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Slide 1: Title AI-Based Public Transport Delay Prediction and Smart Route Recommendation System Presented By Vansh Jindal Manish Rana Shivam Gupta Mayank MCA Department Ajay Kum…
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Slide 1: Title AI-Based Public Transport Delay Prediction and Smart Route Recommendation System Presented By Vansh Jindal Manish Rana Shivam Gupta Mayank MCA Department Ajay Kumar Garg Engineering College (AKGEC), Ghaziabad Academic Year: 2026–27 Slide 2: Problem Statement Existing Problems Public transport is often delayed due to traffic, weather, accidents, and road construction. Passengers do not receive accurate real-time delay information. People waste time waiting at bus stops or railway stations. Existing navigation systems usually focus on the shortest route rather than the most reliable route. Unpredictable delays increase travel time, fuel consumption, and commuter frustration. Slide 3: Market Study Current Market Solutions Google Maps Moovit Citymapper Limitations Delay prediction is often limited or unavailable in many cities. Recommendations may not consider historical delay patterns. Limited AI-based predictive analysis. Some features require continuous internet connectivity. Market Opportunity Increasing number of smart cities. Growing use of AI in transportation. Rising demand for intelligent travel assistants. Slide 4: Feasibility Study Technical Feasibility Python Machine Learning Flask / FastAPI Streamlit or React MySQL / PostgreSQL Maps API Traffic API Weather API Economic Feasibility Open-source libraries reduce development cost. Low-cost cloud deployment. Easily scalable for large cities. Operational Feasibility User-friendly interface. Accessible via web and mobile. Useful for students, office employees, and daily commuters. Slide 5: Proposed Solution Develop an AI-powered system that predicts public transport delays using historical and real-time data and recommends the fastest and most reliable travel route. The system analyzes: Traffic conditions Weather conditions Historical delay records Peak-hour traffic Road congestion Distance Vehicle schedules It then recommends the best route with the estimated arrival time (ETA). Slide 6: Key Features AI-based delay prediction Smart route recommendation Real-time traffic monitoring Estimated Arrival Time (ETA) Weather-aware prediction Alternative route suggestions Delay notifications Interactive dashboard Route comparison Travel history analytics Slide 7: Proposed Platform Frontend HTML CSS JavaScript React (Optional) Backend Python Flask / FastAPI Machine Learning Scikit-learn Pandas NumPy Database MySQL PostgreSQL APIs Google Maps API OpenStreetMap API Weather API Traffic API Deployment Render Railway AWS Azure Slide 8: System Workflow User Request ↓ Collect User Location ↓ Traffic + Weather + Historical Data ↓ AI Prediction Model ↓ Delay Prediction ↓ Smart Route Recommendation ↓ Display ETA + Best Route Slide 9: Expected Benefits Reduce travel time Better journey planning Improve passenger satisfaction Reduce traffic congestion Save fuel Support smart city initiatives Improve public transport efficiency Slide 10: SDG Mapping SDG 9 – Industry, Innovation and Infrastructure Promotes smart transportation infrastructure. Uses AI for intelligent transport systems. SDG 11 – Sustainable Cities and Communities Improves urban mobility. Supports efficient public transportation. Reduces travel delays. SDG 13 – Climate Action Reduces fuel consumption. Lowers carbon emissions. Promotes sustainable transportation. Slide 11: Future Scope Integration with metro and railway systems. Live GPS tracking. Voice assistant support. Personalized travel recommendations. Deep Learning-based prediction. IoT-enabled smart buses. Android & iOS mobile application. Multi-city deployment. Slide 12: Conclusion The AI-Based Public Transport Delay Prediction and Smart Route Recommendation System leverages Artificial Intelligence and real-time data to predict delays and recommend optimal routes. It helps commuters save time, improves travel efficiency, reduces traffic congestion, and supports the vision of Smart Cities and Sustainable Transportation. Slide 13: Thank You Thank You! AI-Based Public Transport Delay Prediction and Smart Route Recommendation System Presented By Vansh Jindal Manish Rana Shivam Gupta Mayank MCA Department Ajay Kumar Garg Engineering College (AKGEC), Ghaziabad Follow Design: {"palette":["Transit blue #0052CC — primary brand and navigation paths","Electric teal #00B8D9 — real-time data signals and AI features","Warning amber #FFAB00 — congestion and delay warnings","Slate black #172B4D — primary headings and text","Station gray #F4F5F7 — secondary card backgrounds","Clean white #FFFFFF — main slide canvas"],"fonts":{"Plus Jakarta Sans":"https://fonts.googleapis.com/css2?family=Plus+Jakarta+Sans:ital,wght@0,200..800;1,200..800&display=swap","Inter":"https://fonts.googleapis.com/css2?family=Inter:ital,wght@0,100..900;1,100..900&display=swap"},"type":"Plus Jakarta Sans for energetic, high-tech headlines with medium-tight tracking; Inter for ultra-legible, clean body text and data tables.","layout":"A structured grid mimicking a live transit dashboard with persistent top-level navigation, clear card splits, and left-aligned structured columns.","framework_treatment":"Crisp cards with subtle 1px borders, colorful warning/status chips for delays, clean flowcharts utilizing actual route-line aesthetics, and high-contrast tables for the tech stack.","feels_like":"A cutting-edge urban mobility dashboard meets a premium smart-city monitoring platform"}
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