Spatiotemporal Dynamic Forecasting and Analysis of Regional Traffic Flow in Urban Road Networks Using Deep Learning Convolutional Neural Network

Abstract

The purpose is to explore the spatial-temporal dynamic prediction performance of urban road network traffic flow based on convolutional neural networks (CNN) of deep learning. A dynamic prediction model of road network traffic flow based on STGCN-BiLSTM (spatial-temporal graph convolution network Bi-directional Long Short-Term Memory) is designed in view of the complex and highly nonlinear traffic data in the actual environment. Finally, the simulation experiment is conducted on the constructed model to verify its performance which is compared with that of the LSTM (Long Short-Term Memory) model, CNN model, RNN (Recurrent Neural Network) model, AlexNet model and STGCN model. The results show that the root mean square error, mean absolute error and mean absolute percentage error of the proposed algorithm model are 4.60%, 5.46% and 7.73%, respectively. The training time is stable at about 45s, the test time is stable at about 33s, and the delay is close to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.86\times 10 ^{-4}\text{s}$ </tex-math></inline-formula> . Additionally, the traffic flow of the test section in the next 15 min, 30 min, 1 h and 2 h is further predicted. Compared with other algorithms, the predicted value of the proposed algorithm is the closest to the actual value, along with the best prediction effect. Therefore, the constructed dynamic prediction model of road network traffic flow can achieve high accuracy and better robustness under the premise of low error, which can provide experimental basis for the spatial-temporal dynamic digital development of transportation in smart cities.