Traffic Signal Control and Speed Offset Coordination Using Q-Learning for Arterial Road Networks
Arterial traffic interacts with freeway traffic, yet the two are controlled independently. Arterial traffic signals do not take into account freeway traffic and how ramps control ingress traffic and have no control over egress traffic from the freeway. This often results in long queues in either direction that block ramps and spill over to arterial streets or freeway lanes. In this paper, we propose an adaptive arterial traffic control strategy that combines traffic signal control (TSC) and dynamic speed offset (DSO) coordination using a Q-learning algorithm for a traffic network that involves a freeway segment and adjacent arterial streets. The TSC agent computes the signal cycle length and split based on observed intersection demands and adjacent freeway off-ramp queues. The DSO agent computes the relative offset and the recommended speeds of both ways between consecutive intersections based on their physical distance, intersection queues, and signal cycles. We evaluate the performance of the proposed arterial traffic control strategy using microscopic traffic simulations of an arterial corridor with seven intersections near the I-710 freeway. The proposed QL-based control significantly outperforms a fixed-time control and MAXBAND in terms of the travel time and the number of stops under low or moderate demands. In high-demand scenarios, the travel-time benefit provided by the QL-based control is reduced as it mitigates off-ramp and intersection queues, which is a necessary trade-off in our perspective. In addition, mutual benefit is obtained by implementing freeway and arterial traffic control simultaneously.
Code (0)
등록된 구현이 없습니다.
Tasks
Q-LearningTraffic Signal ControlMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
C-MP: A decentralized adaptive-coordinated traffic signal control using the Max Pressure framework
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 s…
Traffic Signal ControlAdaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning
Coordinating intersections in arterial networks is critical to the performance of urban transportation systems. Deep reinforcement learning (RL) has gained traction in traffic control research along with data-driven appr…
Deep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)DSIP: A Dynamic Coordination Planner for Signal-Free Intersections using Diffusion-Model-Based Multi-Agent Motion Planning
Traffic signal control at urban intersections inherently introduces stop-and-go behavior, resulting in increased delays and reduced traffic efficiency, especially under high traffic demand. With the emergence of connecte…
Trajectory PlanningMotion PlanningSpatiotemporal Decision Transformer for Traffic Coordination
Traffic signal control is a critical challenge in urban transportation, requiring coordination among multiple intersections to optimize network-wide traffic flow. While reinforcement learning has shown promise for adapti…
Reinforcement LearningCoLight: Learning Network-level Cooperation for Traffic Signal Control
Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating the offsets between two intersections. Su…
Multi-agent Reinforcement LearningReinforcement LearningTraffic Signal Control