Oriented online route recommendation for spatial crowdsourcing task workers

Yu Li, Man Lung Yiu, Wenjian Xu

Research output: Journal article publicationConference articleAcademic researchpeer-review

44 Citations (Scopus)


Emerging spatial crowdsourcing platforms enable the workers (i.e., crowd) to complete spatial crowdsourcing tasks (like taking photos, conducting citizen journalism) that are associated with rewards and tagged with both time and location features. In this paper, we study the problem of online recommending an optimal route for a crowdsourcing worker, such that he can (i) reach his destination on time and (ii) receive the maximum reward from tasks along the route. We show that no optimal online algorithm exists in this problem. Therefore, we propose several heuristics, and powerful pruning rules to speed up our methods. Experimental results on real datasets show that our proposed heuristics are very efficient, and return routes that contain 82–91% of the optimal reward.
Original languageEnglish
Article numberA8
Pages (from-to)137-156
Number of pages20
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 1 Jan 2015
Event14th International on Symposium on Spatial and Temporal Databases, SSTD 2015 - Hong Kong, Hong Kong
Duration: 26 Aug 201528 Aug 2015

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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