Development of a Connected Corridor Real-Time Data-Driven Traffic Digital Twin Simulation Model
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 12
Abstract
Smart cities—being equipped with connected infrastructure—receive significant real-time traffic data. In this paper, a data-driven connected corridor traffic simulation model, i.e., a digital twin, is developed that leverages real-time data streams to model the current traffic state and provide dynamic feedback on traffic and environmental performance measures, e.g., travel time, speed, energy consumption, and vehicular emissions. The developed digital twin model architecture uses real-time 6-min volume aggregate data and 0.1 Hz to 10 Hz signal indication data as input to simulate the connected corridor using a traffic modeling software microscopic simulation. Dynamic data retrieval and transfer from the simulation model are enabled using the software’s COM feature and a Flask web server. The robustness and feasibility of the digital twin architecture and the generated performance measure reasonableness are demonstrated on a smart corridor test bed. Such a model can be used to monitor and provide insights on the impacts of intelligent transportation system technologies on connected corridor traffic and environmental performance.
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Data Availability Statement
1.
Some or all data, models, or code generated or used during the study are available in a repository or online in accordance with funder data retention policies:
•
Python code used to run the digital twin model architecture is available for reference in the given GitHub repository: https://github.com/hunter-guin-gatech/COA-NorthAvenueSimulation.
2.
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request:
•
Digital twin simulation output data: vehicle travel time, energy consumption, and emissions estimates.
3.
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions:
•
The raw volume and signal data of North Avenue Smart Corridor are third-party proprietary data.
Acknowledgments
The information, data, or work presented herein was funded in part by the City of Atlanta (CoA) and in part by the National Center for Sustainable Transportation (NCST) Dissertation Grant. The authors thank CoA for support of this research under Research Project FC-9930 Smart Cities Traffic Congestion Mitigation Program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors. This paper does not constitute a standard, specification, or regulation.
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Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 12 • December 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Nov 25, 2020
Accepted: Jul 27, 2021
Published online: Oct 8, 2021
Published in print: Dec 1, 2021
Discussion open until: Mar 8, 2022
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