Modelling and Simulating of Traffic Flow Considering the Cognitive Differences

Abstract

Abstract:

The advancement of vehicle infrastructure integration technology could provide the drivers with the current and short-term forecasted traffic state. Considering the current and forecasted traffic density provided by such environment, and the heterogeneous traffic information recognition among the driver group, we develop a new class of kinetics model. The linear stability condition of the model is obtained by applying the linear stability theory. We investigate the impact of model parameters on the performance of the model. Results show that the forecasted time of traffic state, the memory time of traffic information, the weight of optimal current change of memory, and the memory time differences have considerable influence on the stability of traffic flow. The stability of traffic flow can be strengthened by increasing the forecasted time, the memory step-size of optimal current changes, the weight of current change of memory, and the cognitive intensity. However, when the cognitive differences of traffic information are higher, the traffic flow stability would be reduced.

Key words: intelligent transportation, cognitive differences, linear stability, lattice model, forecast effects

摘要：

车路通讯技术的发展为驾驶员提供当前和预测的交通状态信息创造了条件.本文考虑驾驶员的预测性及驾驶员群体对交通信息认知的异质性，建立了新的宏观交通流动力学模型.基于线性稳定性理论，获得了新模型的稳定性条件；通过仿真算例，分析了模型参数对交通流稳定性的影响.结果表明，驾驶员的预测时间、记忆时间、最优记忆流量差权重系数、驾驶员类型比例和记忆时间差异对交通流稳定性存在显著影响；而增大预测时间步长、记忆时间步长、权重系数和认知强度系数都可以有效增强交通流的稳定性；但交通信息认知差异增大却会破坏交通流稳定性.

关键词: 智能交通, 认知差异, 线性稳定, 格子模型, 预测性

CLC Number:

TP11

ZHAI Cong, WU Wei-tiao, LIU Wei-ming, HUANG Ling. Modelling and Simulating of Traffic Flow Considering the Cognitive Differences[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(6): 148-156.

翟聪，巫威眺，刘伟铭，黄玲. 基于认知差异的交通流动力学建模及仿真[J]. 交通运输系统工程与信息, 2018, 18(6): 148-156.

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URL: http://www.tseit.org.cn/EN/abstract/abstract19742.shtml

http://www.tseit.org.cn/EN/Y2018/V18/I6/148

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