RESEARCH ARTICLE
A Parameters Calibration Method in Simulated Complex Traffic Network
Hu Xinghua*, 1, 2, Zhang Yu2
Article Information
Identifiers and Pagination:
Year: 2015Volume: 9
First Page: 262
Last Page: 265
Publisher ID: TOBCTJ-9-262
DOI: 10.2174/1874836801509010262
Article History:
Received Date: 26/5/2015Revision Received Date: 14/7/2015
Acceptance Date: 10/8/2015
Electronic publication date: 27/10/2015
Collection year: 2015
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.
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.