Open AccessParameter Correction of VISSIM Multi-intersection Simulation Model Based on Combined Genetic Algorithm
Author(s) -
Jing Zhang,
Zhang Lin,
Changwei Wang
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/688/3/033018
Subject(s) - vissim , intersection (aeronautics) , genetic algorithm , algorithm , computer science , traffic simulation , line (geometry) , artificial neural network , field (mathematics) , mathematical optimization , mathematics , artificial intelligence , engineering , machine learning , geometry , pure mathematics , aerospace engineering
In the field of traffic simulation research, the requirements of microscopic traffic simulation model accuracy and intersection popularity are getting higher and higher. Aiming at the shortcomings of traditional genetic algorithm, such as slow speed, huge time-consuming and easy to fall into local optimum, a parameter correction algorithm for VISSIM simulation model based on combined genetic algorithm is proposed in this paper. Neural network is used to predict and analyze, and genetic algorithm with partitioning operator is used to correct the parameters, and the error of simulation delay and measured delay is designed as the objective function. Finally, taking three typical continuous intersections of a main line as an example, a microscopic traffic simulation model is established and the parameters are corrected. It is showed by the results that the corrected error is as low as 9.63%, which proves that the proposed method is feasible and robust.