Applying Archived Operations Data in Transportation Planning: A Primer

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Contact Information: Operations Feedback at OperationsFeedback@dot.gov

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

FHWA-HOP-16-082

December 2016

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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