STARIMA-based traffic prediction with time-varying lags
发布时间：2022-04-08点击次数：
第一作者：
段沛博
合写作者：
张长胜,Guoqiang Mao
发表刊物：
2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)
DOI码：
10.1109/ITSC.2016.7795773
摘要：
Based on the observation that the correlation between observed traffic at two measurement points or traffic stations may be time-varying, attributable to the time-varying speed which subsequently causes variations in the time required to travel between the two points, in this paper, we develop a modified Space-Time Autoregressive Integrated Moving Average (STARIMA) model with time-varying lags for short-term traffic flow prediction. Particularly, the temporal lags in the modified STARIMA change with the time-varying speed at different time of the day or equivalently change with the (time-varying) time required to travel between two measurement points. Firstly, a technique is developed to evaluate the temporal lag in the STARIMA model, where the temporal lag is formulated as a function of the spatial lag (spatial distance) and the average speed. Secondly, an unsupervised classification algorithm based on ISODATA algorithm is designed to classify different time periods of the day according to the variation of the speed. The classification helps to determine the appropriate time lag to use in the STARIMA model. Finally, a STARIMA-based model with time-varying lags is developed for short-term traffic prediction. Experimental results using real traffic data show that the developed STARIMA-based model with time-varying lags has superior accuracy compared with its counterpart developed using the traditional cross-correlation function and without employing time-varying lags.
关键字：
Predictive models;Hidden Markov models;Correlation;Roads;Data models;Mathematical model;Classification algorithms
页面范围：
1610-1615
是否译文：
否
