@Article{AJ_,
    doi     = { 10.17093/alphanumeric.1402897 },
    author  = { Mehmet Ali Ertürk },
    title   = { Time Series Prediction with Digital Twins in Public Transportation Systems },
    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. }
    journal = { Alphanumeric Journal },
    year    = { 2023 },
    volume  = { 11 },
    number  = { 2 },
    pages   = { 183-192 },
    url     = { https://alphanumericjournal.com/article/time-series-prediction-with-digital-twins-in-public-transportation-systems },
}