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Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway. Previous studies generally update signal timings in real-time based on predefined traffic measurements collected by point detectors, such as traffic volumes and occupancies. Comparing with point detectors, traffic cameras—which have been increasingly deployed on road networks—could cover larger areas and provide more detailed traffic information. In this work, we propose a deep reinforcement learning (DRL) method to explore the potential of traffic video data in improving the efficiency of ramp metering. Vehicle locations are extracted from the traffic video frames and are reformed as position matrices. The proposed method takes the preprocessed video data as inputs and learns the optimal control strategies directly from the high-dimensional inputs. A series of simulation experiments based on real-world traffic data are conducted to evaluate the proposed approach. The results demonstrate that, in comparison with a state-of-the-practice method, the proposed DRL method results in (1) lower travel times in the mainline, (2) shorter vehicle queues at the on-ramp, and (3) higher traffic flows downstream of the merging area. The results suggest that the proposed method is able to extract useful information from the video data for better ramp metering controls. [ABSTRACT FROM AUTHOR]

Modernized trams usually run on exclusive rail lanes along urban streets, but they share the right of way with general vehicles at intersections and often get interrupted by traffic signals. We developed a mixed integer model to resynchronize traffic signal timings to favor tram movements. The objective is to balance the operational needs between minimizing bidirectional tram travel times and reducing the likelihood of activating the green extensions. The model depicts both tram and vehicle progressions in one signal timing plan, making it possible to control the impact of signal timing resynchronization through traffic. Trams following the tram bands produced by the proposed model are prevented from being stopped by red phases at signalized intersections. The applicability and effectiveness of the proposed model were demonstrated in a real-world case study. Compared with the state-of-the-art practice approach, the developed model reduced tram travel time by 10% with lower negative impacts on traffic on side streets. The reduction in tram travel time was obtained without sacrificing the mobility of through traffic. [ABSTRACT FROM AUTHOR]