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  • 标题:Information Model For Refining The Transportation Jamming In Sultanate Of Oman
  • 本地全文:下载
  • 作者:Afra Alzidi ; Boumedyen Shannaq
  • 期刊名称:Webology
  • 印刷版ISSN:1735-188X
  • 出版年度:2022
  • 卷号:19
  • 期号:2
  • 页码:7795-7811
  • 语种:English
  • 出版社:University of Tehran
  • 摘要:This work intends to build a model to predict the time hour/day/date of an accident on specific city and street location, which causes traffic congestion. The proposed model could alert the driver of an accident occurrence in specific location and time. Therefore, this Forecasting Information Model Scheme will minimize the traffic congestion as most of the drivers will change their path route. A large data set has been collected from the Traffic Database for the past 10 years .Data Mining Methodology has been customized to build the forecasting model. 4011 Time series instances have been used to train the forecasting model. The MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) have been used for Evaluation on training data. The MAE scored ~5.1, and RMSE scored ~6.8. The obtained results are promising and could be useful for improving road safety and traffic congestion strategy.
  • 关键词:Traffic Congestion;Traffic Accidents;Data Mining;Forecasting;Information Systems;Time series
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