Intelligent Traffic Management System

Final year project. A machine learning system that detects vehicles per lane and adjusts traffic signal timing based on real-time density instead of fixed timers.

Motivation

Fixed-timer traffic signals ignore actual traffic conditions. A lane with three cars gets the same green time as a lane with thirty. The result is unnecessary congestion on busy lanes and wasted green time on empty ones.

Design

YOLO model trained on the Indian Driving Dataset for vehicle detection. The pipeline runs non-max suppression, counts vehicles per lane, and feeds the count into a signal timing function that allocates green time proportionally. When conditions fall outside normal parameters, the system falls back to static timing.