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

Chien Hung Wei, Ying Lee

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Intelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005
頁面790-803
頁數14
出版狀態Published - 2009 十二月 1
事件12th World Congress on Intelligent Transport Systems 2005 - San Francisco, CA, United States
持續時間: 2005 十一月 62005 十一月 10

出版系列

名字Intelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005
2

Other

Other12th World Congress on Intelligent Transport Systems 2005
國家United States
城市San Francisco, CA
期間05-11-0605-11-10

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

指紋 深入研究「Multi-period forecasting of accident duration based on multiple sources of real-time traffic data」主題。共同形成了獨特的指紋。

  • 引用此

    Wei, C. H., & Lee, Y. (2009). Multi-period forecasting of accident duration based on multiple sources of real-time traffic data. 於 Intelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005 (頁 790-803). (Intelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005; 卷 2).