The model is overfitting in low-traffic areas, capturing noise as if it were a pattern, and underfitting in high-traffic areas, failing to capture the complexity of congested scenarios.

The AUC metric is inherently unsuitable for evaluating the performance of image segmentation models, regardless of the application context.

The training dataset was disproportionately composed of images from congested areas, leading to a bias that affects model performance in less congested scenarios.

The model suffers from the vanishing gradient problem, where gradients become too small during backpropagation, significantly hindering learning in layers close to the input.

Both A and C are correct, indicating that the model's issues stem from overfitting in low-traffic conditions and a biased training dataset favoring congested scenarios.