MODELING THE IMPACT OF COVID-19 PANDEMIC ON PUBLIC TRANSIT RIDERSHIP: A MACHINE LEARNING APPROACH

Abstract

Transit demand prediction is critical for the effective planning of public transit operations, particularly in the very unpredictable environment caused by the COVID- 19 pandemic. Since the beginning of 2020, many governments worldwide have implemented various non-pharmaceutical interventions to control the propagation of the pandemic. These interventions have disrupted daily routines and severely impacted multiple sectors of life, including education, health, economy, tourism, industries, agriculture, employment, and, most notably, the transport sector. However, the impacts of these interventions on transit ridership have not been measured or quantified, making it a challenging task. This study shows that machine learning may be used to develop a model that correlates the impact of imposed interventions with other relevant variables on transit ridership. An LSTM based four prediction models were developed, each of which using different variables input groups, that are related to both ridership and the pandemic. The three input groups are seasonal data, non-pharmaceutical interventions, and COVID-19 statistics data. Although these models differ in their input variables, they are all built to predict the same output, which is the daily transit ridership. The findings suggest that the proposed model is able to map the complex relationship with a high degree of accuracy and reliability. The predicted results and the actual results are fairly comparable. With a coefficient of determination (R2) of 0.96, they are clustered along the regression line. The application of this model will be highly beneficial for managing transit demand during disasters and emergency situations, where the prediction of transit demand is critical for operational and planning purposes.