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

Ying Lee, Chien-Hung Wei

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)132-148
Number of pages17
JournalComputer-Aided Civil and Infrastructure Engineering
Volume25
Issue number2
DOIs
Publication statusPublished - 2010 Feb 1

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics

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