To improve the contrast, clarity, and color fidelity of the traffic image in haze weather and reduce the negative impact of image degradation, a haze traffic image enhancement algorithm based on fast-guided filtering smoothing constraint for Retinex and adaptive fractional differential was proposed herein. First, an original image was converted from RGB space to YCbCr space, and the brightness component Y was extracted to construct the initial image . Second, a variational model was constructed, and the fast-guided filter was used to construct the smoothing constraints of the objective function to accurately estimate the initial irradiation component . Then, the Retinex model was used to obtain the initial reflection component , and the adaptive fractional differential mask was applied to enhance in order to obtain ,which was the enhancement result of the Y component. The method showed good performance in terms of image noise suppression and detail enhancement. Finally, the reflection component was converted from YCbCr to RGB space by combining with the color difference information of Cb and Cr, to obtain the final enhanced image . In this study, the contrast experiments of different haze traffic images were tested. The experimental results indicate that the standard deviationand average gradient of the new method are at least 1.12 times and 4 times higher than those of the original image, and the information entropyis at least 4.76% higher.The comprehensive performance of the proposed method is better than that of other comparison algorithms. Thus, the method is satisfactory for image enhancement and detail retention.It effectively improves the color fidelity, intensity contrast, and texture clarity forroad traffic images in haze weather.Moreover, it makes the image more visible, and the color more natural.