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

Considerations of Current and Emerging Transportation Management Center Data

Printable version [PDF 12MB]
You may need the Adobe® Reader® to view the PDFs on this page.
Contact Information: Operations Feedback at OperationsFeedback@dot.gov

United States Department of Transportation logo.

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

FHWA-HOP-18-084

July 2019

Table of Contents

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

Executive Summary
Chapter 1. Introduction
No Value Purpose
No Value Background
No Value Sources of Information and Methodology
Chapter 2. Emerging Data Sources
No Value Differences and Similarities in Collection, Processing, and Aggregating Data
No Value No Value Processing and Analytics Differences
No Value Crowdsourced Incident and Congestion Data
No Value No Value Description
No Value No Value Applications of Crowdsourced Incident and Congestion Data
No Value No Value General Attributes
No Value No Value Data Availability
No Value No Value Pros and Cons
No Value No Value Use Cases for Crowdsourced Incident and Congestion Data
No Value Roadside Basic Safety Message Data
No Value No Value Description
No Value No Value Applications
No Value No Value Attributes
No Value No Value Details
No Value No Value Data Availability
No Value No Value Pros and Cons
No Value No Value Use Cases for Roadside Basic Safety Message Data
No Value Realtime and Archived Trajectory Data
No Value No Value Description
No Value No Value Applications of Realtime Trajectory Data
No Value No Value Attributes
No Value No Value Data Availability
No Value No Value Pros and Cons
No Value No Value Use Cases for Realtime and Archived Trajectory Data
No Value Crowdsourced Map Data
No Value No Value Description
No Value No Value Applications of Crowdsourced Map Data
No Value No Value Attributes
No Value No Value Data Availability
No Value No Value Pros and Cons
No Value No Value Use Cases for Crowdsourced Map Data
No Value Probe-Based Speed Data
No Value No Value Description
No Value No Value Applications of Probe-Based Speed Data
No Value No Value Attributes
No Value No Value Data Availability
No Value No Value Pros and Cons
No Value No Value Use Cases for Probe-Based Speed Data
No Value High-Resolution Asset Data
No Value No Value Description
No Value No Value Applications of High-Resolution Asset Data
No Value No Value Attributes
No Value No Value Data Availability
No Value No Value Pros and Cons
No Value Wi-Fi and Bluetooth Re-Identification Data
No Value No Value Description
No Value No Value Applications of Wi-Fi and Bluetooth Re-identification Data
No Value No Value Attributes
No Value No Value Data Availability
No Value No Value Pros and Cons
No Value Credit Card Transaction Data
No Value No Value Applications of Credit Card Transaction Data
No Value No Value Attributes
No Value No Value Pros and Cons
No Value Connected Vehicle Data From Third Parties
No Value No Value Description
No Value No Value Data Availability
No Value No Value Applications of Connected Vehicle Data
No Value No Value Attributes
No Value No Value Pros and Cons
No Value Realtime Turning Movement Data
No Value No Value Description
No Value No Value Applications of Realtime Turning Movement Data
No Value No Value Attributes
No Value No Value Pros and Cons
No Value High-Resolution Signal Data
No Value No Value Description
No Value No Value Applications of High-Resolution Signal Data
No Value No Value Attributes
No Value No Value Pros and Cons
No Value Air Quality Sensor Data
No Value No Value Description
No Value No Value Applications of Air Quality Sensor Data
No Value No Value Attributes
No Value No Value Data Availability
No Value No Value Pros and Cons
No Value Roadway Weather Predictions
No Value No Value Description
No Value No Value Applications of Weather Predictions
No Value No Value Attributes
No Value No Value Data Availability
No Value No Value Pros and Cons
No Value No Value Use Cases for Roadway Weather Predictions
No Value Computer-Aided Dispatch (CAD) Data
No Value No Value Description
No Value No Value Applications of CAD Data
No Value No Value Attributes
No Value No Value Data Availability
No Value No Value Pros and Cons
No Value No Value Use Cases for CAD Data
Chapter 3. Current and Emerging Private-Sector Business Models
No Value Pricing Structures
No Value Revenue Sharing With Agencies
No Value Exclusive Access
No Value The Value of Agency Data
No Value Use Cases of Value Exchange for Agency Data
No Value Agency Data Valuation
Chapter 4. Procurement Strategies
No Value Sole Source
No Value Traditional Requests for Proposals
No Value Intergovernmental Agreements
No Value Pooled Fund Study
No Value On-Call Consultant as the Procurer
No Value Procured as Part of a Larger System
Chapter 5. Policy Considerations
No Value Acceptable Use Terms and Conditions
No Value Data Verification and Validation
No Value Payment Terms
No Value No Value Raw Data Procurement
No Value No Value Data and Service Procurement
No Value No Value Single-Use Data and/or Service Package
No Value No Value Bartering
No Value No Value Data Quality
No Value Data Management and Maintenance
No Value No Value In-House versus Hosted Options
No Value No Value In-House Data Management
No Value No Value Commercial Cloud Hosting
No Value No Value Trusted Partner Hosting
No Value No Value Disaster Recovery and Security Considerations
No Value Open Source Software Versus Open Data
Chapter 6. Contract Considerations
Appendix. Example Data Use and Data Sharing Agreements

List of Figures

Figure 1. Graphic. How agency traffic management centers integrate private sector data.
Figure 2 Diagram. Examples of Waze event types and subtypes.
Figure 3. Screenshots. Red boxes highlight the coverage differences in the Waze data versus the Virginia Department of Transportation data.
Figure 4. Screenshot. Many agencies filter out Waze data that has a lower reliability level.
Figure 5. Realtime Waze data integrated into the Regional Integrated Transportation Information System platform.
Figure 6. Screenshot. Waze smartphone app.
Figure 7. Chart. Waze events and vehicle miles traveled by State (excluding jams).
Figure 8. Screenshot. A small portion of the MATOC Twitter monitoring system.
Figure 9. Photo. The MATOC Operations room with TweetDeck prominently displayed on the upper-right media wall panel.
Figure 10. Screenshot. The Denver Police Department uses the #Traffic hashtag to alert the public (and other agencies) about traffic events.
Figure 11. Screenshot. Example of a crowdsourcing event.
Figure 12. Screenshot. Utah Department of Transportation's Click'n Fix widget accompanies the Click'n Fix application.
Figure 13. Illustration. Utah Department of Transportation's smartphone app empowers citizens and the agency to resolve maintenance and safety issues more quickly.
Figure 14. Illustration. U.S. Department of Transportation connected vehicle and connected infrastructure concept.
Figure 15. Illustration. The Pikalert concept.
Figure 16. Diagram. Vehicle Data Translator Architecture.
Figure 17. Map. Example trajectory data for a single trip.
Figure 18. Illustration. Snapping waypoints to routes can sometimes be a challenge.
Figure 19. Illustration. The CATT Lab's Origin-Destination Data Suite uses trajectory data to conduct midblock analyses.
Figure 20. Screenshot. The CATT Lab's Origini-Destination Data Suite provides ranked intersection movements by zip code and date range based on trajectory data.
Figure 21. Screenshot. Example data feeds.
Figure 22. Screenshot. The Origin-Destination Analytics suite illustrates the origins for trips that passed over a very specific road segment.
Figure 23. Screenshot. Example OpenStreetMap.
Figure 24. Screenshot. Example of OpenStreetMap data in XML format.
Figure 25. Screenshot. License rules for OpenStreetMap.
Figure 26. Screenshot. The Virginia Department of Transportation 511 home page.
Figure 27. Screenshot. Example of a MapBox map visualizing trees in New York City.
Figure 28. Screenshot. Inputs panel on the Waze Map Editor.
Figure 29. Screenshot. Google Map.
Figure 30. Screenshot. Google StreetView.
Figure 31. Screenshot. Example routing result code from Google Maps.
Figure 32. Diagram. Toll tag travel time calculation.
Figure 33. Screenshot. Probe data provides ubiquitous coverage.
Figure 34. Screenshot. Bottleneck ranking.
Figure 35. Screenshot. Trend map compares performance before, during, and after a major event.
Figure 36. Screenshot. User delay cost resulting from a major event.
Figure 37. Screenshot. Example of a performance dashboard travel time widget.
Figure 38. Screenshot. Example performance report.
Figure 39. Screenshot. Example of an interstate travel forecast for the Baltimore, MD region during the week of Thanksgiving in 2016.
Figure 40. Screenshot. Example of INRIX traffic management center-based metadata.
Figure 41. Screenshot. Example speed and travel time data.
Figure 42. Screenshot. Extreme Definition metadata.
Figure 43. Screenshot. Example speed and travel time data for Extreme Definition format.
Figure 44. Screenshot. HERE speed data in Orlando, FL.
Figure 45. Screenshot. Example HERE traffic message channel per-lane data.
Figure 46. Screenshot. Example HERE sub-segment per-lane data.
Figure 47. Screenshot. Example TomTom data.
Figure 48. Illustrations. Examples of point clouds and Asset Mapping.
Figure 49. Illustration. Deep learning object identification presented.
Figure 50. Illustration. Third-party, connected-vehicle events from the Washington, D.C. region.
Figure 51. Screenshot. A California Highway Patrol computer-aided dispatch message.
Figure 52. Screenshot. Prince George's County Maryland computer-aided dispatch system message.
Figure 53. Screenshot. California Highway Patrol computer-aided dispatch log example.
Figure 54. Diagram. Virginia Department of Transportation Realtime Traffic Incident Management Information System high level architecture diagram.

Tables

Table 1. Newer and emerging data sources.
Table 2. Where the private sector obtains its data.
Table 3. Public sector alternatives to obtaining third-party data.
Table 4. Sample list of agencies using crowdsourced data.
Table 5. Comparison of Waze versus department of transportation event reporting.
Table 6. Waze versus department of transportation events per day (excluding jams).
Table 7. User-generated alert types and subtypes supported by Waze.
Table 8. Weather hazard event notification elements.
Table 9. Traffic jam parameters.
Table 10. Realtime closure options for Waze Connected Citizens Program agency members.
Table 11. Example connected data sets.
Table 12. Weather impacts on roads, traffic, and operational decisions.
Table 13. Industry data procurement and sharing business models and delivery mechanisms.
Table 14. Comparison of Florida and North Carolina procurement strategies for probe-based speed and travel time data.
Table 15. Comparison of origin-destination data procurement strategies by two agencies.
Table 16. Agency data value as described by private sector companies.
Table 17. Pros and cons of hosting options.
  Last modified: August 22, 2019  
Office of Operations