Feature selection with genetic algorithms for accident duration forecasting on freeway

Ying Lee, Chien-Hung Wei

研究成果: Conference contribution

摘要

This study develops two Artificial Neural Network-based models to provide 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 select suitable data features, Genetic Algorithm is employed to decrease the number of model inputs while preserving relevant traffic characteristics with fewer inputs. This study shows the proposed models are feasible ones in the Intelligent Transportation Systems (ITS) context.

原文English
主出版物標題14th World Congress on Intelligent Transport Systems, ITS 2007
頁面3773-3780
頁數8
5
出版狀態Published - 2007
事件14th World Congress on Intelligent Transport Systems, ITS 2007 - Beijing, China
持續時間: 2007 10月 92007 10月 13

Other

Other14th World Congress on Intelligent Transport Systems, ITS 2007
國家/地區China
城市Beijing
期間07-10-0907-10-13

All Science Journal Classification (ASJC) codes

  • 機械工業
  • 電氣與電子工程
  • 控制與系統工程
  • 運輸
  • 汽車工程
  • 電腦網路與通信
  • 人工智慧
  • 電腦科學應用

指紋

深入研究「Feature selection with genetic algorithms for accident duration forecasting on freeway」主題。共同形成了獨特的指紋。

引用此