Optimization-based Coordination and Control of Traffic Lights and Mixed Traffic in Multi-Intersection Environments

Suriyarachchi, Nilesh; Quirynen, Rien; Baras, John S; Di_Cairano, Stefano

doi:10.23919/ACC55779.2023.10156306

The traffic congestion hits most big cities in the world - threatening long delays and serious reductions in air quality. City and local government officials continue to face challenges in optimizing crowd flow, synchronizing traffic and mitigating threats or dangerous situations. One of the major challenges faced by city planners and traffic engineers is developing a robust traffic controller that eliminates traffic congestion and imbalanced traffic flow at intersections. Ensuring that traffic moves smoothly and minimizing the waiting time in intersections requires automated vehicle detection techniques for controlling the traffic light automatically, which are still challenging problems. In this paper, we propose an intelligent traffic pattern collection and analysis model, named TPCAM, based on traffic cameras to help in smooth vehicular movement on junctions and set to reduce the traffic congestion. Our traffic detection and pattern analysis model aims at detecting and calculating the traffic flux of vehicles and pedestrians at intersections in real-time. Our system can utilize one camera to capture all the traffic flows in one intersection instead of multiple cameras, which will reduce the infrastructure requirement and potential for easy deployment. We propose a new deep learning model based on YOLOv2 and adapt the model for the traffic detection scenarios. To reduce the network burdens and eliminate the deployment of network backbone at the intersections, we propose to process the traffic video data at the network edge without transmitting the big data back to the cloud. To improve the processing frame rate at the edge, we further propose deep object tracking algorithm leveraging adaptive multi-modal models and make it robust to object occlusions and varying lighting conditions. Based on the deep learning based detection and tracking, we can achieve pseudo-30FPS via adaptive key frame selection.

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