Smart Mobility in the Cloud: Enabling Real-Time Situational Awareness and Cyber-Physical Control Through a Digital Twin for Traffic

Xu, Haowen; Berres, Andy; Yoginath, Srikanth B.; Sorensen, Harry; Nugent, Phil J.; Severino, Joseph; Tennille, Sarah A.; Moore, Alex; Jones, Wesley; Sanyal, Jibonananda

doi:10.1109/tits.2022.3226746

Title: Smart Mobility in the Cloud: Enabling Real-Time Situational Awareness and Cyber-Physical Control Through a Digital Twin for Traffic

Journal Article · 16 January 2023 · IEEE Transactions on Intelligent Transportation Systems

DOI:https://doi.org/10.1109/tits.2022.3226746· OSTI ID:1958153

^[1];

^[1]; Sorensen, Harry ^[2];

^[3]; Severino, Joseph ^[2];

^[1]; Moore, Alex ^[4]; Jones, Wesley ^[2];

^[2]

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
National Renewable Energy Laboratory, Golden, CO (United States)
Descartes Labs, Santa Fe, NM (United States)
Y-12 National Security Complex, Oak Ridge, TN (United States)

This article presents the design, implementation, and use cases of the Chattanooga Digital Twin (CTwin) towards the vision for next-generation smart city applications for urban mobility management. CTwin is an end-to-end web-based platform that incorporates various aspects of the decision-making process for optimizing urban transportation systems in Chattanooga, Tennessee, to reduce traffic congestion, incidents, and vehicle fuel consumption. The platform serves as a cyberinfrastructure to collect and integrate multi-domain urban mobility data from various online repositories and Internet of Things (IoT) sensors, covering multiple urban aspects (e.g., traffic, natural hazards, weather, and safety) that are relevant to urban mobility management. The platform enables advanced capabilities for: (a) real-time situational awareness on traffic and infrastructure conditions on highways and urban roads, (b) cyber-physical control for optimizing traffic signal timing, and (c) interactive visual analytics on big urban mobility data and various metrics for traffic prediction and transportation performance evaluation. The platform is designed using a multi-level componentization paradigm and is implemented using modular and adaptive architecture, rendering it as a generalizable and extendable prototype for other urban management applications. We present several use cases to demonstrate CTwin's core capabilities for supporting decision-making in smart urban mobility management.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office

Grant/Contract Number:: AC36-08GO28308; AC05-00OR22725

OSTI ID:: 1958153

Alternate ID(s):: OSTI ID: 1959637

Report Number(s):: NREL/JA-2C00-85379; MainId:86152; UUID:e999137e-7c78-4aef-beaf-ecae7b7a2349; MainAdminID:68808

Journal Information:: IEEE Transactions on Intelligent Transportation Systems, Vol. 24, Issue 3; ISSN 1524-9050

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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