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

Ying Lee, Chien-Hung Wei

研究成果: Conference contribution

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 13th International Conference of Hong Kong Society for Transportation Studies
主出版物子標題Transportation and Management Science
頁面271-280
頁數10
出版狀態Published - 2008
事件13th International Conference of Hong Kong Society for Transportation Studies: Transportation and Management Science - Kowloon, Hong Kong
持續時間: 2008 12月 132008 12月 15

Other

Other13th International Conference of Hong Kong Society for Transportation Studies: Transportation and Management Science
國家/地區Hong Kong
城市Kowloon
期間08-12-1308-12-15

All Science Journal Classification (ASJC) codes

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