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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Main Authors: CHIANG, Meng-Fen, LIM, Ee-peng, LEE, Wang-Chien, HOANG, Tuan-Anh
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access: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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Institution: Singapore Management University
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spelling 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
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic occupancy inference
trajectory segmentation
ride-hailing services
Databases and Information Systems
Transportation
spellingShingle 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
description 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.
format 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
_version_ 1770574550262611968