基于SOGA的VISSIM仿真模型参数标定方法
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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.
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.
引文
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[2]王殿海,陶鹏飞,金盛,等.跟驰模型参数标定及验证方法[J].吉林大学学报(工学版).2011,41(1):59-65[WANG D H,TAO P F,JIN S,et al.Method o calibrating and validating car-following model[J]Journal of Jilin University(Engineering and Technolog Edition),2011,41(1):59-65.]
[3]LI J,ZUYLEN H V,CHEN Y,et al.Calibration of microscopic simulation model for emission calculation[J].Transportaiton Research Part C,2013(31)172-184.
[4]CHEU R L,JIN X,SRINIVASA D,et al.Calibration o FRESIM for Singapore expressway using geneti algorithm[J].Journal of Transportation Engineering1998,124(6):526-535.
[5]PARK B,H.Development and evaluation mode calibration procedure[J].Transportation Research Record:Journal of Transportation Research Board2005:208-217.
[6]KIM S J,KIM W,RILETT L.Calibration of microsimulation models using non-parametric statistica techniques[J].Transportation Research Record:Journa of Transportation Research Board,2005:111-119.
[7]吴伟,时柏营,谢军.面向交通控制的实时在线仿真参数标定[J].同济大学学报(自然科学版),2011,39(6):842-847.[WU W,SHI B Y,XIE J.Paramete calibration in real-time traffic control micr simulation[J].Journal of Tongji University(Natura Science),2011,39(6):842-847.]
[8]章玉,于雷,赵娜乐,等.SPSA算法在微观交通仿真模型VISSIM参数标定中的应用[J].交通运输系统工程与信息,2010,10(4):44-49.[ZHANG Y,YU L,ZHAON L,et al.Application of simulation perturbation stochastic approximation algorithm in paramete calibration of VISSIM microscope simulation model[J]Journal of Transportation Systems Engineering and Information Technology,2010,10(4):44-49.]
[9]于泉,王萌,邓小惠.基于正交试验法的单个信号交叉口仿真参数标定[J].交通科技,2012,29(S1):57-64[YU Q,WANG M,DENG X H.Simulation paramete calibration of single signalized intersection based on orthogonal experiment method[J].Transportation Science&Technology,2012,29(S1):57-64.]
[10]OTKOVI?I I,TOLLAZZI T,?RAML M.Calibration o microsimulation traffic model using neural network approach[J].Expert Systems with Application,2013(40)5965-5974.
[11]LEUNG Y W,WANG Y.An orthogonal genetic algorithm with quantization for global numerical optimization[J]IEEE Transactions on Evolutionary Computation,20015(1):41-53.
[12]江中央,蔡自兴,王勇.求解全局优化问题的混合自适应正交遗传算法[J].软件学报,2010,21(6):1296-1307.[JIANG Z Y,CAI Z X,WANG Y.Hybrid selfadaptive orthogonal genetic algorithm for solving globa optimization problems[J].Journal of Software,2010,21(6):1296-1307.]