基于宏微观耦合模型的城市道路交通流在线估计

发布时间：2018-07-12 15:00

本文选题：城市交通 + 宏微观耦合模型　；参考：《交通运输系统工程与信息》2014年06期

【摘要】：实时可靠的交通流估计是城市交通管理与控制的基础.宏观的MCTM模型不能获取引道路段的微观信息,微观的Paramics仿真则需路网OD的准确估计,为避开单一模型使用的缺陷,本文提出建立宏微观耦合模型.在模型估计的单位间隔内,先利用MCTM估计基本元胞有效密度和引道元胞初步密度,并在接口处计算仿真发车数量;再转用Paramics进行引道微观仿真,利用仿真检测数据计算交叉口排队长度和引道元胞有效密度,取代初步密度,作为下一个间隔计算的初始输入,实现交通流的在线估计.仿真中,为符合转向需求实时变化特性,建立基于约束卡尔曼滤波的转向需求估计模型,实时更新单位间隔的转向需求.实例分析结果表明,宏微观耦合模型满足城市道路交通流在线估计要求.
[Abstract]:Real-time and reliable traffic flow estimation is the basis of urban traffic management and control. The macroscopic MCTM model can not obtain the micro information of the leading road section, while the microscopic Paramics simulation needs the accurate estimation of road network OD. In order to avoid the defects of the single model, a macro and micro coupling model is proposed in this paper. In the unit interval estimated by the model, MCTM is used to estimate the basic cell effective density and the initial cell density, and the number of simulated trains is calculated at the interface, and then Paramics is used for the micro-simulation of the approach path. The queue length and cell effective density of intersection are calculated by simulation data instead of the initial density which is used as the initial input of the next interval calculation to realize the on-line estimation of traffic flow. In order to meet the real-time change characteristics of steering requirements, a steering requirement estimation model based on constrained Kalman filter is established to update the steering requirements per unit interval in real time. The results of case study show that the macro and micro coupling model can meet the requirements of online traffic flow estimation on urban roads.
【作者单位】：中山大学东莞研究院智能交通工程中心;中山大学工学院智能交通研究中心;
【基金】：东莞市智能交通信息处理及服务关键技术与应用示范(2012B010900012) 新能源汽车应用示范监控平台研发和东莞交通信息服务示范(201201B3-0400) 广州市科技计划项目资助(2011J2200092)
【分类号】：U491.112

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