Paper Info
Title
A Neural-Fuzzy Framework for Modeling Car-following Behavior
Abstract
A general framework is introduced to model driver behavior from real car-following data acquired on Swedish roads using an advanced instrumented vehicle. In early research, the data was classified into different car-following regimes based on fuzzy clustering methods and knowledge obtained from video analysis. In this paper, we propose a multi-regime framework based on the statistical property in each regime and mathematical models adopted in those regimes. This framework is an extension of TSK fuzzy inference system and can be expressed by a neural-fuzzy system. Genetic algorithm (GA) is designed as the main learning method for this system. In practice, this model structure illustrates human knowledge of car-following in a more understandable manner and can be rather flexible as the regime parameters and model forms may vary according to the application context.
Year
DOI
Venue
2006
10.1109/ICSMC.2006.384560
SMC
Keywords
Field
DocType
car-following behavior,video analysis,fuzzy clustering methods,multi-regime framework,statistical analysis,traffic engineering computing,advanced instrumented vehicle,automobiles,tsk fuzzy inference system,genetic algorithm,genetic algorithms,statistical property,fuzzy neural nets,neural-fuzzy framework,fuzzy clustering,fuzzy system,mathematical model,computer science
Car following,Fuzzy clustering,Data mining,Computer science,Fuzzy logic,Human knowledge,Artificial intelligence,Application Context,Mathematical model,Genetic algorithm,Machine learning,Fuzzy inference system
Conference
Volume
ISSN
ISBN
2
1062-922X
1-4244-0100-3
Citations 
PageRank 
References 
2
0.43
1
Authors
1
Name
Order
Citations
PageRank
Xiaoliang Ma118218.51