A Matching Based Spatial Crowdsourcing Framework for Egalitarian Task Assignment

Ramneek Kaur, Vikram Goyal, Venkata M. V. Gunturi, Cheng Long

Abstract: The ubiquity of mobile internet has led to the success of Spatial Crowdsourcing platforms like real-time taxi-hailing services, online food ordering services, etc. A critical component of such services is the task assignment algorithm employed for assigning the tasks to the workers of the platform. Our study of the literature in this domain shows that most of the task assignment algorithms developed for spatial crowdsourcing platforms address the problem from a utilitarian perspective, i.e., they optimise for only kind of entity. In contrast, we address the task assignment problem in spatial crowdsourcing platforms from an egalitarian perspective. An egalitarian approach aims to optimise the expectation of all entities involved. Specifically, we aim to minimise the waiting time for the customers and workers, while maximising the profit earned by the platform. To the best of our knowledge, ours is the only study that achieves this objective in a fully-online setting, with deadlines for both customers and workers. We propose two heuristic algorithms to solve the problem, and evaluate our algorithms on a real taxi-trips records dataset. Our algorithms exhibit a superior performance than the state-of-the-art algorithm for the fully-online bottleneck matching problem with deadlines, in terms of solution quality, running time and response time.

External IDs:dblp:conf/mdm/KaurGGL22