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Assessing effects of pandemic-related policies on individual public transit travel patterns: A Bayesian online changepoint detection based framework

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  • Lin, Yuqian
  • Xu, Yang
  • Zhao, Zhan
  • Tu, Wei
  • Park, Sangwon
  • Li, Qingquan

Abstract

During a pandemic or natural disaster, people may alter transit usage behavior due to perception of changes in the environment. To effectively respond to these crises, it is important for governments and public transit agencies to understand when these changes occurred and how they were affected by relevant policies and responsive strategies. In this study, we develop a methodological framework based on Bayesian online changepoint detection (BOCD) to identify the occurrence time, direction, and persistency of changes in individual-level transit usage. We demonstrate the effectiveness of this framework in informing government decision-making in the context of COVID-19. Using Jeju Island, South Korea as a case study, we apply the framework over a nearly two-year smart card dataset collected from the beginning of 2019 till nine months into the pandemic. By focusing on frequent transit users, we detect when these users significantly changed their transit usage frequency during the pandemic and identify several types of users who experienced different behavior change patterns. Besides demonstrating the great heterogeneity in individual-level behavior changes, we perform a regression analysis to further understand how these changes were affected by key government policies (e.g., Risk alert, Social distancing, Public transit policy, and Eased social distancing). Our results suggest that only certain sets of policies appear to have significant effects. In particular, introducing Risk alert would cause a 277% to 317% increase in the number of users who reduced transit usage frequency. Policies that eased social distancing, though, would cause a 134% to 155% increase in the number of users with travel frequency increase. The proposed BOCD framework enables a scalable solution to identifying and understanding changes of individual transit behavior. The methodology and findings are beneficial for developing targeted policies and interventions to facilitate daily travel and public transit operations during public health crises.

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  • Lin, Yuqian & Xu, Yang & Zhao, Zhan & Tu, Wei & Park, Sangwon & Li, Qingquan, 2024. "Assessing effects of pandemic-related policies on individual public transit travel patterns: A Bayesian online changepoint detection based framework," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:transa:v:181:y:2024:i:c:s096585642400051x
    DOI: 10.1016/j.tra.2024.104003
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