Where to Meet a Driver Privately: Recommending Pick-Up Locations for Ride-Hailing Services

Yifei Chen, Meng Li, Shuli Zheng, C. Lal, M. Conti

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Ride-Hailing Service (RHS) has motivated the rise of innovative transportation services. It enables riders to hail a cab or private vehicle at the roadside by sending a ride request to the Ride-Hailing Service Provider (RHSP). Such a request collects rider’s real-time locations, which incur serious privacy concerns for riders. While there are many location privacy-preserving mechanisms in the literature, few of them consider mobility patterns or location semantics in RHS. In this work, we propose a pick-up location recommendation scheme with location indistinguishability and semantic indistinguishability for RHS. Specifically, we give formal definitions of location indistinguishability and semantic indistinguishability. We model the rider mobility as a time-dependent first-order Markov chain and generates a rider’s mobility profile. Next, it calculates the geographic similarity between riders by using the Mallows distance and classifies them into different geographic groups. To comprehend the semantics of a location, it extracts such information through user-generated content from two popular social networks and obtains the semantic representations of locations. Cosine similarity and unified hypergraph are used to compute the semantic similarities between locations. Finally, it outputs a set of recommended pick-up locations. To evaluate the performance, we build our mobility model over the real-world dataset GeoLife, analyze the computational costs of a rider, show the utility, and implement it on an Android smartphone. The experimental results show that it costs less than 0.12 ms to recommend 10 pick-up locations within 500 m of walking distance.
Original languageEnglish
Title of host publicationSecurity and Trust Management - 17th International Workshop, STM 2021, Proceedings
EditorsRodrigo Roman, Jianying Zhou
Place of PublicationDarmstadt, Germany
Number of pages19
ISBN (Electronic)978-3-030-91859-0
ISBN (Print)978-3-030-91858-3
Publication statusPublished - 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13075 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Android
  • Location privacy
  • Location semantics
  • Mobility pattern
  • Ride-hailing service


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