Publication Type

Conference Proceeding Article

Publication Date

8-2016

Abstract

Vehicle trajectories are one of the most important data in location-based services. The quality of trajectories directly affects the services. However, in the real applications, trajectory data are not always sampled densely. In this paper, we study the problem of recovering the entire route between two distant consecutive locations in a trajectory. Most existing works solve the problem without using those informative historical data or solve it in an empirical way. We claim that a data-driven and probabilistic approach is actually more suitable as long as data sparsity can be well handled. We propose a novel route recovery system in a fully probabilistic way which incorporates both temporal and spatial dynamics and addresses all the data sparsity problem introduced by the probabilistic method. It outperforms the existing works with a high accuracy (over 80%) and shows a strong robustness even when the length of routes to be recovered is very long (about 30 road segments) or the data is very sparse.

Keywords

Location-based services, Route recovery, Spatio-temporal, Trajectory

Discipline

Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

KDD '16:Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: San Francisco, California, August 13-17, 2016

First Page

1915

Last Page

1924

ISBN

9781450342322

Identifier

10.1145/2939672.2939843

Publisher

ACM

City or Country

New York

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1145/2939672.2939843

Share

COinS