摘要Model calibration ensures the reliability of traffic simulation models for designing and evaluating traffic management schemes. This paper introduces a metamodel-based method for calibrating a microscopic simulation model developed with Vissim. Initially, a Vissim simulation was devised for an expressway merging zone. A parameter sensitivity analysis was conducted using a backpropagation neural network (BPNN). Several key parameters of the Vissim model were identified based on sensitivity indices. A metamodel called the Kriging model was created to show how Vissim parameter inputs affect simulation outputs. Based on the metamodel, the differential evolution (DE) algorithm was applied to find the best parameters for the VISSIM mode. The average travel time simulation of the calibrated Vissim model deviated from field survey data by less than 3%. The statistical analyses affirm the efficacy of the Kriging-DE calibration method. The metamodel-based method offers a promising tool for model calibration in traffic management.
Abstract：Model calibration ensures the reliability of traffic simulation models for designing and evaluating traffic management schemes. This paper introduces a metamodel-based method for calibrating a microscopic simulation model developed with Vissim. Initially, a Vissim simulation was devised for an expressway merging zone. A parameter sensitivity analysis was conducted using a backpropagation neural network (BPNN). Several key parameters of the Vissim model were identified based on sensitivity indices. A metamodel called the Kriging model was created to show how Vissim parameter inputs affect simulation outputs. Based on the metamodel, the differential evolution (DE) algorithm was applied to find the best parameters for the VISSIM mode. The average travel time simulation of the calibrated Vissim model deviated from field survey data by less than 3%. The statistical analyses affirm the efficacy of the Kriging-DE calibration method. The metamodel-based method offers a promising tool for model calibration in traffic management.
基金资助:Supported by the Henan Provincial Department of Transportation (No. 2020G11); the Department of Transportation of Hunan Province (No. 202322)
通讯作者:
XIE Hai-yang, LI Jie
引用本文:
谢海洋, 秦晨洋, 支鹏, 董亚冰, 李洁. Metamodel-based Calibration of Microscopic Traffic Simulation: A Vissim Case Study[J]. Journal of Highway and Transportation Research and Development, 2023, 17(4): 49-58.
XIE Hai-yang, QIN Chen-yang, ZHI Peng, DONG Ya-bing, LI Jie. Metamodel-based Calibration of Microscopic Traffic Simulation: A Vissim Case Study. Journal of Highway and Transportation Research and Development, 2023, 17(4): 49-58.
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