智能网联公交信号优先的博弈机理和引导策略

doi:10.16097/j.cnki.1009-6744.2025.02.009

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (2): 95-107.DOI: 10.16097/j.cnki.1009-6744.2025.02.009

• 智能交通系统与信息技术 • 上一篇下一篇

智能网联公交信号优先的博弈机理和引导策略

魏丽英^*1，冯梅^1,2，吴润泽¹

1. 北京交通大学，交通运输学院，北京100044；2.北京市首都规划设计工程咨询开发有限公司，北京100031

收稿日期:2024-06-05 修回日期:2024-09-04 接受日期:2025-03-13 出版日期:2025-04-25 发布日期:2025-04-19
作者简介:魏丽英(1974—)，女，黑龙江七台河人，教授，博士。
基金资助:
国家自然科学基金(52472311)；城市公共交通智能化交通运输行业重点实验室开放课题(2023-APTS-02)；中央引导地方科技发展资金项目(自由探索类基础研究)(236Z0802G)。

Game Mechanism and Guiding Strategy of Intelligent Connected Transit Signal Priority

WEI Liying^*1,FENG Mei^1,2,WU Runze¹

1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; 2. Capital Urban Planning & Design Consulting Development Co Ltd, Beijing 100031, China

Received:2024-06-05 Revised:2024-09-04 Accepted:2025-03-13 Online:2025-04-25 Published:2025-04-19
Supported by:
National Natural Science Foundation of China(52472311)；Open Foundation of Key Laboratory of Advanced Public Transportation Science, Ministry of Transport, PRC (2023-APTS-02)；Foundation of Central Guidance for Local Development on Science and Technology(Fundamental Research on Free Exploration) (236Z0802G)。

摘要/Abstract

摘要： 智能网联技术的不断发展为实现公交信号优先提供了技术支持，也将助力智能网联公交向“精准公交”“安全公交”方向发展。本文从不同相位间的冲突博弈关系出发，构建智能网联环境下基于斗鸡博弈的公交信号优先引导策略。首先，利用斗鸡博弈，分析以公交优先相位与非优先相位作为博弈双方的博弈行为，建立以加权延误为收益矩阵的博弈模型；其次，考虑优先公交车的准时性、最小绿灯时长限制、优先相位及非优先相位延误等因素，结合建立的博弈模型，采取主动优先与车速引导相结合的方法，提出智能网联环境下公交优先引导策略及优化流程；最后，利用SUMO(SimulationofUrbanMobility)和采集的交叉口数据对提出的优先引导策略进行仿真。结果表明：与初始配时相比，本文提出的公交信号优先策略可有效提高公交优先相位的通行效益，减少对非优先相位的负面影响；50%渗透率条件下，对比未实施策略，20%的优先公交车准点情况优化显著，平均排队长度、平均停车次数、延误等通行效益指标至少降低33.27%，油耗及CO2排放至少降低12.20%；非优先相位各指标的劣化程度均低于8%。

关键词: 智能交通, 公交信号优先, 博弈论, 信号交叉口, 智能网联环境

Abstract: The ongoing development of intelligent connected technology provides crucial support for achieving transit signal priority (TSP) and assisting the development of the intelligent connected public transit towards "precision public transit" and "safe public transit". This paper starts from the conflict game relationship between different phases, and constructs a TSP guiding strategy based on chicken game in the intelligent connected environment. Firstly, the chicken game theory is used to analyze the game behavior of priority and non-priority phases of public transit, establishing a game model with weighted delay as the benefit matrix. Then, adopting the active priority and speed guidance, a TSP guiding strategy and optimization process based on the proposed game model is proposed by considering factors such as punctuality, limitation of minimum green time, priority and non priority phase delay of priority transit. Finally, to validate the strategy, a case study is conducted using an actual intersection in Beijing, employing SUMO for simulation. The results show that the TSP guiding strategy can effectively improve the traffic efficiency of priority phases and reduce the negative impact on non-priority phases compared to the initial timing; under the condition of 50% penetration rate, compared to the implementation of strategy, 20% of priority buses have been optimized significantly for punctuality, and the traffic efficiency indicators such as average queue length, average parking times and delay are reduced by at least 33.27%. Additionally, fuel consumption and CO2 emissions are reduced by at least 12.20%, and the negative influence of non-priority phase indicators is less than 8%.

Key words: intelligent transportation, transit signal priority, game theory, signalized intersection, intelligent connected environment

中图分类号:

U491

魏丽英, 冯梅, 吴润泽. 智能网联公交信号优先的博弈机理和引导策略[J]. 交通运输系统工程与信息, 2025, 25(2): 95-107.

WEI Liying, FENG Mei, WU Runze. Game Mechanism and Guiding Strategy of Intelligent Connected Transit Signal Priority[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 95-107.

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智能网联公交信号优先的博弈机理和引导策略

Game Mechanism and Guiding Strategy of Intelligent Connected Transit Signal Priority

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