Research Reports |
Title: | Framework of Adaptive Intersection Traffic Control Strategy for Urban Traffic Network Subjected to Disruptions |
Authors: | Kaisen Yao and Suren Chen |
University: | Colorado State University |
Publication Date: | Aug 2023 |
Report #: | MPC-23-503 |
Project #: | MPC-679 |
TRID #: | 01896014 |
Keywords: | adaptive control, incident management, traffic queuing, traffic signal control systems, urban highways |
Type: | Research Report – MPC Publications |
Mitigating congestion at urban traffic system intersections following major hazards and incidents is a crucial step to maximize the evacuation, rescue and recovery efficiency and prevent a hazard from turning into a disaster. An optimized traffic signal design strategy can effectively contribute to maintaining an efficient traffic system operation despite various disruptions. Most existing studies focus on static and generic congestion scenarios during the recovery stage rather than realistic time-progressive scenarios covering the entire process following a disruption. An adaptive traffic signal control strategy in response to traffic disruptions at a single intersection is proposed by covering both the incident and recovery stages. Dynamic phase selection (DPS) technology is applied to adjust the traffic signal control plan adaptively during the incident stage, while the queue length dissipation (QLD) algorithm is adopted to carry out optimal green time calculation during the recovery stage. The proposed methodology is demonstrated by considering disruptions caused by several typical vehicle crashes at intersections. The proposed DPS+QLD traffic signal strategy is found to improve the resiliency of a typical intersection against disruptions by clearing the queue faster, reducing overall traffic loss time, and maintaining stable mobility with superior performance over conventional fixed and actuated traffic signal plans.
Yao, Kaisen, and Suren Chen. Framework of Adaptive Intersection Traffic Control Strategy for Urban Traffic Network Subjected to Disruptions, MPC-23-503. North Dakota State University - Upper Great Plains Transportation Institute, Fargo: Mountain-Plains Consortium, 2023.