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

Ying Lee
, Chien-Hung Wei

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

All Science Journal Classification (ASJC) codes

Transportation

Cite this

@inproceedings{91da654c9dc446669c74e8037d64e1d9,

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",

}

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

TY - GEN

T1 - Applying cluster method to reduce the traffic data feature for accident duration forecasting on freeway

AU - Lee, Ying

AU - Wei, Chien-Hung

PY - 2008

Y1 - 2008

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.

UR - https://www.scopus.com/pages/publications/84896841739

UR - https://www.scopus.com/pages/publications/84896841739#tab=citedBy

M3 - Conference contribution

AN - SCOPUS:84896841739

SN - 9789889884734

SP - 271

EP - 280

BT - Proceedings of the 13th International Conference of Hong Kong Society for Transportation Studies

T2 - 13th International Conference of Hong Kong Society for Transportation Studies: Transportation and Management Science

Y2 - 13 December 2008 through 15 December 2008

ER -

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

Abstract

Other

UN SDGs

All Science Journal Classification (ASJC) codes

Other files and links

Fingerprint

Cite this