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  • 标题:Traffic Incident Duration Prediction based on Partial Least Squares Regression
  • 本地全文:下载
  • 作者:Xuanqiang Wang ; Xuanqiang Wang ; Shuyan Chen
  • 期刊名称:Procedia - Social and Behavioral Sciences
  • 印刷版ISSN:1877-0428
  • 出版年度:2013
  • 卷号:96
  • 页码:425-432
  • DOI:10.1016/j.sbspro.2013.08.050
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThe prediction of the traffic incident duration is a very important issue to the Advanced Traffic Incident Management (ATIM). An accurate prediction of incident duration makes a lot contributes to making appropriate decisions to deal with incidents for traffic managers. The paper employed the Partial Least Squares Regression (PLSR) to build model between incident duration and its influence factors. Three models were established for three types of incident correspondingly, i.e. stopped vehicle, lost load and accident. Meanwhile, a model without distinguishing the incident type was built as a comparison. The experiments results indicated that the model obtained high prediction accuracy for those incidents which last 20minutes to 90minutes. The models got prediction accuracy of 77.24%, 86.59%, 83.33% and 71.30% for stopped vehicle, lost load, accident and all incidents within 20minutes error, respectively. The results indicated that the PLSR has a promising application to predict traffic incident duration
  • 关键词:Incident Management;Incident Duration;Prediction;Partial Least Squares Regression.
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