smart-mobility

Eclipse MOSAIC is a Multi-Domain and Multi-Scale Simulation Framework for Automated and Connected Mobility Scenarios.

Berlin Sumo Traffic (BeST) Scenario

Data from 267 bike sharing schemes across Europe - 43 million km & 88000 bikes

🏆 1st Place Winner, Microsoft AI+ Academy | LightBus: AI-powered bus guidance for visually impaired users using Azure ML, Computer Vision, OCR, STT/TTS, and real-time transit data.

mobinha is a self driving system implementation project of INHA university.

Service that matches signal lane geometries to bike routes (IEEE ISC2 2022, ACM SIGSPATIAL 2023)

A comprehensive ride-pooling simulation engine featuring VRP optimization (OR-Tools), dynamic pricing algorithms and ML-based demand prediction.

Next-Generation Intelligent Decision Support System (IDSS) for Indian Railways. A "Co-Pilot" for controllers that uses a Hybrid Intelligence Architecture (Reinforcement Learning + Operations Research) to optimize train scheduling, minimize delays, and ensure safety via Explainable AI. 🚂🤖🇮🇳

Smart mobility web app that recommends safer urban routes using real-time signals, AI safety scoring, and explainable insights..

An AI-driven Smart Mobility solution for India, providing EV trip planning, on-route charging stops, and CO2 emission savings calculations.

AI-powered traffic management system using YOLOv8 + edge computing to dynamically optimize signal timings and create emergency green corridors for ambulances in real time.

GNSS Drift Doctor – Accuracy & Compliance Tracker

AI-powered EV charging platform with nearest station finder, CO₂ comparison, smart route planning, and AI-based EV infrastructure location analysis using Map and Weather APIs.

This repository contains the 3D models developed for better visualization of a smart mobility system Hyperloop, first publicly mentioned the Hyperloop by Elon Musk in 2012.

Template repository for Smart Mobility Lab research papers and datasets

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.

Welcome to Lab-42 - Open-Techlab for Makers 🤖🛠️

SynapticGrid is an AI-driven system designed to make cities more efficient, sustainable, and livable by optimizing smart energy grids, waste management, and traffic flow through IoT sensors, real-time data processing, and reinforcement learning algorithms. The modular platform continuously learns and improves, helping urban environments