Incentive Design and Task Allocation for Safe Delivery to Prevent Traffic Accidents

Abstract

In the field of online food delivery, which is expanding worldwide, the increasing number of traffic accidents during delivery is becoming problematic. To ensure the safety of delivery workers and residents, it is necessary to understand the incentives of workers for behavior choices. While many existing sharing platforms pull workers into the online labor process by incentives like on-peak/off-peak surcharges, workers try accomplishing more than their goals within a limited time. In this study, to prevent speeding during delivery, we compared and evaluated task allocation methods and incentive schemes using multi-agent simulation. We model workers' rational choices of behaviors based on reinforcement learning, considering the profit and speeding of delivery workers.