This study explores the impact of video resolution on the performance of machine learning-based traffic monitoring systems. Using a combination of empirical analysis and theoretical modeling, we assess how different resolutions affect detection accuracy, resource consumption, and computational efficiency. While other factors such as noise level, or compression artifacts also influence performance, resolution was chosen as a primary variable due to its critical role in balancing detail capture and computational cost. Higher resolutions can enhance object detection accuracy but also significantly increase data processing demands, making resolution a key trade-off in designing efficient surveillance systems. Findings of this study show significant insights into these trade-offs, guiding transportation authorities and system developers in making informed decisions to design scalable traffic monitoring solutions that meet the demands of modern urban environments.