Short-term traffic flow prediction using a self-adaptive two-dimensional forecasting method

Short-term traffic volume forecasting is widely recognized as an important element of intelligent transportation systems, because the accuracy of predictive methods determines the performance of real-time traffic control and management to some extent. The goal of this article is to propose a two-dimensional prediction method using the Kalman filtering theory based on historical data. In the first dimension, using Kalman filtering, we predict the values of traffic flows based on data from the current day and historical data separately. The two predicted values are fused using an equation with weight coefficients where the weight coefficients can be generated in real time in the process of prediction. Accordingly, in the second dimension, using Kalman filtering again, we obtain the predicted value of weight coefficients. In addition, some extreme cases during the process of weight coefficient prediction are discussed, and solutions are proposed as well. The accuracy of the two-dimensional forecasting method is studied based on a set of performance criteria. Comparison of the results of different methods based on field test data of road networks shows that the proposed method outperforms the standard Kalman filtering method, and more accurate traffic flow prediction is obtained using the framework incorporating Fusion method 3 proposed in this article.

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