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.

Many essential services for autonomous vehicles, e.g., navigation on high-quality maps, are designed based on the understanding of traffic conditions, e.g., travel time/speed on road segments, traffic flow, etc. However, most existing traffic condition models lack the consideration of the differentiation for vehicles with different types (e.g., personal vehicles or trucks) and thus they cannot satisfy some type-specific services, e.g., traffic-condition-based routing for autonomous vehicles with different types. To address this challenge, we design a novel vehicular mobility based sensing model called mDrive to predict the travel speed on the road segments, which is targeted for different types of vehicles by utilizing the camera data obtained from the traffic cameras equipped in the road intersections only, without any in-vehicle GPS devices. mDrive addresses the type-aware traffic speed prediction problem with sparse sensors based on three correlations: (1) the spatial correlation of travel speed on the connected road segments; (2) the temporal correlation of travel speed on the consecutive time slots; (3) the type correlation of different vehicular types’ speed on the same road segment. We implement mDrive on traffic camera data from the Chinese city Suzhou and evaluate it by using the detailed GPS data from personal vehicles, taxis, and trucks, with road contextual data as ground truth. The experiment show mDrive outperforms state-of-the-art methods by reducing 6.2% mean relative error on average for all types of vehicles.