Co-Optimization and Interpretability of Intelligent–Traditional Signal Control Based on Spatiotemporal Pressure Perception in Hybrid Control Scenarios

Yingchang Xiong, Guoyang Qin, Jinglei Zeng, Keshuang Tang, Hong Zhu () and Edward Chung
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Yingchang Xiong: Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Guoyang Qin: Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Jinglei Zeng: Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA
Keshuang Tang: Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Hong Zhu: Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Edward Chung: Department of Electrical and Electronic Engineering, Hong Kong Polytenic University, Hong Kong, China

Sustainability, 2025, vol. 17, issue 16, 1-30

Abstract: As cities transition toward intelligent traffic systems, hybrid networks combining AI and traditional intersections raise challenges for efficiency and sustainability. Existing studies primarily focus on global intelligence assumptions, overlooking the practical complexities of hybrid control environments. Moreover, the decision-making processes of AI-based controllers remain opaque, limiting their reliability in dynamic traffic conditions. To address these challenges, this study investigates the following realistic scenario: a Deep Reinforcement Learning (DRL) intersection surrounded by max–pressure-controlled neighbors. A spatiotemporal pressure perception agent is proposed, which (a) uses a novel Holistic Traffic Dynamo State (HTDS) representation that integrates real-time queue, predicted vehicle merging patterns, and approaching traffic flows and (b) innovatively proposes Neighbor–Pressure–Adaptive Reward Weighting (NP-ARW) mechanism to dynamically adjust queue penalties at incoming lanes based on relative pressure differences. Additionally, spatial–temporal pressure features are modeled using 1D convolutional layers (Conv1D) and attention mechanisms. Finally, our Strategy Imitation–Mechanism Attribution framework leverages XGBoost and Decision Trees to systematically analyze traffic condition impacts on phase selection, fundamentally enabling explainable control logic. Experimental results demonstrate the following significant improvements: compared to fixed-time control, our method reduces average travel time by 65.45% and loss time by 85.04%, while simultaneously decreasing average queue lengths and pressure at neighboring intersections by 91.20% and 95.21%, respectively.

Keywords: intelligent–traditional signal control; spatiotemporal pressure perception; deep reinforcement learning; random forest interpretation; carbon emission optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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