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基于流量速度图对Wiedemann74模型参数标定的宏观方法进行了研究,建立了集成VISSIM、Matlab、ExcelVBA的参数标定平台,以最小化流量速度图的实测值与仿真值的差异为优化目标,应用图像识别方法判别图像的差异性,利用遗传算法优化参数值,实现了参数自动寻优的迭代过程。建立的参数标定平台能够利用流量、速度等宏观运行数据标定驾驶行为阈值参数,为利用检测器数据实现自动化标定提供了有效手段,为分析驾驶行为特点提供了方法,解决了VISSIM软件中默认参数不适合我国交通状况导致仿真精度不高的问题。利用路侧激光检测器采集长沙市南二环路断面交通数据,根据标定后的参数、实测数据对Wiedemann模型的驾驶行为阈值曲线进行了拟合,根据驾驶行为分区对长沙市南二环路的驾驶行为进行了分析。
The macroscopic method to calibrate the Wiedemann74 model parameters is studied based on the flow rate map. A parameter calibration platform integrating VISSIM, Matlab and ExcelVBA is established. The objective of this paper is to minimize the difference between the measured value and the simulated value of the flow rate graph. The method discriminates the difference of the images, and optimizes the parameter values by using genetic algorithm to realize the iterative process of automatic optimization of parameters. The established parameter calibration platform can calibrate the driving behavior threshold parameters by using the macro operation data such as flow rate and speed, provide an effective means for automatic calibration with the detector data, and provide a method for analyzing the driving behavior characteristics, and solve the problem that the default parameters in the VISSIM software Suitable for traffic conditions in China led to the problem of simulation accuracy is not high. Based on the calibrated parameters and measured data, the Wiedemann model’s driving behavior threshold curve was fitted by using the roadside laser detector. According to the driving behavior, the data of the second-ring road in Changsha Driving behavior was analyzed.