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The National Academies

NCHRP 08-145 [Active]

Utilizing Cooperative Automated Transportation (CAT) Data to Enhance Freeway Operational Strategies

  Project Data
Funds: $500,000
Staff Responsibility: B. Ray Derr
Research Agency: Noblis, Inc.
Principal Investigator: Meenakshy Vasudevan
Effective Date: 7/14/2021
Completion Date: 7/14/2023

OBJECTIVE
 
The objective of this research is to assess operational scenarios and use cases where freeway operations strategies could be improved through the transmission of data between a traffic management system (TMS) and the larger cooperative automated transportation (CAT) system (either directly or through a third party). This assessment should (1) spur development of enhanced and new operational strategies and (2) help agencies justify gaining access to additional CAT data.

STATUS

The contract is just underway.
 
TASKS
 
Task 1. Project Management. This task will ensure that the team meets the objectives, understands expectations, communicates issues and results, adheres to the schedule, and stays on budget. This will include development of the amplified work plan, holding the kick-off teleconference, submitting monthly and quarterly progress reports, and adopting agile project management approaches.
 
Task 2. Literature Review. Conduct a literature review on CAT-derived data and the data’s use in freeway performance measure estimation and operational strategies to understand the strengths and weaknesses as well as identify opportunities for enhancing strategies.
 
Task 3. Strategy Evaluation. Freeway operational strategies will be catalogued and CAT data that could be used to enhance them documented. Operational concepts for enhanced strategies will be developed. After establishing a framework for prioritizing the potential enhanced strategies, a subset of the strategies will be selected for development in Task 5.
 
Task 4. Interim Report and Meeting. Submit an interim report documenting the results of Tasks 1-3 and presenting an updated plan for Tasks 5 and 6. Meet with the NCHRP to review the report and obtain approval for subsequent tasks.
 
Task 5. Strategy Development. After assessing the suitability of CAT data sources, core logic for the selected enhanced strategies will be developed, tested, and evaluated.

BACKGROUND

The application of new technologies in transportation operations and management began more than 50 years ago, with the introduction of digital computers. Continuous developments in computer technology, emerging sources of data, and communications have created new opportunities for operational strategies and performance measures to improve freeway network safety and mobility. Traffic management systems (TMS) continue to evolve and are incorporating the collection and use of real-time information from fixed sources (e.g., loop detectors, radar, cameras), mobile sources (e.g., probes, smart phones), other systems (e.g., weather, pavement monitoring), and other sources (e.g., third party providers).

AASHTO’s Infrastructure Owner Operators Guiding Principles for Connected Infrastructure Supporting Cooperative Transportation: Supporting Technical Concepts states “Cooperative Automated Transportation (CAT) envisions all stakeholders and elements of the transportation system working together to improve safety, mobility, equity, and operations efficiency through interdependent vehicle, infrastructure, and systems automation enabled by connectivity and information exchange. The concept is intentionally expansive. It looks beyond existing, developing, and planned transportation concepts to a fully integrated system serving travelers, goods, and services.” The emergence of CAT, particularly Connected and Automated Vehicles (CAVs), will provide public agencies with the opportunity to collect, use, and share data among vehicles, infrastructure, and other devices and could transform how agencies actively manage and operate traffic, improving safety and mobility. Agencies will be able to issue advisory, warning, and regulatory messages based on current and projected conditions unique to a specific location (e.g., section of roadway, corridor, geo-fenced area), direction of travel, and possibly specific vehicles. In addition to allowing new operational approaches, these data may reduce the need for fixed sensors that are costly to deploy and maintain.

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