Smart Transport System Using LabVIEW and Machine Learning

This study provides a new method of controlling traffic signals at a congested intersection by combining LabVIEW, machine learning, and image processing. Incoming vehicles are tracked in real time by placing cameras at key locations around the intersection. In order to optimize traffic flow and avoid congestion, machine learning algorithms dynamically modify signal timings based on the analysis of collected photos to detect traffic volume. LabVIEW makes it easy to integrate control logic and sensor data, which makes signal management effective. The system also uses Firebase as a database to prioritize special vehicles for accelerated transit and record operational data. The hardware platform is the Raspberry Pi, which guarantees dependable and strong performance. The suggested method provides a viable framework for intelligent traffic control, making it safer and more effective urban transportation systems.