Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2018

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 DeepTravel, 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 DeepTravel over existing approaches.

Keywords

Complex factors, Real data sets, Road segments, Supervision models, Trajectory data, Trajectory points, Travel time estimation, Urban mobility, Travel time

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Transportation

Research Areas

Data Science and Engineering

Publication

Proceedings of the 27th International Joint Conference on Artificial Intelligence: IJCAI 2018, Stockholm, Sweden, July 13-19

First Page

3655

Last Page

3661

ISBN

9780999241127

Identifier

10.24963/ijcai.2018/508

Publisher

IJCAI

City or Country

San Francisco, CA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.24963/ijcai.2018/508

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