A computerized feature reduction using cluster methods for accident duration forecasting on freeway

Ying Lee, Chien-Hung Wei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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 languageEnglish
Title of host publicationProceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008
PublisherIEEE Computer Society
Pages1459-1464
Number of pages6
ISBN (Print)9780769534732
DOIs
Publication statusPublished - 2008 Jan 1
Event3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008 - Yilan, Taiwan
Duration: 2008 Dec 92008 Dec 12

Publication series

NameProceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008

Other

Other3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008
CountryTaiwan
CityYilan
Period08-12-0908-12-12

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software
  • Electrical and Electronic Engineering

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