考虑驾驶员个性的博弈协商机制与出行路径选择研究

刘捷; 王曈; 孙恒飞; 沐波

doi:10.16503/j.cnki.2095-9931.2023.01.009

交通运输研究 ›› 2023, Vol. 9 ›› Issue (1) : 86-95. DOI: 10.16503/j.cnki.2095-9931.2023.01.009

考虑驾驶员个性的博弈协商机制与出行路径选择研究

刘捷 ¹ ,
王曈 ²^,³ ,
孙恒飞 ²^,³ ,
沐波 ²^,³

作者信息 +

Game Negotiation Mechanism and Travel Route Choice Considering Driver′s Personality

LIU Jie ¹ ,
WANG Tong ²^,³ ,
SUN Heng-fei ²^,³ ,
MU Bo ²^,³

Author information +

文章历史 +

摘要

为解决网联环境下驾驶员个性化和系统最优之间的冲突而导致的交通拥堵问题，采用博弈论、效用理论和多智能体建模技术，对驾驶员、信息发布单元以及路网管理者建立多智能体仿真模型，并提出了一种路网管理者与驾驶员之间的博弈协商机制。为了验证该方法的有效性，基于Net-Logo多智能体仿真软件，搭建了网联交通系统仿真平台，对不同路网饱和度和不同类型驾驶员比例下的出行路径信息服务策略进行仿真。仿真结果表明：所有实验的博弈协商成功率均达到90%以上，路网管理者的系统最优策略与驾驶员的个性化策略得到充分结合；当路网饱和度为1、个性化驾驶员比例为20%时，博弈协商成功率达到98%，博弈协商机制效果最明显。

Abstract

To solve the problem of traffic congestion caused by the conflict between driver personalization and system optimization in a connected environment, a multi-agent simulation model was established for drivers, information release units, and road network managers by using game theory, utility theory, and multi-agent modeling technology. Then a game negotiation mechanism between road network managers and drivers was proposed. To verify the effectiveness of the proposed method, a connected traffic system simulation platform was built based on Net-Logo multi-agent simulation software, and the travel routing information service strategies under different road network saturation and different types of driver proportions were simulated and verified. The simulation results show that the success rate of game negotiation in all experiments is more than 90%, the network manager′s system optimal strategy is fully combined with the driver′s personalized strategy. The success rate of game negotiation reaches 98% when the road network saturation is 1 and the proportion of personalized drivers is 20%, and the effect of the game negotiation mechanism is the most obvious.

导出引用

刘捷, 王曈, 孙恒飞, 等. 考虑驾驶员个性的博弈协商机制与出行路径选择研究[J]. 交通运输研究. 2023, 9(1): 86-95 https://doi.org/10.16503/j.cnki.2095-9931.2023.01.009

LIU Jie, WANG Tong, SUN Heng-fei, et al. Game Negotiation Mechanism and Travel Route Choice Considering Driver′s Personality[J]. Transport Research. 2023, 9(1): 86-95 https://doi.org/10.16503/j.cnki.2095-9931.2023.01.009

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[1]	吕丹丹. 驾驶员路径选择行为建模及计算[D]. 青岛: 山东理工大学, 2012.

[2]	DU L L, HAN L S, CHEN S W. Coordinated online in-vehicle routing balancing user optimality and system optimality through information perturbation[J]. Transportation Research Part B: Methodological, 2015, 79: 121-133.

[3]	LI M, HUANG H J. A regret theory-based route choice model[J]. Transportmetrica A: Transport Science, 2017, 13: 250-272.

[4]	RAMOS G D M, DAAMEN W, HOOGENDOORN S. Expected utility theory, prospect theory, and regret theory compared for prediction of route choice behavior[J]. Transportation Research Record: Journal of the Transportation Research Board, 2011, 2230(1):19-28.

[5]	SAXENA N, WANG R, DIXIT V V, et al. Frequentist and bayesian approaches for understanding route choice of drivers under stop-and-go traffic[J]. Transportation Research Record: Journal of the Transportation Research Board, 2020, 2674(2): 371-382.

[6]	WANG Z, YANG H, NI L. The effect of regret-based risky route choice on the traffic equilibrium for emergency evacuation[J]. Journal of Advanced Transportation, 2020, 2020: 8858302.1-8858302.8.

[7]	XU T D, HAO Y. Real-time traffic state predictor based on dynamic traveler behavior[J]. Transport, 2021, 4: 1-32.

[8]	CHEN C, LIU X M, QIU T, et al. A short-term traffic prediction model in the vehicular cyber-physical systems[J]. Future Generation Computer Systems, 2020, 105: 894-903.

[9]	YUN M P, QIN W W, YANG X G, et al. Estimation of urban route travel time distribution using Markov chains and pair-copula construction[J]. Transportmetrica B: Transport Dynamics, 2019, 7(1): 1521-1552.

[10]	LUCA S D, PACE R D, MEMOLI S, et al. Sustainable traffic management in an urban area: an integrated framework for real-time traffic control and route guidance design[J]. Sustainability, 2020, 12: 726.

[11]	RAGAVAN K, VENKATALAKSHMI K, VIJAYALAKSHMI K. Traffic video-based intelligent traffic control system for smart cities using modified ant colony optimizer[J]. Computational Intelligence, 2020, 37: 538-558.

[12]	FAN Y Q. Research on the development of a traffic signal control model based on route travel time equilibrium[J]. IOP Conference Series: Earth and Environmental Science, 2020, 587(1): 012045.

[13]	DIA H. An agent-based approach to modeling driver route choice behavior under the influence of real-time information[J]. Transportation Research Part C: Emerging Technologies, 2002, 10: 331-349.

[14]	DAI R J, LU Y R, DING C, et al. A simulation-based approach to investigate the driver route choice behavior under the connected vehicle environment[J]. Transportation Research Part F: Psychology and Behaviour, 2019, 65(C): 548-563.

[15]	SHEN J J, YANG G C. Integrated empirical analysis of the effect of variable message sign on driver route choice behavior[J]. Journal of Transportation Engineering Part A: Systems, 2020, 146(2): 4019063.1-4019063.9.

[16]	YU Y, HAN K, OCHIENG W. Day-to-day dynamic traffic assignment with imperfect information, bounded rationality and information sharing[J]. Transportation Research Part C: Emerging Technologies, 2020, 114: 59-83.

[17]	ADLER J L. A cooperative multi-agent transportation management and route guidance system[J]. Transportation Research Part C: Emerging Techno-logies, 2002, 10: 433-454.

[18]	ADLER J L, SATAPATHY G, MANIKONDA V, et al. A multi-agent approach to cooperative traffic management and route guidance[J]. Transportation Research Part B: Methodological, 2005, 39(4): 297-318.

[19]	陈秀锋, 陈伟. 诱导信息条件下驾驶路径选择行为分析[J]. 现代电子技术, 2022, 45(1): 132-135.

[20]	李建敏. 驾驶员路径选择行为的演化博弈研究[D]. 大连: 大连海事大学, 2019.

[21]	孙笑宇. 大数据环境下车联网个性化信息服务模式研究[D]. 长春: 吉林大学, 2016.

[22]	马力, 施树明. 基于车联网的驾驶博弈行为仿真[C]// 2016中国汽车工程学会年会论文集. 上海: 机械工业出版社, 2016:1989-1994.

[23]	安实, 崔娜, 于航. 基于Multi-agent协商的出行信息个性化服务策略[J]. 西南交通大学学报, 2010, 45(4):627-634.

[24]	崔娜. 基于多智能体协商的驾驶员路径选择行为仿真研究[D]. 哈尔滨: 哈尔滨工业大学, 2007.

[25]	LI K, WANG S, CONG R. Game dynamics of route choice in heterogenous population[J]. Physics Letters A, 2022, 421: 127775.

[26]	赵春晓, 魏楚元. 多智能体系统建模、仿真及应用[M]. 北京: 中国水利水电出版社, 2021:1-9.

[27]	任福田, 刘小明, 荣建, 等. 交通工程学[M]. 北京: 人民交通出版社, 2008: 113-119.

[28]	HERMAN R, MONTROLL E W, POTTS R B, et al. Traffic dynamics: analysis of stability in car following[J]. Operations Research, 1959, 7(1): 1-139.

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收稿日期	出版日期
2022-07-18	2023-02-25
发布日期
2023-03-08

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