Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers
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 inc...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2017/8523495 |
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| _version_ | 1832549777402757120 |
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| author | Dawei Li Xiaojian Hu Cheng-jie Jin Jun Zhou |
| author_facet | Dawei Li Xiaojian Hu Cheng-jie Jin Jun Zhou |
| author_sort | Dawei Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3176978dfe7c4a52b36e050ea69551ab |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-3176978dfe7c4a52b36e050ea69551ab2025-02-03T06:08:30ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2017-01-01201710.1155/2017/85234958523495Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian ClassifiersDawei Li0Xiaojian Hu1Cheng-jie Jin2Jun Zhou3Jiangsu 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, ChinaJiangsu 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, ChinaJiangsu 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, ChinaHuaiyin Institute of Technology, Key Laboratory for Traffic and Transportation Security of Jiangsu Province, Meicheng Rd, Huaian 223003, ChinaThis 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.http://dx.doi.org/10.1155/2017/8523495 |
| spellingShingle | Dawei Li Xiaojian Hu Cheng-jie Jin Jun Zhou Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers Discrete Dynamics in Nature and Society |
| title | Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers |
| title_full | Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers |
| title_fullStr | Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers |
| title_full_unstemmed | Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers |
| title_short | Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers |
| title_sort | learning to detect traffic incidents from data based on tree augmented naive bayesian classifiers |
| url | http://dx.doi.org/10.1155/2017/8523495 |
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