Efficient Matching of Offers and Requests in Social-Aware Ridesharing

Xiaoyi Fu, Ce Zhang, Hua Lu, Jianliang Xu

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


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. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches on real-world datasets.
Original languageEnglish
Title of host publication19th IEEE International Conference on Mobile Data Management, MDM 2018, Aalborg, Denmark, June 25-28, 2018
EditorsRandall Bilof
Number of pages10
PublisherIEEE Computer Society Press
Publication date2018
ISBN (Print)9781538641330
Publication statusPublished - 2018
Externally publishedYes
Event19th IEEE International Conference on Mobile Data Management - Aalborg, Denmark
Duration: 26 Jun 201828 Jun 2018


Conference19th IEEE International Conference on Mobile Data Management
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