Travel Demand Forecasting to Predict Parking Behavior At University Campuses: A Case Study at The University of Texas at Tyler

With limited capacity, space, and funds to expand parking facilities, there is a dire need to better understand parking behavior at university campuses so as to better utilize the limited resources available. One potential methodology, which is used by cities and Metropolitan Planning Organizations (MPOs) is known as Travel Demand Forecasting (TDF). The socioeconomic data used includes income, number of households, people in a given household, number of working people, etc. This data is used to divide a city into different Traffic Analysis Zones (TAZ). Similar to how a city is zoned, a university campus has parking zones, each of which is adjacent to a specific building and could serve as a TAZ. Therefore, course schedule data and floor space utilization data of buildings on a campus could be some predictors of parking demand at each zone on a university campus. If true, changes to the course schedule data could be made to better handle parking demand in the future. This study aims to use the University of Texas at Tyler (UT Tyler) as a case study. The trips that are arriving to and departing from each zone were counted during a given week using pneumatic tube counters. Then, the course schedule data and floor space utilization data for each building on campus were extracted. The extracted data served as input data to predict the parking demand at each TAZ on campus. Lastly, parking demand data was used to predict the interzonal trips within the campus network.