Focus Area
Transportation Systems Management & Operations
Submitting On Behalf Of
TRB Committee on Freeway Operations
Critical - High Priority
$250,000 - 499,000
1 - 2 years
Type of Research
Full Research Project
Date Posted
Jul 1, 2019

Research Description

The application of new technologies in traffic operations and management began more than fifty years ago, with the introduction of digital computers. Continuous developments in computer technology, data sources, and communications have created new opportunities for developing and evaluating new strategies to improve safety and mobility on freeway networks. Traffic management systems have evolved around the collection and use of real-time information from fixed data sources (e.g., loop detectors, radars, cameras), as well as mobile sources (probes, smart phones). The presence of Connected & Automated Vehicles (CAVs) will provide public agencies with the opportunity to share, collect, and use data with these vehicles to transform the safety and mobility on these facilities. The data generated and shared between CAVs and transmitted to other vehicles (V2V), the infrastructure (V2I), and connected mobile devices (D2X) in real time will provide the opportunity for agencies to improve how they actively manage traffic and travel. Furthermore, the ability for agencies to collect and use this information will potentially minimize the need to use data that is now collected by fixed sensors that are costly to acquire, install and maintain.

The active management and operation of freeways can be enhanced with the use of CAVs data could include, but is not limited to, speed harmonization and warnings, queue warning, wrong-way movement detection, lane management strategies (e.g., variable speeds, vehicle platooning, part-time shoulder use, pricing), ramp metering, incident detection, and provision of route guidance based on a vehicle’s destination. Also, cooperative merging and lane changing will improve freeway operations especially in merging and weaving areas. Furthermore, management and operational strategies (e.g., bottleneck throughput, network travel time) could be applied across a region, network, corridors or multiple facilities.

CAVs also bring several challenges in data acquisition, management, analytics and utilization. Existing ITS field devices and Transportation Management Systems and Centers (TMCs) are not equipped to handle the data streams that will be generated from CAVs both in terms of the amount and type of data. The data generated from CAVs depend on the number and type of vehicles in operation (sharing information to full automation). Also, there will be variations in data availability in parts of the network and at different times. Furthermore, the ability of systems to collect, compile, and send CAV data in real-time to the TMC or a central location could be very cost prohibitive or require information to be synthesized before sending.

Guidance is needed on how the CAVs data will be utilized alone and in combination with other existing sources of data systems already use to provide reliable estimates of performance measures. Agencies will also be challenged with collecting, compiling, using, and for storing different types of data. Research is also needed to explore how CAV data could be integrated into existing or new algorithms may need to be developed for existing or new operational strategies that may be possible for freeway facilities or networks.

Sharing and using with CAVs will require collecting electronic messages (e.g., basic safety message), processing, and converting data elements for specific point measurements into traffic measures (e.g., speed, speed or flow profiles). These measures will then need to be processed at the edge (e.g., ITS field device), hub (e.g., traffic controller, on-street master) or transmitted to the central processing point for a TMS or the TMC. These traffic measures could be integrated into existing or new algorithms that could be located on the software platform of a hub, TMS or TMC.

Research is needed to assess the operational scenarios, use cases, conditions (e.g., varying levels of CAV market penetration, spacing of ITS devices to collect information), and how electronic messages expected to be shared with CAVs could be integrated TMSs to improve the operation and use of different operational strategies on freeway facilities. This problem statement will address how the electronic messages expected to be shared with CAVs could augment or replace the algorithms that support different operation strategies used for managing travel on freeway facilities. This proposed research will review literature, findings from existing projects, and assess what measures could be used and integrated into existing or incorporated into new algorithms for freeway operational strategies. Where the research will assess a range of electronic messages, data elements and performance measures to augment or develop, test, validate, publish, and share information to support use and integration of the results of this project into other future research.

This proposed research will build off of the initial findings, interim report and Phase 2 Plan (identification and development of selected algorithms) which is scheduled to be approved in May of 2020 by the panel for the “Algorithms to Convert BSMs into Traffic Measures” (NCHRP 03-137) project. Due to the limited resources available for this project, it is not expected to select and develop algorithms to support operational strategies specific to freeway facilities. A review of current or planned NCHRP Projects (e.g., NCHRP 08-119) has verified this proposed research will not duplicate what these projects are addressing.

This proposed research project will build off of and not duplicate current or planned research FHWA or the USDOT ITS Program is advancing. This proposed research will build off of and will not duplicate research FHWA is advancing to explore the feasibility of integrating transportation system management and operations strategies with cooperative automation research mobility applications (CARMA) and the feasibility of sharing and using electronic messages with CAVs to identify and issue warnings related to the formation of queue associated with work zones.

Additional Supporting Information

The main objective of this research is to develop and demonstate a framework for how to utilize the information from CAVs to enhance and optimize the use of operations strategies, algorithms and performance measures for freeway facilities. The research will investigate how the CAV data will be fused with the data from conventional sources to determine the state of the system, and the required CAVs penetration rate for ATDM strategies. The research will also investigate the best location for data processing to occur, whether it can occur at the roadside or needs to be transmitted back to the traffic management center. Finally, this research will address how agencies can manage and mitigate the challenges with collecting, compiling, using, and storing different types of data.

This research will focus on how CAV data can be used to augment or replace existing operational strategies and/or the supporting algorithms. The research will provide examples of how operational strategies (e.g., ramp metering) could change with the use of only CAV data or the fusion of CAV data with existing sources of data typically being used by a traffic management system. This research will also cover how CAV data can be effectively utilized alone and in combination with other existing sources of data to provide reliable estimates of performance measures around which freeway operational strategies are implemented, managed, and operated. The initial phase of this project will prioritize new operational strategies that could be developed or existing strategies modified through the integration and use of data generated from CAV electronic messages. The second phase of the project will develop, evaluate, test and validate, document, and publish algorithms to support the operational strategies selected for freeway facilities.

Submitted By
Jim Katsafanas
Michael Baker International


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