Multi-period forecasting of accident duration based on multiple sources of real-time traffic data

Chien Hung Wei, Ying Lee

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

Abstract

The aim of this study is to build the adaptive ANN-based models for accident duration prediction. Two types of accident duration models are conducted. Model A is used to forecast the duration at the accident notification and Model B provides multi-period update of incident duration after accident notification. The APE and MAPE of forecasted accident duration at each time point of forecast are mostly under 20%. With these two models, the estimated duration can be provided by plugging in relevant traffic data as soon as the incident is notified. The traveler and traffic management unit can generally realize the impact by the forecasted accident duration. From the assessment of model effects, this study shows very promising practical applicability of the proposed models in the Intelligent Transportation Systems (ITS) context.

Original languageEnglish
Title of host publicationIntelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005
Pages790-803
Number of pages14
Publication statusPublished - 2009 Dec 1
Event12th World Congress on Intelligent Transport Systems 2005 - San Francisco, CA, United States
Duration: 2005 Nov 62005 Nov 10

Publication series

NameIntelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005
Volume2

Other

Other12th World Congress on Intelligent Transport Systems 2005
CountryUnited States
CitySan Francisco, CA
Period05-11-0605-11-10

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Transportation
  • Automotive Engineering
  • Computer Networks and Communications
  • Artificial Intelligence
  • Computer Science Applications

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