Predicting University Campus Parking Demand Using Machine Learning Models

Abstract

Parking demand at university campuses has been an issue for decades and is gradually increasing each year. With limited capacity, space, and funds to expand parking facilities, there is a dire need to better understand parking behavior on a university campus so that universities can better utilize the limited resources available. One methodology that has been used by Metropolitan Planning Organizations to predict traveler behavior is known as travel demand modeling, where the most common modeling technique is a four-step procedure that utilizes socioeconomic data to predict current and future traffic volumes in a network (e.g., a city). This study focuses primarily on the trip generation step, and The University of Texas at Tyler campus was used as a case study. First, each parking lot on campus was assigned to a parking demand zone (PDZ) based on its proximity to classroom buildings, and the hourly arrival demand for each PDZ was measured using pneumatic tube counters. The course schedule and floor space utilization data for each building on campus were then extracted as input parameters to predict the parking demand at each PDZ. A linear regression model and two artificial neural network (ANN) frameworks were developed. One ANN model was recommended, as it had an R-squared value of 0.846. From the selected ANN model, an equation has been extracted, which has the potential to be used by course schedulers to modify the course schedule to better mitigate parking demand on campus without the need to develop new parking facilities.