Engineering

Transport engineering

Real-time computing

Travel time

Data mining

Computer science

Physics

Trajectory

Database

Mathematics

Astronomy

Timestamp

Leverage (statistics)

Path (computing)

Artificial intelligence

Geometry

Point (geometry)

Programming language

Deep learning

Artificial neural network

Machine learning

Data modeling

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 leverage the natural 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.

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