DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision
This work addresses travel time estimation for smart urban mobility, presenting an incremental improvement by combining deep learning with auxiliary supervision to better utilize data.
The paper tackles travel time estimation for urban mobility by proposing DeepTravel, a neural network model that uses auxiliary supervision to extract features and leverage temporal labels from trajectory data, achieving superior performance over existing methods in experiments on real datasets.
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.