A computerized feature reduction using cluster methods for accident duration forecasting on freeway

Ying Lee; Chien-Hung Wei

doi:10.1109/APSCC.2008.36

A computerized feature reduction using cluster methods for accident duration forecasting on freeway

Ying Lee
, Chien-Hung Wei

交通管理科學系

研究成果: Conference contribution

3 引文斯高帕斯（Scopus）

摘要

This study creates two Artificial Neural Network-based models and provides a sequential forecast of accident duration from the accident notification to the accident site clearance. With these two models, the estimated duration time can be provided by plugging in relevant traffic data as soon as an accident is notified. To reduce data feature, cluster method can decreases the number of model inputs and preserves the relevant traffic characteristics with fewer inputs. This study shows proposed models are feasible ones in the Intelligent Transportation Systems (ITS) context.

原文	English
主出版物標題	Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008
發行者	IEEE Computer Society
頁面	1459-1464
頁數	6
ISBN（列印）	9780769534732
DOIs	https://doi.org/10.1109/APSCC.2008.36
出版狀態	Published - 2008 1月 1
事件	3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008 - Yilan, Taiwan 持續時間: 2008 12月 9 → 2008 12月 12

出版系列

名字	Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008

Other

Other	3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008
國家/地區	Taiwan
城市	Yilan
期間	08-12-09 → 08-12-12

UN SDG

此研究成果有助於以下永續發展目標

SDG 11 永續發展的城市與社群

All Science Journal Classification (ASJC) codes

電腦科學應用
軟體
電氣與電子工程

存取文件

10.1109/APSCC.2008.36

其它文件與鏈接

引用此

Lee, Y., & Wei, C.-H. (2008). A computerized feature reduction using cluster methods for accident duration forecasting on freeway. 於 Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008 (頁 1459-1464). 文章 4780885 (Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008). IEEE Computer Society. https://doi.org/10.1109/APSCC.2008.36

@inproceedings{5bb4b7feb10346ca80db36617daf71eb,

title = "A computerized feature reduction using cluster methods for accident duration forecasting on freeway",

abstract = "This study creates two Artificial Neural Network-based models and provides a sequential forecast of accident duration from the accident notification to the accident site clearance. With these two models, the estimated duration time can be provided by plugging in relevant traffic data as soon as an accident is notified. To reduce data feature, cluster method can decreases the number of model inputs and preserves the relevant traffic characteristics with fewer inputs. This study shows proposed models are feasible ones in the Intelligent Transportation Systems (ITS) context.",

author = "Ying Lee and Chien-Hung Wei",

year = "2008",

month = jan,

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doi = "10.1109/APSCC.2008.36",

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series = "Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008",

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booktitle = "Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008",

address = "United States",

note = "3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008 ; Conference date: 09-12-2008 Through 12-12-2008",

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Lee, Y & Wei, C-H 2008, A computerized feature reduction using cluster methods for accident duration forecasting on freeway. 於 Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008., 4780885, Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008, IEEE Computer Society, 頁 1459-1464, 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008, Yilan, Taiwan, 08-12-09. https://doi.org/10.1109/APSCC.2008.36

A computerized feature reduction using cluster methods for accident duration forecasting on freeway. / Lee, Ying; Wei, Chien-Hung.
Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008. IEEE Computer Society, 2008. p. 1459-1464 4780885 (Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008).

研究成果: Conference contribution

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N2 - This study creates two Artificial Neural Network-based models and provides a sequential forecast of accident duration from the accident notification to the accident site clearance. With these two models, the estimated duration time can be provided by plugging in relevant traffic data as soon as an accident is notified. To reduce data feature, cluster method can decreases the number of model inputs and preserves the relevant traffic characteristics with fewer inputs. This study shows proposed models are feasible ones in the Intelligent Transportation Systems (ITS) context.

AB - This study creates two Artificial Neural Network-based models and provides a sequential forecast of accident duration from the accident notification to the accident site clearance. With these two models, the estimated duration time can be provided by plugging in relevant traffic data as soon as an accident is notified. To reduce data feature, cluster method can decreases the number of model inputs and preserves the relevant traffic characteristics with fewer inputs. This study shows proposed models are feasible ones in the Intelligent Transportation Systems (ITS) context.

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BT - Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008

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A computerized feature reduction using cluster methods for accident duration forecasting on freeway

摘要

出版系列

Other

UN SDG

All Science Journal Classification (ASJC) codes

存取文件

其它文件與鏈接

指紋

引用此