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

被引：5
|
作者：
Ma, T [1 ]
Abdulhai, B [1 ]
机构：
[1] Univ Toronto, Dept Civil Engn, Intelligent Transportat Syst Ctr & Testbed, Toronto, ON, Canada
来源：
2001 IEEE INTELLIGENT TRANSPORTATION SYSTEMS - PROCEEDINGS | 2001年
关键词：
D O I：
10.1109/ITSC.2001.948771
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, 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. Obtained results are very encouraging.
引用
收藏
页码：848 / 853
页数：6
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