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Discrete Dynamics in Nature and Society
Volume 2017, Article ID 8523495, 9 pages
https://doi.org/10.1155/2017/8523495
Research Article

Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers

1Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic, Sipailou No. 2, Xuanwu District, Nanjing 210096, China
2Huaiyin Institute of Technology, Key Laboratory for Traffic and Transportation Security of Jiangsu Province, Meicheng Rd, Huaian 223003, China

Correspondence should be addressed to Dawei Li; nc.ude.ues@iewadil

Received 25 April 2017; Revised 17 July 2017; Accepted 27 July 2017; Published 2 October 2017

Academic Editor: Gabriella Bretti

Copyright © 2017 Dawei Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.