AI powered traffic management system.pptx

Artificial intelligence (AI) is used in traffic management to reduce congestion, improve safety, and optimize traffic flow. AI can analyze data from cameras, sensors, and other sources to identify patterns and trends. This data is used to adjust traffic signals and reroute vehicles.

1. MAHENDRA ENGINEERING COLLEGE (Autonomous) Name of the College : MAHENDRA ENGINEERING COLLEGE Name of the Team : TECH TREK Name of the Team Leader : HARINI N Name of the Department : CSE Problem Statement : AI- POWERED TRAFFIC MANAGEMENT SYSTEM : Develop areal time traffic monitoring and optimization system using AI. Date : 21/02/2025 CATCH ‘25 – Hackathon

2. CREW DETAILS NAME OF THE STUDENT YEAR DEPARTMENT DESIGNATION E MAIL ID HARINI N III CSE Team Leader [email protected] ABARNASRI S III CSE Team Member 1 [email protected] ELAKKIYA E III CSE Team Member 2 [email protected] INDHUJA T III CSE Team Member 3 [email protected] DEEPADHARSHINI K III CSE Team Member 4 [email protected]

3. ABSTRACT IDEA/APPROACH DETAILS FLOW CHART TECH STACK AND DEPENDENCIES ANALYSIS PART OF THEIR PROJECT METHODOLIGIES OR ALGORITHMS FOCUSED MODULE LIST EXPECTED OUTCOME CONCLUSION AND FUTURE WORK SOCIAL RELEVANCE

4. ABSTRACT Our AI-Powered Traffic Management System aims to optimize urban traffic flow using open- source AI models and real-time data analysis. The system leverages computer vision, reinforcement learning, and IoT integration to detect congestion, predict traffic patterns, and adjust signals dynamically. Key Features: Real-time traffic monitoring using YOLOv5 & OpenCV. Traffic flow prediction with LSTMs & Reinforcement Learning. Sensor fusion for better weather-resistant detection. MQTT & Node-RED for seamless integration with traffic lights. Secure & private edge AI ( TinyML , OpenVINO , Snort IDS)

5. IDEA/APPROACH DETAILS Problem: Existing traffic systems face high costs, privacy concerns, and inefficiency. Approach : Develop a low-cost AI-powered adaptive system using open-source software. How It Works: Capture real-time traffic data from cameras, sensors, and crowdsourced sources. AI analyzes vehicle count, congestion levels, and unexpected events. The system dynamically adjusts signals and suggests optimized routes. Traffic authorities access a dashboard for monitoring & decision-making.

6. FLOW CHART Traffic Data Collection AI Based Vehicle Detection Preprocessing & Data Aggregation AI Decision Making Feedback & Continuous Improvement Route Optimization & Navigation Traffic Signal Control & Optimization Data Input Feature Extraction Data Analysis Route Planning Navigation Feedback AI Decision Making Traffic Adjustment Feedback Loop

7. METHODOLIGIES OR ALGORITHMS FOCUSED Computer Vision (YOLOv5 + OpenCV): Detects vehicles & congestion levels. Reinforcement Learning (Stable-Baselines3): Adaptive signal control. LSTMs (TensorFlow/ PyTorch ): Traffic pattern prediction. Sensor Fusion (ROS + TinyML ): Handles bad weather conditions. SUMO (Simulation of Urban Mobility): AI model testing in a virtual city.

8. ANALYSIS OF THE PROJECT Strengths: Low-cost solution using free open-source software Privacy-focused – processes data locally on edge devices Adaptable AI system – Reinforcement Learning improves over time Works with existing traffic infrastructure (easy to integrate via MQTT) Challenges & Solutions : Challenge: Low visibility in fog → Solution: Sensor Fusion with radar + AI Challenge: High processing power required → Solution: Optimize models using OpenVINO & ONNX .

9. EXPECTED OUTCOME Real-time traffic management with AI-powered dynamic signal control. Live traffic dashboard for authorities with congestion & rerouting data. Accident & anomaly detection with automatic alerts. Weather-aware AI traffic predictions for better planning.

10. SOCIAL RELEVANCE Reduces Traffic Congestion: Optimized signals decrease waiting time. Saves Fuel & Reduces Pollution: Less idling leads to lower emissions. Enhances Road Safety: AI detects & responds to accidents in real-time. Reduces Government Expenses: Uses existing infrastructure with free AI models.

11. CONCLUSION AND FUTURE WORK Conclusion: Our AI-powered traffic management system provides a cost-effective, adaptive, and privacy-focused solution for smart cities. It reduces congestion, improves road safety, and integrates seamlessly with existing infrastructure. Future Work: 🔹 AI-powered pedestrian safety detection 🔹 Integration with emergency vehicle routing systems 🔹 Machine learning models for long-term traffic pattern analysis

12. THANK YOU !