Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers
Joint Authors
Hu, Xiaojian
Li, Dawei
Jin, Cheng-jie
Zhou, Jun
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-02
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
This study develops a tree augmented naive Bayesian (TAN) classifier based incident detection algorithm.
Compared with the Bayesian networks based detection algorithms developed in the previous studies, this algorithm has less dependency on experts’ knowledge.
The structure of TAN classifier for incident detection is learned from data.
The discretization of continuous attributes is processed using an entropy-based method automatically.
A simulation dataset on the section of the Ayer Rajah Expressway (AYE) in Singapore is used to demonstrate the development of proposed algorithm, including wavelet denoising, normalization, entropy-based discretization, and structure learning.
The performance of TAN based algorithm is evaluated compared with the previous developed Bayesian network (BN) based and multilayer feed forward (MLF) neural networks based algorithms with the same AYE data.
The experiment results show that the TAN based algorithms perform better than the BN classifiers and have a similar performance to the MLF based algorithm.
However, TAN based algorithm would have wider vista of applications because the theory of TAN classifiers is much less complicated than MLF.
It should be found from the experiment that the TAN classifier based algorithm has a significant superiority over the speed of model training and calibration compared with MLF.
American Psychological Association (APA)
Li, Dawei& Hu, Xiaojian& Jin, Cheng-jie& Zhou, Jun. 2017. Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers. Discrete Dynamics in Nature and Society،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1151843
Modern Language Association (MLA)
Li, Dawei…[et al.]. Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers. Discrete Dynamics in Nature and Society No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1151843
American Medical Association (AMA)
Li, Dawei& Hu, Xiaojian& Jin, Cheng-jie& Zhou, Jun. Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers. Discrete Dynamics in Nature and Society. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1151843
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1151843