A benchmarking framework for understanding bus performance in the U.S.

Abstract

Purpose This paper describes a benchmarking framework applied to medium-sized urban public bus agencies in the United States which has overcome the challenges of data quality, comparability and understanding. Design/methodology/approach The benchmarking methodology described in this paper is based on lessons learned through seven years of development of a fixed route key performance indicator (KPI) system for the American Bus Benchmarking Group (ABBG). Founded in 2011, the ABBG is a group of public medium-sized urban bus agencies that compare performance and share best practices with peers throughout the United States. The methodology is adapted from the process used within international benchmarking groups facilitated by Imperial College and consists of four main elements: peer selection, KPI system development, processes to achieve high-quality data, and processes to understand relative performance and change. Findings The four main elements of the ABBG benchmarking methodology consist of eighteen sub-elements, which when applied overcome three main benchmarking challenges; comparability, data quality, and understanding. While serving as examples for the methodology elements, the paper provides specific insights into service characteristics and performance among ABBG agencies. Research limitations/implications The benchmarking approach described in this paper requires time and commitment and thus is most suitably applied to a concise group of agencies. Practical implications This methodology provides transit agencies, authorities and benchmarking practitioners a framework for effective benchmarking. It will lead to high-quality comparable data and a strong understanding of the performance context to serve as a basis for organizational changes, whether for policy, planning, operations, stakeholder communication, or program development. Originality/value The methodology, while consistent with recommendations from literature, is unique in its scale, in-depth validation and analysis, and holistic and multi-dimensional approach.