C-MP: A decentralized adaptive-coordinated traffic signal control using the Max Pressure framework
원문 초록
Coordinated traffic signals seek to provide uninterrupted flow through a series of closely spaced intersections, typically using pre-defined fixed signal timings and offsets. Adaptive traffic signals dynamically change signal timings based on observed traffic conditions in a way that might disrupt coordinated movements, particularly when these decisions are made independently at each intersection. To alleviate this issue, this paper introduces a novel Max Pressure-based traffic signal framework that can provide coordination even under decentralized decision-making. The proposed Coordinated Max Pressure (C-MP) algorithm uses the space mean speeds of vehicles to explicitly detect freely flowing platoons of vehicles and prioritizes their movement along a corridor. Specifically, upstream platoons are detected and their weight in the MP framework increased to provide priority, while downstream platoons are detected and their weight reduced to ensure smooth traffic flow across corridors. The study analytically proves that C-MP maintains the desirable maximum stability property, while micro-simulation analyses conducted on an arterial network demonstrate its ability to achieve a larger stable region compared to benchmark MP control policies. Simulation results also reveal that the proposed control algorithm can effectively coordinate traffic signals in both directions along an arterial without explicitly assigned offsets or constraints. The results also reveal C-MP's superiority to benchmark coordination strategies in reducing travel time, and fuel consumption both at the corridor level and the network level by balancing the negative impact imparted to vehicles in the minor direction.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control
Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal Control
A Parallel Hybrid Action Space Reinforcement Learning Model for Real-world Adaptive Traffic Signal Control
CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control
MacLight: Multi-scene Aggregation Convolutional Learning for Traffic Signal Control
토론
이 논문과 연결된 포럼 글이 아직 없습니다.