Top-k publish/subscribe for ride hitching

Yafei Li, Hongyan Gu, Rui Chen, Jianliang Xu, Mingliang Xu*

*Corresponding author for this work

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

Abstract

With the continued proliferation of mobile Internet and geo-locating technologies, carpooling as a green transport mode is widely accepted and becoming tremendously popular worldwide. In this paper, we focus on a popular carpooling service called ride hitching, which is typically implemented using a publish/subscribe approach. In a ride hitching service, drivers subscribe the ride orders published by riders and continuously receive the matching ride orders until one is picked. The current systems (e.g., Didi Hitch) adopt a threshold-based approach to filter ride orders. That is, a new ride order will be sent to all subscribing drivers whose planned trips can match the ride order within a pre-defined detour threshold. A limitation of this approach is that it is difficult for drivers to specify a reasonable detour threshold in practice. In addressing this problem, we propose a novel type of top-k subscription queries called Top-k Ride Subscription (TkRS) query, which continuously returns to drivers the best k ride orders that match their trip plans. We propose two efficient algorithms to enable the top-k result maintenance. Finally, extensive experiments on real-life datasets suggest that our proposed algorithms are capable of achieving desirable performance in practical settings.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
PublisherIEEE
Pages2291-2296
Number of pages6
ISBN (Electronic)9781728191843
ISBN (Print)9781728191850
DOIs
Publication statusPublished - Apr 2021
Event37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Chania, Greece
Duration: 19 Apr 202122 Apr 2021
https://ieeexplore.ieee.org/xpl/conhome/9458599/proceeding

Publication series

NameProceedings of IEEE International Conference on Data Engineering (ICDE)
Volume2021-April
ISSN (Print)1063-6382
ISSN (Electronic)2375-026X

Conference

Conference37th IEEE International Conference on Data Engineering, ICDE 2021
Country/TerritoryGreece
CityVirtual, Chania
Period19/04/2122/04/21
Internet address

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems

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