Improved Dynamic Route Guidance based on Holt-Winters-Taylor method for Traffic Flow Prediction

Abstract

Accurate short-term traffic flow prediction is necessary for the implementation of Dynamic Route Guidance as motorists need to know traffic conditions ahead. The accuracy of short-term traffic flow prediction depends on how prediction models handle traffic flow characteristics such as temporal correlation, overdispersion, and seasonal patterns. Several data mining methods have been proposed to model and forecast traffic flow 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 flow 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. Then the proposed method is used in a dynamic route guidance systems to enhance the efficiency of guidance.