Mining overdispersed and autocorrelated vehicular traffic volume

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.