Worker movement information can help the spatial crowdsourcing platform to identify the right time to assign a task to a worker for successful completion of the task. However, the majority of the current assignment strategies do not consider worker movement information. This paper aims to utilize the worker movement information via transits in an online task assignment setting. The idea is to harness the waiting periods at different transit stops in a worker transit route (WTR) for performing the tasks. Given the limited availability of workers’ waiting periods at transit stops, task deadlines and workers’ preference of performing tasks with higher rewards, we define the Transit-based Task Assignment (TTA) problem. The objective of the TTA problem is to maximize the average worker rewards for motivating workers, considering the fixed worker transit models. We solve the TTA problem by considering three variants, step-by-step, from offline to batch-based online versions. The first variant is the offline version of the TTA, which can be reduced to a maximum bipartite matching problem, and be leveraged for the second variant. The second variant is the batch-based online version of the TTA, for which, we propose dividing each batch into an offline version of the TTA problem, along with additional credibility constraints to ensure a certain level of worker response quality. The third variant is the extension of the batch-based online version of the TTA (Flexible-TTA) that relaxes the strict nature of the WTR model and assumes that a task with higher reward than a worker-defined threshold value will convince the worker to stay longer at the transit stop. Through our extensive evaluation, we observe that the algorithm solving the Flexible-TTA problem outperforms the algorithms proposed to solve other variants of the TTA problems, by 55% in terms of the number of assigned tasks, and by at least 35% in terms of average reward for the worker. With respect to the baseline (online task assignment) algorithm, the algorithm solving the Flexible-TTA problem results in three times higher reward and at least three times faster runtime.
|Konference||SSDBM 2020: 32nd International Conference on Scientific and Statistical Database Management|
|Periode||07/07/2020 → 09/07/2020|