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.

Keywords

• digital twin
• intelligent transportation systems
• iot
• time series prediction