Literature Review and Data Analysis on the Impacts of Prolonged Events on Transit System Ridership

Liu, Yining; Osorio, Jesus; Ouyang, Yanfeng

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https://hdl.handle.net/2142/110365 Copy

Description

Title

Literature Review and Data Analysis on the Impacts of Prolonged Events on Transit System Ridership

Author(s)

• Liu, Yining
• Osorio, Jesus
• Ouyang, Yanfeng

Issue Date

2021-08

Keyword(s)

• COVID-19
• Transit Ridership
• Historic Events
• Bayesian Structural Time Series
• Dynamics Model
• Forecast

Abstract

The COVID-19 pandemic has drastically disrupted transit operations and induced significant transit ridership losses worldwide. Given its unprecedented duration, magnitude, and scale, the long-term effects are still unclear. Despite the differences, we can learn from previous disruptive events, such as terrorist attacks and epidemics, in the past 30 years and draw qualitative and quantitative insights about public reactions, ridership recovery periods, and transit agency responses during and after those events. This study sought to understand ridership variations during the current COVID-19 pandemic and inform transit agencies’ future decisions. This project’s research team therefore reviewed the impacts of selected historical events. They observed the following: (i) that most of the reviewed incidents (except for the 9/11 attacks) did not impose prolonged post-event effects on transit ridership for more than one year; (ii) that executive orders (e.g., school closures), transportation services (e.g., intensified airport safety screening and rail station closures), public fear, media reports, and reduced tourism were frequently mentioned as key factors that impacted transit ridership; and (iii) that measures, such as sanitizing vehicles and facilities, improving communications with the public, and promotions and advertisements, can potentially help restore transit ridership. The research team also developed a modeling framework that integrated a Bayesian structural time-series model, a dynamics model for daily transit ridership loss, a prediction module, and ordinary least squares regression to study COVID-19’s effects on the Chicago Transit Authority’s rail ridership. The researchers undertook a model of ridership on the CTA rail system as a potential first step to modeling COVID-19’s effects on transit ridership in northeastern Illinois. The researchers have not modeled ridership on any other transportation mode in northeastern Illinois at this time. The statistical analysis showed that remote learning/work policies and executive orders had answered for most of the ridership loss. Socioeconomic and land-use characteristics could effectively capture their effects. However, these characteristics could not explain people’s different reactions to reported deaths and media attention. Different population groups may have reacted differently to policy decisions, but their responses to reported deaths and media coverage seem random and independent of sociodemographic factors.

Publisher

Illinois Center for Transportation/Illinois Department of Transportation

Series/Report Name or Number

FHWA-ICT-21-021

ISSN

0197-9191

Type of Resource

text

Language

en

Permalink

http://hdl.handle.net/2142/110365

DOI

https://doi.org/10.36501/0197-9191/21-026

Sponsor(s)/Grant Number(s)

IDOT-ICT-SP45

Copyright and License Information

No restrictions. This document is available through the National Technical Information Service, Springfield, VA 22161.

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