Cluster Based Methodology for Scheduling a University Shuttle System

Abstract

A university transit service owns and operates a shuttle fleet that serves thousands of students that reside on and around the campus within a fairly compact area as a special form of public transit. Usually, there is a central drop off point in the center of the campus. Each shuttle goes on a tour that covers a certain area where apartment complexes housing the students are located. The main goal of this study is to find a practical scheduling methodology for the university shuttle fleet considering students’ travel behaviors and class times. The 2018–2019 annual ridership data of Texas A&M University (TAMU) shuttle system is used to identify distinct travel patterns in weekday and weekend services. A clustering method is proposed to develop homogenous departure times within the current schedules respectively for on- and off-campus routes by taking the high student loads and graduate/undergraduate behaviors into account. A new scheduling method is developed to update the current shuttle departure times by leaning on a generous/highly frequent system usage. This easy to use method considers the dynamics of daily activities conveniently. Results of the developed new schedules for on-campus and off-campus routes show that the current university shuttle fleet could operate more efficiently during the time when class changes take place. This new methodology could potentially increase the efficiency of the system in relation to total shuttle runs by up to 25%.