TY - GEN
T1 - A computerized feature reduction using cluster methods for accident duration forecasting on freeway
AU - Lee, Ying
AU - Wei, Chien-Hung
PY - 2008/1/1
Y1 - 2008/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=67049173101&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67049173101&partnerID=8YFLogxK
U2 - 10.1109/APSCC.2008.36
DO - 10.1109/APSCC.2008.36
M3 - Conference contribution
AN - SCOPUS:67049173101
SN - 9780769534732
T3 - Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008
SP - 1459
EP - 1464
BT - Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008
PB - IEEE Computer Society
T2 - 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008
Y2 - 9 December 2008 through 12 December 2008
ER -