Abstract

Rapid transit systems are critical components of urban public transportation networks in their impact, not only on personal mobility but also on the energy and environmental costs associated with network operations. To facilitate effective planning for current and future needs, a framework is required that provides important consumption metrics and also explains the various contributors to energy consumption, along with their interactions. To address this gap, we estimated models that utilized operational and ridership data for the Massachusetts Bay Transportation Authority?s rapid transit system, as well as ambient temperature, to accurately predict system-wide electricity consumption. The models were trained with data from 2019 and tested with data from 2020. The estimated multiple linear regression (MLR) and random forest (RF) models explained 93% and 95% of the variance in the data set, respectively. The MLR model provided predictions with a root mean squared error (RMSE) of 2.7?MWh and mean absolute percentage error (MAPE) of 4.68%, while the RF model resulted in an RMSE of 2.94?MWh and MAPE of 5.01%. We also investigated the impacts of COVID-19 on the transit system by exploring the effects on ridership, energy consumption, cost, and train movement metrics before and during the pandemic. We find that the models are robust and perform well, even with the significant disruptions associated with the COVID-19 pandemic.