Abigail Adams

Date

5-11-2021

Document Type

Thesis

Abstract

Bridgewater State University first established its on-campus transportation service in January of 1984. While it began only running as an on-campus service for students throughout the day, the service grew to expand by offering an off-campus connection to the neighboring city of Brockton and absorbed the night service system from the campus safety team. As BSU Transit continues to grow, the organization is seeking ways to improve their overall service and better prepare their fleet and driver pool to accommodate this growth. The purpose of this research is to analyze trends among the data collected by BSU Transit and assist in making educated predictions for future trends within the organization. With the collection of about 5-10 years of data, descriptive statistics and statistical modeling were utilized to analyze important factors such as passenger counts and total mileage reporting to produce informative results for BSU Transit. Time series analysis and forecasting was implemented in order to predict future observations of passenger counts and total mileage counts, while the descriptive statistics breakdown provided the overall view of the system’s growth through the years. Several seasonal ARIMA models were fit to the data in an attempt to forecast monthly predictions for each metric of focus. Through the analysis of residual auto-correlation function (ACF), partial auto-correlation function (PACF) plots, and Ljung-Box Statistics test, all assumptions were met for each model. Normality and stationarity of the data was also checked through diagnostics in order to clarify the model’s true fit and accuracy. The 95% Confidence intervals of each model’s forecasts were constructed and utilized as well as mean squared errors to ensure accuracy of such predictions. Extensive data cleaning and organizing from former BSU Transit monthly reports was completed to achieve such results. A description of the data was also performed in order to highlight trends and understand patterns among the data, and further explain these trends recognized in the time series models.

Department

Mathematics

Thesis Comittee

Dr. Wanchunzi Yu, Thesis Advisor

Dr. Laura K. Gross, Committee Member

Dr. Kevin Rion, Committee Member

Copyright and Permissions

Original document was submitted as an Honors Program requirement. Copyright is held by the author.

Download

Share

COinS