Publication Type
Journal Article
Version
publishedVersion
Publication Date
2-2022
Abstract
In this study, we utilized a random forest model to predict the “L” train’s daily ridership in the Chicago downtown area during the pandemic based on environmental, transportation, and COVID-19-related factors. The results indicated that the model accurately predicts ridership one month in advance. However, its accuracy degraded over time. Moreover, average temperature, stay-at-home order status, and percentage of home renters were found to be the most important factors contributing to ridership.
Discipline
Urban Studies
Research Areas
Integrative Research Areas
Publication
Transport Findings
First Page
1
Last Page
6
Identifier
10.32866/001c.30181
Publisher
Findings Press
Citation
AZIMIAN, Amin and JIAO, Junfeng.
Prediction of “L” train’s daily ridership in downtown Chicago during the COVID-19 pandemic. (2022). Transport Findings. 1-6.
Available at: https://ink.library.smu.edu.sg/cis_research/540
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.32866/001c.30181