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

DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision