With the comprehensive deployment of urban traffic monitoring systems, the massive amount of video data poses severe challenges to storage and transmission systems, thus highlighting the increasing importance of video compression technology, which alleviates these pressures by reducing data volume through algorithms. However, compression processing and video transmission inevitably lead to a certain degree of degradation in video quality, potentially affecting the accuracy and efficiency of monitoring negatively. Currently, the evaluation system for the compression quality of intelligent traffic monitoring videos is not yet well-established and cannot comprehensively and accurately reflect the practical application effects of compressed videos. To address these limitations, we propose a method for evaluating the quality of compressed videos in intelligent traffic surveillance systems. This method comprehensively assesses the quality level of compressed videos from the aspects of single-frame image pixels, single-frame image structure, inter-frame spatiotemporal information, and the application performance of traffic compressed videos. By employing the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), RSR (Rank Sum Ratio), and a fuzzy combination of both methods, it aims to provide a comprehensive evaluation of the overall performance of compressed videos in intelligent traffic surveillance systems.