Attention mechanism-driven freeway traffic flow prediction model under the fusion of ETC gantry and multivariate monitoring data

ETC gantry data and other monitoring data provide data support for highway traffic flow prediction, for this reason, this paper proposes an attention mechanism-driven traffic flow prediction model to scientifically coordinate and schedule highway traffic conditions. Based on the fusion of multivariate monitoring data, the model utilizes ConvLSTM to generate global location coding, learns the data characteristics through the jump expansion attention structure, and completes the traffic flow prediction using the mask attention structure. The example analysis verifies that the predicted values of traffic flow and speed of this paper’s model are closer to the real values, and compared with the models such as ARIMA, LSTM and BiLSTM, this paper’s model has lower values of RMSE and MAE indexes in the prediction of traffic flow and speed, and the prediction error is smaller. The article also validates the model’s prediction under 5min, 15min and 30min prediction lengths, showing that the model has excellent performance and good prediction stability.