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Major
Accountancy
Anticipated Graduation Year
2023
Access Type
Open Access
Abstract
The Chicago Transit Authority transports millions of people around the city annually; however, several different factors impact ridership at each stop and, therefore, the efficiency of the rail system as a whole. These factors include average temperatures, precipitation levels, weather patterns, and day of the week; considering these variables are both numerical and categorical and the relationship between the inputs and daily ridership is quite complex, the data mining technique known as neural networks most adequately analyzes the various relationships. In this study, we collected data from three years of CTA ridership at the Chicago Red Line stop, and created a neural network model that ultimately determined that day of the week has the most significant impact on ridership.
Faculty Mentors & Instructors
Professor Kmet, BHNR 343
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
CTA Red Line Ridership
The Chicago Transit Authority transports millions of people around the city annually; however, several different factors impact ridership at each stop and, therefore, the efficiency of the rail system as a whole. These factors include average temperatures, precipitation levels, weather patterns, and day of the week; considering these variables are both numerical and categorical and the relationship between the inputs and daily ridership is quite complex, the data mining technique known as neural networks most adequately analyzes the various relationships. In this study, we collected data from three years of CTA ridership at the Chicago Red Line stop, and created a neural network model that ultimately determined that day of the week has the most significant impact on ridership.