Abstract
Real-time traffic prediction is essential for mitigating traffic congestion and reducing travel time. Recent advancement in graph convolutional network (GCN) has motivated a series of GCN-based models for traffic forecasting on an urban road network. However, these graph-based methods cannot capture the intricate dependencies of consecutive road segments such as no left turn, and dynamic spatial dependency. In this paper, we propose Trajectory WaveNet, or T-wave in short, a traffic forecasting model that utilizes the actual vehicle trajectories to capture the above intricate dependencies to improve prediction performance. Both trajectories and traffic data are obtained from the floating car data collected by ridesharing companies such as DiDi. T-wave treats vehicle trajectories as first-class citizens, and applies dilated causal convolutions along both the temporal dimension (i.e., recent, daily-periodic and weekly-periodic historical traffic data) and the spatial dimension (i.e., trajectories). For effective training, a trajectory mini-batch sampling technique is devised considering both spatial and temporal proximity. Extensive experiments on real datasets show that T-wave consistently bests the state-of-the-art models.