Mobility Digital Twin: Concept, Architecture, Case Study, and Future Challenges

Abstract

A Digital Twin is a digital replica of a living or nonliving physical entity, and this emerging technology attracted extensive attention from different industries during the past decade. Although a few Digital Twin studies have been conducted in the transportation domain very recently, there is no systematic research with a holistic framework connecting various mobility entities together. In this study, a mobility digital twin (MDT) framework is developed, which is defined as an artificial intelligence (AI)-based data-driven cloud–edge–device framework for mobility services. This MDT consists of three building blocks in the physical space (namely,Human,Vehicle, andTraffic), and their associated Digital Twins in the digital space. An example cloud–edge architecture is built with Amazon Web Services (AWS) to accommodate the proposed MDT framework and to fulfill its digital functionalities of storage, modeling, learning, simulation, and prediction. A case study of the personalized adaptive cruise control (P-ACC) system is conducted, which integrates the key microservices of all three digital building blocks of the MDT framework: 1) theHuman Digital Twinwith user management and driver type classification; 2) theVehicle Digital Twinwith cloud-based advanced driver-assistance systems (ADAS); and 3) theTraffic Digital Twinwith traffic flow monitoring and variable speed limit. Future challenges of the proposed MDT framework are discussed toward the end of the article, including standardization, AI for computing, public or private cloud service, and network heterogeneity.