Applying cluster method to reduce the traffic data feature for accident duration forecasting on freeway

Ying Lee; Chien-Hung Wei

Applying cluster method to reduce the traffic data feature for accident duration forecasting on freeway

Department of Transportation and Communication Management Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings of the 13th International Conference of Hong Kong Society for Transportation Studies
Subtitle of host publication	Transportation and Management Science
Pages	271-280
Number of pages	10
Publication status	Published - 2008
Event	13th International Conference of Hong Kong Society for Transportation Studies: Transportation and Management Science - Kowloon, Hong Kong Duration: 2008 Dec 13 → 2008 Dec 15

Other

Other	13th International Conference of Hong Kong Society for Transportation Studies: Transportation and Management Science
Country/Territory	Hong Kong
City	Kowloon
Period	08-12-13 → 08-12-15

All Science Journal Classification (ASJC) codes

Transportation

Cite this

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title = "Applying cluster method to reduce the traffic data feature 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",

language = "English",

isbn = "9789889884734",

pages = "271--280",

booktitle = "Proceedings of the 13th International Conference of Hong Kong Society for Transportation Studies",

note = "13th International Conference of Hong Kong Society for Transportation Studies: Transportation and Management Science ; Conference date: 13-12-2008 Through 15-12-2008",

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Lee, Y & Wei, C-H 2008, Applying cluster method to reduce the traffic data feature for accident duration forecasting on freeway. in Proceedings of the 13th International Conference of Hong Kong Society for Transportation Studies: Transportation and Management Science. pp. 271-280, 13th International Conference of Hong Kong Society for Transportation Studies: Transportation and Management Science, Kowloon, Hong Kong, 08-12-13.

Applying cluster method to reduce the traffic data feature for accident duration forecasting on freeway. / Lee, Ying; Wei, Chien-Hung.
Proceedings of the 13th International Conference of Hong Kong Society for Transportation Studies: Transportation and Management Science. 2008. p. 271-280.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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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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Applying cluster method to reduce the traffic data feature for accident duration forecasting on freeway

Abstract

Other

All Science Journal Classification (ASJC) codes

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