Efficient matching of offers and requests in social-aware ridesharing

Xiaoyi Fu, Ce Zhang, Hua Lu, Jianliang Xu

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environmental friendliness. Much research attention has been drawn to the optimization of travel costs in shared rides. However, other important factors in ridesharing, such as the social comfort and trust issues, have not been fully considered in the existing works. In this paper, we formulate a new problem, named Assignment of Requests to Offers (ARO), that aims to maximize the number of served riders while satisfying the social comfort constraints as well as spatial-temporal constraints. We prove that the ARO problem is NP-hard. We then propose an exact algorithm for a simplified ARO problem. We further propose three pruning strategies to efficiently narrow down the searching space and speed up the assignment processing. Based on these pruning strategies, we develop two novel heuristic algorithms, the request-oriented approach and offer-oriented approach, to tackle the ARO problem. We also study the dynamic ARO problem and present a novel algorithm to tackle this problem. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches on real-world datasets.
Original languageEnglish
JournalGeoinformatica
Volume23
Issue number4
Pages (from-to)559-589
Number of pages31
ISSN1384-6175
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • Ridesharing
  • LBS Services
  • Spatio-temporal databases

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