Paper Info
Title
A novel car-following model considering conditional heteroskedasticity of acceleration fluctuation and driving force.
Abstract
Based on the conditional heteroskedasticity of following vehicle acceleration fluctuation in car-following behavior, this paper combines the idea of driving force, Intelligent Driving Model (IDM) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH model) to establish a novel car-following model. First, the concept of safety driving force and efficiency driving force are recommended, and both of them are defined by the idea of IDM, and the dynamic balance of them are the reason for acceleration fluctuation. Then, according to the heteroskedasticity of acceleration sequence, the classic GARCH model is introduced to establish the relationship among the variance of acceleration fluctuation items, driving force items and fluctuating memory items. On this basis, a novel car-following model is established, and the relational properties are studied. Finally, an example is used to verify our model, the results show that the novel car-following model is more accurate than IDM model, and the forward prediction result is close to the actual acceleration change value, which can predict the occurrence of dangerous driving behavior.
Year
DOI
Venue
2018
10.3233/JIFS-171351
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Conditional heteroskedasticity,IDM model,GARCH model,driving force
Car following,Heteroscedasticity,Control theory,Acceleration,Artificial intelligence,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
34
4
1064-1246
Citations 
PageRank 
References 
0
0.34
2
Authors
4
Name
Order
Citations
PageRank
Xinping Xiao1204.78
Meng Jiang211.02
Jianghui Wen3141.59
Chao-zhong Wu48014.18