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<p>Urban Rail Transit (URT) systems play a critical role in modern cities but consume significant amounts of energy. Understanding and predicting energy consumption patterns in these systems is vital for sustainable urban planning and cost-effective decision-making. This study develops a comprehensive framework for forecasting energy consumption based on planning metrics, ridership data, and historical energy and weather, using the Massachusetts Bay Transportation Authority (MBTA)’s URT system as a case study. The fitted eXtreme Gradient Boosting (XGBoost) model demonstrated high predictive accuracy, with a mean absolute percentage error (MAPE) of 3.3%, while ridge regression models reliably forecasted ridership and temperature to support future planning. We further evaluated eight operational plans under nine ridership–temperature scenarios. Each plan represents a set of operational adjustments that may be pursued to regulate service conditions or respond to disruptions. These analyses highlight the model’s capability to provide insightful forecasts that inform decision-making processes and optimize energy usage, thereby supporting sustainable and efficient urban rail transit operations.</p>

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Robust Forecasting Framework for Energy-Informed Planning in Urban Rail Transit Systems

  • 期刊论文

  • 发表日期：

    2025年12月17日

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  • Zhuo Han,
  • Eleni Christofa,
  • Eric J. Gonzales,
  • Sean Donaghy,
  • Jimi Oke

摘要

Urban Rail Transit (URT) systems play a critical role in modern cities but consume significant amounts of energy. Understanding and predicting energy consumption patterns in these systems is vital for sustainable urban planning and cost-effective decision-making. This study develops a comprehensive framework for forecasting energy consumption based on planning metrics, ridership data, and historical energy and weather, using the Massachusetts Bay Transportation Authority (MBTA)’s URT system as a case study. The fitted eXtreme Gradient Boosting (XGBoost) model demonstrated high predictive accuracy, with a mean absolute percentage error (MAPE) of 3.3%, while ridge regression models reliably forecasted ridership and temperature to support future planning. We further evaluated eight operational plans under nine ridership–temperature scenarios. Each plan represents a set of operational adjustments that may be pursued to regulate service conditions or respond to disruptions. These analyses highlight the model’s capability to provide insightful forecasts that inform decision-making processes and optimize energy usage, thereby supporting sustainable and efficient urban rail transit operations.