TuSeRACT: Turn-Sample-Based Real-Time Traffic Signal Control
Real-time traffic signal control is a challenging problem owing to constantly changing traffic demand patterns, limited planning time and various sources of uncertainty (e.g., turn movements, vehicle detection) in the real world. SURTRAC (Scalable URban TRAffic Control) is a recently developed traffic signal control approach which computes delay-minimizing and coordinated (across neighbouring traffic lights) schedules of oncoming vehicle clusters in real time. To ensure real-time responsiveness in the presence of turn-induced uncertainty, SURTRAC computes schedules which minimize the delay for the expected turn movements as opposed to minimizing the expected delay under turn-induced uncertainty. This approximation ensures real-time tractability, but degrades solution quality in the presence of turn-induced uncertainty. To address this limitation, we introduce TuSeRACT (Turn Sample based Real-time trAffic signal ConTrol), a distributed sample-based scheduling approach to traffic signal control. Unlike SURTRAC, TuSeRACT computes schedules that minimize expected delay over sampled turn movements of observed traffic, and communicates samples of traffic outflows to neighbouring intersections. We formulate this sample-based scheduling problem as a constraint program and empirically evaluate our approach on synthetic traffic networks. Our approach provides substantially lower mean vehicular waiting times relative to SURTRAC.
Code (0)
등록된 구현이 없습니다.
Tasks
SchedulingTraffic Signal Controlvehicle detectionSimilar Papers 제목 키워드 기반
Scalable Adaptive Traffic Light Control Over a Traffic Network Including Turns, Transit Delays, and Blocking
We develop adaptive data-driven traffic light controllers for a grid-like traffic network considering straight, left-turn, and right-turn traffic flows. The analysis incorporates transit delays and blocking effects on ve…
BlockingSpatiotemporal 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 LearningReinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion
The growing demand for road use in urban areas has led to significant traffic congestion, posing challenges that are costly to mitigate through infrastructure expansion alone. As an alternative, optimizing existing traff…
reinforcement-learningReinforcement Learning (RL)Traffic Signal ControlEffortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images
Deep learning has been successfully applied to several problems related to autonomous driving. Often, these solutions rely on large networks that require databases of real image samples of the problem (i.e., real world) …
Autonomous DrivingData AugmentationTraffic Sign DetectionEMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles
Emergency vehicles (EMVs) play a crucial role in responding to time-critical events such as medical emergencies and fire outbreaks in an urban area. The less time EMVs spend traveling through the traffic, the more likely…
reinforcement-learningReinforcement Learning (RL)Traffic Signal Control