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

•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.

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.