基于SOGA的VISSIM仿真模型参数标定方法  

VISSIM Model Calibration Based on SOGA

作　　者：杨艳芳[1] 秦勇[2] 努尔兰.木汉 

机构地区：[1]北京交通大学交通运输学院,北京100044 [2]北京交通大学轨道交通控制与安全国家重点实验室,北京100044 [3]北京交通大学城市交通信息智能感知与服务工程技术研究中心,北京100044

出　　处：《交通运输系统工程与信息》2017年第3期91-97,共7页Journal of Transportation Systems Engineering and Information Technology

基　　金：国家科技支撑计划课题(2014BAG01B02)~~

摘　　要：微观交通仿真模型是对交通系统进行管理、控制和优化的重要试验手段和工具,而微观交通模型的参数标定是确保微观交通仿真模型能真实、直观地反映交通流运行情况的必要前提.针对遗传算法(GA)的不足,提出了基于自适应正交遗传算法(SOGA)的微观交通仿真模型参数标定方法.选取应用较为广泛的VISSIM仿真模型作为基础平台,给出了该优化方法中染色体的编码解码、适应度函数和自适应正交交叉算子的详细设计.最后将算法应用到北京市荣华中路与荣京西街交叉口模型参数标定中,通过与GA算法、正交试验法对比,SOGA算法得到的适应度函数值为19.43,优于其他标定算法的适应度函数值;同时,SOGA算法迭代时间比GA算法少了40.5%,验证了SOGA算法在VISSIM参数标定上的优越性.Traffic flow simulation models have become one major tool in evaluating both traffic operation and transportation planning application, with the progress of simulation technologies. In this paper, a microscope simulation parameter calibration method based on self-adaptive orthogonal algorithm（SOGA） is presented. The widely used VISSIM model is selected as the basic platform for the parameter calibration.The questions about how to encoding and decoding chromosomes and how to design the fitness function and the self-adaptive orthogonal crossover are answered in this paper. Finally, the proposed method is applied to the intersection model of the Ronghua mid-road and the Rongjing west street in Beijing, China. Through comparing with the GA and the orthogonal experiment method, the fitness value of SOGA is 19.43, which is better than other calibration algorithms, and the convergence time of SOGA is 40.5% less than the calibration method using GA algorithm. The advantage of the proposed method is shown.

关 键 词：智能交通 微观交通仿真 参数标定 自适应正交遗传算法 VISSIM 

分 类 号：U491.1[交通运输工程—交通运输规划与管理]