Mining overdispersed and autocorrelated vehicular traffic volume

Yousef Awwad Daraghmi, Tsi-Ui Ik, Tsun Chieh Chiang

研究成果: Conference contribution同行評審

4 引文 斯高帕斯(Scopus)

摘要

Vehicular congestion is a major problem in urban cities and is managed by real time control of traffic that requires accurate modeling and forecasting of traffic volumes. Traffic volume is a time series that has complex characteristics such as autocorrelation, trend, seasonality and overdispersion. Several data mining methods have been proposed to model and forecast traffic volume for the support of congestion control strategies. However, these methods focus on some of the characteristics and ignore others. Some methods address the autocorrelation and ignore the overdispersion and vice versa. In this research, we propose a data mining method that can consider all characteristics by capturing the volume autocorrelation, trend, and seasonality and by handling the overdispersion. The proposed method adopts the Holt-Winters-Taylor (HWT) count data method. Data from Taipei city are used to evaluate the proposed method which outperforms other methods by achieving a lower root mean square error.

原文English
主出版物標題2013 5th International Conference on Computer Science and Information Technology, CSIT 2013 - Proceedings
頁面194-200
頁數7
DOIs
出版狀態Published - 7 十月 2013
事件2013 5th International Conference on Computer Science and Information Technology, CSIT 2013 - Amman, Jordan
持續時間: 27 三月 201328 三月 2013

出版系列

名字2013 5th International Conference on Computer Science and Information Technology, CSIT 2013 - Proceedings

Conference

Conference2013 5th International Conference on Computer Science and Information Technology, CSIT 2013
國家/地區Jordan
城市Amman
期間27/03/1328/03/13

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