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Connected Traffic Signal Coordination Optimization Framework through Network-Wide Adaptive Linear Quadratic Regulator–Based Control Strategy.

Authors :
Park, Jiho
Liu, Tong
Wang, Chieh "Ross"
Berres, Andy
Severino, Joseph
Ugirumurera, Juliette
Kohls, Airton G.
Wang, Hong
Sanyal, Jibonananda
Jiang, Zhong-Ping
Source :
Journal of Transportation Engineering. Part A. Systems. Feb2025, Vol. 151 Issue 2, p1-15. 15p.
Publication Year :
2025

Abstract

Traffic congestion in metropolitan areas causes several significant challenges, such as longer travel times, decreased productivity, increased fuel consumption and vehicle emissions, and even severe injuries during crashes. Traffic signal control is a management approach to reduce traffic congestion and allocate the appropriate right of way for safety and mobility efficiency, both in temporal and spatial domains. This study proposes a network-wide adaptive signal control coordination optimization framework based on the linear quadratic regulator algorithm. The traffic flow conditions driven by signal control inputs are formulated based on their network-wide state-space representation. After modeling traffic control regulation constraints, an adaptive linear quadratic regulator algorithm is designed to maximize the network-wide total throughput under the current conditions. Optimal signal control split time durations for multiple intersections in the network are derived by solving the algebraic Riccati equation. Furthermore, the recursive least square parameter estimation method is employed to quantify dynamic traffic condition changes. To verify the effectiveness of this proposed signal control framework, both simulation and real-world experimental tests are conducted for multiple intersections in downtown Chattanooga, Tennessee, United States. In preparation for real-world experimental tests, pipelines for real-time data processing implementation and historical traffic flow data analysis are conducted. The test results demonstrate that the proposed control framework achieves a decrease in travel time by up to 19.4%, total time spent (TTS) by up to 11.9%, and relative queue balance (RQB) by up to 15.6%. The research findings indicate that the proposed signal control framework can be generalized to handle large scale signal control optimization network-wide. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
24732907
Volume :
151
Issue :
2
Database :
Academic Search Index
Journal :
Journal of Transportation Engineering. Part A. Systems
Publication Type :
Academic Journal
Accession number :
181709554
Full Text :
https://doi.org/10.1061/JTEPBS.TEENG-8376