Applying cluster method to reduce the traffic data feature for accident duration forecasting on freeway

Ying Lee, Chien-Hung Wei

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

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 13th International Conference of Hong Kong Society for Transportation Studies
Subtitle of host publicationTransportation and Management Science
Pages271-280
Number of pages10
Publication statusPublished - 2008
Event13th International Conference of Hong Kong Society for Transportation Studies: Transportation and Management Science - Kowloon, Hong Kong
Duration: 2008 Dec 132008 Dec 15

Other

Other13th International Conference of Hong Kong Society for Transportation Studies: Transportation and Management Science
CountryHong Kong
CityKowloon
Period08-12-1308-12-15

All Science Journal Classification (ASJC) codes

  • Transportation

Fingerprint Dive into the research topics of 'Applying cluster method to reduce the traffic data feature for accident duration forecasting on freeway'. Together they form a unique fingerprint.

  • Cite this

    Lee, Y., & Wei, C-H. (2008). Applying cluster method to reduce the traffic data feature for accident duration forecasting on freeway. In Proceedings of the 13th International Conference of Hong Kong Society for Transportation Studies: Transportation and Management Science (pp. 271-280)