Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
10-2018
Abstract
The knowledge of all occupied and unoccupied trips made by self-employed drivers are essential for optimized vehicle dispatch by ride-hailing services (e.g., Didi Dache, Uber, Lyft, Grab, etc.). However, the occupancy status of vehicles is not always known to the service operators due to adoption of multiple ride-hailing apps. In this paper, we propose a novel framework, Learning to INfer Trips (LINT), to infer occupancy of car trips by exploring characteristics of observed occupied trips. Two main research steps, stop point classification and structural segmentation, are included in LINT. In the stop point classification step, we represent a vehicle trajectory as a sequence of stop points, and assign stop points with pick-up, drop-off, and intermediate labels. The classification of vehicle trajectory stop points produces a stop point label sequence. For structural segmentation, we further propose several segmentation algorithms, including greedy segmentation (GS), efficient greedy segmentation (EGS), and dynamic programming-based segmentation (DP) to infer occupied trip from stop point label sequences. Our comprehensive experiments on real vehicle trajectories from self-employed drivers show that (1) the proposed stop point classifier predicts stop point labels with high accuracy, and (2) the proposed segmentation algorithm GS delivers the best accuracy performance with efficient running time.
Keywords
occupancy inference, trajectory segmentation, ride-hailing services
Discipline
Databases and Information Systems | Transportation
Research Areas
Data Science and Engineering
Publication
CIKM'18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management: October 22-26, Torino, Italy
First Page
2097
Last Page
2105
ISBN
9781450360142
Identifier
10.1145/3269206.3272025
Publisher
ACM
City or Country
New York
Citation
CHIANG, Meng-Fen; LIM, Ee-peng; LEE, Wang-Chien; and HOANG, Tuan-Anh.
Inferring trip occupancies in the rise of ride-hailing services. (2018). CIKM'18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management: October 22-26, Torino, Italy. 2097-2105.
Available at: https://ink.library.smu.edu.sg/sis_research/4265
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3269206.3272025