Title :
A genetic fuzzy system for modeling mandatory lane changing
Author :
Hou, Yi ; Edara, Praveen ; Sun, Carlos
Author_Institution :
Univ. of Missouri-Columbia, Columbia, SC, USA
Abstract :
A Fuzzy Logic-based lane changing model was developed for mandatory lane changes at lane drops. Genetic Algorithm was used for optimizing the widths of membership functions. The Next Generation Simulation (NGSIM) dataset of vehicle trajectories was used for model development and validation. The model performed better than a comparable binary Logit model in terms of predicting the merge and non-merge events. The model has applications in traffic simulation and driver assistance systems.
Keywords :
driver information systems; fuzzy logic; genetic algorithms; road traffic; road vehicles; roads; NGSIM dataset; driver assistance systems; fuzzy logic-based lane changing model; genetic algorithm; genetic fuzzy system; lane drops; mandatory lane changing modelling; membership functions; merge events prediction; next generation simulation dataset; nonmerge events prediction; traffic simulation; vehicle trajectories; Accuracy; Data models; Fuzzy logic; Merging; Predictive models; Vehicles;
Conference_Titel :
Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4673-3064-0
Electronic_ISBN :
2153-0009
DOI :
10.1109/ITSC.2012.6338877
