Traditional fixed-time traffic signals fail when weather degrades driving speed and visibility.

I built an adaptive traffic signal control model combining computer vision and environmental data to dynamically adjust green light durations during rain, fog, and poor road conditions.

Identical preprocessing and weather-shift domain adaptation proved critical to maintaining object detection accuracy when camera lenses face adverse conditions.