Open AccessExamining Bayesian network modeling in identification of dangerous driving behavior
Author(s) -
Yichuan Peng,
Leyi Cheng,
Yuming Jiang,
Shengqi Zhu
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0252484
Subject(s) - computer science , bayesian network , identification (biology) , bayesian probability , machine learning , cluster analysis , bayesian inference , crash , data mining , dynamic bayesian network , inference , toolbox , principal component analysis , artificial intelligence , botany , biology , programming language
Traffic safety problems are still very serious and human factor is the one of most important factors affecting traffic crashes. Taking Next Generation Simulation (NGSIM) data as the research object, this study defines six control indicators and uses principal component analysis and K-means++ clustering methods to get the driving style of different drivers. Then use the Bayesian Networks Toolbox (BNT) and MCMC algorithm to realize the structure learning of Bayesian network. and parameter learning was completed through Netica software. Finally, the vehicle-based traffic crash risk model was created to conduct sensitivity analysis, posterior probability inference, and simulation data was used to detect the feasibility of the model. The results show that the Bayesian network modeling can not only express the relationship between the crash risk and various driving behaviors, but also dig out the inherent relationship between different influencing factors and investigate the causes of driving risks. The results will be beneficial to accurately identify and prevent risky driving behavior.