🚏 TMS-GNN: Revolutionizing Bus Passenger Flow Prediction with AI & Traffic Intelligence 🚀

🔍 The Problem: Why Do We Need Smarter Public Transit?

Every day, millions of people rely on public transportation, yet bus networks still struggle with unpredictable demand and traffic delays. Traditional forecasting models fail to adapt to real-time conditions, leading to inefficient schedules, overcrowding, and delays.

What if AI could predict the future of urban mobility? Enter TMS-GNN—a Traffic-Aware Multistep Graph Neural Network that revolutionizes how cities predict and manage bus passenger flow!

🌟 The Breakthrough: AI That Predicts Passenger Flow Like Never Before!

TMS-GNN is an advanced graph-based deep learning model that sees beyond traditional forecasting. It learns from:

✅ Real-time traffic conditions 🌍

✅ Historical passenger trends 📊

✅ Dynamic connectivity between bus stops 🚍

✅ Multi-scale time dependencies (hourly, daily, weekly) 🕒

The result? Smarter bus scheduling, fewer delays, and an enhanced commuter experience!

🧠 How It Works: The AI Behind TMS-GNN

Think of TMS-GNN as the brain of public transit 🧠 —analyzing how passengers move and how traffic impacts their journeys.

🔹 Graph Neural Networks (GNNs): Understand connections between bus stops just like social networks analyze relationships!

🔹 Traffic-Aware Learning: Unlike traditional models, TMS-GNN considers real-time road conditions 🚦.

🔹 Multi-Step Forecasting: Predicts not just the next few minutes—but the entire journey ahead!

🔹 AI-Powered Smart Scheduling: Helps cities adjust bus frequencies dynamically to reduce overcrowding!

🌍 Real-World Impact: Transforming Urban Mobility!

• 🚇 Tested on real-world bus networks across Canada & the USA



• 📈 Outperforms traditional forecasting models by up to 20%



• 🌍 Can scale to any city with high-traffic urban transit



• 🎯 Potential integration with autonomous public transport

🚀 Imagine a future where buses arrive right when you need them!

🔗 Want to See the Future of Smart Transit?

📄 Interested in this Project? → Send me an Email

💡 Get in Touch for Collaborations!