AI-powered intelligent traffic management and congestion prediction system using Fuzzy Logic, Machine Learning, and Deep Learning for smart city traffic optimization.
TrafficFlow AI is a hybrid AI-based traffic control and prediction platform designed to improve urban traffic management using:
- Artificial Neural Networks
- LSTM Networks
- Random Forest
- LightGBM
- Fuzzy Logic
- Graph Theory
- Queue-based Traffic Modeling
The system predicts congestion levels, optimizes signal timing, estimates travel time and CO₂ emissions, and provides real-time monitoring dashboards for intelligent traffic administration.
- Predicts traffic congestion levels
- Estimates traffic volume and travel time index
- Supports real-time traffic analysis
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Combines fuzzy logic with AI models
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Human-like reasoning using:
- Low congestion
- Medium congestion
- High congestion
- LSTM
- Sequential Neural Networks (SNN)
- Random Forest
- LightGBM
- Adaptive signal timing
- Route optimization
- Delay reduction
- Flow efficiency analysis
- CO₂ emission estimation
- Traffic efficiency metrics
- Sustainable traffic management support
- Dashboard visualization
- Incident monitoring
- Live congestion analysis
- Traffic analytics
The platform consists of 3 major layers:
- Traffic datasets
- IoT sensor inputs
- CCTV feeds
- Manual traffic parameters
- Fuzzification
- Rule-based inference
- ML/DL model execution
- Interactive dashboards
- Real-time alerts
- Adaptive traffic optimization
The project compares baseline AI models with fuzzy-enhanced models for urban traffic prediction.
- Fuzzy-enhanced Random Forest and LightGBM achieved near-perfect regression accuracy
- LSTM achieved strong congestion classification performance
- Hybrid fuzzy-AI models improved interpretability and decision-making
- Real-time adaptive traffic management improved efficiency and reduced delays
- Python
- JavaScript
- HTML/CSS
- TensorFlow
- Keras
- Scikit-learn
- LightGBM
- NumPy
- Pandas
- Fuzzy Logic
- Neural Networks
- Deep Learning
- Queue Theory
- Graph Theory
- Smart City Systems
- Traffic Congestion Prediction
- Traffic Volume Estimation
- Travel Time Prediction
- Adaptive Signal Control
- Traffic Optimization Dashboard
- AI Monitoring System
- Reinforcement Learning integration
- CNN + Vision Transformer traffic analysis
- Real-time cloud deployment
- Edge AI traffic processing
- Multi-city scalable infrastructure
- Autonomous traffic signal systems
Fuzzy Feature Engineering for Urban Traffic Prediction: A Comparative Performance Analysis of LSTM, SNN, Random Forest, and LightGBM
- Kaushal S
- Rishitha C
- Divya Rithanya S
- Karthikeya Y