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Key to the effectiveness of schedule-driven approaches to real-time traffic control is an ability to accurately predict when sensed vehicles will arrive at and pass through the intersection. Prior work in schedule-driven traffic control has assumed a static vehicle arrival model. However, this static predictive model ignores the fact that the queue count and the incurred delay should vary as different partial signal timing schedules (i.e., different possible futures) are explored during the online planning process. In this paper, we propose an alternative arrival time model that incorporates queueing dynamics into this forward search process for a signal timing schedule, to more accurately capture how the intersection’s queues vary over time. As each search state is generated, an incremental queueing delay is dynamically projected for each vehicle. The resulting total queueing delay is then considered in addition to the cumulative delay caused by signal operations. We demonstrate the potential of this approach through microscopic traffic simulation of a real-world road network, showing a 10 − 15% reduction in average wait times over the schedule-driven traffic signal control system in heavy traffic scenarios. 

Reconstructing 4D vehicular activity (3D space and time) from cameras is useful for autonomous vehicles, commuters and local authorities to plan for smarter and safer cities. Traffic is inherently repetitious over long periods, yet current deep learning-based 3D reconstruction methods have not considered such repetitions and have difficulty generalizing to new intersection-installed cameras. We present a novel approach exploiting longitudinal (long-term) repetitious motion as self-supervision to reconstruct 3D vehicular activity from a video captured by a single fixed camera. Starting from off-the-shelf 2D keypoint detections, our algorithm optimizes 3D vehicle shapes and poses, and then clusters their trajectories in 3D space. The 2D keypoints and trajectory clusters accumulated over long-term are later used to improve the 2D and 3D keypoints via self-supervision without any human annotation. Our method improves reconstruction accuracy over state of the art on scenes with a significant visual difference from the keypoint detector’s training data, and has many applications including velocity estimation, anomaly detection and vehicle counting. We demonstrate results on traffic videos captured at multiple city intersections, collected using our smartphones, YouTube, and other public datasets.