ML-Powered Intelligent Traffic Signal Optimization System with LiDAR-based Vehicle Detection

This project develops an intelligent traffic signal optimization system that enhances urban traffic flow using LiDAR-based vehicle detection and machine learning algorithms. LiDAR sensors continuously monitor vehicle movements at intersections, collecting real-time data on traffic density and vehicle speed. This data is then transmitted to a cloud platform for analysis, enabling the system to adapt traffic light timings based on real-time traffic conditions.

Machine learning algorithms process the vehicle data to predict traffic patterns, optimize signal timings, and reduce congestion, ensuring smoother traffic flow and minimizing wait times at intersections. This system is ideal for city planners, transportation authorities, and smart city developers aiming to improve traffic efficiency and reduce road congestion in urban areas.

A mobile app integrates with the system to provide real-time traffic updates, signal status, and route optimization suggestions, ensuring that drivers benefit from more efficient routes and reduced travel times.

Key Features:

• Real-time vehicle detection and traffic flow monitoring using LiDAR and IoT integration.
• ML-powered traffic signal optimization and congestion reduction.
• Cloud-based data storage for long-term traffic analysis and optimization.
• Mobile app with real-time updates, signal status, and route suggestions.

This project includes source code, complete hardware setup, detailed analysis, and a 200-page project report in soft copy, making it an excellent choice for students exploring IoT and AI applications in smart transportation systems.