Decentralized Deep Reinforcement Learning based Real-World Applicable Traffic Signal Optimization

dITC through RL Code Foundation

SUMO

SynapticGrid is an AI-driven system designed to make cities more efficient, sustainable, and livable by optimizing smart energy grids, waste management, and traffic flow through IoT sensors, real-time data processing, and reinforcement learning algorithms. The modular platform continuously learns and improves, helping urban environments

An open-source Python implementation and evaluation of the Priority Bidding Mechanism (PBM) for adaptive traffic signal control. This is an active collaboration between the Illinois Mathematics and Science Academy and Southern Illinois University, Carbondale.

An intelligent traffic optimization system using Deep Reinforcement Learning (DQN & Actor-Critic) to control vehicle speed and lane changes for improved traffic flow and safety.

A Traffic Optimization system in C++ using a rudimentary ant colony optimization technique.

DeepTrafficQ is a reinforcement learning-based traffic signal control system that uses Deep Q-Networks (DQN) to minimize vehicle waiting times at a 4-way intersection. By leveraging Q-learning with experience replay and a convolutional neural network (CNN), the agent dynamically adjusts traffic light phases to optimize traffic flow.

This project uses reinforcement learning to optimize traffic signals, reducing congestion and improving flow through dynamic adjustments and simulation analysis.

a prototype dashboard interface for the EV management via traffic and battery SoC, SoH optimisation

Network Dynamics and Learning — Coursework and projects exploring network theory, optimization, and learning on graphs. Includes Jupyter notebooks with simulations, analysis, and visualizations using Python

Analysis of modern network protocols designed to maintain data integrity and availability in adversarial environments.