Accurate and timely traffic flow prediction is important for the successful deployment of intelligent transportation systems. Most of existing methods have not made good use of information from adjacent sections to analyse the trends of the object section. A new method for traffic flow prediction of highway network, namely network-constrained Lasso (Least absolute shrinkage and selection operator) and Neural Networks, was proposed. Unlike existing methods, our approach incorporated all the spatial and temporal information available in a highway network to carry our short-term traffic flow prediction for the objective section. To capture the spatial correlation of traffic network, the Laplacian matrix was introduced to describe the highway network structure. Subsequently, a network-constrained Lasso method was applied for sparse variable selection. With the extracted historic and real-time data, the back propagation neural networks were implemented to predict traffic flow at different time intervals in future. The experimental results verified that the proposed method could achieve above 90% average accuracy in the 30-minutes speed predictions for 78 road sections.
Effect of Multi-Scale Decomposition on Performance of Neural Networks in Short-Term Traffic Flow Prediction
