intelligent-transportation

🏆 Official implementation of LangCoop: Collaborative Driving with Natural Language

[ICLR'25] Official Implementation of STAMP: Scalable Task And Model-agnostic Collaborative Perception

Official code for "Expression is enough: Improving traffic signal control with advanced traffic state representation ".

Official code for "Efficient Pressure: Improving efficiency for signalized intersections ".

Vehicle detection and traffic analysis system leveraging YOLOv11L, with data logging and dynamic visualization of traffic metrics

🚦 Next-generation AI Traffic Management System with real-time computer vision, reinforcement learning optimization, emergency vehicle detection, and immersive 3D visualization

Multi-Vehicle V2V simulation platform with UDP mesh networking, custom 28-byte BSM protocol, collision prediction, and real-time dashboard telemetry.

Repo for the Bikestream project (aka Databike Pilot Project)

Jev-powered traffic signal control on CityFlow with structured phase and green-time decisions.

This repository addresses popular content routing design for public transportation as discussed in my Ph.D. thesis titled as: "Popular Content Distribution in Public Transportation Using Artificial Intelligence Techniques". The code used for the entire content routing design is provided twice using two programming languages, namely: Python and M…

AI-powered Smart Traffic Management System built with Kotlin Multiplatform. Real-time traffic monitoring, adaptive signal control, and emergency vehicle prioritization across Android, iOS, Desktop, and Server platforms.

Physics-injected Graph Attention Networks for trajectory prediction in heterogeneous Indian urban traffic. M.Tech thesis, IIT Guwahati.

A high-precision real-time vehicle speed detection and monitoring system built with YOLOv8 and OpenCV. Features include dual-line accuracy, multi-class classification (Car, Bus, Truck), automated CSV data logging, and overspeed alerts

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.

Multi-agent Deep RL (Double+Dueling DQN + PER) for adaptive traffic signal control on a 2x2 grid, with SUMO and built-in simulators, emergency-vehicle preemption and a real-time dashboard.

Adaptive traffic signal management with YOLOv8 + SORT: real‑time vehicle counting, line crossing, and congestion‑aware timing

Digital twin framework for 6G ITS using mmWave sensing and beamforming

End-to-end AI-driven congestion pricing prototype for Auckland using Python, SUMO, ARIMA, XGBoost, LSTM, MQTT and Gradio.

AI-powered real-time traffic monitoring and emergency priority system using FastAPI, YOLOv8, WebSockets, and React with live congestion, intersection intelligence, alerts, solar, and environmental monitoring.

Boost productivity with calendar, reminders, and focus tools for seamless workflow management.