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[1] Li Manman, , Lu Jian, et al. Network traffic flow evolution with battery electric vehiclesand conventional gasoline vehicles [J]. Journal of Southeast University (English Edition), 2019, 35 (2): 213-219. [doi:10.3969/j.issn.1003-7985.2019.02.011]
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Network traffic flow evolution with battery electric vehiclesand conventional gasoline vehicles()
混合纯电动汽车与传统汽油车的网络交通流演化
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
35
Issue:
2019 2
Page:
213-219
Research Field:
Traffic and Transportation Engineering
Publishing date:
2019-06-30

Info

Title:
Network traffic flow evolution with battery electric vehiclesand conventional gasoline vehicles
混合纯电动汽车与传统汽油车的网络交通流演化
Author(s):
Li Manman1, 2, 3, Lu Jian1, 2, 3 , Sun Jiahui4, Tu Qiang3
1Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China
2Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, China
3School of Transportation, Southeast University, Nanjing 210096, China
4Xi’an Aerospace Power Test Technology Institute, Xi’an 710100, China
李嫚嫚1, 2, 3 , 陆建1, 2, 3, 孙加辉4, 涂强3
1东南大学江苏省城市智能交通重点实验室, 南京 210096; 2东南大学现代城市交通技术协同创新中心, 南京 210096; 3东南大学交通学院, 南京 210096; 4西安航天动力试验技术研究所, 西安 710100
Keywords:
battery electric vehicles;  constrained path;  reinforcement learning;  bounded rationality;  traffic dynamics
纯电动汽车;  约束路径;  强化学习;  有限理性;  交通流动态
PACS:
U121
DOI:
10.3969/j.issn.1003-7985.2019.02.011
Abstract:
In order to investigate the effect of the use of battery electric vehicles on traffic dynamics, the valid paths of electric battery vehicles are defined and a check-based method is proposed to obtain them. Then, assuming that travelers only focus on their past travel experience, a day-to-day traffic assignment model is established based on reinforcement learning and bounded rationality. In the proposed model, the Bush-Mosteller model, a reinforcement learning model, is modified to calculate path choice probability according to bounded rationality. The modified model updates the path choice probability only if the gap between expected travel time and perceived travel time is beyond the cognitive threshold. Numerical experiments validate the effectiveness of the model and show that traffic flows can converge to the equilibrium in any case of cognitive thresholds and penetration rates of battery electric vehicles. The cognitive threshold has a positive influence on the variation of traffic flows while it has a negative influence on the differences between traffic flows. The adaptation of battery electric vehicles leads to the poor performance of the traffic system.
为了研究纯电动汽车使用对交通流演化特性的影响, 定义了纯电动汽车的有效路径, 提出了一种获得纯电动汽车有效路径的检查依据方法.假设出行者仅能获得出行经验信息, 依据强化学习和有限理性理论建立了出行者逐日路径选择模型.在所提出的模型中, 依据有限理性理论改进了Bush-Mosteller强化学习模型计算路径选择概率.改进的模型只在出行者的期望出行时间与认知出行时间差异高于认知阈值时, 才更新路径的选择概率.数值实验证实了模型的有效性, 表明交通流不受出行者认知阈值和纯电动车渗透率的影响总能汇聚到均衡状态;交通流的波动与出行者认知阈值成正相关;交通流差异与出行者认知阈值成负相关;纯电动汽车的使用会降低交通系统的效率.

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Memo

Memo:
Biographies: Li Manman(1991—), female, Ph.D. candidate; Lu Jian(corresponding author), male, doctor, professor, lujian_1972@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.51478110), Postgraduate Research & Practice Innovation Program of Jiangsu Province(No.KYCX18_0139).
Citation: Li Manman, Lu Jian, Sun Jiahui, et al. Network traffic flow evolution with battery electric vehicles and conventional gasoline vehicles[J].Journal of Southeast University(English Edition), 2019, 35(2):213-219.DOI:10.3969/j.issn.1003-7985.2019.02.011.
Last Update: 2019-06-20