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Short-Term Traffic Forecasting Using the Double Seasonal Holt-Winters Method

机译：基于双季节霍尔特-冬天方法的短期交通量预测

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In a typical traffic time series, a within-day seasonal cycle is evident from the similarity of traffic data from one day to the next; a within-week seasonal cycle can be observed when comparing traffic data on corresponding days of adjacent weeks. Recently a new method called the double seasonal (DS) Holt-Winters method has been proposed to deal with double seasonality in time series. The primary objective of this study is to investigate the possibility of using the DS method to account for both within-day and within-week seasonal cycles in traffic time series. Based on speed data from Freeway I-5, California, the DS method, the standard Holt-Winters method and the ARIMA method are employed to perform short-term traffic forecasting. The result shows that the proposed DS model outperforms the other two methods, indicating that the double seasonal Holt-Winters approach is a promising and effective method for short-term traffic forecasting.
机译：在典型的交通时间序列中，一天之间到第二天之间的交通数据相似性可以明显看出一天之内的季节性周期。比较相邻两周相应日期的流量数据时，可以观察到一周内的季节性周期。最近，提出了一种称为双季节（DS）Holt-Winters方法的新方法来处理时间序列中的双季节。这项研究的主要目的是研究使用DS方法来计算交通时间序列中一天之内和一周之内的季节性周期的可能性。基于加利福尼亚州高速公路I-5的速度数据，采用DS方法，标准Holt-Winters方法和ARIMA方法进行短期交通流量预测。结果表明，所提出的DS模型优于其他两种方法，表明双季节Holt-Winters方法是一种有前途和有效的短期交通流量预测方法。

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