Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections

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Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections

Zhu, Jia Zheng / Cao, Jin Xin / Zhu, Yuan

Highlights • We combined traffic flow forecasting with flocking theory from biology. • We analyzed the correlation of the traffic volume between adjacent intersections. • We proposed a new method for real-time traffic flow forecasting considering the influence of adjacent intersection flows. • A new method was proposed for real-time traffic flow forecasting under missing data.

Abstract The forecasting of short-term traffic flow is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting could be a challenging task. Artificial Neural Network (ANN) could be a good solution to this issue as it is possible to obtain a higher forecasting accuracy within relatively short time through this tool. Traditional methods for traffic flow forecasting generally based on a separated single point. However, it is found that traffic flows from adjacent intersections show a similar trend. It indicates that the vehicle accumulation and dissipation influence the traffic volumes of the adjacent intersections. This paper presents a novel method, which considers the travel flows of the adjacent intersections when forecasting the one of the middle. Computational experiments show that the proposed model is both effective and practical.