Dynamic pricing and task assignment in real-time spatial crowdsourcing platforms

2018-12-12A new platform, termed spatial crowdsourcing (SC), is emerging that enables a requester to commission workers to physically travel to some specified locations to perform a set of spatial tasks (i.e., tasks related to a geographical location and time) by paying the workers a certain fee for each completed task. Similar to any other marketplace, a spatial crowdsourcing platform's success relies on the participation of requesters and workers. Consequently, the fee for each task should be high enough for workers to participate in the platform but not too high to discourage the requesters. Furthermore, for spatial crowdsourcing to scale to thousands of workers and tasks, it should be able to efficiently assign tasks to workers, which in turn consists of both matching tasks to workers and computing a schedule for each worker. ❧ In this dissertation, we discuss two key problems in spatial crowdsourcing. Assuming that the goal of a spatial crowdsourcing platform is to ultimately increase its revenue, we first focus on the problem of dynamically pricing tasks in spatial crowdsourcing. Current approaches consider the platform's current supply and demand at the location where the task is posted and price the task accordingly. Instead, we introduce a pricing method that also considers the network's future demand and can increase the platform's revenue while lowering the prices as compared to state-of-the-art algorithms. Next, we consider the task assignment problem in spatial crowdsourcing. As mentioned this consists of both matching tasks to workers and computing a schedule for each worker. The existing approaches for task assignment in spatial crowdsourcing cannot scale as either task matching or task scheduling will become a bottleneck. Instead, we propose an on-line assignment approach utilizing an auction-based framework where workers bid on every arriving task and the server determines the highest bidder, resulting in splitting the assignment responsibility between workers (for scheduling) and the server (for matching) and thus eliminating all bottlenecks. Finally, we apply this auction framework to a commercial ridesharing platform as a real-world application of an SC system. We utilize the framework for maximizing the platform provider's profit while satisfying both the workers' and requesters' monetary constraints