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道路交通流预测不仅可以为出行者提供实时有效的信息,而且可以帮助他们选择最佳路径,减少出行时间,实现道路交通路径诱导,缓解交通拥堵。本文提出了一种基于ARIMA模型和Kalman滤波算法的道路交通流预测方法。首先,基于道路交通历史数据建立时间序列的ARIMA模型。其次,结合ARIMA模型和Kalman滤波法构建道路交通预测算法,获取Kalman滤波的测量方程和更新方程。然后,基于历史道路交通数据进行算法的参数设定。最后,以北京的四条路段作为案例,对所提出的方法进行了分析。实验结果表明,基于ARIMA模型和Kalman滤波的实时道路交通状态预测方法是可行的,并且可以获得很高的精度。
Road traffic flow prediction not only provides travelers with real-time and effective information, but also helps them to choose the best route, reduce the travel time, realize the guidance of road traffic and relieve the traffic congestion. This paper presents a road traffic flow prediction method based on ARIMA model and Kalman filter algorithm. First, an ARIMA model of time series is established based on historical traffic data. Secondly, combining the ARIMA model and Kalman filter method to construct the road traffic prediction algorithm, the Kalman filter measurement equation and the updated equation are obtained. Then, the parameters of the algorithm are set based on the historical road traffic data. Finally, taking the four sections of Beijing as examples, the method proposed is analyzed. Experimental results show that real-time prediction of road traffic status based on ARIMA model and Kalman filtering is feasible and highly accurate.