Fairness-Guaranteed Task Assignment for Crowdsourced Mobility Services

Yafei Li, Huiling Li, Baolong Mei, Xin Huang, Jianliang Xu*, Mingliang Xu*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

As a new computing paradigm, crowdsourced mobility service is booming with the rapid development of sharing economy. In the typical crowdsourced mobility service, a large number of part-time workers perform the spatial tasks offered by the platform and share the benefits in proportion, thereby, the strategy of task assignment directly affects the level of revenue and fairness among workers. In order to balance the revenue and fairness of workers, in this paper we study a novel type of fairness-aware spatial crowdsourcing problem, namely F airness- G uaranteed T ask A ssignment (FGTA), which aims to maximize the total revenue of workers at a certain level of fairness guarantee and that is proved to be NP-hard. To solve this problem, we propose an efficient game-theory based approach for task assignment, which makes use of the best-response framework to iteratively select the best strategy for each worker until a Nash equilibrium is reached. Inspired by the observation that tasks with similar spatial and temporal features can be assigned together to a worker, we propose a spatial-temporal grouping based optimization to further improve the efficiency of task assignment. Furthermore, to improve the quality of Nash equilibrium, we present an effective large neighborhood search based optimization that trains a DQN decision model as destroy operator to accelerate the convergence of optimal task assignment. Finally, extensive experiments conducted on two real-world datasets demonstrate that our proposed approaches achieve better effectiveness and efficiency than the state-of-the-arts.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Mobile Computing
DOIs
Publication statusE-pub ahead of print - 31 Aug 2023

Scopus Subject Areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Crowdsourced mobility services
  • fairness
  • game theory
  • reinforcement learning
  • task assignment

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