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Taxi or Hitchhiking: Predicting Passenger's Preferred Service on Ride Sharing Platforms

Published: 27 June 2018 Publication History

Abstract

Ride sharing apps like Uber and Didi Chuxing have played an important role in addressing the users' transportation needs, which come not only in huge volumes, but also in great variety. While some users prefer low-cost services such as carpooling or hitchhiking, others prefer more pricey options like taxi or premier services. Further analyses suggest that such preference may also be associated with different time and location. In this paper, we empirically analyze the preferred services and propose a recommender system which provides service recommendation based on temporal, spatial, and behavioral features. Offline simulations show that our system achieves a high prediction accuracy and reduces the user's effort in finding the desired service. Such a recommender system allows a more precise scheduling for the platform, and enables personalized promotions.

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Cited By

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  • (2024)Lightweight GCN Encoder and Sequential Decoder for Multi-Candidate Carpooling Route Planning in Road NetworkCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648328(302-310)Online publication date: 13-May-2024
  • (2024)SpecFL: An Efficient Speculative Federated Learning System for Tree-based Model Training2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00068(817-831)Online publication date: 2-Mar-2024
  • (2023)Leveraging Web Application to Enhance Transport Mobility: Optimize Bus Schedules and Ride-Share in Fiji's Central Division2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)10.1109/CSDE59766.2023.10487728(1-6)Online publication date: 4-Dec-2023
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      cover image ACM Conferences
      SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
      June 2018
      1509 pages
      ISBN:9781450356572
      DOI:10.1145/3209978
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 27 June 2018

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      Author Tags

      1. recommender system
      2. ride sharing
      3. spatio-temporal mining

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      SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;
      Overall Acceptance Rate 792 of 3,983 submissions, 20%

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      Cited By

      View all
      • (2024)Lightweight GCN Encoder and Sequential Decoder for Multi-Candidate Carpooling Route Planning in Road NetworkCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648328(302-310)Online publication date: 13-May-2024
      • (2024)SpecFL: An Efficient Speculative Federated Learning System for Tree-based Model Training2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00068(817-831)Online publication date: 2-Mar-2024
      • (2023)Leveraging Web Application to Enhance Transport Mobility: Optimize Bus Schedules and Ride-Share in Fiji's Central Division2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)10.1109/CSDE59766.2023.10487728(1-6)Online publication date: 4-Dec-2023
      • (2023)Algorithms for Shortest Path Tour Problem in Large-Scale Road NetworkComputing and Combinatorics10.1007/978-3-031-49193-1_26(340-352)Online publication date: 9-Dec-2023
      • (2021)A Utility-Aware General Framework With Quantifiable Privacy Preservation for Destination Prediction in LBSsIEEE/ACM Transactions on Networking10.1109/TNET.2021.308425129:5(2228-2241)Online publication date: Oct-2021
      • (2021)Research and Application of Key Technologies for Request Prediction and Assignment on Ridesharing PlatformsChina’s e-Science Blue Book 202010.1007/978-981-15-8342-1_28(487-502)Online publication date: 9-Jan-2021

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