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Fairness-Guaranteed Task Assignment for Crowdsourced Mobility Services
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AI
DOI：10.1109/TMC.2023.3310591.png)
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Abstract
En 中文
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 Fairness-Guaranteed Task Assignment (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.
Keywords:
Task analysis
Mobile computing
Shared transport
Roads
Optimization
Schedules
Real-time systems
Crowdsourced mobility services
fairness
game theory
reinforcement learning
task assignment
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