基于三方演化博弈的外卖骑手违规驾驶协同治理研究
On Collaborative Governance of Illegal Driving by Delivery Riders Based on Tripartite Evolutionary Game
耿纪超 1李雪 2杨力1
作者信息
- 1. 安徽理工大学 经济与管理学院,安徽 淮南 232001;智能决策博弈与数字经济创新 安徽省哲学社会科学重点实验室,安徽 合肥 230026
- 2. 安徽理工大学 经济与管理学院,安徽 淮南 232001
- 折叠
摘要
近年来,外卖已成为了大城市年轻人首选的用餐方式.由于消费者对快速送达的需求、平台企业对订单准时率的追求,外卖骑手不得不加快自己的送餐速度,导致违规驾驶,交通事故屡屡发生.为了科学、系统治理外卖人员的违规驾驶问题,本文设计平台监管惩罚、商家规范出餐、骑手规范驾驶的合作机制,构建外卖平台、商家、骑手之间的三方演化博弈模型,分析三方主体策略选择和博弈系统均衡点的稳定性,并结合数值仿真方法考察不同参数取值对三方主体演化稳定策略的影响.最后,以企业、商家、骑手三方协同治理为抓手,针对配送机制、商家环境和政府治理等方面提出相关对策建议,形成利益三方共享,矛盾问题三方联调的良性互动.
Abstract
In recent years,take-out has become the preferred dining method for young people in big cities.Due to consumers'demand for fast delivery and platform companies'pursuit of on-time order rates,take-out riders have to speed up their food delivery speed,leading to illegal driving and frequent traffic accidents.In order to scientifically and systematically manage the illegal driving problem of delivery personnel,this paper designs a cooperation mechanism of platform supervision and punishment,business regulation of food delivery,and rider regulation of driving,builds a tripartite evolutionary game model between delivery platform,business and rider,and analyzes the strategic choice of the tripartite main body and the stability of the equilibrium point of the game system.Combined with the numerical simulation method,the effects of different parameter values on the evolutionary stability strategy of the tripartite agents are investigated.Finally,taking the collaborative governance of enterprises,merchants and riders as the starting point,relevant countermeasures and suggestions are put forward for the distribution mechanism,business environment and government governance,etc.,so as to form a benign interaction of tripartite sharing of interests and tripartite joint coordination of contradictory problems.
关键词
外卖配送/骑手安全/三方演化博弈/协同治理Key words
take-out delivery/rider safety/tripartite evolutionary game引用本文复制引用
基金项目
安徽省高校优秀青年科研项目(2022AH030082)
国家自然科学基金项目(72104001)
出版年
2024
NETL
NSTL
万方数据