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Time Series Prediction with Digital Twins in Public Transportation Systems

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  • Mehmet Ali Ertürk

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.

Suggested Citation

  • Mehmet Ali Ertürk, 2023. "Time Series Prediction with Digital Twins in Public Transportation Systems," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 11(2), pages 183-192, December.
    • Handle: RePEc:anm:alpnmr:v:11:y:2023:i:2:p:183-192
    DOI: https://doi.org/10.17093/alphanumeric.1402897
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    References listed on IDEAS

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    1. Asaf Tzachor & Soheil Sabri & Catherine E. Richards & Abbas Rajabifard & Michele Acuto, 2022. "Potential and limitations of digital twins to achieve the Sustainable Development Goals," Nature Sustainability, Nature, vol. 5(10), pages 822-829, October.
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    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

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