TrajNet: A Trajectory-Based Deep Learning Model for Traffic Prediction

Ridesharing companies such as Ube and DiDi provide ride-hailing services where passengers and drivers are matched via mobile apps. As a result, large amounts of vehicle trajectories and vehicle speed data are collected that can be used for traffic prediction. The recent popularity of graph convolutional networks (GCNs) has opened up new possibilities for real-time traffic prediction and many GCN-based models have been proposed to capture the spatial correlation on the urban road network. However, the graph-based approaches fail to capture the intricate dependencies of consecutive road segments that are well captured by trajectories.