Top-k Vehicle Matching in Social Ridesharing: A Price-Aware Approach

Yafei Li, Ji Wan, Rui Chen, Jianliang XU, Xiaoyi FU, Hongyan Gu, Pei Lv, Mingliang Xu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

In the past few years ridesharing has largely reshaped the transportation marketplace. It is envisioned as a promising solution to transportation-related problems in metropolitan cities, such as traffic congestion and air pollution. In the current ridesharing research, social ridesharing, which makes use of social relations among drivers and riders to address safety issues, and dynamic pricing are two active directions with important business implications. Simultaneously optimizing social cohesion and revenue is vital to a commercial ridesharing platform's sustainable development, which, however, has not been previously studied. In this paper, we first present a new pricing scheme that better incentivizes drivers and riders to participate in ridesharing, and then propose a novel type of Price-aware Top-$k$k Matching (PTkM) queries which retrieve the top-$k$k vehicles for a rider's request by taking into account both social relations and revenue. We design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. Moreover, we propose a novel index tailored to our problem to further speed up query processing. Extensive experimental results on real datasets show that our proposed algorithms achieve desirable performance for real-world deployment.

Original languageEnglish
Article number8812916
Pages (from-to)1251-1263
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Volume33
Issue number3
DOIs
Publication statusPublished - 1 Mar 2021

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • location-based services
  • Price revenue
  • query processing
  • social acquaintance
  • social ridesharing

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