A computerized feature selection method using genetic algorithms to forecast freeway accident duration times

Ying Lee, Chien-Hung Wei

研究成果: Article同行評審

71 引文 斯高帕斯(Scopus)

摘要

This study presents a feature selection method that uses genetic algorithms to create two artificial neural network-based models that provide a sequential forecast of accident duration from the time of accident notification to the accident site clearance. These two models can provide the estimated duration time by plugging in relevant traffic data as soon as an accident is notified. To select data feature, the genetic algorithm is designed to decrease the number of model inputs while preserving the relevant traffic characteristics. Using the proposed feature selection method, the mean absolute percentage error for forecasting accident duration at each time point is mostly under 29%, which indicates that these models have a reasonable forecasting ability. Thanks to this model, travelers and traffic management units can better understand the impact of accidents. This study shows that the proposed models are feasible in the Intelligent Transportation Systems context.

原文English
頁(從 - 到)132-148
頁數17
期刊Computer-Aided Civil and Infrastructure Engineering
25
發行號2
DOIs
出版狀態Published - 2010 二月 1

All Science Journal Classification (ASJC) codes

  • 土木與結構工程
  • 電腦科學應用
  • 電腦繪圖與電腦輔助設計
  • 計算機理論與數學

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