Trip Distribution Patterns on a University Campus: A Smarter Travel Demand Forecasting Approach

Parking demand at university campuses has been steadily increasing each year. With limited capacity, space, and funds to expand parking facilities, there is a dire need to better understand parking behavior at university campuses so that universities can better utilize the limited resources available. One methodology to predict traveler behavior, which is used by cities and Metropolitan Planning Organizations (MPOs), is known as Travel Demand Forecasting (TDF). TDF is a four-step procedure, which utilizes socioeconomic data to predict the current and future traffic volume on each segment of a roadway in a network (e.g., within a city). Typically, this four-step procedure has only been applied at a larger scale (i.e., at the city level). Although micro-level studies have been conducted using the TDF procedure, it has not been applied to a university campus specifically to predict parking behavior. This paper specifically follows the trip-distribution step of the TDF procedure, by applying the Gravity Model to predict the number of interzonal trips within a university campus. For this research, The University of Texas at Tyler campus was used as a case study, and an optimal travel time calibration constant is recommended for the Gravity Model for a small-scaled application such as a university campus. This optimal travel time calibration constant could be used at other universities to determine its accuracy at predicting interzonal trips within other university campuses.