The constraint means you cannot use standard end-to-end Deep Learning models where the raw video or image pixels are fed directly into a neural network, and the entire network is then tuned using back-propagation to predict the congestion rating.

We can use computer vision/deep learning models for feature extraction from the raw videos, then train classical ML models on them. So the flow would be computer vision - time series feature engineering - classical ML prediction