  Estimating the travel time of a path is of great importance to smart urban
mobility. Existing approaches are either based on estimating the time cost of
each road segment which are not able to capture many cross-segment complex
factors, or designed heuristically in a non-learning-based way which fail to
utilize the existing abundant temporal labels of the data, i.e., the time stamp
of each trajectory point. In this paper, we leverage on new development of deep
neural networks and propose a novel auxiliary supervision model, namely
DeepTravel, that can automatically and effectively extract different features,
as well as make full use of the temporal labels of the trajectory data. We have
conducted comprehensive experiments on real datasets to demonstrate the
out-performance of DeepTravel over existing approaches.
