A Practical Approach for Evaluating and Scheduling a Campus Bus System Based on Clustering

Optimal fixed-route transit scheduling can help transit planners and managers save finite resources and increase service efficiency. Compared with urban transportation systems, a university campus transit system can carry specific characteristics such as high service frequencies, overcrowding and capacity concerns, and unique challenges in service allocation, which have not yet been widely studied. The Texas A&M transit service is investigated in this text in order to optimize its large network of 18 on and off-campus routes. The main aim of the paper is to find a practical scheduling methodology for the campus system. A comprehensive analysis of ridership data is performed to evaluate the delay, running time, on-time performance, and efficiency of the current network. Then, two clustering methods of hierarchical and K-means are used to categorize the ridership data considering the class times. The data is analyzed to determine the similar patterns in both weekday and weekend services. Then homogeneous clusters within each day are detected to find the trip departure times. A concept of the generous, high-frequency system is defined to assign the bus trips to the new departure times. Results show that the current university bus system could operate more efficiently during class changes; currently, only even headways are provided regardless of periods with higher demand. The proposed methodology could potentially increase the efficiency of the system by up to 40 percent.

• Authors:
  • Mahmoudzadeh, Ahmadreza
  • Firoozi Yeganeh, Sayna
  • Ochoa, Barrett
  • Wang, Xiubin Bruce
  • Tippy, Justin
• Conference:
  • Transportation Research Board 98th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2019-1-13 to 2019-1-17
• Date: 2019

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; References;
• Pagination: 23p

Subject/Index Terms

• TRT Terms: Bus transit; Campus transportation; Cluster analysis; Data analysis; Ridership; Scheduling
• Identifier Terms: Texas A&M University
• Subject Areas: Data and Information Technology; Operations and Traffic Management; Passenger Transportation;

Filing Info

• Accession Number: 01729201
• Record Type: Publication
• Report/Paper Numbers: 19-03045
• Files: TRIS, TRB, ATRI
• Created Date: Jan 29 2020 9:32AM