Inferring trip occupancies in the rise of ride-hailing services
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...
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sg-smu-ink.sis_research-52682019-06-18T00:57:51Z Inferring trip occupancies in the rise of ride-hailing services CHIANG, Meng-Fen LIM, Ee-peng LEE, Wang-Chien HOANG, Tuan-Anh 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. 2018-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4265 info:doi/10.1145/3269206.3272025 https://ink.library.smu.edu.sg/context/sis_research/article/5268/viewcontent/21._Dec03_2018___Inferring_Trip_Occupancies_in_the_rise_of_ride_hailing_services_CIKM18_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University occupancy inference trajectory segmentation ride-hailing services Databases and Information Systems Transportation |
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occupancy inference trajectory segmentation ride-hailing services Databases and Information Systems Transportation CHIANG, Meng-Fen LIM, Ee-peng LEE, Wang-Chien HOANG, Tuan-Anh Inferring trip occupancies in the rise of ride-hailing services |
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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. |
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text |
author |
CHIANG, Meng-Fen LIM, Ee-peng LEE, Wang-Chien HOANG, Tuan-Anh |
author_facet |
CHIANG, Meng-Fen LIM, Ee-peng LEE, Wang-Chien HOANG, Tuan-Anh |
author_sort |
CHIANG, Meng-Fen |
title |
Inferring trip occupancies in the rise of ride-hailing services |
title_short |
Inferring trip occupancies in the rise of ride-hailing services |
title_full |
Inferring trip occupancies in the rise of ride-hailing services |
title_fullStr |
Inferring trip occupancies in the rise of ride-hailing services |
title_full_unstemmed |
Inferring trip occupancies in the rise of ride-hailing services |
title_sort |
inferring trip occupancies in the rise of ride-hailing services |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2018 |
url |
https://ink.library.smu.edu.sg/sis_research/4265 https://ink.library.smu.edu.sg/context/sis_research/article/5268/viewcontent/21._Dec03_2018___Inferring_Trip_Occupancies_in_the_rise_of_ride_hailing_services_CIKM18_.pdf |
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