A lightweight Python-based Web-GUI for Linux traffic control (tc) to set, view and delete traffic shaping rules.
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Updated
Jun 17, 2024 - Python
A lightweight Python-based Web-GUI for Linux traffic control (tc) to set, view and delete traffic shaping rules.
Traffic detection and notify C&C (prototype)
Official code for article <DynamicLight: Dynamically Tuning Traffic Signal Duration with DRL>.
The official Python SDK for adding Stanza Systems fault tolerance to your python 3 service.
Computational framework for reinforcement learning in traffic control
This model is very useful to detecting cars, buses, and trucks in a video.
NetLimiter-like bandwidth limiting and QoS for Linux
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 Flight Control service coordinates a drone fleet by managing flight permissions and creating flight routes. This is a part of the Autonomous Drone Delivery System, which I implemented for my Bachelor's Thesis.
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 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.
The name says everything...
Dynamic Real-time Traffic Control System
An environment for core simulation based on Docker Swarm.
Traffic Lights Control with Deep Learning
Implementation of Universal Multi-Agent Reinforcement Learning via Policy Decoupling with Transformers (UDPET) on Multi-Agent Traffic Control
An AI project about dynamic traffic light configuration which use openCV2 to analyze the current scene. Using colored blocks as a replacement for motorcycle, cars and trucks.
Experiments in which Deep Reinforcement Learning agents try to choose the correct traffic light phase at an intersection to maximize the traffic efficiency. (Deep Q-Learning and Independent Deep Q-Networks)
We developed a system that leverages on YOLO Machine Learning Model for managing the traffic flow based on the vehicle density.
Python API for the SUMO environment of Plymouth Rd.
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