Genetic algorithm-based combinatorial parametric optimization for the calibration of microscopic traffic simulation models

Author

Ma, Tao ; Abdulhai, Baher

Author_Institution

Dept. of Civil Eng., Toronto Univ., Downsview, Ont., Canada

fYear

2001

fDate

2001

Firstpage

848

Lastpage

853

Abstract

We introduce GENOSIM, genetic optimizer for traffic micro-simulation models. GENOSIM is developed as a pilot software, employing state of the art combinatorial parametric optimization to automate the tedious task of calibrating traffic microscopic simulation models. The employed global search technique, genetic algorithms, is integrated with a dynamic traffic microscopic simulation model for the City of Toronto, Canada using Paramics microsimulation suite. The output of GENOSIM is the near-optimal values of its car-following, lane changing and dynamic routing parameters. The results obtained are very encouraging

Keywords

calibration; digital simulation; genetic algorithms; road traffic; traffic engineering computing; Calibration; Combinatorial Parametric Optimization; GENOSIM; Paramics; car-following; dynamic routing; genetic algorithms; lane changing; microscopic traffic simulation; road traffic; simulation models; Calibration; Civil engineering; Genetic algorithms; Intelligent transportation systems; Microscopy; Predictive models; Search methods; System testing; Telecommunication traffic; Traffic control;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Transportation Systems, 2001. Proceedings. 2001 IEEE

Conference_Location

Oakland, CA

Print_ISBN

0-7803-7194-1

Type

conf

DOI

10.1109/ITSC.2001.948771

Filename

948771