Computational framework for reinforcement learning in traffic control

NetLimiter-like bandwidth limiting and QoS for Linux

Traffic Lights Control with Deep Learning

Adaptive real-time traffic light signal control system using Deep Multi-Agent Reinforcement Learning

We developed a system that leverages on YOLO Machine Learning Model for managing the traffic flow based on the vehicle density.

The name says everything...

Using reinforcement learning and genetic algorithms to improve traffic flow and reduce vehicle waiting times in a single-lane two-way junction simulator by coordinating traffic signal schedules.

This model is very useful to detecting cars, buses, and trucks in a video.

A dynamic traffic control system using image processing

Python API for the SUMO environment of Plymouth Rd.

Geo-Distributed Infrastructure Emulation using Traffic Shaping

NOCD is a micro NOC (Network Operations Center) that aims to help people with little to no experience in networking to create and manage Linux network.

The PiWall project is a Raspberry Pi based, secure and standalone low-level (Layer 2 OSI) network firewall with enchanced flexibility as its rules and policies may directly be defined in Python (3.4).

Documentation:

Implementation of Universal Multi-Agent Reinforcement Learning via Policy Decoupling with Transformers (UDPET) on Multi-Agent Traffic Control

Implementation of the FQ-PIE algorithm in ns-3

This research focused on developing a mainline metering policy for freeways. The mainline metering policy was controlled by a DRL agent, alongside an ALINEA algorithm to control the ramp metering policy. To model and evaluate the effectiveness of these policies, we utilized Vissim, a traffic simulation software.

Web scrapping and Image processing modules for traffic light control assistance for an Isolated Intersection in Dhaka City

Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning