Freeway Incident Frequency Analysis Based on CART Method
<p<Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadw...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xuecai Xu [verfasserIn] Željko Šarić [verfasserIn] Ahmad Kouhpanejade [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Promet (Zagreb) - University of Zagreb, Faculty of Transport and Traffic Sciences, 2016, 26(2014), 3, Seite 191-199 |
|---|---|
| Übergeordnetes Werk: |
volume:26 ; year:2014 ; number:3 ; pages:191-199 |
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10.7307/ptt.v26i3.1308 |
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DOAJ050800396 |
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10.7307/ptt.v26i3.1308 doi (DE-627)DOAJ050800396 (DE-599)DOAJd03dc55cf2154f5da659ca7b6509369c DE-627 ger DE-627 rakwb eng TA1001-1280 Xuecai Xu verfasserin aut Freeway Incident Frequency Analysis Based on CART Method 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.</p< data mining classification and regression tree incident frequency binary tree Transportation engineering Željko Šarić verfasserin aut Ahmad Kouhpanejade verfasserin aut In Promet (Zagreb) University of Zagreb, Faculty of Transport and Traffic Sciences, 2016 26(2014), 3, Seite 191-199 (DE-627)864193793 (DE-600)2863683-1 18484069 nnns volume:26 year:2014 number:3 pages:191-199 https://doi.org/10.7307/ptt.v26i3.1308 kostenfrei https://doaj.org/article/d03dc55cf2154f5da659ca7b6509369c kostenfrei http://www.fpz.unizg.hr/traffic/index.php/PROMTT/article/view/1308 kostenfrei https://doaj.org/toc/0353-5320 Journal toc kostenfrei https://doaj.org/toc/1848-4069 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 26 2014 3 191-199 |
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10.7307/ptt.v26i3.1308 doi (DE-627)DOAJ050800396 (DE-599)DOAJd03dc55cf2154f5da659ca7b6509369c DE-627 ger DE-627 rakwb eng TA1001-1280 Xuecai Xu verfasserin aut Freeway Incident Frequency Analysis Based on CART Method 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.</p< data mining classification and regression tree incident frequency binary tree Transportation engineering Željko Šarić verfasserin aut Ahmad Kouhpanejade verfasserin aut In Promet (Zagreb) University of Zagreb, Faculty of Transport and Traffic Sciences, 2016 26(2014), 3, Seite 191-199 (DE-627)864193793 (DE-600)2863683-1 18484069 nnns volume:26 year:2014 number:3 pages:191-199 https://doi.org/10.7307/ptt.v26i3.1308 kostenfrei https://doaj.org/article/d03dc55cf2154f5da659ca7b6509369c kostenfrei http://www.fpz.unizg.hr/traffic/index.php/PROMTT/article/view/1308 kostenfrei https://doaj.org/toc/0353-5320 Journal toc kostenfrei https://doaj.org/toc/1848-4069 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 26 2014 3 191-199 |
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10.7307/ptt.v26i3.1308 doi (DE-627)DOAJ050800396 (DE-599)DOAJd03dc55cf2154f5da659ca7b6509369c DE-627 ger DE-627 rakwb eng TA1001-1280 Xuecai Xu verfasserin aut Freeway Incident Frequency Analysis Based on CART Method 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.</p< data mining classification and regression tree incident frequency binary tree Transportation engineering Željko Šarić verfasserin aut Ahmad Kouhpanejade verfasserin aut In Promet (Zagreb) University of Zagreb, Faculty of Transport and Traffic Sciences, 2016 26(2014), 3, Seite 191-199 (DE-627)864193793 (DE-600)2863683-1 18484069 nnns volume:26 year:2014 number:3 pages:191-199 https://doi.org/10.7307/ptt.v26i3.1308 kostenfrei https://doaj.org/article/d03dc55cf2154f5da659ca7b6509369c kostenfrei http://www.fpz.unizg.hr/traffic/index.php/PROMTT/article/view/1308 kostenfrei https://doaj.org/toc/0353-5320 Journal toc kostenfrei https://doaj.org/toc/1848-4069 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 26 2014 3 191-199 |
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10.7307/ptt.v26i3.1308 doi (DE-627)DOAJ050800396 (DE-599)DOAJd03dc55cf2154f5da659ca7b6509369c DE-627 ger DE-627 rakwb eng TA1001-1280 Xuecai Xu verfasserin aut Freeway Incident Frequency Analysis Based on CART Method 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.</p< data mining classification and regression tree incident frequency binary tree Transportation engineering Željko Šarić verfasserin aut Ahmad Kouhpanejade verfasserin aut In Promet (Zagreb) University of Zagreb, Faculty of Transport and Traffic Sciences, 2016 26(2014), 3, Seite 191-199 (DE-627)864193793 (DE-600)2863683-1 18484069 nnns volume:26 year:2014 number:3 pages:191-199 https://doi.org/10.7307/ptt.v26i3.1308 kostenfrei https://doaj.org/article/d03dc55cf2154f5da659ca7b6509369c kostenfrei http://www.fpz.unizg.hr/traffic/index.php/PROMTT/article/view/1308 kostenfrei https://doaj.org/toc/0353-5320 Journal toc kostenfrei https://doaj.org/toc/1848-4069 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 26 2014 3 191-199 |
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10.7307/ptt.v26i3.1308 doi (DE-627)DOAJ050800396 (DE-599)DOAJd03dc55cf2154f5da659ca7b6509369c DE-627 ger DE-627 rakwb eng TA1001-1280 Xuecai Xu verfasserin aut Freeway Incident Frequency Analysis Based on CART Method 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.</p< data mining classification and regression tree incident frequency binary tree Transportation engineering Željko Šarić verfasserin aut Ahmad Kouhpanejade verfasserin aut In Promet (Zagreb) University of Zagreb, Faculty of Transport and Traffic Sciences, 2016 26(2014), 3, Seite 191-199 (DE-627)864193793 (DE-600)2863683-1 18484069 nnns volume:26 year:2014 number:3 pages:191-199 https://doi.org/10.7307/ptt.v26i3.1308 kostenfrei https://doaj.org/article/d03dc55cf2154f5da659ca7b6509369c kostenfrei http://www.fpz.unizg.hr/traffic/index.php/PROMTT/article/view/1308 kostenfrei https://doaj.org/toc/0353-5320 Journal toc kostenfrei https://doaj.org/toc/1848-4069 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 26 2014 3 191-199 |
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<p<Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.</p< |
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<p<Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.</p< |
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<p<Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.</p< |
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10.7307/ptt.v26i3.1308 |
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