Development of Multimodal Traffic Signal Control (Core Project)

Project Abstract

Traffic congestion affects traveler mobility and also has an impact on air quality, which negatively affects
public health. Sustainable mobility could enhance air quality and alleviate congestion. Accordingly, optimizing
the utilization of the available infrastructure using advanced traffic signal controllers has become necessary to
mitigate traffic congestion in a world with growing pressure on financial and physical resources. Hence, this
work develops a novel real-time adaptive multi-modal decentralized traffic signal controller that integrates
connected vehicles using a Nash bargaining game-theoretic framework by optimizing total queue length. This
framework has a flexible phasing sequence and free cycle length, and thus can adapt to dynamic changes in
traffic demand. The controller was implemented and evaluated using INTEGRATION microscopic traffic
assignment and simulation software. The proposed controller was tested and compared to state-of-the-art isolated
and coordinated traffic signal controllers. The developed controller integrates transit signal priority and freight
signal priority to maximize flows in real-time using data collected from vehicles through vehicle-to-infrastructure
wireless communications. The proposed controller was tested on an isolated intersection, arterial network, and
large-scale networks. The simulation results demonstrate that the proposed decentralized controller reduces
traffic congestion, fuel consumption and vehicle emission levels, and produces major improvements over other
state-of-the-art centralized and de-centralized traffic signal controllers.

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Impacts and Outcomes

The DNB controller developed and tested in this research reduced delays and traffic congestion at signalized intersections, which in turn reduced travel times and emissions. Decentralized systems are scalable and easy to expand by inserting new controllers into the system. Additionally, decentralized systems are often inexpensive to establish and operate, as there is no essential need for a reliable and direct communication network between a central computer and the local controllers in the field.

Universities and Sponsoring Organizations Involved

U.S. Department of Transportation Office of the Secretary-Research, Virginia Tech

Principal Investigator(s)

Hesham Rakha: hrakha@vt.edu
Kyoungho Ahn: kahn@vt.edu

Funding Sources and Amounts (Split By Organization and Type of Funding)

USDOT: $300,000 (Federal), Virginia Tech: $150,000 (Match)

Completion Date

May 2019

Keywords

Traffic Signal Control