TrajForesee: How limited detailed trajectories enhance large-scale sparse information to predict vehicle trajectories?

Foreseeing detailed vehicle future trajectories collectively enables a large scope of urban applications such as route planning and commercial advertising. Existing methods focused on predicting future trajectories of urban vehicles with their own fine-grained historical trajectories. Unfortunately, in real-world scenarios, fine-grained trajectories provided by GPS are limited to obtain due to privacy issues and business competitions. In this paper, our solution enables the ubiquitous but coarse-grained location-based surveillance information to predict the fine-grained trajectories of all vehicles with limited number of fine-grained trajectories. We first capture the vectorized semantic representation of trajectories by training the spatiotemporal embedding in large coarse trajectory set. Then, we propose a new measurement to calculate the trajectory similarity, which combines the vehicles' historical behavior similarity and short-term trajectory similarity. The obtained trajectory similarity is then seamlessly embedded into the dynamic graph convolution network in the manner of spatial attention. The dynamic graph convolution sequence-to-sequence module and the fully-connected layer are devised to generate final sequential trajectory predictions. The whole process is to train in a multi-task framework. Extensive experiments on real-world datasets show the excellent performance of our method.