Efficiency of urban public transit: A meta analysis

Abstract

Abstract. The aim of this paper is twofold. First, to provide a statistical overview of the literature on public transit efficiency performance. Second, to statistically explain the variation in efficiency findings reported in the literature. To this end, first some key concepts of efficiency analysis will be introduced, while next the different frontier methodologies that are used in the literature will be discussed. The empirical part of this paper consists of a statistical summary of the literature as well as meta-regression analyses for different samples of the literature in order to identify key determinants of technical efficiency (TE) of public transit operators. For a broad sample of observations, we found significant and consistent effects of the type of database, region and output measurement method. For the sample of non-parametric studies we found that the type of frontier assumptions also have an impact on the efficiency ratio. Further results show that there is no statistical difference in TE ratio's between parametric and non-parametric studies. Finally, we found a positive univariate relationship between the number of inputs in the estimated specification and the efficiency ratio.

Defining critical success factors in TOD implementation using rough set analysis

“…Another advantage is the ability to use completed case studies, resulting in a much shorter amount of time than that needed to gather primary data in a multiple-case study. Within meta-analysis, a number of techniques can be used to identify cross-case patterns, e.g., meta-matrices, qualitative metasynthesis (Schofield 1990), meta-regression (Gim 2012;Brons et al 2005;Bamberg and Möser 2007), and rough set analysis (Baaijens and Nijkamp 2001). Meta-matrices can be used in a meta-analysis to allow the researcher to approach data (both qualitative and quantitative) in a systematic, transparent way; coded case data is entered into one or more tables and can be organized as needed by case or variable (Miles and Huberman 1994).…”

Comparing the Efficiency of Public Transportation Subunits Using Data Envelopment Analysis

“…Since the first papers applying DEA to public transportation were published in 992, the procedure has become increasingly popular for comparing transit organizations with each other (Brons et al 2005;De Borger, Kerstens, and Costa 2002). However, DEA has not been used to compare subunits within a given transit organization.…”

The Influence of Purchase Date and Flight Duration over the Dispersion of Airline Ticket Prices

“…Demand for flight tickets bought for business reasons is less elastic than the demand for tickets bought for tourist reasons (Brons, et al, 2002). People travelling for business are willing to pay a higher price for an airline ticket because they need to be able to change their travel plans (Brons, et al, 2002).…”

“…People travelling for business are willing to pay a higher price for an airline ticket because they need to be able to change their travel plans (Brons, et al, 2002). Typically, the longer the journey, the smaller the price elasticity for clients buying airline tickets.…”

“…Typically, the longer the journey, the smaller the price elasticity for clients buying airline tickets. This is because of the lack of substitute transport on long-distance routes (Brons, et al, 2002). (Borenstein & Rose, 1994).…”