Abstract:

Traffic flow prediction for highway tunnels is a crucial technical foundation for rationally optimizing tunnel operation and management strategies. To address the nonlinearity, spatiotemporal coupling of traffic flow data, and the loss of disturbance information during noise reduction, a highway tunnel traffic flow prediction model combining trend modeling and residual compensation is proposed. Firstly, based on the analysis of the trend and statistical characteristics of traffic flow data, the original data is divided into two categories: passenger cars and trucks. Secondly, combined with the stationarity test results, the Gaussian Smoothing method is applied to denoise the non-stationary data. Then, both the stationary and denoised data are input into a backbone network that integrates the Convolutional Neural Network, the Bidirectional Long Short-Term Memory network and the Temporal Self-Attention mechanism to extract trend features, and the Temporal Convolutional Network is introduced to model the residual data to restore the disturbance features with temporal structure lost during denoising. Finally, the trend features and disturbance features are fused to generate the final prediction results. The hourly traffic volume data of the Qinling Zhongnanshan Highway Tunnel is used for model training and testing. The experimental results show that in the continuous prediction of the overall traffic volume, the Root Mean Square Error of the prediction by the Integrated Deep Learning Model is 42.29±5.66 pcu, and the Weighted Mean Absolute Percentage Error is 4.18±1.06%. Compared with the best results of other prediction models (64.28±7.84 pcu, 6.57±1.08%), the two types of errors are reduced by 34.20% and 36.37%, respectively. In the ablation experiment, the residual compensation module has the most significant impact on the model performance, reducing the two types of errors by 26.97% and 35.00%, respectively. The research results provide a theoretical basis for dynamic traffic flow prediction in intelligent transportation systems of highway tunnels.

Key words: intelligent transportation, traffic flow prediction, deep learning,  , highway tunnel, residual modeling