Aiming at challenges such as low contrast， detail loss， blurring， and distortion in foggy images of traffic roads， a road traffic image dehazing method was proposed based on Wavelet DehazeFormer model.To enhance dehazing capability of the model，a Wavelet DehazeFormer network with an encoder-decoder structure was constructed. The encoder employed the DehazeFormer and the Selective Kernel Feature Fusion module （SKFF） as basic units in a cascaded manner. The encoding section consisted of three levels of such basic units to fuse the original information and post-dehazing information， capturing critical features more effectively. The middle feature layer adopted a local residual structure， incorporating the Convolutional Block Attention Module （CBAM） for different weights assigned to features of different levels. Additionally， a Content-guided Attention based Mixup Fusion Scheme （CGAFusion） was introduced to adjust features by learning spatial weights. The decoder comprised DehazeFormer and SKFF， utilizing pointwise convolution to reduce parameter count while maintaining network performance. Jump connections introduced wavelet transform to analyze feature maps of different scales， obtaining high and low-frequency features at various resolutions. This helped amplify details of the traffic fog image for enhanced texture retention in the dehazed image. Finally， the original image and the decoded output feature map were fused to gather more detailed information.Experimental results demonstrate that， in comparison with the baseline DehazeFormer network， the proposed method achieves a PSNR improvement of over 1.32 on public datasets and 0.56 on synthetic datasets. The SSIM metric is increased by 0.015 or more， and there is a substantial reduction in MSE， with a decrease of 23.15 or more. The entropy metric shows an increase of 0.06 or more. The proposed dehazing algorithm exhibits excellent performance in enhancing contrast， reducing fog-induced blurring and distortion， which preserves details in traffic fog images.This contributes to the advancement of intelligent visual surveillance and management in road traffic.