Short term traffic flow prediction of expressway service area based on STL-OMS

Abstract

To improve the management ability of expressway service area and formulate strategies for traffic flow changes in time, a short-term traffic flow prediction model is proposed. Firstly, cleaning the extracted data according to the rules and constructing four kinds of features (temporal, spatial, statistical and external factors). Then, a short-term traffic flow prediction model WADNN (wide attention and deep neural networks) is constructed. In the model, LSTM (long and short-term memory neural network), CNN (convolution neural network) and self-attention mechanism are used to extract different features respectively. In addition, the STL (Seasonal-Trend decomposition procedure based on LOESS) algorithm is used to decompose the traffic flow to fit the trend better. For the three decomposed components, the OMS (optimal model selection) operation is carried out, the prediction of each component is added to obtain the final predicted value, and the model effect is measured according to the RMSE (root mean square error), MAE (mean absolute error), MAPE (Mean Absolute Percentage Error) and R2 coefficient. Finally, taking an expressway service area as an example, the proposed model is compared with some common models. The results show that the prediction effect of WADNN is better and STL-OMS can further improve the accuracy.