Abstract 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 or designed heuristically in a non-learning-based way. The former is not able to capture many cross-segment complex factors while the latter fails 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 D EEP T RAVEL, that can automatically and effectively extract different features, as well as make full use of temporal labels of the trajectory data. We have conducted comprehensive experiments on real datasets to demonstrate the out-performance of D EEP T RAVEL over existing approaches.