This folder contains code that can be used by alternative Flask implementations, for example Quart. The code therefore cannot do any IO, nor be part of a likely IO path. Finally this code cannot use the Flask globals.
🚦 UrbanFlow AI AI-Driven Predictive Urban Navigation & Mobility Optimization System An intelligent navigation platform that predicts traffic congestion, recommends optimal departure times and optimizes urban mobility using AI and Machine Learning.
📌 Problem Statement Urban commuters in metropolitan cities such as Mumbai face significant challenges including: • Unpredictable traffic congestion • Inefficient departure planning • Difficulty finding available parking spaces • Increased travel time and fuel consumption • Existing navigation platforms mainly provide real-time traffic updates, but they lack predictive capabilities to forecast congestion in advance.
💡 Solution UrbanFlow AI introduces a predictive mobility platform that leverages: • Historical traffic data • Machine learning models • User behavior analysis to provide smart travel recommendations before the journey begins. The system predicts future traffic patterns, recommends optimal departure times, and estimates parking availability.
🚀 Core Features 🔮 Predictive Traffic Forecasting Uses LSTM neural networks to predict future traffic congestion based on historical traffic patterns. ⏱ Smart Departure Planning Recommends the best time to leave in order to avoid peak congestion. 🅿 Parking Intelligence Predicts parking availability near destinations using historical parking usage data. 👤 Personalized Navigation Learns from user travel behavior to provide personalized route suggestions. 🌍 CO₂ Emission Estimation Calculates carbon emissions for each route, enabling eco-friendly travel choices. 🗺 Route Optimization Users can choose routes based on: • Fastest route • Eco-friendly route • Safest route 📊 Interactive Dashboard Displays: • traffic predictions • congestion heatmaps • route recommendations • trip analytics
🏗 System Architecture The system follows a three-layer architecture. WhatsApp Image 2026-03-10 at 8 01 24 PM
⚙️ Tech Stack Frontend • React 18 • React Router • TailwindCSS • Recharts • Lucide React Icons Backend • Python • Flask • SQLAlchemy Machine Learning • TensorFlow / Keras (LSTM) • Scikit-learn • Pandas • NumPy Database • SQLite (development) • PostgreSQL (production) • Redis (caching) APIs • RESTful APIs • Maps & Traffic APIs
🧠 Machine Learning Workflow 1️⃣ Data Collection Traffic datasets and parking data are collected. 2️⃣ Data Processing Cleaning, feature extraction, and normalization. 3️⃣ Model Training LSTM models are trained on time-series traffic patterns. 4️⃣ Prediction The system predicts: • hourly traffic congestion • best departure times • parking availability
🛠 Installation Guide Clone Repository git clone https://github.com/Anisca-hub/TechDiva-s_AIML_Horizon26 cd urbanflow-ai Backend Setup cd backend python -m venv venv source venv/bin/activate pip install -r requirements.txt python run.py
Backend runs at: http://localhost:5000
Frontend Setup cd frontend npm install npm start Frontend runs at: http://localhost:3000
📊 Example Use Case 1️⃣ User enters destination 2️⃣ System predicts traffic conditions 3️⃣ AI recommends optimal departure time 4️⃣ Routes are generated 5️⃣ CO₂ emissions are calculated 6️⃣ User selects best route
🌱 Impact UrbanFlow AI helps: • Reduce travel time • Improve traffic efficiency • Minimize congestion impact • Support sustainable urban mobility
🔮 Future Improvements • Real-time reinforcement learning for route optimization • Integration with public transport systems • Smart city traffic signal coordination • AI congestion simulation for urban planning
🤝 Contributors Team Members: Anisca-hub SakshiBhaingade Pranali Bamne Siddhi4503
⭐ If you like this project, please star the repository!
