result in the same basic data elements. Speeds are measured over 

very short distances—the length of the detection zone—so they are essentially “spot” speeds 

(time mean speeds). The speeds are usually transformed to travel times by assuming a length 

of highway where the speed is constant, usually half the distance to the next upstream and 

downstream detectors. 

On signalized highways, use of data from roadway-based detectors is far less direct. Some 

detectors are placed at midblock locations (away from signals) and measure the same conditions 

as freeway detectors. However, midblock speeds are not indicative of signalized highway 

performance, as the effect of the signals is not included.

 Probe Detection - Travel Times

Toll tag reader and roadway-based vehicle re-identification technologies can provide travel times 

measured between two fixed points. Global Positioning System (GPS) vehicle re-identification 

provides travel times between two points in time or instantaneous speeds. These measurements 

are usually “snapped” to a roadway segment, as vendor-supplied travel time data is. As discussed 

above, companion volume measurements are not available with these technologies, as only a 

sample of vehicles are collected. 

 Incident Data

Incident characteristics are an extremely 

important data source for conducting 

planning analyses. This data describes 

incident location, characteristics, and 

the nature of blockages (duration of 

lane or shoulder blockages). Although 

incident data is being collected today by 

transportation and emergency response 

agencies, complete uniformity has not yet 

been achieved. In general, the types of 

data available are shown in Figure 11.9 

9 Cambridge Systematics et al., Guide to Effective Freeway Performance Measurement: Final Report and Guidebook, Web-Only Document 

97, Transportation Research Board, Washington, D.C., 2006. Available at: http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w97.pdf.

Figure 11. Diagram. Incident data model for 

performance monitoring.9

Incident 

Characteristics

Incident 

Location

Responders Blockage Vehicles 

Involved

Motorist 

Assistance

Source: Transportation Research Board, National Cooperative Highway Research Program. 
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

4Obtaining Archived Data That Planners Need

 The most important data elements in these categories are:

■ Incident type (crash, disabled vehicle, fire, debris, and abandoned vehicle).

■ Incident collision type (fixed object, overturn, vehicle/side, vehicle/head-on, and vehicle/

rear-end).

■ Incident “timeline” data (time stamps for the start of the incident, detection, verification, 

on-scene arrival, lane/shoulder open, and all clear).

■ Incident severity (“KABCO” injury scale).

■ Incident location (route, travel direction, milepost, or GPS coordinates).

■ Incident blockage.10

◦ Cross-section feature affected (lane, partial lane, right shoulder, left shoulder, median, 

and off of maintained way).

◦ Lane type (through/general purpose, through/HOV, auxiliary, on-ramp, and off-ramp).

◦ Begin/end time.

■ Number of involved vehicles.

The duration of the blockages caused by incidents 

is the most important piece of information 

needed for planning applications, but others 

affect adjacent traffic flow conditions. Also, 

some incident data may be incomplete, and the 

type of incident may be used as an indicator of 

the lane closure impacts (e.g., for data-checking 

purposes). This data is usually as readily available 

from databases, especially those that already have 

incident blockage/duration information; therefore, 

there is no additional effort involved in assembling 

this data.

Possible sources of data include the following 

(these are not mutually exclusive, and multiples 

may be used in combination to feed data into the 

system):

■ Service patrol/incident response team data 

entry – Personal digital assistant or laptop in 

the vehicles.

■ Transportation management center (TMC) operator data entry – Automate the conversion 

of web information to database entries.

■ Police computer-aided dispatch (CAD) files.

10 Ideally, blockage is updated every time it changes during a given incident.

Figure 12. Photo. Incident clearance.

Source: Thinkstock/csakisit.
33

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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

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 Work Zone Data

For planning applications, work zone data is useful in understanding congestion patterns and 

explaining why certain congestion trends emerged. Work zone data is sometimes collected as part 

of incident data, but also independently by construction and maintenance personnel. Figure 13 

shows the basic categories and structure for work zone data.11Major types of data on work zone 

characteristics are lane information, shoulder information, and sign information. For each of those 

categories, there is data such as lane modification, lane shift, and shoulder restrictions.

Work zone characteristics – The actual and planned changes in the roadway environment 

created by the work zone. These are used to measure the extent of work zones in time (duration) 

and space (amount of existing highway removed for the work zone) and their impact on safety 

and mobility. They also can be used in traveler information services to alert motorists to expected 

work zone conditions. In general, the types of information that should be collected are work zone 

type, longitudinal characteristics and extent (including details on transition zones and tapers), 

duration of work zone characteristics, and major cross-section characteristics of the work zone. 

Both actual and planned changes should be tracked separately.

Work zone activity – The activities related to traffic management and construction/rehabilitation 

in a work zone. These are used to assess mobility and safety impacts of traffic control plans and 

motorist guidance as well as improvements in construction planning and execution. Data includes 

specifications in traffic control plans, times traffic control plans are in effect, traffic control device 

placement in the field and times used (e.g., pavement markings, dynamic message signs (DMS) 

and static signage, positive guidance devices, barriers, and other new technologies), construction 

and rehabilitation field activities (e.g., crew size by task, task duration, and equipment used 

on-scene), and time-of-day and where in the work zone the work occurred. This level of detail 

is usually not necessary for planning applications unless evaluations of work zone management 

practices are undertaken.

11 Cambridge Systematics, et al., Guide to Effective Freeway Performance Measurement: Final Report and Guidebook, Web-Only Document 

97, Transportation Research Board, Washington, D.C., 2006. Available at: http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w97.pdf.

Figure 13. Diagram. Work zone data for planning applications.11

Work Zone

Characteristics

Lane Information Shoulder Information Sign Information

Lane 

Modification

Lane 

Shift

Sign

Location

Sign 

Message

Shoulder 

Restrictions

Source: Transportation Research Board, National Cooperative Highway Research Program. 
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

4Obtaining Archived Data That Planners Need

 Special Event Data

These kinds of events are somewhat different than work zone events in that they occur repeatedly 

and more often in the same location (e.g., sports stadiums, concert halls). Only a minimum amount 

of data on special events is required and is used primarily to document surges in traffic volumes:

■ Event name and location.

■ Start/end dates and times.

■ Approximate attendance (may be segmented by time periods).

■ Changes in operating policies, including start/end dates and times: HOV restrictions, 

contraflow, and ramp meter timing.

As with work zone data, special event data is useful in explaining and understanding congestion 

patterns; that is, continuously collected volume data will detect the volume increases due to 

special events, but planners will want to tag those surges as special events for performance 

reporting.

 Weather Data

Unlike volumes, travel times, incidents, and work zones, the vast majority of weather data 

collection is conducted by non-transportation agencies. There has been limited activity in 

developing performance measures for weather activities; of those that do exist, they relate to the 

duration/extent of weather events, weather prediction versus actual weather that materialized, and 

snow and ice removal activities. However, the influence of weather on traffic flow, safety, and 

customer satisfaction is substantial. Details on the nature of weather events that influence traffic 

characteristics, travel decisions by users, and activities by agencies need to be collected:

■ Type of weather – Precipitation type, fog, dust, and high winds.

■ Duration of the event.

■ Intensity of the event – Depends on type of weather: rainfall/snowfall rates, visibility, and 

wind speed.

■ Geographic coverage of the event – Ideally, roadway segments affected by the weather event.

■ Treatment strategies – Treatment strategies related to snow and ice control require 

coordination with maintenance personnel. These include pretreatment of roadway surfaces, as 

well as removal of snow and ice. Other treatment strategies that are more directly controlled 

by operations personnel include supporting access for emergency and maintenance vehicles 

and using RWIS and forecast data to identify areas for pretreatment.

■ Control strategies – Speed limit control, retiming of ramp meters, and traffic signals to 

accommodate reduced capacity and vehicle speeds; road closures; lane use control; vehicle 

type restrictions; and aggressive incident management (e.g., more service patrols than 

normal).

■ Advisory strategies – Highway advisory radio (HAR) and DMS weather messages, 511 

weather messages, website weather messages, and weather messages to private Internet 

service providers.
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

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 Signal Control Data

This data includes: 

■ Operational status of signals at intersections or ramp meters, 

such as operational, maintenance mode, flashing, or offline.

■ Signal timing plans and current or future timing schemes.

For planning applications, signal timing and phasing can be used 

to set the capacity used in travel demand forecasting (TDF) and 

mesoscopic simulation models. In addition, recent work has 

highlighted how data from signals and approaches can be used to 

develop certain types of arterial performance measures. Day et al. 

developed an approach based on using high-resolution controller 

event data.12 They developed a portfolio of performance measures 

for system maintenance and asset management; signal operations; 

non-vehicle modes, including pedestrians; and travel time-based 

performance measures for assessing arterial performance. The focus of this work is clearly on the 

signal operation and maintenance, but several aspects are of interest to measuring arterial corridor 

performance.

 Other Operations Data Useful for Evaluations

Planners are increasingly relied upon to conduct evaluations of completed projects and new 

policies. In fact, an ongoing evaluation program is a hallmark of performance management. 

Several other sources of operations data are available for these evaluations, even if this data is not 

useful for other traditional planning applications:

■ Device operational status – Data on the operational status of roadway devices, including 

traffic detectors, DMS, traffic signals, HARs, RWIS, and closed circuit television (CCTV) 

cameras, where available.

■ Managed lane status – Data on when HOV restrictions are in effect, direction of reversible 

lanes by time of day, and price of high-occupancy toll lanes by time.

■ Parking data – The location of parking facilities, number of spaces occupied and available, 

time and duration of parking space utilization, current fees, restrictions, and data on how to 

reserve a space.

■ Incident activity: institutional – The institutional setting, arrangements, and protocols for 

incident management are extremely important determinants of performance; these will 

determine how long incidents are in effect (incident duration), thereby influencing 

performance. Institutional activities may include:

◦ Agency participates in a formal multi-agency regional or Statewide program to 

coordinate management of traffic incidents that contains strategic planning, a program 

plan, and annual work plan. 

12 Day, C.M., E.J. Smaglik, D.M. Bullock, and J.R. Sturdevant, Real-Time Arterial Traffic Signal Performance Measures, Publication FHWA/

IN/JTRP-2008/09, Joint Transportation Research Program, Indiana Department of Transportation and Purdue University, West Lafayette, 

Indiana, 2008. doi: 10.5703/1288284313439.

Figure 14. Photo. 

Traffic signal.

Source: Thinkstock/Carsten Reisinger.
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

4Obtaining Archived Data That Planners Need

◦ Agency participates in a team that meets on a regular basis to evaluate and improve 

coordinated incident response and to address traffic problems as well.

◦ Agency has developed a concept of operations document for incident management.

◦ Multi-agency contact list has been developed in the area containing the names, phone 

numbers, pager numbers, and other pertinent information for the appropriate response 

personnel.

◦ Incident Management (Incident Command) System used on-scene to manage traffic incidents.

◦ Legal specification by State law or formal agreement as to who is in charge at the 

scene of a traffic incident (Incident Commander).

◦ A plan has been developed and adopted by responding agencies for staging and 

parking response vehicles and equipment at a traffic incident site in a manner that 

minimizes lane blockage and facilitates the re-opening of lanes.

◦ Respondents protected through law or court opinion for liability claims for damages to 

vehicles or cargoes during clearance activities so long as the removal was not done in a 

careless or grossly negligent manner.

◦ TMC and Service Patrol active incident management times (e.g., 24 hours, 7 days).

◦ State or local jurisdiction has a law that requires drivers involved in a propertydamage-only accident (where vehicles can be driven) to move the vehicles from travel 

lanes to a safe location to exchange information or wait for police.

◦ Quick clearance laws or policies.

◦ Length of time abandoned vehicles are allowed to remain on a freeway shoulder 

(assuming they are not an imminent hazard).

◦ Laws or policies regarding the removal of stalled or abandoned vehicles from freeway 

shoulders are in effect.

◦ Policies and procedures are in place to facilitate quick removal of heavily damaged 

vehicles and non-hazardous cargoes.

◦ Procedures are in place for removing crash victims from incident scene.

◦ Transportation agency and public safety agency are colocated in a TMC.

■ Incident activity: physical – The equipment and resources necessary to conduct effective 

incident management also will have an effect on incident duration and performance:

◦ Incident detection algorithms (used with traffic sensors).

◦ Method of incident notification to all responders (communication between agencies).

◦ CCTV coverage (percent of study segment capable of being viewed with CCTV).

◦ Degree of wireless communication for on-scene management.

◦ Service patrol vehicles per centerline mile by time period.

◦ TMC floor personnel per centerline mile.
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

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 Connected Vehicle Data

Data from connected vehicles, once considered a futuristic data source, is now available through 

both third-party vendors at the national level and from connected-vehicle pilot sites in several 

States. Presently, at the national level, commercial vehicles rolling off of the assembly line have 

the ability to provide connected vehicle data in real-time. This can be information about driver 

behavior and vehicle diagnostics, including:

■ Speeding.

■ Harsh braking/acceleration.

■ Antilock braking system (ABS) engagement.

■ Stability control.

■ Temperature sensors.

■ Wiper use.

■ Headlight use.

■ Air bag deployments.

■ Emissions data.

■ Oxygen sensors.

■ Seat belt use.

■ Tire pressure.

The same providers also are able to 

furnish real-time intersection turning 

movements and near-real-time O-D 

data in an anonymized way. This 

real-time operations O-D data reflects 

actual trip-making patterns between 

a subset of nodes. At the smallest 

scale, this data can include turning 

movements at interchanges. At 

medium scales, it can track corridor 

distribution of traffic. At the largest 

scale, it reflects region-to-region trip 

demand.

Figure 15. Image. An illustration of interconnected 

infrastructure.

Source: Federal Highway Administration.
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

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Having access to this type of data has significant impacts on real-time operations, but can also 

have major implications for planners and researchers. Having increased access to O-D data and 

safety condition information from connected vehicles will enable new safety analytics, new 

methods of problem identification, and more extensive model validation data sets.

Data Sources and Collection Techniques to Ensure Usefulness in Planning

Archived operations data exists at very detailed temporal and spatial scales. Continuous data from 

ITS roadway detectors can be archived at as low a level as 20-second intervals by lane. Currently, 

available data from commercial travel time vendors exists at 1- to 5-minute intervals by roadway 

link. As vendors increase the sophistication of their products, additional detail may become 

available. Data from traffic signals can include phase-by-phase and, sometimes, second-by-second 

conditions. Processing this data into a useable form poses several challenges.

What levels of aggregation should be used for storing the data? Aggregation is a tradeoff 

between computational efficiency and storage capacity versus level of detail available for 

applications. Developers should carefully consider what level of aggregation best balances these 

tradeoffs. Sound archiving practice suggests that all raw or originally collected data should also be 

maintained for a period of time, even if it is offline.13 When aggregating for storage, data is often 

transformed by combining it with other data (e.g., delay is a combination of travel time and demand).

For both straight aggregation and transformations, it is useful to document the “lineage” of the 

data so that users can better understand its structure. This documentation requires that structured 

metadata be maintained as part of system that houses the data.14 

What QC procedures should be used to check the data for accuracy? True QC procedures 

would be based on comparison to independently collected data, even if it is on an audit basis, as 

is done for the I-95 Corridor Coalition.15 However, most agencies do not have the resources to 

conduct continuous data audits, although some limited one-time tests may be conducted. Instead, 

the majority of QC processing must be done post-hoc with reasonableness checks, comparison 

to traffic flow theory, and outlier detection. Once potential data errors are detected, it must be 

decided if the data should be deleted or flagged as erroneous. (Flagging is preferred so as to 

preserve the original data for other uses.) Then, a decision has to be made if the erroneous data are 

to be replaced with imputation.16 Imputation carries its own set of issues, and users must decide 

if imputed data should be used. For example, performance measures that are “counts” or “sums” 

require complete data for the time period in question (e.g., traffic volume and delay). Other 

measures, such as speed, may be treated as a sample and computed without imputed data, if users 

determine a sufficient sample size exists.

How should the data be turned into measures? The starting point for developing performance 

13 As specified in ASTM Standard Guide for Archiving and Retrieving ITS-Generated Data. Available at: http://www.astm.org/Standards/

E2259.htm.

14 As specified in ASTM Standard Practice for Metadata to Support Archived Data Management Systems. Available at: http://www.astm.org/

Standards/E2468.htm.

15 Hamedi, M., Haghani, A., Zamir, K., and Wang, Z., I-95 Corridor Coalition Vehicle Probe Project: Validation of HERE Data, Report 

for North Carolina (#7) US-29 and US-74, February 2016. Available at: http://i95coalition.org/wp-content/uploads/2015/02/I-95-CCValidation_Report-NC07-HERE-FINAL2.pdf?dd650d.

16 See Chapter 3 of the Strategic Highway Research Program 2 L02 Final Report. Available at: http://onlinepubs.trb.org/onlinepubs/shrp2/

SHRP2_S2-L02-RR-2.pdf.
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measures for mobility is travel time data. Over the past 15 years, travel time data availability 

has increased exponentially, starting with data from ITS roadway detectors and continuing to 

the current generation of vehicle probe data from private vendors. However, none of this data 

represents directly measured travel times as defined by measuring the passage of individual 

vehicles as they traverse the network. To create travel times from this data, transformations are 

required. For roadway detectors and the current generation of privately-supplied vehicle probes, 

this means assuming that the measurements of speeds apply equally over some distance, usually 

the length of the reporting segment. Users should be aware that the travel times so created are an 

approximation of actual travel times. These restrictions do not apply to data collected from individual 

vehicle probes, such as GPS-equipped “floating cars,” roadway systems that measure the passage of 

individual vehicles via electronic signatures (e.g., Bluetooth readers), and toll tag systems. 

In the near future, it is expected that directly measured travel times will become available from 

private sources. As of this writing, some vendors are developing travel times for origin-destination 

pairs. In the longer term, data from connected or automated vehicles could supply origindestination travel times. As this data becomes available, it should replace the current generation of 

data used for performance measurement. Until that time, the following guidance is offered.

Performance measures and data inputs to models typically are summarized temporally and 

spatially. For example, mobility performance often is computed for “facilities,” usually several 

miles in length and encompassing several ITS roadway detectors or probe data links. Two 

methods can be used to compute performance measures on such facilities. The first method 

computes the measures for each spatial unit, and then combines them to get facility performance 

(e.g., speed and travel time distributions). The problem posed by this method is that, while it 

is possible to combine means from links on the facility (the harmonic mean would be used for 

speed), statistical theory indicates that variances and percentiles cannot be combined because 

speeds (or travel times) on successive links are correlated.17 The second method is based on first 

developing travel times (and vehicle miles of travel (VMT) where available) for the entire facility 

by the temporal level in the original data. The distributions from which measures are produced are 

then based on the entire facility, not stitched together from its components. Figure 16 presents a 

graphic summary for how facility travel times are computed from ITS roadway detectors reporting 

data by individual lanes.18 (Note: A computation procedure for developing the measures using 

this approach will likely be included in Chapter 24 of the 2016 update to the Highway Capacity 

Manual (HCM).)

17 A summary discussion and extension of this phenomenon can be found in the Strategic Highway Research Program 2 L04 Final Report. 

Available at: http://onlinepubs.trb.org/onlinepubs/shrp2/SHRP2_S2-L04-RR-1.pdf.

18 Turner, S., R. Margiotta, and T. Lomax, Monitoring Urban Freeways in 2003: Current Conditions and Trends from Archived Operations 

Data, Report No. FHWA-HOP-05-018, December 2004. Available at: http://mobility.tamu.edu/mmp/FHWA-HOP-05-018/.
40

ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

4Obtaining Archived Data That Planners Need

 Figure 16. Diagram. Computing facility travel times from intelligent transportation system 

roadway detectors.

However, the second method is not a complete reflection of travel time conditions on the 

facility, because it is not based on the actual passage of vehicles over time. That is, it is a single 

“snapshot” of conditions for a given time period. To more closely replicate true travel times, 

the “virtual probe” (also known as “vehicle trajectory”) method can be used to synthesize travel 

times. The virtual probe method “traces” the vehicle trip in time and applies the link travel time 

traffic sensors collect data in each lane at 0.5-mile nominal spacing

summary statistics computed across all lanes in a given direction

link travel time &

vehicle-miles of travel

link travel time &

vehicle-miles of travel

point-based properties extrapolated to roadway links 1-3 miles in length

directional roadway section

travel time & vehicle-miles of travel

directional roadway section 

travel time & vehicle-miles of travel

link properties summed to analysis sections 5-10 miles in length

Laneby-Lane

Level

Section

Level

Link

Level

Station

Level

Source: Federal Highway Administration.
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ARCHIVED OPERATIONS DATA IN TRANSPORTATION PLANNING

Obtaining Archived Data That Planners Need

corresponding to the precise time in which a vehicle is expected to traverse the link. For example, 

a section travel time that begins at 7:00 AM will use a link travel time for 7:00 to 7:05 at the trip 

origin, but could use a link travel time from 7:05 to 7:10, or 7:10 to 7:15 at the trip destination. 

The virtual probe method attempts to more closely model the actual link travel times experienced 

by motorists as they traverse the freeway system.

For relatively short segments, the difference in results of the snapshot versus the virtual probe 

method is thought to be small. As the length of the facility increases, the differences are 

exaggerated by the time/space offset. For example, the Florida DOT (FDOT) recently developed 

a monitoring program for long-distance freight trips in the state based on the trajectory method to 

address this problem.19

The virtual probe method also can be applied to signalized arterials to develop performance 

measures from signal system and control data. This data is “event-based,” as it relates to either 

the presence or absence of a vehicle at a point on or near the signal approach, or the status of the 

signal phasing. Liu et al. used the virtual probe method with both vehicle-actuation and signal 

phase change data in a synchronized manner.20 This data is time-stamped, thus allowing the 

reconstruction of the history of traffic signal events along the arterial street. In the simulation, the 

virtual probe vehicle’s trajectory is traced in time and space, and its status at any point in time 

is dependent on the underlying data. When the vehicle “completes” its trip, total travel time is 

recorded. In the absence of directly measured travel times and delays, this approach would work 

for measuring arterial performance.

Another major issue in the development of mobility performance measures is developing a 

complete data set from which to compute the measures. Such a complete set would include 

simultaneous measurements of both speed (or travel time) and traffic volumes. Volumes are used 

to compute some measures (VMT and delay) and are used to weight the results for sections with 

varying amount of traffic. ITS roadway detectors provide this structure, but vendor-supplied 

vehicle probe data does not; this data should be treated as a sample. As mentioned above in the 

section, Challenges in Changing Planning Methods and Products, vehicle probe and traffic count 

data sets are conflated to provide the complete set of data. Conflation brings its own problems, 

especially when many-to-many spatial relationships require allocation of the traffic data. Further, 

nearly all traffic counts are daily counts, which have to be decomposed into the time periods 

represented by the probe data. For this process, default temporal distributions are used that are 

developed from continuous count stations.21

19 Cambridge Systematics, Inc., Trip-Oriented Mobility Monitoring Framework Development Final Report, Prepared for Florida Department 

of Transportation, November 5, 2014. Available at: http://floridampms.com/Final%20Reports/Task%2012%20Trip%20Based%20Travel%20

Time%20Reliability.docx.

20 Liu, H.X., W. Ma, X. Wu, and H. Hu, Development of a Real-Time Arterial Performance Monitoring System Using Traffic Data Available 

from Existing Signal Systems, Prepared for Minnesota Department of Transportation, December 2008. Available at: http://www.lrrb.org/

PDF/200901.pdf.

21 See Appendix A of Texas Agricultural and Mechanical University’s 2012 Urban Mobility Report. Available at: http://d2dtl5nnlpfr0r.

cloudfront.net/tti.tamu.edu/documents/mobility-report-2012-appx-a.pdf.
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4Obtaining Archived Data That Planners Need

Data Archives: Implementation Options and Common Mistakes Associated 

with Each

There are two options from which to choose when deciding how to implement an operations data 

archive (Figure 17):

■ Agency builds/creates in-house.

■ A third party specializing in data archiving or offering an existing product for sale can be 

procured. That third party can handle all aspects of the data archive, including ongoing 

maintenance.

In addition, it may be possible to find another agency that is willing to build and deploy the data 

archive; however, that agency becomes responsible for operations and maintenance moving forward.

Figure 17. Diagram. Operations data archives built in-house or hosted by a third party.

There are pros and cons to each of these approaches, some of which may not be obvious at first.

 In-House

If an agency believes it has the capacity and drive to develop, deploy, and host its own archived 

operations data, then it is important to keep the following in mind:

■ Identify the data (where is it archived and who is responsible for it).

■ Meet with potential users.

■ Identify needs (but realize positive feedback may not initially occur).

■ Let the needs dictate the technology (rather than vice versa).

■ Focus more on usability, the user interface, and the questions or problems to be addressed.

Source: University of Maryland CATT Laboratory.
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Costs for databases vary greatly. Licensing of the database engine alone can range from no cost 

(for open-source database technologies) to several hundred thousand dollars per year. Databases 

are well suited for storing smaller data sets that are relational in nature. However, with massive 

amounts of data (hundreds of terabytes, petabytes, etc.), it begins to become quite expensive 

to implement fast search. To combat these growing costs and the need for speed and failover 

capabilities, Google developed some algorithms that allowed data to be divided into smaller 

“chunks” that were mapped to many computers. When calculations needed to be made, the data 

“chunks” were brought back together and processed rapidly. These algorithms were eventually 

open sourced and named Hadoop, which is available on many cloud platforms. Many public and 

private-sector companies have now adopted Hadoop-style technologies.

 Third-party Hosting

Often, one or more archives will be implemented through consultant support. This could be as 

simple as drafting a new task order for an existing ATMS provider, or it may be more complex and 

involve a new procurement going out to bid. If bidding a new procurement, there are a few things 

to consider, including:

■ Avoid over-specifying implementation technologies. For example, it may be best not to tell 

a consultant that they must deploy something in “the cloud,” or that they must use a specific 

type of relational database technology (e.g., Oracle, MsSQL, etc.).

■ Consider how available an archive needs to be. If it is not being used for 24-hour/7-day mission 

critical operations, then it is likely not worth the extra expense to have a fully redundant/

hot-swappable system. Ensure data is backed up, but it may not be necessary to spend additional 

money on a hot-swappabl system with just over 5 minutes of downtime annually.

■ Dedicate more time to working on requirements for usability, functionality, and recruiting 

multiple user groups to determine expected usage.

■ Attempt to procure the services of a consultant who has performed similar work for other 

agencies. The archive may need customization and tailoring, but a proven provider is often 

more reliable than a standard consultant.

■ Recognize that initial startup will be costly. There are several private-sector and university 

providers that have excellent archiving, fusion, and analytics products. Some of these 

systems work across borders and across multiple agencies. Consider adopting similar 

technologies or products as neighboring jurisdictions when possible so that shared 

experiences, knowledge, and benefits from shared resources can be leveraged.

■ Avoid “black box” solutions that do not explain the underlying technologies, algorithms, or 

methods used to calculate the performance measures, despite the notion that it is prudent to 

purchase existing products. Ensure the chosen provider has documented procedures that 

can be shared with engineers and analysts. Some providers have multi-State or multiagency steering committees that collectively drive the features of the archive products to 

ensure they are constantly meeting user needs.
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 Common Mistakes:

Mistakes made while procuring new data collection systems. When procuring a new system 

(e.g., ATMS, 511, or other), ensuring the “home” agency has access to and ownership of any data 

that is created is essential. With software-as-a-service-type contracts for some of these systems, 

it is not uncommon for commercial off-the-shelf products to claim ownership of all data created 

in a particular system. This is a particular issue for transit system operators who have purchased 

systems from third parties. These third parties have historically claimed all of the automated 

vehicle location (AVL), routing, and other data as “proprietary,” and have either refused to allow 

it to be archived for other agency use or have charged exceedingly high fees to make the data 

available to other archives and for other use.

All agency systems (e.g., ATMS, AVL, 511, CAD, or other) should be capable of sharing, in 

real-time, all data fields in their database in some sort of data feed to trusted third parties (e.g., 

other DOTs, public safety agencies, trusted university partners). This includes any device 

configuration information, lookup codes, incident information, detector data, operator actions, 

static and dynamic Geographic Information System information (e.g., device locations, mile 

marker locations, etc.), or video. Making these statements within individual systems procurement 

documents will assist in avoiding large future expenses or being unable to access data in the 

desired ways.

Data ownership and acceptable use. As mentioned above, it is imperative for an agency to 

ensure ownership of all data entered into any of its systems. In other words, the agency should 

ensure that it is freely available to share the data with any third parties it so chooses. The one 

exception to this requirement is with third-party data that the agency may choose to purchase from 

a private company (e.g., Waze, SpeedInfo, Traffic.com, INRIX, HERE, TomTom). Whenever 

possible, the agency should negotiate terms of use for sharing third-party or privately collected 

data, so that the agency is free to share the data with any other non-commercial entity (e.g., other 

operational agencies, universities, public safety agencies). For agency-created and -owned data, 

the agency should always make every effort to be able to share their data with external parties 

without the need for complex legal agreements. If a data sharing agreement is necessary, the DOT 

should only reference Federal laws and should remain silent on governing law so that inter-State 

data sharing will be possible.

Persistence of data. All data feeds to an archive should be persistent, such that eventual 

data delivery to the data archive is guaranteed and no data is lost. Should there be a network 

connectivity issue or a power failure at the archive, the providing system should continue to hold 

the data in a queue until such time as the connection can be re-established and all of the data can 

be sent.
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Planning Opportunities for Archived Operations Data 

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5. PLANNING OPPORTUNITIES FOR ARCHIVED 

OPERATIONS DATA – BASIC TO INNOVATIVE

This chapter showcases five important planning activities that are enabled by archived operations data:

■ Identifying and confirming the problem.

■ Developing and reporting mobility performance measures.

■ Using archived operations data for loading into and calibrating reliability prediction tools.

■ Performing before and after studies to assess projects and program impacts.

■ Identifying causes of congestion.

The sections below introduce each planning activity by identifying the need for archived 

operations data for this activity, the types of relevant data, analytical procedures that transform the 

archived data into useful information, and the impact that using archived operations data for that 

planning activity can achieve. Following the introduction to each planning activity, one or more 

case studies are provided to demonstrate, in detail, how archived operations data can be used and 

what agency outcomes were achieved. Each case study highlights the use of visualization and its 

influence on the planning activity.

Problem Identification and Confirmation

 Need

All agencies have limited dollars available to improve 

mobility, safety, and aging infrastructure; therefore, most 

agencies attempt to identify the “worst” problems in their 

jurisdiction (i.e., problems that have the greatest impact, 

and most likely, the greatest potential for a high return 

on investment (ROI) should countermeasures be put into 

place). An agency needs an efficient way to identify, 

quantify, and rank safety and mobility issues, and to 

determine the cause of these issues. Because of the dynamic 

nature of transportation, it is important that an agency 

be able to perform these types of analysis efficiently and 

cost-effectively many times throughout a year to respond 

to external political influences and/or to public concern. 

The results of these analyses can then drive the long-range 

transportation plan (LRTP), influence funding decisions, 

or simply be used to respond to public concerns over why a 

particular project was chosen over another.

Figure 18. Photo. Congestion on 

freeway.

Source: Thinkstock/tomwachs.
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 Relevant Data

Historically, continuously collected permanent count station data and police accident reports 

would have been influential in this type of analysis. In some cases, temporary count station data 

may have been used, or density data may have been collected by aerial photography surveys. 

Both the count station and police accident data, while collected in real-time, will often have many 

months of lag between when the data is collected and when it becomes available to analysts. 

Aerial photography surveys may be collected only for a few hours once every few years, and 

only at select locations where problems have been anecdotally noted. Temporary count stations 

are deployed only once every few years on select roadways, and they rarely collect data for more 

than 1 week. Because these data sources are not collected continuously, agencies must rely on 

modeling and forecasting techniques, which may not account for seasonal variations, incidents, 

or other dynamic conditions. Archived operations data, however, can help to reduce or even 

eliminate some of the challenges associated with the above data sources. Real-time flow data can 

be collected from sensors, toll tag readers, license plate readers, or probe data from third-party 

vendors (e.g., HERE, INRIX, or TomTom). Advanced transportation management system (ATMS) 

platforms also collect real-time event data and lane closure information from construction events, 

disabled vehicles, accidents, and special events.

 Analytical Procedures

Operations data from probes or other high-density sensors can be used to identify congested areas 

or bottlenecks. When this data is integrated with ATMS event data, it becomes possible to identify 

if the congestion is recurring or non-recurring.

Flow data from probes and dense sensor networks can sometimes be challenging to work 

with because of the sheer volume of data, complex linear referencing systems that might not 

match up with agency centerline road databases, and limited agency technical and resource 

capacity. Agencies with strong, integrated information technology (IT) departments, dedicated 

data analysts, sufficient data storage hardware, and access to statistical software, such as SAS 

(Statistical Analysis System), can handle this type of analysis given time and proper funding.

However, many agencies subcontract their analysis to consultants, universities, or transportation 

departments, or they purchase off-the-shelf congestion analytics software and data integration 

services tailored for transportation planners. 

 Impact

Regardless of the method used to analyze operations data, agencies often find that the use of 

operations data can allow them to identify problems based on more timely data, which helps to 

instill greater confidence in the decisions that are made as a result. Also, when probe-based data 

is used for the analysis, the data has greater coverage than traditional loop detectors. Therefore, 

there is more confidence that problem identification analysis is covering “all roads” instead of just 

detected roads. Lastly, agencies who purchase off-the-shelf congestion analytics software find that 

the speed of analysis increases so significantly that resources are freed up and the agency is able to 

respond to requests much faster.
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 Case Study: Problem Identification and Confirmation at the New Jersey Department 

of Transportation

Overview

The New Jersey Department of Transportation (NJDOT) uses a combination of archived 

operations data and data processing and visualization tools to identify performance issues on the 

transportation system and develop easy-to-understand performance measures with visualization 

that speak to senior leadership, elected officials, and the public, in alignment with Fixing 

America’s Surface Transportation (FAST) Act and organizational goals. NJDOT is leveraging 

a mixture of archived operations probe data, volume data, weather data, and event data to help 

identify congestion issues on the State’s roadway network in part to create problem statements for 

consideration in its project delivery process.

The archive of these data sets has been made available to NJDOT planners through a web-based 

visual analytics tool known as the Vehicle Probe Project (VPP) suite. This suite gives planners 

the ability, with minimal effort, to automatically detect and rank the worst bottleneck locations 

in a county or entire State, determine if the congestion is recurring or non-recurring, determine 

causality, measure the economic impact, and produce graphics that can be inserted into analysis 

documents and presentation materials to be used with decision makers. The VPP suite is an 

outcome of a project directed by the I-95 Corridor Coalition that began in 2008 with the primary 

goal of providing Coalition members with the ability to acquire reliable travel time and speed data 

for their roadways without the need for sensors and other hardware. The VPP has evolved since 

that time and now also provides tools to support the use of the data and integrates several vehicle 

probe data sources.22

Data

NJDOT is using four archived data sets: probe, volume, event, and weather. The probe data is 

derived from a private-sector data provider and is archived at 1-minute intervals from a real-time 

feed that is originally used for travel time and queue estimation. The probe data is represented as 

the average speed across the length of a road segment for each 1-minute period.

The volume data used by the agency is derived from Highway Performance Management System 

(HPMS) data that is also provided back to the agency through a third-party data provider.

The event data derives from NJDOT’s OpenReach system, which is their ATMS and 511 platform. 

This data is entered by operations personnel into their ATMS platform in real-time. Types of 

information recorded include the type of event (e.g., construction, accident, special event, disabled 

vehicle, police activity, etc.), location, and lane closure information.

The weather data is a mixture of both weather alerts and radar that derives from freely available 

National Weather Service data feeds.

All of the above-mentioned data sources are fed into the Regional Integrated Transportation 

Information System (RITIS) archive and the VPP suite of analytics tools housed outside of the 

agency. RITIS also began as a project under the I-95 Corridor Coalition and is an automated data 

fusion and dissemination system with three main components, including: (1) real-time data feeds, 

(2) real-time situational awareness tools, and (3) archived data analysis tools.

22 I-95 Corridor Coalition, Vehicle Probe Project web site. Available at: http://i95coalition.org/projects/vehicle-probe-project/.
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Agency Process

NJDOT identifies and analyzes congestion issues on the system and develops communication 

materials using the following basic process with the VPP suite of analytics tools: 

1. Rank the worst congested (Figure 19) locations within the State by:

a. Road class.

b. Time of day.

c. Day of week.

d. Date range.

e. Geography (statewide, by county, by zip code, by corridor, or by user-defined regions).

2. Determine if the congestion is recurring or non-recurring (Figure 20).

3. If non-recurring, identify causality through event data analysis (Figure 21), including: 

a. Weather events.

b. Accidents.

c. Construction.

4. For each congested location or corridor, study the economic impact of that congestion through 

user delay cost analysis (Figures 22 and 23).

5. Develop compelling reports and graphics that are presented to high-level decision makers to 

communicate the impact of the problem in a meaningful way (Figures 24 - 27).

Figure 19. Screenshot. Ranked bottleneck locations for all of 

New Jersey during March 2014. 

Source: Bottleneck Ranking Tool from the Probe Data Analytics Suite. University of 

Maryland CATT Laboratory.
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The bottleneck ranking tool allows an agency to “rank” the worst congested locations in any 

geographic region (multi-State, State, county(ies), city, etc.) based on archived data (e.g., probebased speed data, weather data, and ATMS incident logs), which informs users on certain statistics 

(e.g., on how significant each congested location is, when the congestion occurs, how it may have 

been affected by incidents and events, etc.).

The graph in Figure 20 is one of the ways NJDOT views information on bottlenecks using the 

VPP suite. This graph shows the Monday through Friday pattern of congestion in the mornings 

(6:30AM through 10:30AM) with little to no congestion on the weekends. The spiral graphs are 

useful in identifying temporal patterns in the data. The repeated patterns that temporal data exhibit 

over longer time spans are often more illuminating than the linearity of the data and can be used in 

tandem with event and weather data to determine if the bottleneck location is recurring or nonrecurring. The spiral graph renders data along a temporal axis that spirals outward at regular, userdefined intervals (e.g., days, weeks, or months). Bottleneck events are rendered as bands along the 

axis, creating visual clusters 

among data that contribute 

to patterns. The particular 

bottleneck shown in Figure 

20 is rendered on a daily 

cycle—each revolution in the 

graph is a single day where 

the inner-most revolution is 

March 1, and the outermost 

revolution is March 31. 

Bottlenecks at this location 

are more prevalent in the 

morning between the hours 

of 6:30AM and 10AM, with 

a few less-severe bottlenecks 

occurring in the evening 

between 5PM and 6PM. The 

color denotes the maximum 

length of the bottleneck. The 

5-day work week pattern also 

is shown in this bottleneck. 

Figure 20. Chart. Time spiral showing when a bottleneck 

occurred and length during that time. 

Source: Bottleneck Ranking Tool from the Probe Data Analytics Suite. 

University of Maryland CATT Laboratory.
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The graph in Figure 

21 is another way 

that identifies 

congestion issues 

based on archived 

data. The graphic 

depicts a 20-mile 

section of I-95 in the 

State of Maryland 

in which the right 

side represents 

northbound traffic 

while the left side 

represents southbound 

traffic. Green shading 

denotes uncongested conditions, while red and orange shadings represent more congested 

conditions. Daily occurrences of incidents and events are overlayed. Figure 21 shows a large 

number of events that contributed to abnormal (non-recurring) congestion in the northbound 

direction of travel during the morning. 

Figure 22 displays the user delay costs for I-295 in New Jersey that contribute to an economic 

impact study of congestion. Each cell represents 1 hour of the day on a particular day of the 

week. The cells are color coded to represent hourly user delay in thousands of dollars. Figure 23 

provides a closer look at a summary report of delay costs for a Friday at 5 PM.

Figure 21. Graph. Congestion scan graphic for I-95. 

Source: Bottleneck Ranking Tool from the Probe Data Analytics Suite. University of Maryland CATT Laboratory.

Figure 22. Screenshot. User delay costs for a 17-mile stretch of I-295 in New Jersey (both 

directions of travel).

Source: User Delay Cost Table from the Probe Data Analytics Suite. University of Maryland CATT Laboratory.
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The following figures are examples of reports and facts sheets that NJDOT developed based on 

archived operations data to communicate to decision makers and the public about the problems on 

the system and the need for improvements.

Figure 23. Screenshot. View of I-295 showing detailed statistics in a 

mouse tooltip for a particular hour of the day. 

Source: Probe Data Analytics Suite. University of Maryland CATT Laboratory.

Figure 24. Screenshot. Project confirmation presentation graphic to confirm a “high-need” 

signalized intersection in New Jersey. 

Source: New Jersey Department of Transportation.
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Figure 25 shows confirmation of significant congestion at the I-280/Rte 21 interchange.

Figure 25. Screenshot. Example of problem identification and project confirmation at 

New Jersey Department of Transportation. 

Source: New Jersey Department of Transportation.
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The evaluation in Figure 26 of the McClellan Street Interchange is part of the Port Authority of 

New York and New Jersey’s Southern Access Roadway Project. This location is not considered a 

high priority for the NJDOT from a congestion relief perspective.

Figure 26. Screenshot. Example analysis from New Jersey Department of Transportation 

using the Vehicle Probe Project Suite, 511 New Jersey cameras, and the New Jersey 

Consortium for Middle Schools.

Source: New Jersey Department of Transportation.
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Tool

The VPP suite allows the user to generate a congestion impact factor, which is a simple 

multiplication of the average duration, average maximum length, and the number of occurrences. 

The impact factor helps the user filter out high occurrence—but short duration and short length—

bottlenecks that are of lower significance (e.g., those that might be a result of normal stoppages as 

a result of traffic signals on arterials).

Once NJDOT identifies a candidate location for congestion reduction, it can conduct an economic 

analysis within the VPP suite. NJDOT can select a study region and use existing statistics on the 

regional value of time for both commercial and passenger vehicles. The suite produces a series of 

interactive tables that show hourly, daily, and total:

■ User delay cost:

◦ Total.

◦ Per vehicle.

◦ Per person.

■ Hours of delay:

◦ Person-hours.

◦ Vehicle-hours.

◦ Per vehicle.

■ Volume:

◦ Total.

◦ Passenger.

◦ Commercial.

■ Data validity metrics.

After conducting their analysis 

within the VPP suite, NJDOT 

planners integrate some of the 

supporting graphics into slide 

shows, pamphlets, and posters that 

are used to help communicate with 

decision makers and the public. The 

embedded graphics are supported 

by explanatory text, call-out boxes, 

and other materials that combine 

to display all the information about 

the nature of the identified problem. 

Figures 24-27 are examples of 

some of these problem identification 

slides and pamphlets. It should be 

noted that many of these examples 

Figure 27. Screenshot. Concept graphics explaining where 

high accident locations contribute to bottlenecks.

Source: New Jersey Department of Transportation.
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are taken from larger slide decks and other materials that, when combined, provide further 

background and backup materials to help explain the nature of a problem, and in some cases, 

recommended countermeasures.

Calculations Behind the Visualizations

The calculations behind all of the performance measures within the VPP suite are posted online.23

It is important to note that, as knowledge improves and technical capabilities mature, these 

calculations will frequently be updated and revamped within the suite to reflect the state of the 

art. One of the many advantages of using a web-based analytics tool is that the enhancements 

made to these calculations propagate instantaneously to every user, thus NJDOT and the relevant 

metropolitan planning organizations (MPOs) benefit from the new knowledge, and analysts 

are not burdened with having to re-implement complex functions multiple times within an 

organization. Another advantage is that these calculations are instantaneously and effectively 

standardized across multiple agencies and multiple departments, which makes it much easier to 

compare performance and ensure consistent and reliable analysis from State to State.

Impact and Lessons Learned

NJDOT has, within only a few short years, significantly increased the abilities of their own 

planning staff, their consultant partners, researchers, and MPOs through the adoption of these 

archived data analytics tools. They are saving time and money while providing much-needed 

insights into mobility and the problem of congestion. 

With the advancements of these archived data tools, 

NJDOT can now answer difficult questions that were 

either previously unanswerable, or could have taken 

up to 1 year to analyze and report. As a result of 

this tool, NJDOT is able to operate more efficiently, 

respond to media and public inquiries rapidly, use a 

data-driven approach to problem solving, and focus 

more time and effort in identifying solutions to 

complex transportation problems, rather than chasing down other data.

The improved analysis tools provide NJDOT with a stronger basis for the recommendations, and 

enable staff to bring a fresh perspective to previous studies, as appropriate. The VPP suite also is 

being used to evaluate projects in concept development to help decide whether projects should 

continue to advance through NJDOT’s Project Delivery Process. This type of evaluation is used 

primarily for projects that were initiated outside of the normal problem statement or management 

systems screening processes and can be particularly helpful in ranking the priority of projects 

throughout a region so that political influences do not overly impact a project’s likelihood of 

moving forward.

The greatest lesson learned from NJDOT’s experience with archived operations data is that data 

alone will not solve its problems. It needs tools that can empower a wide range of planners and 

analysts by removing the stress and burden of managing massive data sets and relying on external 

processing power to make them more efficient and proactive analysts.

23 Go to https://vpp.ritis.org/suite/faq/#/how-are-bottleneck-conditions-tracked for information on bottlenecks, and https://vpp.ritis.org/suite/

faq/#/calculations for information on user delay cost.

“Archived operations data may be 

the most important advance in data 

for planning purposes.”

John Allen, Formerly of the NJDOT 

Planning Office
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Development and Reporting of Mobility Performance Measures

 Need

Performance measurement for mobility concerns is a key aspect of planning activities. It has 

been defined as a major step in the congestion management process (CMP) and is the basis 

for conducting sound performance management. In addition to fulfilling these requirements, 

undertaking performance measurement improves the quality of planning in several ways:

■ Provides accountability for investment decisions. Performance measures assist 

transportation professionals in gauging what benefit was gained for the cost. As such, they 

enable the investment process to be transparent.

■ Demonstrates that planners understand where the problems are. Instead of relying on 

partial information from models, planners now can identify exactly where problems exist. 

Therefore, performance measurement can be an effective tool for public relations; it is the 

first step in developing solutions to problems. 

■ Helps to guide investment decisions and to understand the impacts of those decisions 

on travelers. As with the private sector, performance measures help agencies and firms 

improve their products and labor by continuously monitoring impacts of policies and 

investments.

Performance measures should not be undertaken in isolation. Rather, they should be part of a 

process that relates to the strategic vision an agency has. That vision is embodied in goals and 

objectives and in performance measures that are used to monitor progress toward meeting them 

(Figure 28). Performance measures also should permeate all aspects of the planning process and 

planning products 

(Figure 29).24 A high 

degree of commonality 

in measures should exist 

across planning stages, 

recognizing some 

measures will be unique 

to specific stages. To 

the degree possible, 

however, consistency 

in performance 

measures across 

the different stages 

should be maintained. 

Documentation of past 

performance, through 

the production of 

performance reports, is 

the focus of this section.

24 Cambridge Systematics et al., Guide to Effective Freeway Performance Measurement: Final Report and Guidebook, Web-Only Document 

97, Transportation Research Board, August 2006, Available at: http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w97.pdf.

Figure 28.Diagram. Performance measures provide a 

quantifiable means of implementing goals and objectives 

from the transportation planning process.

Source: Transportation Research Board, National Cooperative Highway Research Program. 
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Archived data enables performance measures to track trends in system performance and to 

identify deficiencies and needs. Performance measures can be classified into different types:

■ Outcome measures relate to how well the firm or agency is meeting its mission and stated 

goals. Outcome measures are sometimes called “efficiency” measures. In the private sector, 

outcome measures relate to the “bottom-line” (i.e., the financial viability of the firm: profit 

and revenue). For transportation agencies, outcomes are more related to the nature and 

extent of the services provided to transportation users.

■ Output measures relate to the levels of effort expended, or the scale or scope of actions. 

Output measures are often called “activity measures” because they relate to specific 

activities undertaken by agencies.

■ Input measures relate to the physical quantities of items used to undertake activities.

Figure 30 shows an example of a “program logic model” adapted from the general performance 

measurement literature for incident management. The model breaks down the three above 

categories (outcome, output, and input measures) into additional categories. “External influences” 

relate to the forces that create changes in demand that are outside the control of transportation 

agencies (i.e., changes in economic activity and demographic and migration trends). The 

main point of Figure 30 is that a comprehensive performance management program includes 

performance measurement at all levels and that a clear linkage exists between successively higher 

levels. That is, a change in a level leads to a change in the higher levels.

Performance measures for both congestion and reliability should be based on measurements of 

travel time. Travel times are easily understood by practitioners and the public, and are applicable 

to both the user and facility perspectives of performance.

Reliability is a key aspect of mobility that should be measured. There are two widely held ways 

that reliability can be defined. Each is valid and leads to a set of reliability performance measures 

that capture the nature of travel time reliability. Reliability can be defined as:

■ The variability in travel times that occurs on a facility or for a trip over the course of time. 

■ The number of times (trips) that either “fail” or “succeed” in accordance with a 

predetermined performance standard or schedule.

Figure 29. Diagram. Performance measures should be carried 

across planning applications throughout the entire time horizon.

Source: Transportation Research Board, National Cooperative Highway Research Program. 
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In both cases, reliability 

(more appropriately, 

unreliability) is caused 

by the interaction of the 

factors that influence 

travel times: fluctuations 

in demand (which may be 

due to daily or seasonal 

variation, or by special 

events), traffic control 

devices, traffic incidents, 

inclement weather, work 

zones, and physical 

capacity (based on 

prevailing geometrics and 

traffic patterns). These 

factors produce travel 

times that are different 

from day to day for the 

same trip.

From a measurement 

perspective, travel time 

reliability is quantified 

from the distribution of 

travel times, for a given 

facility/trip and time period (e.g., weekday peak period), that occurs over a significant span of 

time; 1 year is generally long enough to capture nearly all of the variability caused by disruptions. 

Figure 31 shows an actual travel time distribution derived from roadway detector data, and how 

it can be used to define reliability metrics.25 The shape of the distribution in Figure 31 is typical 

of what is found on congested freeways; it is skewed toward higher travel times. The skew is 

reflective of the impacts of disruptions, such as incidents, weather, work zones, and high demand 

on traffic flow. Therefore, most of the useful metrics for reliability are focused on the right half 

of the distribution; this is the region of interest for reliability. Note that a number of metrics are 

expressed relative to the free-flow travel time, travel time under low traffic-flow conditions, which 

becomes the benchmark for any reliability analysis. The degree of unreliability then becomes a 

relative comparison to the free-flow travel time.26 Table 7 shows a suite of reliability metrics. All 

of these are based on the travel time distribution.27

25 Kittelson & Associates et al., Incorporation of Travel Time Reliability into the Highway Capacity Manual, SHRP 2 Report S2-L08-RW1,TRB, 2014. Available at: http://onlinepubs.trb.org/onlinepubs/shrp2/SHRP2_S2-L08-RW-1.pdf.

26 Ibid.

27 Ibid.

Figure 30. Diagram. Program logic model adapted for

 incident management.

Source: Federal Highway Administration, Operations 

Performance Measurement Workshop Presentation. 
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28Table 7. Recommended set of reliability performance measures from Strategic Highway 

Research Program 2 Project L08.

29

Reliability Performance 

Measure Definition

Core Measures

Planning time index (PTI) 95th percentile travel time index (TTI) (95th percentile 

travel time divided by the free-flow travel time).

80th percentile TTI 80th percentile TTI (80th percentile travel time divided by 

the free-flow travel time).

Semi-standard deviation The standard deviation of travel time pegged to the free 

flow rather than the mean travel time. 

Failure/On-time measures Percent of trips with space mean speed less than 50 miles 

per hour (mph), 45 mph, and 30 mph.

Supplemental Measures

Standard deviation Usual statistical definition.

Misery index (modified) The average of the highest 5 percent of travel times divided 

by the free-flow travel time.

28 From SHRP 2 Report SR-L08-RW-1: Incorporation of Travel Time Reliability into the Highway Capacity Manual, Figure ES.2, p. 3. 

Copyright, National Academy of Sciences, Washington, D.C., 2014. Reproduced with permission of the Transportation Research Board. 

Available at: http://www.trb.org/Main/Blurbs/169594.aspx.

29 From SHRP 2 Report SR-L08-RW-1: Incorporation of Travel Time Reliability into the Highway Capacity Manual, Table 2.1, p. 17. 

Copyright, National Academy of Sciences, Washington, D.C., 2014. Reproduced with permission of the Transportation Research Board.

Figure 31. Graph. Travel time distribution is the basis for defining reliability metrics.28

Source: Transportation Research Board, Strategic Highway Research Program 2, Report S2-L08-RW-1.

Source: Transportation Research Board, Strategic Highway Research Program 2, Report S2-L08-RW-1.
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 Relevant Data

Prior to the appearance of archived operations data, congestion performance report production by 

planning agencies was rare. Where they existed, they were based on results from travel demand 

forecasting (TDF) models or small sample “floating car” travel time runs. The measurement of 

reliability was non-existent.

The data to accomplish performance reporting starts with field measurements of travel times 

or speeds from intelligent transportation system (ITS) roadway detectors or vehicle probes. At 

a basic level, this data can be used to produce mobility performance measures. However, more 

sophisticated and in-depth measures can be constructed with the integration of other data:

■ Traffic volumes (volumes are paired with speed measurements when ITS detectors are used 

but there are no companion volume measurements from the current generation of vehicle 

probe data).

■ Incident data from incident management logs (includes the nature, severity, duration, and 

blockage characteristics of incidents).

■ Work zone data (similar to incident data).

■ Weather data.

 Analytical Procedures

Data preparation. Routine quality control (QC) procedures need to be applied to the data. These 

are documented elsewhere.30 Travel time data is extremely voluminous and requires special 

software to be processed. Incident data is much more manageable and can be analyzed with 

spreadsheets.

Calculation methods. Detailed guidance on processing the raw travel time data into 

measurements, including QC, may be found in the literature.31 In their simplest form, the steps are 

as follows:

1. Compute travel time for individual links (if not already provided). Compute these by the time 

periods of interest (e.g., PM peak period).

2. Compute volume-weighted moments of the travel time distribution for each link (e.g., mean 

and various percentiles). Each observation in the distribution should be an individual travel 

time record. This assumes that volumes are available at the same temporal and spatial level as 

the travel time measurements.32 If volumes are not available, then a straight average is used. 

Note that an “observation” could in fact be an average value; for example, the average travel 

time for a 5-minute period. In such cases, some variability has been lost due the aggregation; if 

travel times from individual vehicles were used, the resulting reliability metrics would indicate 

higher variability.

30 Cambridge Systematics, et al., Guide to Effective Freeway Performance Measurement: Final Report and Guidebook, Web-Only Document 

97, Transportation Research Board, August 2006. Available at: http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w97.pdf.

31 For example: Cambridge Systematics et al., Guide to Effective Freeway Performance Measurement: Final Report and Guidebook, 

Web-Only Document 97, Transportation Research Board, August 2006, Available at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w97.

pdf. Also: Cambridge Systematics, Inc. et al., Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies, The 

Second Strategic Highway Research Program, Report S2-L03-RR-1, Transportation Research Board (Washington, DC: 2013).

32 The Texas Agricultural and Mechanical University’s Urban Mobility Report demonstrates a method for deriving volumes to be used with 

vehicle probe data: http://mobility.tamu.edu/ums/report/.
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3. Compute additional metrics, such as the travel time indices, at the link level.

4. Compute performance measures at aggregated spatial levels, such as facility and area wide, by 

taking the volume-weighted average of the metrics previously computed.

If results are desired at the facility level, a more statistically sound procedure is to compute the 

travel time over the entire facility by summing up the individual link travel times from Step 1 and 

using the aggregated travel times as the basis for travel time distribution. In this case, the weights 

become sum of the volumes for the links that comprise the facility. In both cases, the results 

achieved by volume-weighting will be different than not weighting. The difference will be more 

pronounced if both low volume and high volume periods are used in the calculation.

The calculation of certain performance measures that are defined by exposure requires special 

treatment. For example, delay is the number of vehicles or persons exposed to the delay time. The 

most accurate way of computing these measures is from paired volume-travel time measurements; 

i.e., measurements that were collected at the same point in time and space. The current generation 

of privately-supplied vehicle probe data does not contain volume estimates – only speed or 

travel time. Computing exposure-based measures from this data requires using volume data from 

another source, namely, average traffic counts. Users should be aware that mixing average traffic 

counts with vehicle probe data produces only crude estimates for exposure-based measures. 

The analysis of incident, work zone, and weather characteristics data is much more 

straightforward; simple summaries usually suffice. Integrating incident and work zone data with 

travel time data is more problematic. First, the data must be matched temporally and spatially. The 

more difficult task is to assign congestion to a specific incident, work zone, or weather event. On 

facilities where recurring congestion rarely occurs, it is safe to assume that any congestion that 

occurs is due to an event. Where recurring congestion is routine, the assignment becomes much 

more difficult because some congestion would have occurred in the absence of the event.

Display of results. A variety of methods are used to display performance measures, including the 

display of trends:

■ Figure 32 shows one example of how to combine travel time and event data into a single 

graphic for a corridor. This analysis shows the dramatically negative effect that disruptions 

can have on travel times when they occur.33

■ Figure 33 shows how the Metropolitan Washington Council of Governments (MWCOG) 

depicts travel time reliability trends over time. This information is part of MWCOG’s 

program to communicate problems to stakeholders and to be accountable for investment 

decisions. The data source for this analysis is vehicle probe data.34

■ Figure 34 shows how the Georgia Department of Transportation (GDOT) displays trends in 

incident service times. This information is used to monitor the effectiveness of new incident 

management policies and practices. The data was collected by GDOT as part of their 

incident management system.

33 Cambridge Systematics et al., Analytic Procedures for Determining the Impacts of Reliability Mitigation Strategies, SHRP 2 Report S2-

L03-RR-1, Transportation Research Board, 2013. Available at: http://onlinepubs.trb.org/onlinepubs/shrp2/SHRP2_S2-L03-RR-1.pdf.

34 For more information, see the Metropolitan Washington Council of Governments (MWCOG) Transportation Congestion Dashboard 

at:https://www.mwcog.org/congestion/.
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■

Figure 32. Graph. Mean travel times under rain, crash, or non-crash traffic incident 

conditions for I-5 southbound, North Seattle Corridor, Tuesdays through Thursdays, 2006.35

Source: Transportation Research Board, Strategic Highway Research Program 2, Report S2-L03-RR-1.

Figure 33. Graph. Reliability trends reported by the Metropolitan Washington 

Council of Governments.

Source: Pu, Wenjing, Presentation at 5th International Transportation Systems Performance Measurement and Data Conference, June 1-2, 2015, Denver, Colorado. 
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■ Figure 35 shows how the Performance Measurement System (PeMS) displays congestionby-source information for a corridor. PeMS is a data archival and diagnostic tool that is 

used by many agencies to identify the source of problems and to match countermeasures to 

them. The travel time data source for this particular analysis was ITS detectors.35

35 California Department of Transportation, Performance Measurement System. Available at: http://pems.dot.ca.gov/.

Figure 34. Charts. Incident timeline trends reported by the Georgia Department of 

Transportation for the Atlanta region.

Source: California Department of Transportation

Figure 35. Screenshot. Heat map of congestion on a corridor using the Performance 

Measurement System.

Source: Georgia Department of Transportation, H.E.R.O Monthly Statistics Report, 

Available at:More information available at: http://www.dot.ga.gov/DS/Travel/HEROs.

0:15

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 Impact

In addition to demonstrating accountability, providing transparency, and promoting effective 

public relations, developing performance measures is the starting point for several other agency 

functions. These include:

■ Target setting – Understanding the current state of the system is crucial to understanding 

what is achievable in the future. In addition, the same data used to measure current 

performance can be used as input to forecasting models.

■ Project and program evaluations – Evaluating implemented projects is revealing in 

several ways. A library of impact factors can be developed for future project planning. 

Benefits can be highlighted as success stories, and lessons can be learned from evaluations 

where expected benefits did not materialize.

 Case Study: Tracking Performance Measures at Delaware Valley Regional Planning 

Commission 

Overview

The Delaware Valley Regional Planning Commission (DVRPC) is the metropolitan planning 

organization for the greater Philadelphia region. The region includes five counties in Pennsylvania 

and four in New Jersey. The most congested freeway miles in the region are along major routes 

into and out of the City of Philadelphia. DVPRC uses archived operations data to inform a variety 

of planning efforts, including problem identification and confirmation, calibrating regional and 

corridor models, conducting before and after studies, and developing and reporting transportation 

performance measures for the regional congestion management process (CMP) and long-range 

transportation plan (LRTP). 

Data

DVRPC primarily uses the VPP data purchased by the I-95 Corridor Coalition for developing 

performance measures based on archived operations data. It is currently exploring other vehicle 

probe-based data sources, including the National Performance Measures Research Data Set 

(NPMRDS), particularly for tracking and reporting progress toward the system performance 

measures. However, given DVRPC’s concerns with the quality of the NPMRDS data, DVRPC 

is working with its partners at the Pennsylvania DOT and NJDOT to use the VPP data instead. 

Incident data from agency-collected traffic management systems also are analyzed. 

Agency Process

DVRPC uses archived operations data to track the performance of the regional transportation 

network, identify and prioritize congested locations, and justify funding for the appropriate 

projects. In the MPO’s 2015 CMP, DVRPC used two measures based on archived operations data: 

travel time index (TTI) and planning time index (PTI). DVRPC selected these measures because 

it anticipated that they were likely to become part of the FAST Act reporting requirements at 

the time of the CMP update. DVRPC used VPP data to analyze the TTI and PTI for all limitedaccess highway and arterial road segments with data coverage at the time of the update. It then 

used the results of TTI and PTI analysis, along with other performance measures, to identify 
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Figure 36. Image. Delaware Valley Regional Planning Commission’s outreach document is 

based on archived data.

Source: Delaware Valley Regional Planning Commission. Available at: http://www.dvrpc.org/reports/NL13011.pdf..
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Figure 37. Image. Inside view of Delaware Valley Regional Planning Commission’s 

outreach document.

Source: Delaware Valley Regional Planning Commission. Available at: http://www.dvrpc.org/reports/NL13011.pdf..
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congested corridors throughout the region. These congested corridors were used as a basis for 

selecting corridors for investment in ITS infrastructure through the Transportation Operations 

Master Plan.

36 The designation of CMP corridor was also used as an evaluation criterion for 

projects to be programmed in the LRTP and TIP. In addition, DVRPC has developed several 

outreach documents based on archived operations data to communicate its approach to mitigating 

congestion with the general public and elected officials (See Figures 36 and 37 for an example). 

In addition to developing and reporting performance measures, DVRPC participated in an 

evacuation planning effort for the City of Philadelphia. The project involved extracting a 

microsimulation model of the city center from DVRPC’s travel demand model, and then 

calibrating the model with the VPP’s archived travel time data on key arterial, collector, and local 

roadways in the city. The model was used to analyze how to best evacuate buildings, which routes 

all modes would likely take, which intersections would cause major bottlenecks, and other factors 

that may impact efficient evacuation of the city during a major event. DVRPC also was using 

archived operations data to spot-check calibration of the regional model and has begun an effort to 

use it more extensively in calibration. 

Impact and Lessons Learned

Overall, access to archived operations data has been a significant benefit to planning at DVRPC, 

especially because the DVRPC staff have access to a user-friendly interface through the VPP Suite. 

The VPP Suite allows for both quick analysis at the corridor level and more complex regional 

analyses. The archived operations data enhances the agency’s knowledge of key travel attributes, 

including peak hour travel times and reliability, but DVRPC has had challenges. One challenge has 

been working with the Traffic Message Channel network on which the archived speed and travel time 

data is reported and translating data from the Traffic Message Channel network to the base GIS linework for analysis with other data sets.

DVRPC manages eight traffic incident management task forces (IMTFs) throughout the region. 

Incident data is reported at quarterly meetings of these corridor-based IMTFs. Using data from TMCs, 

incident durations help the task forces identify which incidents should be discussed in more detail in post 

incident debriefs. Occasionally, the VPP data is used to show congestion impacts of incidents. 

DVRPC, a bi-state MPO, has several hurdles to overcome to report incident data on a regional 

basis. DVRPC would need to perform the non-standardized geospatial referencing of data, expend 

significant resources fusing data from two States, and clean up inconsistencies. The reporting of 

incident data would need to be standardized as well as geo-referenced across all corridors and 

agencies to support a complete regional picture. For example, incident duration, roadway clearance 

times, and other data may be entered differently depending on the agency, and NJDOT and 

Pennsylvania DOT—DVRPC’s two primary resources for incident data—do not necessarily capture 

this data in the same way. DVRPC has considered cross-checking the incident data against police 

crash databases, although this data lags by 6 months or more and does not contain municipal crash 

reports. The integration of police CAD data with agency traffic incident data would help create 

regional incident data.

36 Delaware Valley Regional Planning Commission, Transportation Operations Master Plan (Philadelphia: PA, 2009). Available at: http://

www.dvrpc.org/Transportation/TSMO/MasterPlan/.
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DVRPC also faces challenges with inconsistent data reporting methods and formats with other types 

of archived operations data because it covers a bi-state area. Agencies in the region have various 

capabilities to collect, analyze, and share archived data, as well. The transit agencies in the region are 

an example of this discrepancy, which makes it difficult to perform an analysis for the entire region. 

Another barrier to using archived operations data is the sheer amount of data that needs to be 

processed. The probe data for the DVRPC region amasses nearly 3.4 billion records per year. Working 

with this data is extremely difficult without powerful tools and hardware.

Use of Archived Operations Data for Input and Calibration of Reliability 

Prediction Tools

 Need

Including travel time reliability as an aspect of mobility is becoming an important consideration 

for transportation agencies. Reliability is valued by users as an additional factor in how they 

experience the transportation system. Transportation improvements affect not only the average 

congestion experienced by users, but reliability as well. Therefore, adding reliability to the benefit 

stream emphasizes the positive impact of improvements.

Archived operations data enables the direct measurement of reliability, and many agencies 

have added reliability as a key performance measure. However, the ability to forecast reliability 

has lagged behind the ability to measure it. Being able to predict the reliability impacts of 

improvements, in addition to traditional notions of performance, provides a more complete 

perspective for planners. It also provides continuity in tracking the performance of programs 

because the tracking includes measures of reliability.

Fortunately, the Strategic Highway Research Program 2 (SHRP 2) produced several tools for 

predicting travel time reliability.37, 38 These tools require a variety of inputs that can be developed from 

archived operations data and are used here to provide specific examples of how archived data can be 

used in conjunction with advanced modeling procedures. Several other modeling platforms—as well 

as those likely to emerge in the near future—will require similar levels of inputs.

 Relevant Data

Data for input and calibration of reliability prediction tools include travel time (or speed) data, 

preferably continuously collected; volume data; and incident characteristics data.

 Analytical Procedures

Procedures to predict travel time reliability are just now emerging in the profession (as of this 

writing). There are two basic approaches to predicting reliability: (1) statistical methods that 

develop predictive equations for reliability as a function of either congestion source factors (e.g., 

demand, capacity, incident characteristics, and weather conditions) or model-produced values for 

the average or typical condition, and (2) direct assessment of reliability by creating a travel time 

distribution that reflects varying conditions from day-to-day. 

37 Economic Development Research Group, Development of Improved Economic Analysis Tools Based on Recommendations from Project 

C03 (2), The Second Strategic Highway Research Program, Project C11, Transportation Research Board (Washington, DC: 2013). Available 

at: http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=2350.

38 Kittelson & Associates et al., Incorporation of Travel Time Reliability into the Highway Capacity Manual, The Second Strategic Highway 

Research Program, Report S2-L08-RW-1, Transportation Research Board (Washington, DC: 2014). Available at: http://onlinepubs.trb.org/

onlinepubs/shrp2/SHRP2_S2-L08-RW-1.pdf.
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 Impact

Reliability prediction tools are needed by analysts as a way to capture the impacts of operations 

strategies that are primarily aimed at improving travel time reliability. Many agencies have 

identified the need to improve reliability, yet have not had the means to predict it as a benefit of 

transportation improvements. Reliability tools provide a means for evaluating operations projects 

side-by-side with capacity expansion projects. When applied to planning activities, the tools will 

help transportation agencies better identify and implement strategies to reduce the variability and 

uncertainty of travel times for commuters and other travelers as well as the freight industry.

 Case Study: Developing Data for Reliability Prediction Tools

Overview

A hypothetical MPO is embarking on an update to its LRTP. It has previously developed a scoring 

process for ranking potential projects in the plan, and it wants to add travel time reliability as one of 

the scoring factors. However, the MPO’s TDF model only produces average speed estimates, and 

these estimates are artifacts of the traffic assignment process that are not reflective of actual speeds. 

Data

The MPO has ITS detector data (volumes and speeds) on regional freeways available. It also 

has the NPMRDS travel time data for National Highway System (NHS) roadways in the region. 

Incident data on freeways is available via the State-run traffic management center.

Agency Process

The TDF model produces outputs by individual network link in a readable file. The time periods 

are a 2-hour AM peak and a 3-hour PM peak. Outputs include assigned traffic volumes, speeds, 

and delay for each of the periods.

Specific activities that can be undertaken with archived operations data to improve reliability 

prediction models include:

■ Customize a volume-delay function for estimating recurring delay. Archived ITS detector 

data (volumes and speeds) can be used to develop a freeway function. This would involve 

removing observations that were influenced by incidents, weather, and work zones, then 

computing the volume to capacity (v/c) ratio for each speed measurement. Alternately, the 

volume-delay function could be developed using the speeds that include all sources of 

congestion.

■ Customize the reliability prediction relationship. Archived ITS detector or vehicle probe 

data is used for this purpose. For a set of study links, the mean travel index is computed 

over the course of a year along with the reliability metrics of interest. Curve fitting is then 

performed to derive the relationships.

■ Develop relationships between LRTP project types and the factors considered by the 

recurring and incident delay relationships, especially for operations strategies. This is done 

by searching the literature, not by using archived data, but it is a critical step. (Archived 

data may have been used by the researchers to develop these relationships, however.) The 

factors that can be influenced are free-flow speed, volume (demand), capacity, incident 

duration, and incident frequency.
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The staff determined that operations strategies would best be handled by creating “bundles” of 

investments scenarios. These were combinations of strategies at different deployment intensity 

levels. Benefits and costs for multiple investment scenarios (bundles) were developed for further 

study. Then, the resulting system performance forecasts from the TDF model and the postprocessor were used as part of a public engagement program for the LRTP, asking citizens to 

choose their preferred level of investment and corresponding performance outcome.

For example, travel time reliability was a performance measure in the operations bundles of 

projects. Investing in advanced traffic management systems does not produce a quantifiable 

benefit when using the TDF models alone, because it only considers demand, capacity, and 

recurring delay. Focusing instead on reliability produced a quantifiable benefit using the post 

processor. Those benefits were then paired with cost estimates for the ATMS and other operational 

improvements in the project scoring process. It also allowed the public and elected officials to 

make informed investment decisions when finalizing the project list.

 Developing Capacity Values (Freeways)

Use of localized capacity values is extremely significant in enabling forecasting models to 

replicate field conditions. Data from ITS detectors that produce simultaneous measurements 

of volume and speed can be used in two ways to determine the capacity of roadway segments, 

especially bottlenecks. First, classic speed-flow plots can be constructed. For this exercise, we 

assume that the volume and speed measurements have been archived in 5-minute time intervals:

■ Aggregate the 5-minute measurements to 15-minute intervals. This is done for consistency 

with the definition of capacity used in the Highway Capacity Manual (HCM). Take only 

15-minute time intervals with all three 5 minute intervals present.

■ For each directional location, sum the volumes and compute the volume-weighted harmonic 

average speed.

■ Convert 15-minute volumes to volume per hour per lane by first dividing by the number of 

lanes at the location and then multiplying by four.

■ Plot the results.

Figures 38 through 43 show the results obtained by applying the above procedure to data from I-4 

in Orlando, Florida. Several observations can be made based on these plots:

■ The general shape of the plots conforms to those shown in the HCM and produced by other 

researchers. There is a small amount of “noise” in the data, as shown by points scattered 

away from the general trend lines. These could be errors in the measurements or indicative 

of transitional flow, which can vary in its traffic flow parameters.

■ The flow values are in vehicles per hour per lane, not passenger cars per hour per lane 

(pcphpl), as specified in the HCM. That is, trucks are imbedded in the flow measurements, 

and an average value for percent of trucks must be used to expand vehicles to passenger 

cars.

■ The large number of observations grouped around a horizontal line between 60 to 70 mph 

is indicative of the free-flow speed, although the next section will identify a more direct 

method for computing it.
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■ Capacity values are indicated by the right end “nose” of the curve. Capacity varies 

significantly throughout this highway section. A more formal analysis appears in Table 8. 

Selecting the maximum value as the capacity value is not recommended due to possible 

inaccuracies in the measurements, as can be seen by the spread between the 99.5 percentile 

and the maximum value. Therefore, a capacity value between the 99.5 percentile and the 

maximum value should be selected. For illustration, we have selected the midpoint.

Table 8. Upper end of speed-flow distribution, I-4, Orlando, Florida.

Location

Vehicles Per Hour Per Lane

95th 

percentile

99th 

percentile

99.5 

percentile Maximum

Selected 

Capacity

I-4 at EE Williamson_EB 1,687 1,889 1,944 2,101 2,023

I-4 at EE Williamson_WB 1,704 1,905 1,959 2,119 2,039

I-4 at Kaley Ave_WB 1,445 1,554 1,594 1,752 1,673

I-4 at Michigan Ave_EB 1,515 1,619 1,652 1,836 1,744

I-4 at Wymore_EB 1,999 2,116 2,144 2,247 2,195

I-4 at Wymore_WB 1,892 2,051 2,101 2,315 2,208

I-4 East of Wymore_EB 1,991 2,099 2,124 2,231 2,177

I-4 East of Wymore_WB 1,915 2,071 2,117 2,344 2,231

I-4 West of SR 408_EB 1,500 1,633 1,677 1,820 1,749

EB = eastbound, WB = westbound

Figure 38. Graph. Plot of speed and vehicles per hour per lane on I-4 at Kaley Avenue, westbound.

Plot of SPEED *FLOW

I-4 @ Kaley Ave WB
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Figure 39. Graph. Plot of speed and vehicles per hour per lane 

on I-4 at Michigan Avenue, westbound.

Figure 40. Graph. Plot of speed and vehicles per hour per lane 

on I-4 at Wynmore, eastbound.

Figure 41. Graph. Plot of speed and vehicles per hour per lane 

on I-4 at Wynmore, westbound.

Plot of SPEED *FLOW

I-4 @ Michigan Ave EB

Plot of SPEED *FLOW

I-4 @ Wynmore EB

Plot of SPEED *FLOW

I-4 @ Wynmore WB
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A second approach to determining capacity is heuristic: adjust (calibrate) the capacity value in 

the model to match local traffic conditions. Here, either speed data from ITS detectors or vehicle 

probes can be used; volumes are not used in the calibration but must be input in the models 

separately: 

■ From the speed data in the corridor, identify existing bottlenecks. These will be locations 

(detectors or links) where queues are building upstream while downstream speeds are 

relatively high, unless influenced by another bottleneck (Figure 44).

■ Set the bottleneck segment to a capacity according to the HCM capacity estimate based 

on facility free-flow speed (e.g., 2,400 pcphpl for 70 mph free-flow speed (FFS)). Run 

the model. 

■ If a queue does not result, lower the capacity value in increments of 50 pcphpl until a queue 

results and speeds in the queue roughly match the field data.

Figure 43. Graph. Plot of speed and vehicles per hour per lane on 

I-4 east of Wynmore, westbound.

Figure 42. Graph. Plot of speed and vehicles per hour per lane on 

I-4 east of Wynmore, eastbound.

Plot of SPEED *FLOW

I-4 East of Wynmore EB

Plot of SPEED *FLOW

I-4 East of Wynmore EB
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 Determining Free Flow Speed (FFS)

Both ITS detector and vehicle probe data can be used to determine the FFS of a facility. A variety 

of approaches can be used to determine the FFS. The first method uses only speed data and can be 

used on both freeways and signalized highways. It is based on selecting the 85th percentile speed 

under very light traffic conditions, defined as occurring morning hours on weekend days (e.g., 

from 6:00 AM to 10:00 AM). The second method uses freeway ITS detector volume and speeds. 

It determines the 85th percentile speed when volumes are low and speeds are relatively high (to 

weed out the low volumes that occur in extreme queuing conditions). 

Table 9 compares these approaches for the freeways in the Atlanta, Georgia, region. The values 

in this table are computed for the entire facility, which includes multiple segments. To calculate 

this value, the space mean speed (SMS) over the entire facility is first calculated by the smallest 

time interval in the data (in this case, 5 minutes). With detector data, because companion volumes 

are available, SMS is the facility vehicle-miles of travel (VMT) divided by the facility vehiclehours of travel. With probe data, SMS is the total distance divided by travel time in hours. Then, a 

distribution is developed from those facility space mean speeds.

Figure 44. Graph. Speed contours from archived intelligent transportation system detector 

data are useful for identifying bottleneck locations.

Source: System Metrics Group and Cambridge Systematics, unpublished presentation to California Department of Transportation, August 26, 2005.

Average Speed (mph)

Segment Type: Freeway Segment None: I-880N

10/01/2004 00:00:00 to 10/31/2004 23:59:59 (Days=Tu, We, Th)

Traffic Flows from Bottom to Top
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Table 9. Facility free-flow speed calculations, Atlanta, Georgia, area freeways.

Section

Weekend FFS 

Calculation Method 

(mph)

Low Volume 

FFS Calculation 

Method (mph)

I-75 Northbound from I-285 to Roswell Road 72 70

I-75 Southbound from I-285 to Roswell Road 69 68

I-75 Northbound from I-20 to Brookwood 69 69

I-75 Southbound from I-20 to Brookwood 68 67

I-285 Eastbound from GA-400 to I-75 71 69

I-285 Westbound from GA-400 to I-75 69 68

I-285 Eastbound from GA-400 to I-85 68 66

I-285 Westbound from GA-400 to I-85 68 68

I-75 Northbound from Roswell Road to Barrett 

Parkway 71 70

I-75 Southbound from Roswell Road to Barrett 

Parkway 69 69

FFS=free flow speed

 Identifying Demand (Volumes)

Data from ITS freeway detectors can be used to develop input volumes at the hourly or finer 

level. The main issue with using detectors on already-congested freeways is that, under queuing, 

the volume they measure is lower than the actual demand. If this is the case, then the following 

procedure can be used. It is based on moving upstream from where queues typically occur to 

locate a detector that is relatively free of queuing influence, but its traffic patterns are similar to 

the facility being studied. If a major interchange is present between the study detector and the 

facility, traffic patterns are likely to be different. If the study detector meets these criteria, it is used 

to develop hourly factors for extended peak periods that can be applied to volumes on the freeway 

facility. The extended period should cover times on either side of the peak where queuing is not 

normally a problem (i.e., 1 hour before the peak start and 1 to 2 hours after the peak end). The 

factors are computed as the hourly volume divided by the extended peak volume. These are then 

applied to the extended peak period volumes on the facility.

Once demands have been created from the congested volumes, accurate temporal distributions can 

be created for the study section, such as the percent of daily traffic occurring in each hour. Also, 

the variability in demand can be established since some reliability prediction methods require 

knowledge about demand variability. 
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 Determining Incident Characteristics

Advanced modeling methods require basic data about incidents. For example, annual number and 

average duration of the following incident categories:

■ Crashes – property damage only.

■ Crashes – minor injury.

■ Crashes – major injury and fatal.

■ Non-crashes – disabled vehicle/lane blocking.

■ Non-crashes – disabled vehicle/non-lane blocking.

Two sources of data are used to develop these inputs: (1) traditional crash data, and (2) archived 

incident management data. Crash data has details on crash severity, whereas incident data may 

or may not. Conversely, crash data does not include duration information. One approach is to 

use state- or area-wide values for crash severity and then to rely on incident management data. 

The first step is to estimate incidents by type. Typical categories in incident management data are 

crashes, disabled vehicles, and debris. Table 10 shows data taken for I-66 in Virginia, from the 

I-495 Capital Beltway west for 30 miles.

Table 10. Incident characteristics for I-66, Virginia.

CRASH

Obs

Lateral

Location Frequency

Duration

Average

Standard 

Deviation Median

95th 

Percentile

1 1 Lane Blocked 366 59.2505 75.359 43.5 1.29

2 2 Lanes Blocked 158 67.7142 55.357 55.5 1.54

3 3+ Lanes Blocked 67 77.5970 60.302 57.0 1.82

4 No Blockage 212 52.7137 111.603 32.0 1.39

5 Shoulder Blocked 531 69.5431 101.793 51.0 1.53

Obs = observation
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DEBRIS

Obs

Lateral

Location Frequency

Duration

Average

Standard 

Deviation Median

95th 

Percentile

1 1 Lane Blocked 68 53.0466 65.727 31.5 242

2 2 Lanes Blocked 5 83.6000 103.667 38.0 258

3 3+ Lanes Blocked 2 26.5000 20.506 26.5 41

4 No Blockage 54 33.6296 38.253 27.0 94

5 Shoulder Blocked 27 50.5926 50.400 45.0 165

DISABLED

Obs

Lateral

Location Frequency

Duration

Average

Standard 

Deviation Median

95th 

Percentile

1 1 Lane Blocked 264 58.126 91.48 32.5000 169

2 2 Lanes Blocked 12 49.833 21.02 53.0000 79

3 3+ Lanes Blocked 7 263.143 469.49 38.0000 1297

4 No Blockage 638 577.226 9908.04 8.5833 85

5 Shoulder Blocked 1552 54.147 118.88 29.0000 182

Source: Margiotta, Richard, unpublished internal memo for SHRP 2 Project L08, August 26, 2005.

Impact and Lessons Learned

To explore the various investment options preferred by the public, several financial scenarios 

were prepared for the MPO board’s consideration. The scenarios varied by spending pattern and 

by whether they included new funding sources for transportation, such as mobility fees on new 

development or increases in the gas tax or sales tax. The final outcome of the process was that 

operations projects were successfully evaluated on an equal basis with traditional projects and are 

now part of the LRTP.

Performance of Before and After Studies to Assess Projects and 

Program Impacts

 Need

A key component of performance management is the ability to conduct evaluations of completed 

projects and to use the results to make better-informed investment decisions in the future. State 

departments of transportation (DOTs) and MPOs routinely invest great effort in forecasting the 

impact of capital, operating, and regulatory improvements on general and truck traffic trips, but 

practitioners seldom have the opportunity to evaluate the outcomes of their investments, except in 

isolated cases where special studies are conducted.

Table 10. Incident characteristics for I-66, Virginia. (Continued)

Obs = observation
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However, no standardized method for conducting before and after evaluations with these data 

sources exists, especially for operations projects. Although a rich history of evaluations has 

been accumulated, the vast majority of studies lack a consistent method; common performance 

measures; and, most problematic, controls for dealing with factors that can influence the 

observed performance other than the project treatment.39 A thorough treatment of an evaluation 

methodology is beyond the scope of this primer. Regardless of what a comprehensive 

methodology entails, at its core, it will be examining travel time and demand data. The remainder 

of this section focuses on the use of these data types.

 Relevant Data

A comprehensive and statistically valid evaluation methodology will have to draw on a variety of 

data types to establish controls. Many factors can influence travel time besides an improvement 

project, including incidents, demand (e.g., day-to-day and seasonal variations and special events), 

weather, work zones, traffic controls, and general operations policies. Ideally, the influence of 

these factors is stable (or nearly so) in the before and after periods of project implementation, 

which allows for observed changes in travel times to be untainted. In theory, all of these data types 

can be obtained from archived operations data, although in practice, some operations may choose 

not to archive certain types of data.

 Analytical Procedures

Define the Geographic Scope of the Analysis

The geographic scope is driven by the target area of the project. For example, if an analysis is 

performed to pinpoint the safety benefits from the implementation of an incident management 

strategy over a stretch of a freeway, then the scope would be limited to that stretch. On the other 

hand, if the objective is to quantify safety improvements for an entire downtown area, then the 

scope would cover all arterials and roads in the downtown area.

In operations evaluations, a “test location” typically means a roadway section that has 

implemented certain operations strategies or has seen the impacts of such strategies. For analysis, 

roadway sections are typically 5 to 10 miles in length. If a larger area is covered, it is advisable 

to break it up into multiple sections. Study sections should be relatively homogenous in terms of 

traffic patterns, roadway geometry, and operating characteristics.

Define Analysis Period

The temporal aspects that should be considered in before and after evaluations are:

■ Time period. Because of the need to compute reliability, a year’s worth of data in each of 

the before and after periods is preferred. Six months is an absolute minimum, but it is likely 

that seasonal effects will come into play unless the same months of different years are used. 

■ User-defined peak periods (AM and PM), mid-day, and off peak periods. The peak 

periods should be defined for weekday non-holidays. At a minimum, the peak periods 

should be analyzed; other periods may be added at the analyst’s discretion. Note that, in 

some cases, the time period of interest may be different from those mentioned (e.g., 

weekends in rural recreational areas may be the focus of an operational treatment).

39 United States Department of Transportation, Intelligent Transportation Systems (ITS) benefits database. Available at: http://www.

itsbenefits.its.dot.gov/.
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Establish Travel Time Performance Measures

Many different types of performance measures are available. Below is a non-exhaustive list that 

can be considered:

■ Travel time (minutes) – Because the project length or coverage is usually the same in the 

before and after periods, straight travel time may be used as a performance measure.

■ Mean travel time index (TTI) (unitless) – The ratio of average travel time to the ideal or 

free-flow travel time. For evaluating an individual project, the ideal or free-flow travel time 

should be the same in the before and after periods. Although straight travel time is 

recommended also, the TTI is useful because it is normalized and the project may be 

compared to others with different project lengths.

■ Delay (vehicle-hours and person-hours) – The actual vehicle- or person-hours of travel 

that occur minus those that occur under free-flow/ideal conditions. Delay is a useful 

measure because economic analyses have a long history of applying monetary value to 

delay.

■ PTI – The 95th percentile travel time divided by the free-flow travel time.

■ 80th percentile TTI.

■ Incident Performance Measures:

◦ Total incidents by type: crashes, stalls, and debris.

◦ Incident rate (incidents per 100 million VMT).

◦ Incident duration: mean and standard deviation.

◦ Lane-hours lost due to incidents (number of lanes closed multiplied by the number of 

hours they are closed).

◦ Shoulder-hours lost due to incidents.

Develop Performance Measures from the Data

The following example illustrates the initial steps in conducting a before and after evaluation, 

recognizing that more sophisticated analysis using statistical controls should be performed next.

An operational improvement is made on a freeway section. It was implemented quickly, with 

minimal disruption to traffic. Travel time and volume data were available for 1 year prior to the 

completion of the improvement and 1 year after. VMT, mean TTI, and PTI were computed for 

each week and plotted (Figures 45 and 46). Also, incident lane-hours and shoulder-hours were 

computed (Table 11).
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Table 11. Incident blockage characteristics for case study.

Period Incident Lane-Hours Lost Incident Shoulder-Hours Lost

Before 35.3 51.3

After 26.7 66.1

The results show that the improvement reduced both the mean TTI and PTI and that VMT was 

roughly similar in the before and after periods. As expected, the PTI shows more volatility than 

the mean TTI because it measures extreme cases, which can vary greatly from week to week. 

Incident lane-hours lost is higher in the before period, while incident shoulder-hours lost are 

higher in the after period. A more thorough examination of incidents, as well as other factors, 

should be pursued to ensure that the congestion and reliability improvement are due to the project 

and not to other factors.

Figure 45. Graph. Hypothetical before and after case: mean travel time index 

and vehicle-miles of travel.

Figure 46. Graph. Hypothetical before and after case: planning time index 

and vehicle miles of travel.
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 Impact

Development of an ongoing performance evaluation program enables planning agencies to make 

more informed decisions about investments. The results of evaluations can be used to highlight 

agency activities to the public and to demonstrate the value of transportation investments. The 

knowledge gained from evaluations can lead to development of impact factors for future studies, 

as well as an understanding of where and when certain types of strategies succeed or fail. These 

impact factors, tailored to an agency’s particular setting, are useful for future planning activities 

when alternatives are being developed. For example, evaluations of the conditions under which ramp 

metering is effective can avoid future deployments in situations where they will not be successful.

 Case Study: Florida Department of Transportation Evaluation of the Port of Miami 

Tunnel Project

Overview

The Florida DOT (FDOT) used archived data to evaluate the impact that the Port of Miami Tunnel 

project had on performance. The Port of Miami Tunnel project was built by a private company in 

partnership with FDOT, Miami-Dade, and the City of Miami. By connecting State Route A1A/

MacArthur Causeway to Dodge Island, the project provided direct access between the seaport 

and highways I-395 and I-95, thus creating another entry to Port of Miami Tunnel (Figure 47). 

Additionally, the Port of Miami Tunnel was designed to improve traffic flow in downtown Miami 

by reducing the number of cargo trucks and cruise-related vehicles on congested downtown streets 

and aiding ongoing and future development in and around downtown Miami.

Figure 47. Map. The Port of Miami tunnel project.

Source: Florida Department of Transportation, Port of Miami Tunnel, Project Maps. Available at: 

http://www.portofmiamitunnel.com/press-room/project-maps/.
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Data

FDOT used travel time data from NPMRDS and traffic volume data from their own system to 

conduct the evaluation.

Agency Process

The purpose of this study is to examine the potential impacts to the transportation system resulting 

from the opening of the Port of Miami Tunnel. The study utilizes tabular and geospatial data from 

FDOT and the NPMRDS. Using a geographic information system or GIS, these data sources are 

merged together spatially to create value-added information that would otherwise be unavailable. 

The resulting information provides FDOT with a better understanding of what impacts there are to 

the transportation system, the magnitude of these impacts, and where these impacts are occurring. 

Impacts considered in the study include:

■ Differences in truck flows using truck count data from FDOT.

■ Congestion – travel time data from HERE to compare and analyze average travel speeds 

(pre- and post- tunnel) within the tunnel vicinity.

This study compares the average weekday travel speeds during peak hours in August 2013 and 

August 2015. The speed data for August 2013 was collected prior to the opening of the Port of 

Miami Tunnel. Data for the corresponding month in 2015 was collected after the opening of the 

Port of Miami Tunnel (Figure 48). In Figure 48, the pie symbols represent differences in truck 

counts, with 2015 data shown in dark blue and 2013 data shown in light blue. The red and green 

lines represent a decrease in average weekday travel speed and increase in average weekday travel 

speed, respectively. Results from the evaluation showed:

■ Improved or similar travel speeds through downtown Miami.

■ Improved or similar travel speeds along MacArthur Causeway eastbound, portions of I-395 

East, and portions of Biscayne Boulevard.

■ Slower speeds along portions of I-395 West, I-95, and Biscayne Boulevard westbound.

Impact and Lessons Learned

Analysis of the transportation system resulting from the opening of the Port of Miami Tunnel 

is just an example of how powerful value-added tabular and spatial data can be. The long-term 

vision is to empower planners, freight coordinators, consultants, and others throughout FDOT 

with value-added data and information that can assist with decision making and the achievement 

of Federal guidelines.

Although working with very large and complex data sets presents a number of challenges, 

including human resource, information technology (IT), and financial availability, the long-term 

benefits are expected to outweigh the challenges. Because much value-added data is generated via 

spatial analysis, disparate geometric (geographic information system) networks should be edited 

and aligned with each other in an ongoing fashion. With the various networks merged together, the 

attributes associated with these networks can work together via spatial coincidence. The valueadded tabular and spatial information generated through a variety of complex mechanisms will 

be made available throughout FDOT, including the Central Office in Tallahassee, Florida, and the 

District offices throughout the State.
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Identification of Causes of Congestion 

 Need

The prior case study identifies how patterns in congestion can be identified, ranked, and 

prioritized. However, simply identifying locations with problems is not enough; analysts need 

to know “why” a problem is occurring regardless of whether it is a recurring problem or nonrecurring problem. Perhaps the congestion is related to a geometric issue with the road, or maybe 

it is incident or event related. Maybe it is simply a capacity issue.

The analysis also can be reversed: if a large incident has just occurred, how can the impacts 

(environmental or otherwise) of the related congestion and delays be measured? In this case study, 

the following will be considered:

■ Determine the cause of the recurring congestion.

■ Determine the cause of the non-recurring congestion.

■ Examine the effect of events, incidents, or policy decisions on congestion.

Figure 48. Map. Truck flow and speed impacts of the Port of Miami tunnel project.

Source: O’Rourke, Paul, Florida Department of Transportation, 2016, Unpublished. 

© OpenStreetMap and contributors, CC-BY-SA.
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 Relevant Data

The data required to identify patterns and investigate cause and effect is not that different from 

the FDOT Port of Miami Tunnel case study above. An agency will need a data source that first 

can identify the presence of congestion. This could be a dense network of point-sensor data (e.g., 

inductive loops, side-fired radar/microwave, video detection) or it could be probe-based speed 

and travel time data (from commercial third parties, such as HERE, INRIX, and TomTom, or from 

Bluetooth or WiFi detection systems). The above data helps to identify the presence of congestion. 

To identify the cause of the congestion, the following data sources may be of value: traffic 

management center ATMS event logs, police accident records, weather radar, road weather 

measurements, aerial photography, and volume data profiles.

 Analytical Procedures

Operations data from probes or other high-density sensors can be used to identify congested 

areas or bottlenecks. When these data are comingled with ATMS event data, it becomes possible 

to identify if the congestion is recurring or non-recurring. Each agency may define congestion 

slightly differently, but in its most basic sense, congestion occurs any time speeds and travel 

times drop below some desired threshold. For some, that threshold is the speed limit. For others, 

a segment of a road becomes congested when the space mean speed on it falls below some 

percentage of the speed limit. Queues can be measured on the roadway by simply adding adjacent 

road segments that meet the same condition.

Flow data from probes and dense sensor networks can sometimes be challenging to work with 

because of the sheer volume of data, complex linear referencing systems that might not match up 

with agency centerline road databases, and agency technical and resource capacity. A relatively 

small state, Maryland, has nearly 12,000 road segments. With each segment providing data every 

minute, that comes to well over 6.3 billion data points every year.

To conduct a causality analysis, an agency must be able to associate the non-speed/flow data 

elements to the roadway. ATMS incident, event, or construction data and police accident data 

records will likely need to be assigned to a specific point on the roadway or a roadway segment. 

This could require some basic geospatial queries, but in certain cases may be more complex. 

Some speed data from third parties is provided in a proprietary geospatial standard referred to as 

traffic message channel segments. This georeferencing system will likely be different than most 

agency centerline road networks. Furthermore, some police accident records only store their data 

in log-miles or other georeferencing systems. Aligning all of these different linear referencing 

systems can be time consuming.

As with police incident and ATMS data, weather data will need to be merged with the congestion 

information so that the two can be correlated. Road weather data directly from road weather 

information system (RWIS) stations can usually be linked in the same manner as ATMS records; 

however, RWIS stations are few and far between and do not offer great coverage. Weather data 

from the National Weather Service is fairly ubiquitous, but can be more complex to work with. 

Radar reflectivity data that represents precipitation rates or snowfall can be associated with road 

segments using geospatial queries. There are commercial third-party road weather data products 
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for sale that can be used in a similar way. They are usually pre-processed to reflect conditions 

directly on the roadway, and will be associated with various road segments in the same manner as 

probe-based speed data.

Once all of the disparate operations data are joined both geospatially and temporally, real analytics 

can begin. The analysis can range from big-data style correlation of coefficients—searching for 

any possible pair or set of variables to see if there might be a correlation between them. However, 

the analysis can be less complex: beginning with identifying a problem location, and then 

considering additional questions (what percentage of the time was the congestion at that location 

related to weather and/or incidents, and was the congestion greater when weather was a factor?). 

The opposite can occur; first identify major weather events, and then determine if congestion was 

worse in various locations during those events compared to normal. These types of analyses are 

discussed in the real-world case studies below.

Agencies with strong, integrated IT departments, dedicated data analysts, sufficient data storage 

hardware, and access to statistical software such as SAS can handle this type of analysis given 

time and proper funding. However, many agencies simply subcontract their analysis to consultants 

or university transportation departments, or they purchase off-the-shelf congestion analytics 

software and data integration services tailored for transportation planners. 

 Impact

With the advancement of tools and technologies, roadway problems are more easily identified. 

However, significant effort is needed to effectively understand why a problem exists at a particular 

location—ultimately leading to the identification of solutions to the various problems. Impact and 

causality analysis are the end goal of archived operations data analytics. Impact analysis affords the 

opportunity to change a simple observation (e.g., last week’s snowstorm caused significant delays 

in the region) into a more detailed observation (e.g., last week’s snowstorm impacted 3 million 

travelers, costing some $45 million in user delay and excess fuel consumption, and resulting in 60 

serious collisions that claimed the lives of three individuals in two separate families).

Progressive agencies that use archived operations data can further justify their investment 

(e.g., while the impacts of last week’s snowstorm were significant and deadly per the prior 

statement, the damage could have been worse. The roads were pre-treated with 100,000 tons of 

sodium chloride brine, which reduced the plowing cleanup time by nearly 9 hours. Furthermore, 

safety service patrols reduced the clearance time of the 60 serious collisions by over 20 hours. 

These actions saved area commuters $32.8 million in excess delay, wasted fuel, and the reduction 

of secondary accidents).

These last two statements are only possible with a robust data archive. The results of such an 

analysis allow agencies to better plan decisions for future storms, better justify operating 

budgets, and respond to questions from the media and public officials in a more complete and 

timely manner.
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 Case Study 1: Maryland State Highway Administration Examines Causes of 

Recurring and Non-Recurring Congestion

Overview

This case study determines the cause of both recurring and non-recurring congestion, by 

discussing existing tools, hiring a consultant, and doing the study in-house.

The Maryland State Highway Administration (MD SHA) routinely evaluates the impacts of 

congestion on its roadways with a focus on understanding why congestion is occurring, how 

significant the congestion is in terms of measurable impacts (e.g., queues, user delay costs), 

determining potential solutions to the congestion (e.g., projects, operational strategies), and then 

measuring the effectiveness of the chosen solution.

Data

Five archived operations data sets are used by MD SHA to perform the analysis: probe speed, 

volume, ATMS event, police accident, and weather. The probe data is derived from a private 

sector data provider. This probe data is archived at 1-minute intervals from real-time. The probe 

data represents the average speed across the length of a road segment for each 1-minute period.

The volume data is derived from two sources: HPMS-derived volume profiles and side-fired 

remote traffic microwave sensors (RTMS) detectors.

The event data is derived from two sources: the MD SHA Coordinated Highways Action 

Response Team (CHART) ATMS platform, and police accident reports collected and digitized by 

the Maryland State Police in coordination with MD SHA. The ATMS data is entered by operations 

personnel into their CHART platform in real-time. The type of information recorded includes 

the type of event (e.g., construction, collision, special event, disabled vehicle, police activity), 

location, lane closure information, and number of responders on scene. 

The police accident reports contain additional details about the cause and nature of the incident, 

including the types of individuals involved and their conditions.

Weather data is a mixture of both weather radar data (coming from freely available National 

Weather Service data feeds) and RWIS station data. The RWIS stations are installed and operated 

by a DOT and include measurements such as wind speed and direction, precipitation rates, 

visibility, salinity content of the moisture on the roadway, and barometric pressure. 

Agency Process

As with the case study on problem identification and confirmation with NJDOT, MD SHA uses 

a suite of third-party data analytics tools to collect, fuse, archive, and visualize their archived 

operations data. To evaluate the cause of congestion, MD SHA first determines where congestion 

is occurring. This can be accomplished using the probe data analytics suite and by following the 

same general process as described for NJDOT above.

Figure 49 shows an example of a bottleneck ranking query for September 2015 on all Maryland 

Interstates. By selecting the top-ranked congested location (I-270 southbound), the following can 

be determined:

■ The congestion usually lasts 2 hours and 17 minutes.
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■ The queue usually backs up to just over 19 miles.

■ There were 104 events that fell within the geography of this congested location.

■ The bulk of the congestion at this location occurs between 5:30 AM and 10 AM Monday 

through Friday, confirming this is primarily a recurring congestion event.

■ Significant congestion can occur outside of the morning rush hour, as can be seen by the 

outlier color bands in the afternoon and the nights.

Once a user determines when and where congestion is occurring, he or she can select additional 

tool overlays (e.g., construction, incident, special event) to map out data on top of the time spiral 

to help identify event causality. These event icons are interactive. Figure 49 shows a couple of 

afternoon outliers being the result of significant events. The one highlighted below happens to be 

an injury accident that occurred on a Saturday afternoon.

Figure 49. Screenshot. The bottleneck ranking tool.

Source: Probe Data Analytics Suite. University of Maryland CATT Laboratory.

Ranked list of the most congested locations.

Location of the currently 

selected bottleneck

Non-recurring congestion 

events (outliers) during the 

afternoon and nights

Recurring 

AM rush-hour 

congestion events 

M-F.
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In Figure 50, yellow diamonds denote incidents or disabled vehicles, red diamonds denote injury 

or fatality incidents, and orange diamonds denote construction or work zone events. Selecting the 

congestion lines then displays a congestion scan graphic of the speeds and travel times for that 

particular day and this particular corridor (as seen in Figure 57).

For further study and use, the user can select the congestion event in the time spiral, which will 

create other graphics depicting additional elements. 

■ The congestion on that stretch of road for that specific day of the week (Figure 51).

■ The congestion on that stretch of road on that specific day compared to similar days that 

same month (Figure 52).

■ The estimated user delay cost on that road and that direction of travel by hour of day.

Figure 50. Screenshot. Congestion time spiral graphic with incidents and events overlaid. 

Source: Probe Data Analytics Suite. University of Maryland CATT Laboratory.
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There is unusual congestion on a Saturday evening in the southbound direction. Incident overlays 

show that this was caused by an injury incident (bottom of the screen), and that a vehicle also 

became disabled towards the back of the queue.

Figure 51. Screenshot. Congestion scan graphic of outlier congestion showing both north- and 

southbound traffic on I-270. 

Figure 52. Screenshot. Congestion scan graphic including Saturdays. 

Source: Probe Data Analytics Suite. University of Maryland CATT Laboratory.

Source: Probe Data Analytics Suite. University of Maryland CATT Laboratory.
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Editing the display options, the analyst has now chosen to add an additional date range to the 

graphic. All Saturdays during the month of September are also drawn on the screen in the 

outside columns of data. Note that there is little or no congestion “normally” on Saturdays in the 

southbound direction. This analysis confirms that the congestion and incidents on September 19 

(innermost columns) are non-recurring.

The agency can use other analytic tools found within the RITIS platform to review safety data 

from police accident reports or ATMS event/incident reports to determine if incident hot spots 

exist that may be globally affecting congestion on the roads in question. Some of these tools 

allow extremely deep querying and causality analytics. Figure 53 shows how heat maps can be 

drawn overtop of the roadway to show when and where collisions, weather events, or construction 

projects are most likely to occur for any month, day of week, or other time range during the year.

Figure 54 shows how the tool also can be used for deep analysis (e.g., correlation of coefficients, 

standard deviation of frequencies, number of outliers, maximum frequency, number of unique 

pairs, uniformity of the distribution) to further identify patterns, trends, and outliers within the 

data. The correlation of coefficients ranking function can be used to verify if any pair of variables 

within the data set might be affecting each other (e.g., does a wet road highly correlate with fatal 

collisions, or does 

the day of the week 

in which an incident 

occurs correlate with 

hazmat incidents or 

the number of vehicles 

involved?). 

In Figure 53, the 

heat maps in the 

upper right show 

where incidents have 

occurred over a given 

date range. A list 

of variables in the 

upper left is clickable, 

filterable, and 

interactive. Selecting 

a particular variable 

generates a histogram 

(bottom) that shows 

the breakdown of 

possible values for 

that variable. Here, 

the user has clicked 

on “incident type,” Figure 53. Screenshot. Heat maps function within 

incidents clustering explorer. 

Source: Incident Cluster Explorer, University of Maryland CATT Laboratory.
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which results in an interactive histogram depicting the number of each incident type occurring 

during the particular date range. Clicking on each bar of the histogram then highlights those 

incidents on the map above.

In Figure 54, the correlation of coefficients ranking function in the upper left allows a user to 

compare variable pairs to determine if any correlation exists. Clicking on any of these pairs of 

variables then brings up detailed two-dimensional histograms (not shown) that make it easy 

for users to do a deeper dive into the various relationships, trends, and statistics related to those 

variables.

Figure 54. Screenshot. Correlation of coefficients ranking function in 

incidents clustering explorer. 

Source: Incident Cluster Explorer, University of Maryland CATT Laboratory.
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Impacts and Lessons Learned

This type of analysis has historically been difficult for MD SHA to conduct, as their data sets 

have been unavailable. Creating a central data repository and developing tools that make it faster 

and more intuitive to ask questions has dramatically improved productivity and understanding 

within the organization. Historically, problems were identified through laborious studies that often 

involved dedicated data collection. These studies may have only been initiated if someone within 

the community complained enough about the congestion. Presently, with more ubiquitous data 

from probes and the integration of incident and event data from both law enforcement and the 

ATMS platforms, the agencies are able to identify problems, causality, and overall impact before 

the public raises concerns, and better respond when they do.

In addition to answering these questions more quickly, NJDOT is more confident in the results of 

their studies. The ease of access to the data, coupled with the compelling visualizations, result in 

analysts being able to effectively communicate with different audiences. 

 Case Study 2: Metropolitan Area Transportation Operations Coordination Examines 

Effects of Events, Incidents, and Policy Decisions on Congestion

Overview

Unlike the case study above, which first identified heavily congested locations and then tried to 

determine causality, this case study reviews how to examine the effect of events, incidents, or 

policy decisions on congestion.

The Metropolitan Area Transportation Operations Coordination (MATOC) program is a 

coordinated partnership between transit, highway, and other related transportation agencies in 

the District of Columbia, Maryland, and Virginia, that aims to improve safety and mobility in 

the region through information sharing, planning, and coordination. MATOC is frequently called 

upon to help agencies perform after action reviews of major incidents, produce impact statements 

post-event for the media, or help agencies measure the impact of certain operations decisions 

(e.g., closing the Federal government due to winter weather, closing certain roads, responding in a 

certain way to an event).

This case study shows how MATOC used the archived operations data from the agencies that 

they support to help measure the impacts of delaying the opening of schools and the Federal 

government by 2 hours.

Data

The data for this case study are the same as that used for the case study discussion above; 

however, there is a greater emphasis on weather data—both RWIS and weather radar from the 

National Weather Service.

Agency Process

MATOC uses a series of tools found within RITIS for analyzing weather impacts, user delay cost, 

and travel times.

After a winter weather event ends, MATOC is usually contacted by various groups (e.g., the 

media, transit agencies, DOTs, MPOs, legislators) to help answer questions about the impacts of 
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a particular event on the region. MATOC follows this process to develop graphics, statistics, and 

reports:

1. Choose a corridor of significance that will help tell a compelling story. The corridor must be of 

regional significance and a general pain point for commuters.

2. Use the probe data analytics suite or the detector analytics tools to run travel time queries for a 

common commute route on the corridor of significance.

3. Use the RWIS explorer and/or the other historic weather tools within RITIS to determine which 

dates did or did not have winter weather, other precipitation, construction, or other incidents.

4. Run a user delay cost query to determine the financial impacts of the winter weather event on 

the corridor (or the entire region, if needed).

5. Develop an animated trend map that shows traffic conditions during the winter weather event 

compared to normal days without events.

6. Compile all of the outputs listed above into a report or embed the results online using 

embedding and exporting support tools within the RITIS analytics.

Below are the outputs of each of the steps listed above.

Steps 1 and 2

In Step 1, the MATOC analysts determined that the I-66 corridor leading into Washington D.C. 

would be a well-known corridor of significance. A 20-mile section of the corridor was selected, 

and a travel time query was run for the day of the 2-hour delayed opening. Simultaneously, a 

query was run for two similar days of the week during the same season. Figure 55 is the result of 

this query. Note that the travel time during normal rush hours is nearly double that of the travel 

times during the 2-hour delayed opening. Two hours later, a slightly larger rush hour occurred. 

Note that, in the 

evening, the rush hour 

travel times are nearly 

the same as the normal 

rush hour; however, 

this rush hour is only 1 

hour later than normal 

instead of 2 hours later. 

Therefore, it appears 

that most individuals 

went to work 2 hours 

later than normal but 

only stayed 1 hour later.

Figure 55. Graph. Plot of travel time by hour of day for 

I-66, created via Regional Integrated Transportation 

Information Systems Vehicle Probe Project Suite. 

Source: Metropolitan Area Transportation Operations Coordination 

(MATOC) Program Facilitator, Taran Hutchinson.

Travel Time (minutes) over Hour of Day for I-66 

Averaged for the whole day

EASTBOUND
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Figure 56 depicts travel times along I-66 during normal rush hours compared to travel times when 

there was a winter weather event that resulted in a 2-hour delayed opening for most schools and 

the Federal Government.

Step 3

To ensure that the analysis is based on accurate data, MATOC should verify that the other date 

ranges for which they are comparing the winter weather event are clear of other events (weather 

or incidents). This can be done via the probe data analytics suite by overlaying historic National 

Weather Service radar data and historic incident/event data on top of any date/time in the past 

(Figure 56). This visually confirms that a date or date range is safe for inclusion in the analysis.

In the tool shown in Figure 56, after the date is entered, the map redraws itself to show what was 

happening at that particular time including: weather radar, incidents and events, bottlenecks, and speeds. 

The left side of the screen shows a ranked list of all of the bottlenecks that were occurring at that time. 

Alternatively, MATOC can look at RWIS station data (if there is enough coverage) to see if road 

weather may have impacted travel conditions. This particular analytics tool from RITIS (seen 

in Figure 57) allows MATOC to select any date range and view weather conditions, such as 

precipitation rates, visibility, and surface conditions, while simultaneously viewing incident/event 

data, and flow data. This tool helps evaluate the effects of all forms of weather on congestion and 

incidents. This allows users to compare data from road weather information systems stations (e.g., 

precipitation rates, visibility, wind speeds, surface conditions) to other archived data (e.g., speeds, 

volumes, flow, incidents, events).

Figure 56. Screenshot. Historical dates and times to visualize weather and 

incident details during that date and time. 

Source: University of Maryland, Probe Data Analytics Suite.
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Step 4

In Step 4, MATOC runs a user delay cost query on the I-66 corridor to determine the financial 

impacts of the winter weather compared to normal user delay on the same corridor. Figure 58 

shows the resulting user delay for the morning snow event discussed in this case study. Note how 

much higher the user delay is during the morning of the snow event compared to normal.

Figure 57. Screenshot. The road weather information systems history explorer.

Figure 58. Screenshot. Visual representation of the cost of delay for both 

passenger and commercial vehicles.

Source: Clarus History Explorer: University of Maryland CATT Laboratory.

Source: University of Maryland Probe Data Analytics Suite.
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Step 5

Here MATOC runs a similar query to that in Steps 1 and 2; however, the output is an animated 

trend map instead of a simple line chart. The animated trend map shows travel conditions on 

additional corridors and shows a side-by-side comparison of travel speeds during the event 

compared to normal (Figure 59).

Step 6

Finally, the various statistics and visualizations previously mentioned are consolidated into a 

correspondence with a narrative description of the impacts of the event surrounding it. This 

narrative details the associated impacts of this weather event. 

Impacts and Lessons Learned

MATOC’s ability to efficiently produce these types of reports has allowed them to gain priority 

ranking for after action analysis and media requests, which helps secure their annual operating 

budget. MATOC now trains other agencies on how to perform their own analysis using these tools.

 

Figure 59. Screenshot. An interactive animated map showing conditions during a winter 

weather event (left) compared to conditions during normal days of the week (right).

Source: University of Maryland CATT Laboratory. 

Location: MD-28/Montgomery Ave/Exit 6

Direction: NORTHBOUND

Code: 110+04107

Speed (mph): 50.64

Location: MD-28/Montgomery Ave/Exit 6

Direction: NORTHBOUND

Code: 110P04107

Speed (mph): 28.12
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6. GETTING STARTED 

Any transportation organization can and should begin to make use of their data. Even with 

extremely limited data, staff can begin to make better planning and programming decisions. This 

chapter provides concrete steps and information resources that transportation planners and their 

planning partners can use to begin making better use of existing archived operations data.

Transportation planners and their partners should review the following checklist when they decide 

to begin using archived operations data:

Table 12. Checklist for getting started.

Step # Action Explanation

1 Define your needs/

questions.

What is it that you're trying to do? What questions do 

you need to answer? 

2 Inventory your data.

This can be the hardest step. Ask each department in 

your agency if they have any of the data sources listed 

in Table 1. Create a list of all of the data that you 

believe you can get, from whom the data will come, 

and any other relevant attributes (metadata) related to 

what is available from within the dataset. 

3 Did you find the data you 

needed?

If yes, then skip to step #6, if not, then continue 

through this list.

4 If not, look elsewhere.

If your own agency does not seem to have data (or 

there are political reasons why it cannot be accessed), 

consider reaching out to partner agencies (local 

department of transportations (DOTs), metropolitan 

planning organizations (MPOs), and university 

partners). There are also a number of free or third party 

data sets that are available as shown in Table 15, 16, 

and 17. Consider exploring your regional intelligent 

transportation system (ITS) architecture to identify 

opportunities for obtaining data in the region from 

other systems or agencies. This is one of the benefits of 

a maintained ITS architecture.

5 Purchase data.

If free data sets are not available, you may need to 

procure data from a third party. Consult Chapter 4, 

section "Obtaining Third-party Data and Tools" for 

important considerations.
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Step # Action Explanation

6 Is the data usable?

Just because data exists, does not mean that it is usable. 

Is it a scan of a paper document or is it truly electronic, 

machine readable data? Make sure it is of the right 

spatial and temporal resolution and in a format that you, 

your team, and your systems can use. 

7 Do you trust the data?

While no dataset is perfect, it is important to 

understand where the data came from, how it may have 

been massaged or filtered, and if there are any data 

stewards that can attest to the overall quality of the 

dataset. 

8 Determine your analysis 

process, and get to work!

Will you analyze the data with in-house personnel? 

Will you purchase data analysis tools to help with 

the analysis? Will you use consultant support? Later 

sections of this chapter give guidance on how to work 

with third party data analysts. Another section below 

also discusses what types of skills you might need 

internally for successful data analysis. 

 My Agency Does Not Have Any Data

If you followed the checklist above and have realized that your agency is not producing any of its 

own data (or there are political or technical impediments that make it too difficult to access that 

data), there are still other resources at your disposal. Some of these are free, while others are not. 

Some data are considered “raw,” while other data is accompanied by readily available tools for 

analysis.

Examples of datasets include:

■ National Performance Measures Research Data Set (NPMRDS).

■ National Weather Service Data.

■ Google/Waze Speed and Incident Data.

■ Third Party Probe-based Speed Data.

■ Third Party Origin-Destination Data.

■ Partner Agency Data.

■ Fatality Analysis Reporting System (FARS) Data.

These data are described in greater detail in the following tables.

Table 12. Checklist for getting started. (Continued)
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Table 13. The National Performance Measurement Research Data Set (NPMRDS).

Overview

The NPMRDS was purchased by Federal Highway Administration (FHWA) originally for 

internal purposes, but it has been made available to State and local transportation agencies at 

no cost. It will likely be used by agencies to develop Fixing America’s Surface Transportation 

(FAST) Act congestion-related performance measures. However, there are other potential 

uses, including problem identification and after action review. The first iteration of NPMRDS 

is an unfiltered, unsmoothed data set that has gaps in temporal coverage and only covers the 

National Highway System (NHS). It is only available as an historic data set, made available at 

the end of the each month. Therefore, it has few real-time applications. 

What is it What it 

Costs Access Tool Availability

Historic speed and travel 

time data for the NHS. 

Procured by the FHWA; 

officially referred to as 

the NPMRDS.

Free Send an email to heretraffic.nhs.

data@here.com to receive official 

instructions. More information 

at: http://ops.fhwa.dot.gov/freight/

freight_analysis/performan_meas/

vpds/npmrdsfaws.htm#2.

Data can be accessed through 

several third-party tools, including 

Regional Integrated Transportation 

Information System [RITIS] (https://

npmrds.ritis.org/analytics/) and Iteris 

(http://www.iteris.com). Other tools 

may be online.

Free tools 

are available 

at https://

npmrds.ritis.

org/analytics/.

Table 14. The National Weather Service Data.

Overview

Historical weather data can be used to identify causes of congestion as well as area for 

improvement in road weather management activities. 

What is it What it 

Costs Access Tool Availability

A host of weather 

products including radar, 

images, cloud cover, 

road weather information 

systems (RWIS), 

forecasts, alerts, and 

much more.

Free http://www.nws.

noaa.gov/tg

Yes. The National Weather Service 

has its own host of tools available 

for looking at real-time and historic 

data. Many third-party companies 

also provide tools to analyze 

weather data. Some free tools exist 

(e.g., https://weatherspark.com/). 

There are many other historic 

weather data tools available.
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 Table 15. Google/Waze speed deviation and incident data.

Overview

In addition to real-time operations uses, the archived data can be used for performance 

measures generation, safety data analysis, various congestion studies, and problem 

identification. It is especially useful on roads where coverage by the agency may be limited 

(like on arterials).

What is it What it 

Costs Access Tool Availability

Crowd-sourced 

incident and 

event data 

plus speed 

variation data 

on segments of 

the roadway.

Free 

(with the 

Connected 

Citizens 

Partnership 

Program).

Google/Waze has a relatively new 

public sector data sharing partnership. 

At the time of publication, Google/Waze 

was brokering deals with agencies to 

share their crowd-sourced data with the 

public sector if the public sector was 

willing to provide their own operations 

data back to Google and/or the agency 

agrees to provide attribution to Waze or 

promote Waze on agency 511 websites 

or other materials.

University and 

private sector data 

fusion specialists 

are working on 

tools (similar to 

RITIS) that will 

allow agencies to 

look at trends in 

congestion and 

incidents from the 

Google data sets.

 Table 16. Third party probe-based speed data.

Overview

Probe-based speed data can be used to: develop system performance reports; identify 

problems; prioritize projects; perform after-action incident review; conduct before and after 

studies; make informed, real-time operations decisions; analyze travel time and reliability; 

monitor work zones; develop and publish press releases for public and media consumption; 

measure the economic and environmental impacts of passenger and commercial vehicle user 

delay.

What is it What it Costs Access Tool Availability

Speed and travel 

time data on 

Interstates and 

major and minor 

arterials from 

HERE, INRIX, 

TomTom and 

others.

Varies depending 

on the provider. 

Usually a per-mile 

cost; however, 

some companies 

charge only for 

major roads and 

include smaller 

arterials for free.

Directly from data providers, 

through application program 

interfaces (APIs), or through thirdparty data analytics like Iteris 

Performance Monitoring System 

(iPeMS) or Regional Integrated 

Transportation Information System 

(RITIS) Probe Data Analytics Suite.

Yes. Some 

of the probe 

data providers 

offer their own 

analytics tools. 

RITIS and 

iPeMS also offer 

analytics suites 

for probe data.
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 Table 17. Third party origin-destination data (O-D).

Overview

This data usually comes from third-party probe data providers and includes everything 

from basic turning movements at intersections all the way to direct origins and destinations 

of vehicles moving throughout a network. O-D data can be used to track travel trends and 

calibrate models to support informed investment decisions.

What is it What it Costs Access Tool Availability

Information related to 

the movement of people 

through the trips they 

take, including their 

origin and destination for 

each trip.

Costs depends heavily 

on geography and date 

range.

From thirdparty data 

providers 

like INRIX, 

Airsage, and 

others.

Yes. The companies that 

sell these data usually 

offer analytics tools. 

Some universities are 

also building tools along 

with the RITIS platform.

Table 18. Partner agency data.

Overview

Partner agency data is any and all data that you can get from outside of your own agency. 

For example, if you are a metropolitan planning organization (MPO) and do not have any of 

your own data, you may be able to request data directly from State or local department of 

transportations (DOTs), law enforcement, or other agencies in your region. Partner agency 

data could include any of the data mentioned previously in this document (speeds, volumes, 

incidents, weather, etc.) and can be used for all of the things that have been mentioned under 

any of the case studies that have been presented, plus any of the other relevant uses listed in 

the above data sets.

What is it What it Costs Access Tool 

Availability

Anything. Incident 

data, speeds and 

volumes, computeraided dispatch, signal 

phasing and timing, 

transit schedules, 

and on-time 

performance, etc.

Usually free, 

but it depends 

on the agency/

organization 

for which you 

are asking.

Work with other agencies in a region 

MPOs, State and local DOTs, law 

enforcement, etc.) to request copies 

of databases. In rare instances, these 

agencies may have already invested 

in tools that they may allow to be 

borrowed.

Maybe. It 

depends on 

each agency 

and the 

investments 

that they have 

already made 

in data and 

analytics.
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Table 19. Fatality Analysis Reporting System (FARS) data.

Overview

This dataset can be used to analyze fatality data from motor vehicle traffic crashes. It can be 

used to identify safety issues on roadways.

What is it What it 

Costs Access Tool Availability

Database of all fatal 

collisions in the 

United States.

Free. http://www.

nhtsa.dot.gov/

Main/index.

aspx

Yes. The FARS website has basic tools 

available for querying. Other universities 

and private-sector companies have 

developed additional web-based tools for 

analyzing FARS data. Some of these tools 

also incorporate non-FARS data.

Partnering with other organizations is also a great way to get access to additional data sets and 

tools that enable personnel to analyze the data. If an agency is able to leverage investments that 

others have already made in data collection and analysis, it is advisable to do so.

 My Agency Has Limited Data or the Data is in Silos

Prior chapters of this report have provided examples of how various types of operations data are 

being used. Table 20 below gives an overview of what can be done with limited data sets (or data 

that exists in silos).

Table 20. Options for agencies with limited amounts and types of data.

Data What You Can Do With It

Traffic volume, speed, class, and 

occupancy from point sensors Travel time analysis, model calibration. 

Event, work zone, and incident 

information from an ATMS

Safety analytics, support of before and after studies, 

identification of effective operations strategies.

Weather data N/A when by itself.

Traffic speeds from probe-based 

data sources 

• System performance reports.

• Problem identification/project development.

• Before and after studies of construction projects, signal 

retiming, etc.

• Travel time analysis including reliability measurements.

Transit data

Schedule adherence and fare collection can be used for 

identifying transit accessibility in communities and for 

planning for new stops, schedules, etc.

Signal data

Intersection and ramp meter signal data (timing plans, 

maintenance, etc.) can be used for arterial systems 

performance measures, signal retiming studies, and more.
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Data What You Can Do With It

Police crash reports Safety analytics and model calibration.

Freight movements Model calibration.

 Origin-Destination data

Useful for many planning activities that require an 

understanding of travel patterns for freight and passenger 

vehicles. Also useful for real-time operations when trying 

to measure the impact of various traveler information 

strategies and the impact of incidents on arterials and 

other secondary roads.

 Advanced Transportation Management System (ATMS) Data Example

Most State DOTs collect some form of incident or event data through their ATMS or traveler 

information platforms (e.g., 511). These data usually include (at a minimum) the location of 

an event, the type of the event, and the lane status (e.g., the number of lanes closed). In other 

agencies, these data might include information about which responders are on the scene, how long 

they took to get to the scene, which variable message signs were used to provide information, and 

much more.

Minimally, these data can and should be leveraged to improve operations, but, more importantly, 

to justify budgets and help to effect change within the organization. For example, a quick 

geospatial and temporal query can help an agency to understand when and where certain types 

of incidents are most likely to occur. The agency can then use that data to make better decisions 

about how many safety service patrols are needed for each shift, where they should be positioned, 

and what types of equipment might be needed on the vehicles to better tend to the types of events 

they respond to on a daily basis.

Going a few steps further, planners and operations agencies can use these data to:

■ Identify which responders are arriving on the scene quicker.

■ Identify problem locations that might need countermeasures to reduce the occurrence of 

incidents and events.

■ Justify the need for additional responders or operators during certain shifts.

■ Justify budgets.

 My Agency Has Lots of Data

For agencies that are fortunate enough to have many disparate data sources at their disposal, 

blending and fusion concepts can and should be explored to attempt to get more out of the 

archived operations data sets. Creating links between data sets so that they can function together 

as a much larger database can make possible the use of more sophisticated analytics—searching 

for cause and effect, correlation of weather to safety issues, and others.

Table 20. Options for agencies with limited amounts and types of data. (Continued)
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If a vast array of data is indeed available, big data techniques can allow an agency to enter the 

world of multi-variate data analytics, correlations, what-if scenarios, prediction, and more. Big 

data analytics are less about the size of the data sets than they are about a process for searching 

through multi-variate data sets to search for context, correlation, and other aspects that the agency 

may not have previously thought to ask. The goal is to extract value from the data more quickly 

and easily than traditional data analysis. 

Numerous business and university groups specialize in big data concepts and data science. 

One example of applying big data analytics is the problem of determining the contribution 

of underlying factors to total congestion. Congestion and unreliable travel are caused by the 

interaction of several factors: physical capacity, demand, incidents, weather, work zones, traffic 

control devices, and operating policies. Assigning how much each of the factors contributes to 

measures like total delay is problematic, but big data analytics can help.

Other examples of what can be derived from the fusion of multiple data sets are shown in 

Table 21 below. Ultimately, as the size, quality, and diversity of data continue to grow, the 

potential for new and innovative data uses will be discovered. 

Table 21. Options for agencies with extensive amounts and types of data.

Data What You Can Do With It

Volumes and probe-based speed 

data.

User delay cost, emissions, fuel consumption, model 

calibration.

Advanced transportation 

management systems (ATMS), crash 

reports, speeds and travel times, and 

weather.

Bottleneck analytics, causality, correlation 

of incident management practices with reductions in 

delay and incident severity, and the benefit/costs of 

various strategies.

Signal data (SPaT and sensor 

actuation), Bluetooth re-identification 

or origin-destination data, volumes.

True arterial performance measures including travel 

times between intersections, travel time reliability, 

capacity utilization at intersections, true integrated 

corridor management, safety applications, dynamic 

signal retiming, what-if scenario planning, and more.
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Before starting your analysis, consider the capabilities and availability of your in-house resources. 

Is there a need to hire new staff? What is the expected lead time for them to accomplish your 

goal? If it is lengthy, consider the costs associated with delays and consider purchasing existing 

tools or services. Leverage the investments that others have already made. Whether you are 

looking to hire in-house staff or are looking to hire university/consultant support, the following 

skills and job descriptions should be considered:

■ Database administrator.

■ Data scientist/data analyst.

■ Information visualization.

Ultimately, if resources are lacking and there is neither the time nor ability to hire new staff, 

consider outsourcing to meet these needs. Alternatively, you can talk to other agencies to 

determine what tools they are using—they might be willing to share or offer guidance. Try to 

leverage their investments, if possible.

 Guidance on Procuring Data and Analytics Services

Many agencies will struggle to analyze complex, disparate data from operations groups. When 

this is the case, external support from universities or consultants, and/or the purchase of tools, may 

be necessary. 

Whenever an agency procures either a system or a set of services, a certain amount of rigor is 

needed to ensure the agency’s needs are communicated properly to bidders. When developing 

requirements for consultant support and/or tools, two approaches are typically used:

1. Option 1: A detailed set of system requirements are issued at the beginning of the contract 

to identify the functions and top-level design of the system. This approach requires a 

major effort on the part of the agency, and can become expensive and frustrating if new 

requirements are identified midway through the initiative or the requirements were not 

thought through entirely.

2. Option 2: The second approach is similar to the “design-build” methodology in construction. 

In this option, the requirements are developed as part of the contract prior to development 

beginning. This method can allow for greater dialogue between the bidders and the agency 

which ultimately can lead to a better end product.
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6Conquering the Challenges of Using Archived Operations Data

Both of the options have their advantages; however, the design-build approach typically results in 

an end-state that is more favorable to the agency. Evaluate external service providers (consultants 

in the private sector or universities). Check their references and project portfolios.

If the decision is made to hire a consultant or a university, distinguish between a one-time study 

using available data and the development of a system for ongoing use by the agency. For one-time 

analysis projects, make sure to write-up a detailed scope of work that describes: 

1. The data available (be as detailed as possible and be ready to provide samples for 

evaluation).

2. The questions that need to be answered.

3. Expected deliverables (broken down into phases that ensure success).

4. Desired skills.

5. Avoid specifying technologies or techniques. Allow the consultant team to suggest an 

approach to solving the problem. 

6. Any other expectations.

7. If possible, allow the consultant to suggest alternative approaches and value-added services. 

This will ensure that the best that each consultant has to offer will be available.
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Purchasing Services

For agencies that are not comfortable using analytic tools, and are not 

interested in performing in-house data analysis, hiring outside consultant 

support may prove to be a viable option. Purchasing support from 

universities is a similar option. Consultants and universities frequently have 

access to scientists, statisticians, database programmers, economists, and 

other analysts that would otherwise be difficult to hire at State and local 

agencies. When seeking out-of-agency services, it is wise to review product 

and project portfolios for examples of prior work to ensure an agency’s 

needs match the skills of the consultant or university personnel being 

proposed on a project.

When hiring outside support (consultant or universities), consider a 

phased approach to projects. Start small, and ensure the consultant is able 

to perform basic analysis and fusion tasks with the data available. If the 

consultants are successful, then work can progress on bigger analysis 

tasks—adding layers of complexity and building off of prior work and 

available data sets. Initiating extremely large analysis tasks that are not 

easily broken down into smaller deliverables can be a recipe for confusion, 

cost overruns, disappointment, and waste.

Regardless of who does the work, it is advisable to avoid demanding 

that consultants use specific tools, technologies, or techniques to deliver 

a solution. New technologies, methodologies, and tools are developed 

quickly and often, and requiring outdated technologies can result in 

unnecessarily limiting the agency and the consultant in performing 

analytical tasks. Allow the consultants to drive these decisions based on 

what they perceive to be the most efficient and effective tools and methods.


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

December 2016

FHWA-HOP-16-082