DTUMOS, digital twin for large-scale urban mobility operating system

Abstract

The advancement of digital twin technology has significantly impacted the utilization of virtual cities in the realm of smart cities and mobility. Digital twins provide a platform for the development and testing of various mobility systems, algorithms, and policies. In this research, we introduce DTUMOS, a digital twin framework for urban mobility operating systems. DTUMOS is a versatile, open-source framework that can be flexibly and adaptably integrated into various urban mobility systems. Its novel architecture, combining an AI-based estimated time of arrival model and vehicle routing algorithm, allows DTUMOS to achieve high-speed performance while maintaining accuracy in the implementation of large-scale mobility systems. DTUMOS exhibits distinct advantages in terms of scalability, simulation speed, and visualization compared to current state-of-the-art mobility digital twins and simulations. The performance and scalability of DTUMOS are validated through the use of real data in large metropolitan cities including Seoul, New York City, and Chicago. DTUMOS’ lightweight and open-source environment present opportunities for the development of various simulation-based algorithms and the quantitative evaluation of policies for future mobility systems.

YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility

“…The most related works up to date are as follows. The paper [18] presents a comprehensive case study on the integration of UAM into the Greater Boston transportation network, focusing on a digital twin for Boston and economic analysis. It explores the potential impact of UAM on cities, addressing the need for multimodal transportation planning, economic analysis, and community integration of new air vehicles.…”

“…However, these studies seldom address AI-enhanced collision detection and situational awareness within the digital twin architecture comprehensively. Among the studies most pertinent to our research, Yeon (2023) [18] and Tuchen (2022) [19] explore UAM integration within Boston's transportation system through digital twin and economic analyses, assessing UAM's urban impact and its potential to support employment, environmental sustainability, and urban improvement initiatives. Bachechi (2022) [20] examines digital modeling for urban traffic and air quality, leveraging data and simulations to enhance urban planning, with a focus on ground mobility digital twins.…”