Office of Operations
21st Century Operations Using 21st Century Technologies

Applying Archived Operations Data in Transportation Planning: A Primer

PDF Version [PDF, 11.9 MB]
You may need the Adobe® Reader® to view the PDFs on this page.
Contact Information: Operations Feedback at

United States Department of Transportation Federal Highway Administration

U.S. Department of Transportation
Federal Highway Administration
Office of Operations
1200 New Jersey Avenue, SE
Washington, DC 20590


December 2016

Table of Contents

[ Notice and Quality Assurance Statement ] [ Technical Report Documentation Page ] [ SI Modern Metric Conversion Factors ] [ Acronyms ]

1. Introduction
Making the Case - Using Archived Operations Data for Transportation Planning
Benefits of Archived Data to Transportation Planners
Primer Purpose and Overview
2. Meeting a Range of Planning Needs with Archived Operations Data
Performance-Based Planning and Programming
Planning Model Development: Analyzing Alternatives and Identifying Future Performance Issues
3. Conquering the Challenges of Using Archived Operations Data
Institutional Challenges
Challenges in Changing Planning Methods and Products
4. Obtaining Archived Data that Planners Need
Locating and Accessing Archived Operations Data
Obtaining Third-party Data and Tools
Data Elements Useful for Planning
Data Sources and Collection Techniques to Ensure Usefulness in Planning
Data Archives: Implementation Options and Common Mistakes Associated with Each
5. Planning Opportunities for Archived Operations Data - Basic to Innovative
Problem Identification and Confirmation
Development and Reporting of Mobility Performance Measures
Use of Archived Operations Data for Input and Calibration of Reliability Prediction Tools
Performance of Before and After Studies to Access Projects and Program Impacts
Identification of Causes of Congestion
6. Getting Started

List of Figures

Figure 1 Map. New Jersey Department of Transportation management system integration of candidate project areas
Figure 2 Diagram. Flow chart of performance-based planning and programming
Figure 3 Graphic. Timeline displaying time taken for each activity during the incident
Figure 4 Diagram. Components of an effective archived operations data archive
Figure 5 Photo. Technician
Figure 6 Map. Outline of New York State
Figure 7 Map. Outline of Arizona
Figure 8 Map. Outline of Maryland
Figure 9 Map. Outline of Oregon
Figure 10 Photo. Interstate highway shield for I-95
Figure 11 Diagram. Incident data model for performance monitoring
Figure 12 Photo. Incident clearance
Figure 13 Diagram. Work zone data for planning applications
Figure 14 Photo. Traffic signal
Figure 15 Image. An illustration of interconnected infrastructure
Figure 16 Diagram. Computing facility travel times from intelligent transportation system roadway detectors
Figure 17 Diagram. Operations data archives built in-house or hosted by a third party
Figure 18 Photo. Congestion on freeway
Figure 19 Screenshot. Ranked bottleneck locations for all of New Jersey during March 2014
Figure 20 Chart. Time spiral showing when a bottleneck occurred and length during that time
Figure 21 Graph. Congestion scan graphic for I-95
Figure 22 Screenshot. User delay costs for a 17-mile stretch of I-295 in New Jersey (both directions of travel)
Figure 23 Screenshot. View of I-295 showing detailed statistics in a mouse tooltip for a particular hour of the day
Figure 24 Screenshot. Project confirmation presentation graphic to confirm a "high-need" signalized intersection in New Jersey
Figure 25 Screenshot. Example of problem identification and project confirmation at New Jersey Department of Transportation
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
Figure 27 Screenshot. Concept graphics explaining where high accident locations contribute to bottlenecks
Figure 28 Diagram. Performance measures provide a quantifiable means of implementing goals and objectives from the transportation planning process
Figure 29 Diagram. Performance measures should be carried across planning applications throughout the entire time horizon
Figure 30 Diagram. Program logic model adapted for incident management
Figure 31 Graph. Travel time distribution is the basis for defining reliability metrics
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
Figure 33 Graph. Reliability trends reported by the Metropolitan Washington Council of Governments
Figure 34 Charts. Incident timeline trends reported by the Georgia Department of Transportation for the Atlanta region
Figure 35 Screenshot. Heat map of congestion on a corridor using the Performance Measurement System
Figure 36 Image. Delaware Valley Regional Planning Commission's outreach document is based on archived data
Figure 37 Image. Inside view of Delaware Valley Regional Planning Commission's outreach document
Figure 38 Graph. Plot of speed and vehicles per hour per lane on I-4 at Kaley Avenue, westbound
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
Figure 42 Graph. Plot of speed and vehicles per hour per lane on I-4 east of Wynmore, eastbound
Figure 43 Graph. Plot of speed and vehicles per hour per lane on I-4 east of Wynmore, westbound
Figure 44 Graph. Speed contours from archived intelligent transportation system detector data are useful for identifying bottleneck locations
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
Figure 47 Map. The Port of Miami tunnel project
Figure 48 Map. Truck flow and speed impacts of the Port of Miami tunnel project
Figure 49 Screenshot. The bottleneck ranking tool
Figure 50 Screenshot. Congestion time spiral graphic with incidents and events overlaid
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
Figure 53 Screenshot. Heat maps function within incidents clustering explorer
Figure 54 Screenshot. Correlation of coefficients ranking function in incidents clustering explorer
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
Figure 56 Screenshot. Historical dates and times to visualize weather and incident details during that date and time
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
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)

List of Tables

Table 1 Types of archived operations data
Table 2 Granularity of Strategic Highway Research Program 2 reliability-oriented analysis tools
Table 3 Highlights of operations data available to Capital District Transportation Committee
Table 4 Highlights of operations data available to Maricopa Association of Governments
Table 5 Highlights of operations data available to Maryland Department of Transportation (DOT) State Highway Administration (SHA)
Table 6 Highlights of operations data available to Oregon Department of Transportation
Table 7 Recommended set of reliability performance measures from Strategic Highway Research Program 2 Project L08
Table 8 Upper end of speed-flow distribution, I-4, Orlando, Florida
Table 9 Facility free-flow speed calculations, Atlanta, Georgia, area freeways
Table 10 Incident characteristics for I-66, Virginia
Table 11 Incident blockage characteristics for case study
Table 12 Checklist for getting started
Table 13 The National Performance Measurement Research Data Set (NPMRDS)
Table 14 The National Weather Service Data
Table 15 Google/Waze speed deviation and incident data
Table 16 Third party probe-based speed data (HERE, INRIX, TomTom)
Table 17 Third party origin-destination data (O-D)
Table 18 Partner agency data
Table 19 Fatality Analysis Reporting System (FARS) data
Table 20 Options for agencies with limited amounts and types of data
Table 21 Options for agencies with extensive amounts and types of data
Office of Operations