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AI Traffic Optimisation System

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AI-Driven Predictive Traffic Flow Optimisation System

👥 Research Team

📈 Performance Targets

Project Goal: Target 15-20% reduction in urban traffic congestion for the Athlone "Orange Loop" using Reinforcement Learning.

  • Average Travel Time (ATT): Target -15%
  • Mean Queue Length (MQL): Target -20%
  • Data Integrity: TLS 1.3 secured telemetry pipeline
🌐 API Gateway 🤖 Inference Service
App App
Swagger UI Docs Swagger UI Docs

🏗️ System Architecture

This repository implements a Cloud-Native Microservices Pipeline designed for the Athlone "Orange Loop" case study.

  • Traffic Monitoring Gateway (Java/Spring Boot): Manages secure telemetry ingestion and orchestrates service communication.
  • RL-Inference Service (Python/FastAPI): Hosts a trained PPO (Proximal Policy Optimization) model to predict optimal signal timings based on real-time traffic density.
  • Simulation Layer (SUMO): Integrated high-fidelity environment for testing adaptive signal logic against baseline fixed-time controllers.

This system is specifically modeled to address the saturation flow rates and signal-timing patterns of the Athlone 'Orange Loop' corridor, providing a scalable template for Smart City traffic management in regional Irish hubs.

For more details see System Architecture page.