异构交通场景下的宏观交通流建模

Macroscopic Traffic Flow Modeling Under Heterogeneous Traffic Condition

摘要:

提出了基于动态图混杂自动机与改进的元胞传输模型相结合的建模方法。通过对不同自动驾驶车辆混入率下路段的流量和密度进行三角基本图拟合，讨论了临界拥堵密度、通行能力、反向波速等主要参数的变化规律，并基于此改进了传统的元胞传输模型；利用动态图混杂自动机表征路网分层递阶的拓扑结构，并将改进的元胞传输模型嵌入动态图混杂自动机来建立异构场景下的宏观交通流模型。利用OpenModelica搭建了仿真平台，验证了该建模方法的有效性。结果表明：随着自动驾驶车辆混入率的增加，路段的临界拥堵密度、最大通行能力和反向波速等都有较为显著的变化。

Abstract:

A modeling method of dynamic graph hybrid automata combined with an improved cell transmission model was proposed. Based on the triangular fundamental diagram fitting results of flow volume and density of road segment under different mixing ratios of automated driving vehicles， the variation rules of critical congestion density， traffic capacity， reverse wave velocity and other main parameters were discussed， and the traditional cell transmission model was improved. The dynamic graph hybrid automata was used to characterize the hierarchical topology of road network， and the improved cell transmission model was embedded into the dynamic graph hybrid automata to establish a macroscopic traffic flow model under heterogeneous traffic condition. Finally， simulation platform was built by using the OpenModelica software to verify the effectiveness of the modeling method. The results show that with the increase of the mixing ratio of automated driving vehicles， the critical congestion density， maximum traffic capacity and reverse wave velocity of road segment all have significant changes.