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Oriented Online Route Recommendation for Spatial Crowdsourcing Task Workers

  • Yu LiEmail author
  • Man Lung Yiu
  • Wenjian Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)

Abstract

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.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of ComputingHong Kong Polytechnic UniversityHong KongChina

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