Implementation of robust parameter inversion for SUMO bus simulation using multi-level calibration (Micro/Macro) and Bayesian surrogate models (Kriging/RBF) to match real-world transit data.

FMU data standard and data export with rich metadata in the FMU context

This repository contains the code of the algorithms and tools developed in Work Package 4 of Artificial Intelligence for Urban Low-Emission Autonomous Traffic (AIForLEssAuto) project

Evaluate the effectiveness of Parking Guidance strategies through microscopic simulations using SUMO simulator.

Repository with windows libraries used in SUMO project

24AL733 - Connected Vehicles and Security - 2nd Sem - M.Tech Automotive Electronics - ASE, CBE

Development of a real-time traffic simulation application using Java and other support tools

A multi-stack, ETSI compliant, V2X framework for ns-3.

Interaction with Sumo in the FMU context

Upload to Sumo in the FMU context

Thin python wrapper to interact with Sumo API

This project implements a hybrid quantum-classical approach to optimize traffic signal timings in real-time. It leverages the Quantum Approximate Optimization Algorithm (QAOA), simulated via IBM Qiskit, to determine optimal signal phases that minimize congestion and vehicle wait times. The system integrates the SUMO (Simulation of Urban Mobility)

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.

This project is the realization of a Multi-Mode Mobile Robot based on the Arduino UNO platform, designed for optimal high-speed line following. It combines two distinct control modes to offer maximum flexibility.

Social forces for microscopic traffic simulation of cyclists.

A framework for training and evaluating multi-agent reinforcement learning models for adaptive traffic light control in SUMO.

a website for a Fantasy Sports game following the action of professional Ozumo

Repo for trying out SUMO

Upload files from reservoir simulators to azure assisted by sumo

An AI-powered traffic light controller that uses Deep Reinforcement Learning (DQN) with Python, TensorFlow, and SUMO to intelligently optimize traffic flow and minimize vehicle wait times.