A Traffic Flow Congestion Prediction Model Based on LSTM

Highway congestion has become a normal social problem that needs to be managed more accurately and deeply. In this paper, a traffic flow congestion prediction model is established based on LSTM, and the traffic parameters obtained from four observation points of China's long and deep high-speed video are used for training, so as to provide real-time early warning before the continuous congestion of the road section between the third and fourth points. The results show that the model constructed in this paper can reach 89.25% accuracy after predicting 400 times steps in 50 times Windows. After further evaluation of the constructed traffic flow congestion prediction model, it is found that the mean square error (MSE), mean absolute error (MAE) and R2 is 0.0897, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.2334,0.6028$</tex>, respectively, revealing that the model can well predict the future traffic congestion.