Popular GitHub Repositories for Traffic Forcasting

A Burp Suite extension that integrates OpenAI's GPT to perform an additional passive scan for discovering highly bespoke vulnerabilities and enables running traffic-based analysis of any type.

A selection of state-of-the-art research materials on trajectory prediction

Multi-camera live traffic and object counting with YOLO v4, Deep SORT, and Flask.

Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)

Reinforcement Learning environments for Traffic Signal Control with SUMO. Compatible with Gymnasium, PettingZoo, and popular RL libraries.

Slips, a free software behavioral Python intrusion prevention system (IDS/IPS) that uses machine learning to detect malicious behaviors in the network traffic. Stratosphere Laboratory, AIC, FEL, CVUT in Prague.

Govern, Secure, and Optimize your AI Traffic. AI Gateway provides unified interface to all LLMs using OpenAI API format with a focus on performance and reliability. Built in Rust.

Implementation of Single Shot MultiBox Detector in TensorFlow, to detect and classify traffic signs

An open source advanced driver assistance system (ADAS) that uses Jetson Nano as the hardware. Features: Traffic sign detection, Forward collision warning, Lane departure warning.

A Computer Vision based Traffic Signal Violation Detection System from video footage using YOLOv3 & Tkinter. (GUI Included)

A framework where a deep Q-Learning Reinforcement Learning agent tries to choose the correct traffic light phase at an intersection to maximize traffic efficiency.

Code for our SIGKDD'22 paper Pre-training-Enhanced Spatial-Temporal Graph Neural Network For Multivariate Time Series Forecasting.

:traffic_light: Traffic control for code.

35% faster than ResNet: Harmonic DenseNet, A low memory traffic network

Traffic Sign Detection. Code for the paper entitled "Evaluation of deep neural networks for traffic sign detection systems".

Graph-based Topology Reasoning for Driving Scenes

A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization

Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).

Summary of open source code for deep learning models in the field of traffic prediction

User can set up destination for any agent to navigate on Google Map and learn the best route for the agent based on its current condition and the traffic. Our result is 10% less energy consumption than the route provided by Google map