Real-time traffic volume inference is key to an intelligent city. It is a
challenging task because accurate traffic volumes on the roads can only be
measured at certain locations where sensors are installed. Moreover, the
traffic evolves over time due to the influences of weather, events, holidays,
etc. Existing solutions to the traffic volume inference problem often rely on
dense GPS trajectories, which inevitably fail to account for the vehicles which
carry no GPS devices or have them turned off. Consequently, the results are
biased to taxicabs because they are almost always online for GPS tracking. In
this paper, we propose a novel framework for the citywide traffic volume
inference using both dense GPS trajectories and incomplete trajectories
captured by camera surveillance systems. Our approach employs a high-fidelity
traffic simulator and deep reinforcement learning to recover full vehicle
movements from the incomplete trajectories. In order to jointly model the
recovered trajectories and dense GPS trajectories, we construct spatiotemporal
graphs and use multi-view graph embedding to encode the multi-hop correlations
between road segments into real-valued vectors. Finally, we infer the citywide
traffic volumes by propagating the traffic values of monitored road segments to
the unmonitored ones through masked pairwise similarities. Extensive
experiments with two big regions in a provincial capital city in China verify
the effectiveness of our approach.

本文提出了一种新的城市交通量推断框架，使用探测器和摄像头捕获到的密集 GPS 轨迹以及不完整轨迹，结合高保真交通模拟器和深度强化学习方法从不完整轨迹中恢复完整车辆行驶轨迹，并通过构建时空图和多视图图嵌入模型，推断未监测到的道路交通量。实验结果表明，该方法在中国省会城市的两个大区域中表现出了较高的准确性。