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.
The cover image is sourced from Pexels and is free of copyright issues.
For more information, please visit: https://doaj.org/article/99d1eb6087c548d9bbd655c9dbd11045
