Inferring trip occupancies in the rise of ride-hailing services

Meng Fen Chiang, Ee Peng Lim, Wang-chien Lee, Tuan Anh Hoang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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, vehicles' occupancy status is not always known to 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 first step, we represent a vehicle trajectory as a sequence of stop points, and assign stop points with pick-up, drop-off, and intermediate labels thus producing a stop point label sequence. In the second step, 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.

Original languageEnglish (US)
Title of host publicationCIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
EditorsNorman Paton, Selcuk Candan, Haixun Wang, James Allan, Rakesh Agrawal, Alexandros Labrinidis, Alfredo Cuzzocrea, Mohammed Zaki, Divesh Srivastava, Andrei Broder, Assaf Schuster
PublisherAssociation for Computing Machinery
Pages2097-2106
Number of pages10
ISBN (Electronic)9781450360142
DOIs
StatePublished - Oct 17 2018
Event27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italy
Duration: Oct 22 2018Oct 26 2018

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other27th ACM International Conference on Information and Knowledge Management, CIKM 2018
CountryItaly
CityTorino
Period10/22/1810/26/18

Fingerprint

Segmentation
Greedy algorithm
Trajectory
Operator
Car
Dynamic programming
Experiment
Classifier

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Chiang, M. F., Lim, E. P., Lee, W., & Hoang, T. A. (2018). Inferring trip occupancies in the rise of ride-hailing services. In N. Paton, S. Candan, H. Wang, J. Allan, R. Agrawal, A. Labrinidis, A. Cuzzocrea, M. Zaki, D. Srivastava, A. Broder, ... A. Schuster (Eds.), CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 2097-2106). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3269206.3272025
Chiang, Meng Fen ; Lim, Ee Peng ; Lee, Wang-chien ; Hoang, Tuan Anh. / Inferring trip occupancies in the rise of ride-hailing services. CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management. editor / Norman Paton ; Selcuk Candan ; Haixun Wang ; James Allan ; Rakesh Agrawal ; Alexandros Labrinidis ; Alfredo Cuzzocrea ; Mohammed Zaki ; Divesh Srivastava ; Andrei Broder ; Assaf Schuster. Association for Computing Machinery, 2018. pp. 2097-2106 (International Conference on Information and Knowledge Management, Proceedings).
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title = "Inferring trip occupancies in the rise of ride-hailing services",
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, vehicles' occupancy status is not always known to 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 first step, we represent a vehicle trajectory as a sequence of stop points, and assign stop points with pick-up, drop-off, and intermediate labels thus producing a stop point label sequence. In the second step, 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.",
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Chiang, MF, Lim, EP, Lee, W & Hoang, TA 2018, Inferring trip occupancies in the rise of ride-hailing services. in N Paton, S Candan, H Wang, J Allan, R Agrawal, A Labrinidis, A Cuzzocrea, M Zaki, D Srivastava, A Broder & A Schuster (eds), CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, Association for Computing Machinery, pp. 2097-2106, 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, 10/22/18. https://doi.org/10.1145/3269206.3272025

Inferring trip occupancies in the rise of ride-hailing services. / Chiang, Meng Fen; Lim, Ee Peng; Lee, Wang-chien; Hoang, Tuan Anh.

CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ed. / Norman Paton; Selcuk Candan; Haixun Wang; James Allan; Rakesh Agrawal; Alexandros Labrinidis; Alfredo Cuzzocrea; Mohammed Zaki; Divesh Srivastava; Andrei Broder; Assaf Schuster. Association for Computing Machinery, 2018. p. 2097-2106 (International Conference on Information and Knowledge Management, Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Inferring trip occupancies in the rise of ride-hailing services

AU - Chiang, Meng Fen

AU - Lim, Ee Peng

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AU - Hoang, Tuan Anh

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N2 - 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, vehicles' occupancy status is not always known to 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 first step, we represent a vehicle trajectory as a sequence of stop points, and assign stop points with pick-up, drop-off, and intermediate labels thus producing a stop point label sequence. In the second step, 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.

AB - 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, vehicles' occupancy status is not always known to 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 first step, we represent a vehicle trajectory as a sequence of stop points, and assign stop points with pick-up, drop-off, and intermediate labels thus producing a stop point label sequence. In the second step, 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.

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BT - CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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Chiang MF, Lim EP, Lee W, Hoang TA. Inferring trip occupancies in the rise of ride-hailing services. In Paton N, Candan S, Wang H, Allan J, Agrawal R, Labrinidis A, Cuzzocrea A, Zaki M, Srivastava D, Broder A, Schuster A, editors, CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery. 2018. p. 2097-2106. (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/3269206.3272025