预防交通事故安全配送的激励设计与任务分配

Takashi Nishino, Sin Syo, Yuka Yamanari, Masaru Miyao, Peng Liu, Hisashi Hayashi
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引用次数: 0

摘要

在全球范围内不断扩大的在线外卖领域,外卖过程中日益增多的交通事故正在成为一个问题。为了确保快递员和居民的安全,有必要了解快递员行为选择的激励机制。虽然许多现有的共享平台通过高峰/非高峰附加费等激励措施将员工吸引到在线劳动过程中,但员工们试图在有限的时间内完成更多的目标。在本研究中,为了防止配送过程中的超速,我们使用多智能体仿真对任务分配方法和激励方案进行了比较和评估。我们基于强化学习,在考虑快递员的利润和速度的情况下,对快递员的理性行为选择建模。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incentive Design and Task Allocation for Safe Delivery to Prevent Traffic Accidents
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.
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