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Advanced Traffic Management and Managed Lanes for Connected and Automated Vehicles

Description:

Adoption of dynamic pricing strategies and eligibility/access controls typically increases roadway capacity and throughput, but operational performance is still sub-optimized on most managed lane facilities. While capacity caps are reached for limited portions of the peak period, the ability to manage demand at the margin and to accommodate more demand on most facilities remains limited. Demonstration of dynamic management systems that utilize the latest active traffic management technologies has been limited to a few cities on specific projects; but, demonstration experiences to date have been positive. Researchers also expect that advanced traffic management technologies, coupled with emerging artificial intelligence approaches that can communicate with connected and automated vehicles, will significantly enhance freeway management systems. Researchers expect that practical applications of artificial intelligence and active traffic management will be deployed very soon. This research endeavors to provide guidance for implementing advanced traffic management system and artificial intelligence demonstration projects, to assess the costs and benefits of corridor-level and regional deployments, and to propose criteria that will be needed for the future development of standards of practice for managed lanes system operators. This project paves the way for the introduction of connected and automated vehicles into the eligible managed lane user mix.

Objective:

The goal of the research is to facilitate the implementation of advanced lane management strategy and artificial intelligence demonstration projects, and propose criteria that will be needed for the future development of standards of practice for managed lanes system operators.

Tasks:

The proposed research will:

  • Identify the current levels of performance on existing managed lane facilities across the country, including hourly operating conditions (flows, speeds, and weaving activity by vehicle class where available) and revealed effective capacities (make these data available in an online database)

  • Conduct a literature review of advanced traffic management technology demonstrations implemented on managed lane corridors, identify current advanced traffic management strategies that best fit a managed lane setting, and quantify costs, impacts, and benefits associated with each strategy.

  • Identify current modeling tools and their relevance to assessing the potential spatial and temporal impacts of applying advanced traffic management technologies to managed lanes across variations.

  • Perform a literature review of advanced traffic management systems coupled with artificial intelligence systems technology and assess existing demonstrations

  • Identify advanced traffic management strategies most likely to fit the various project settings and applications outlined above, based on operational and design parameters

  • Develop guidance for effective dynamic lane management strategies that take into account the most common performance shortcomings identified

  • Propose criteria that will be needed for the future development of standards of practice for managed lanes system operators that pave the way for the introduction of connected and automated vehicles into the eligible managed lane user mix.

Sponsoring Committee:ACP35, Managed Lanes
RNS Developer:Nick Wood
Source Info:This research need was originally developed as part of a working group of members and friends of the TRB Managed Lanes Committee (AHB35). The working group consisted of these individuals: Randall Guensler, Robert Bain, Ross Chittenden, Elizach Dembinski, Casey Emoto, Chuck Fuhs, Darren Henderson, Md Sakoat Hossan, Thomas Jacobs, Dave Kristick, Jonathan Peters, Srikanth Panguluri, Myron Swisher, Patrick Vu, and Nick Wood.
Date Posted:09/20/2018
Date Modified:05/02/2019
Index Terms:Advanced traffic management systems, Advanced traffic management systems, Highway traffic control, Managed lanes, Connected vehicles, Autonomous vehicles, Artificial intelligence,
Cosponsoring Committees: 
Subjects    
Highways
Operations and Traffic Management
Vehicles and Equipment

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