基于感知能力的微观交通流动力学建模与仿真  

Dynamic Modeling and Simulation for Microscopic Traffic Flow Based on Sensing Capability

作　　者：曹宝贵[1] CAO Bao-gui(Transportation College, Jilin University, Changchun 130022, China)

机构地区：[1]吉林大学交通学院

出　　处：《交通运输系统工程与信息》2019年第5期101-107,113,共8页Journal of Transportation Systems Engineering and Information Technology

基　　金：国家自然科学基金(61104168)~~

摘　　要：基于驾驶员驾驶行为感知能力,提出了一种新的微观交通流动力学模型.通过理论分析和数值模拟,对新模型的性能进行了详细的研究分析.通过理论分析,基于线性稳定性理论,得到了新模型的稳定性条件.通过数值模拟,深入分析了各参数对密度波和迟滞环的影响,进而对交通流稳定性的影响.仿真算例结果表明:驾驶员感知能力对交通流稳定性有显著影响,车头距离变化信息可有效增强交通流的稳定性,对stop-and-go交通拥堵有显著抑制作用,但不可避免的感知缓冲时间会破坏交通稳定性,进而产生严重的stop-and-go交通拥堵;密度波和迟滞环的数值仿真结果与理论分析结果吻合得很好,验证了理论分析结果.Based on the sensing capacity of driver's driving behavior, a kind of new micro-traffic flow dynamics model is proposed in this paper. Through theoretical analysis and numerical simulation, the performance of the new model is studied in detail. Through theoretical analysis and based on linear stability theory, the stability conditions of the new model are obtained. Through numerical simulation, the influence of various parameters on the optimal speed is analyzed in depth, and then the influence on traffic flow stability is analyzed. The simulation results show that driver's sensing capability have a significant impact on traffic flow stability. The intensity of headway change information can effectively enhance the stability of traffic flow and significantly compress stopand- go traffic jams. However, the increase of perception buffer time will inevitably destroy traffic stability, resulting in serious stop- and- go traffic congestion. The numerical results of density waves and hysteresis loops with different parameters are in good agreement with the theoretical results, which verifies the theoretical results.

关 键 词：智能交通 感知能力 线性稳定 交通流模型 迟滞环 

分 类 号：TP11[自动化与计算机技术—控制理论与控制工程]