基于委托-代理理论的出行者激励机制研究

祁小花; QI Xiaohua; 肖海燕; XIAO Haiyan; 吴新林; WU Xinlin

doi:10.48014/jcss.20250909004

基于委托-代理理论的出行者激励机制研究

Research on the Incentive Mechanism for Travelers Based on the Principal-agent Theory

中国统计科学学报 2025年第3卷第3期页码[43-48] 下载全文[0.5MB]

摘要

城市交通系统运行中的突出矛盾体现为路网交通流分布不均衡, 以及出行者所能掌握的实时路况信息不足。在此背景下, 如何有效引导车辆分流, 提高出行者对诱导路径的依从性, 已成为当前交通管理领域的核心挑战。本文基于委托-代理理论框架, 将政府视为委托人, 出行者作为代理人, 研究路径诱导接受率的提升机制。通过建立数学模型分析政府政策对出行者行为的激励效果, 探讨提高诱导接受率的有效途径。研究结果表明, 通过合理的激励机制设计, 可显著提升出行者的路径诱导接受率, 为城市交通管理提供理论依据和实践指导。

Abstract

The prominent contradictions in the operation of urban transportation systems are reflected in the uneven distribution of road network traffic flow and the insufficient real-time road condition information that travelers can grasp. Against this backdrop, effectively guiding vehicle diversion and improving travelers’ compliance with induced routes have become core challenges in the field of traffic management. Based on the principal-agent theory framework, this study treats the government as the principal and travelers as agents to investigate mechanisms for enhancing the acceptance rate of route guidance. A mathematical model is established to analyze the incentive effects of government policies on traveler behavior. and to explore effective approaches for improving route guidance acceptance. The results demonstrate that well-designed incentive mechanisms can significantly increase travelers’ acceptance of route guidance, providing both a theoretical foundation and practical insights for urban traffic management.

DOI

10.48014/jcss.20250909004

文章类型

研究性论文

收稿日期

2025-09-09

接收日期

2025-09-12

出版日期

2025-09-28

关键词

交通工程, 交通流诱导, 委托-代理理论, 诱导接受率, 激励机制

Keywords

Transportation engineering, traffic flow guidance, principal-agent theory, guidance acceptance rate, incentive mechanism

作者

祁小花¹, 肖海燕², 吴新林^2,*

Author

QI Xiaohua¹, XIAO Haiyan², WU Xinlin^2,*

所在单位

1. 湖北第二师范学院教育科学学院, 武汉 430205
2. 湖北第二师范学院数学与统计学院, 武汉 430205

Company

1. School of Educational Science, Hubei University of Education, Wuhan 430205, China
2. School of Mathematics and Statistics, Hubei University of Education, Wuhan 430205, China

浏览量

下载量

基金项目

湖北省高等学校优秀中青年科技创新团队计划项目(T2022034)。

参考文献

[1] 庄焱, 董春娇, 米雪玉, 等. 基于随机参数Logit的中小城市居民出行方式选择建模[J]. 吉林大学学报(工学版), 2024, 54(02): 461-468.
https://doi.org/10.13229/j.cnki.jdxbgxb.20220312.
[2] 梅朵, 袁梦柯, 郑黎黎, 等. 城市区域交通信号控制与交通诱导协同模型研究[J]. 武汉理工大学学报(交通科学与工程版), 2021, 45(05): 828-833.
https://doi.org/10.3963/j.issn.2095-3844.2021.05.004.
[3] 张立立, 王力, 张玲玉. 城市道路交通控制概述与展望[J]. 科学技术与工程, 2020, 20(16): 6322-6329.
https://doi.org/10.3969/j.issn.1671-1815.2020.16.002.
[4] 龙琼, 胡列格, 张谨帆, 等. 不确定性环境下个性化路径选择的多属性决策方法[J]. 中国公路学报, 2014, 27(9): 105-110.
https://doi.org/10.19721/j.cnki.1001-7372.2014.09.014.
[5] 马东超, 武涛, 王晓亮, 等. 个体开销受限的交通流优化分配方法[J]. 中国公路学报, 2016, 29(7): 134-142.
https://doi.org/10.19721/j.cnki.1001-7372.2016.07.017.
[6] 赵丹, 邵春福, 王军利, 等. 多方式诱导下通勤出行链交通方式组合选择行为模型[J]. 吉林大学学报(工学版), 2015, 45(6): 1763-1770.
https://doi.org/10.13229/j.cnki.jdxbgxb201506005.
[7] 凃强, 程琳, 林芬, 等. 考虑出行者风险态度的最优路径搜索[J]. 吉林大学学报(工学版). 2019, 49(03): 720-726.
https://doi.org/10.13229/j.cnki.jdxbgxb20171143.
[8] Fonzone A, Schmöcker J D, Ma Jiang-shan, et al. Linkbased route choice considering risk aversion, disappointment, and regret[J]. Transportation Research Record, 2018, 1(2322): 119-128.
https://doi.org/10.3141/2322-13.
[9] Papinski D, Scott D M. A GIS-based toolkit for routechoice analysis[J]. Journal of Transport Geography, 2011, 19(3): 434-442.
https://doi.org/10.1016/j.jtrangeo.2010.09.009.
[10] 龚, 李苏剑, 刘启生. 驾驶员分类的路径诱导系统及评价指标[J]. 武汉理工大学学报, 2013, 35(1): 75-81.
https://doi.org/10.3963/j.issn.1671-4431.2013.01.015.
[11] 肖海燕. 出行者向诱导转移的委托代理模型研究[J]. 工程数学学报. 2019, 36(03): 256-264.
https://doi.org/10.3969/j.issn.1005-3085.2019.03.002.

引用本文

祁小花, 肖海燕, 吴新林. 基于委托-代理理论的出行者激励机制研究[J]. 中国统计科学学报, 2025, 3(3): 43-48.

Citation

QI Xiaohua, XIAO Haiyan, WU Xinlin. Research on the incentive mechanism for travelers based on the principal-agent theory[J]. Journal of Chinese Statistical Sciences, 2025, 3(3): 43-48.