RESEARCH ARTICLE


A Parameters Calibration Method in Simulated Complex Traffic Network



Hu Xinghua*, 1, 2, Zhang Yu2
1 School of Traffic and Transportation, Beijing Jiaotong University, Beijing 10044, China
2 Chongqing Communications Planning Survey and Design Institute, Chongqing 401121, China


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Creative Commons License
© 2015 Xinghua and Yu;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Management, Harbin Institute of Technology, Harbin, 150001, China; Tel: +86-23-88913869; E-mail: bjtufox@163.com


Abstract

Traffic simulation models have been extensively used because of their ability to model the dynamic stochastic nature of transportation systems. Parameter calibration is very complex and does not give optimal results easily. Besides, it is also time-consuming especially for large and complex networks. Initially, the procedure of traffic micro-simulation parameter calibration was put forward. A Vehicle Intelligent Simulation Software Model (VISSIM) models were selected for parameter calibration in complex-network, and, the role of Simultaneous Perturbation Genetic Algorithm (SPGA) was examined in the optimization of component. Moreover an automatic calibration methodology for micro-simulation models was developed in order to select the best parameter set based on the observed Intelligent Transportation Systems (ITS) data which proved effective for different networks. Finally, the methodology was applied to calibrate the Beijing city VISSIM models, followed by the comparison of convergence rate of Genetic Algorithm (GA), Simultaneous Perturbation Stochastic Approximation (SPSA) and SPGA algorithm. The results show that the SPGA was effective and had good performance.

Keywords: Complex network simulation, parameter calibration, stochastic approximation genetic algorithm, traffic engineering.