Top-k taxi recommendation in realtime social-aware ridesharing services

Xiaoyi FU*, Jinbin Huang, Hua Lu, Jianliang XU, Yafei Li

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

15 Citations (Scopus)


Ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environment friendliness. In this paper, we introduce social-awareness into realtime ridesharing services. In particular, upon receiving a user’s trip request, the service ranks feasible taxis in a way that integrates detour in time and passengers’ cohesion in social distance. We propose a new system framework to support such a social-aware taxi-sharing service. It provides two methods for selecting candidate taxis for a given trip request. The grid-based method quickly goes through available taxis and returns a relatively larger candidate set, whereas the edge-based method takes more time to obtain a smaller candidate set. Furthermore, we design techniques to speed up taxi route scheduling for a given trip request. We propose travel-time based bounds to rule out unqualified cases quickly, as well as algorithms to find feasible cases efficiently. We evaluate our proposals using a real taxi dataset from New York City. Experimental results demonstrate the efficiency and scalability of the proposed taxi recommendation solution in real-time social-aware ridesharing services.

Original languageEnglish
Title of host publicationAdvances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings
EditorsWei-Shinn Ku, Agnes Voisard, Haiquan Chen, Chang-Tien Lu, Siva Ravada, Matthias Renz, Yan Huang, Michael Gertz, Liang Tang, Chengyang Zhang, Erik Hoel, Xiaofang Zhou
PublisherSpringer Verlag
Number of pages21
ISBN (Print)9783319643663
Publication statusPublished - 2017
Event15th International Symposium on Spatial and Temporal Databases, SSTD 2017 - Arlington, United States
Duration: 21 Aug 201723 Aug 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10411 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Symposium on Spatial and Temporal Databases, SSTD 2017
Country/TerritoryUnited States

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)


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