AURAI – AI-Driven Transit Optimization Simulator
AURAI is an interactive transit simulation that models a large urban subway system and uses reinforcement learning to optimize train frequencies based on passenger demand, congestion, and operating cost. The project combines graph theory, simulation modeling, and real-time visualization.
FEATURES
Large-scale subway network modeled as a graph of stations and connections
Real-time passenger flow simulation with time-of-day demand patterns
Live performance metrics including congestion, cost, and customer satisfaction
Reinforcement learning agent that dynamically adjusts train frequencies
Optional AI-generated daily performance summaries
Interactive graphical interface for visualization and control
TECH STACK
Language
Python 3
Libraries
pygame – graphics, UI, and real-time interaction
networkx – graph modeling and routing
numpy – numerical operations and calculations
Core Concepts
Graph theory
Reinforcement learning (Q-learning)
Simulation modeling
AI-assisted decision making
INSTALLATION
Requirements
Python 3.9 or newer
pip
Install dependencies pip install pygame networkx numpy
HOW TO RUN
Run the main application file python app.py
The application window will automatically scale to your screen resolution.
HOW THE SIMULATION WORKS
The subway system is represented as a graph where nodes are stations and edges are track segments. Passengers are generated dynamically based on the time of day, with higher demand during morning and evening rush hours.
Passengers travel through the network using shortest-path routing. Each connection tracks passenger load, train count, capacity, and congestion levels. These values directly influence system performance metrics.
AI AGENT
The AI agent uses reinforcement learning to decide whether to increase, decrease, or maintain train frequency on different subway lines. Decisions are based on current congestion levels and time-of-day conditions.
The reward function balances customer satisfaction against operating cost, allowing the agent to improve its strategy gradually over time.
NOTES
This project is a simulation intended for experimentation and visualization. Passenger behavior and system dynamics are simplified to improve clarity and performance.