Time Series Prediction with Digital Twins in Public Transportation Systems

Mehmet Ali Ertürk

Alphanumeric Journal, 2023, vol. 11, issue 2, 183-192

Abstract: Classical traffic and transportation control centers must be more robust with the rapid spread of electric, intelligent, autonomous, and software-defined vehicles. Existing traffic management strategies have significant drawbacks in public safety, predictive maintenance, tuning the core functionality of vehicles, and managing mobility. We can renovate this system with next-generation intelligent Digital Twin (DT) technologies. This research proposes a time-series prediction system through Digital Twins to manage the public transportation system with Facebook’s Prophet. This study presents a model framework to build a Digital Twin application in Intelligent Public Transportation Systems and uses a public data set to validate the model with Facebook’s Prophet library by forecasting metro line passenger flows. According to the results, the Mean Absolute Percentage Error (MAPE) is 0.017 for a 1-day horizon.

Keywords: Digital Twin; Intelligent Transportation Systems; IoT; Time Series Prediction (search for similar items in EconPapers)
JEL-codes: C46 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:anm:alpnmr:v:11:y:2023:i:2:p:183-192

DOI: 10.17093/alphanumeric.1402897

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