Reconsidering the Effects of Fare Reductions on Transit Ridership

 

Principal Investigator:                Brian D. Taylor (Associate Professor of Urban Planning)

Investigators:                            Camille Fink (Urban Planning PhD student)

Hiroyuki Iseki (Urban Planning PhD student)

Douglas Miller (ITS Research Associate)

 

UCLA Institute of Transportation Studies

3250 Public Policy Building

Los Angeles, CA 90095-1656

310-825-7442

btaylor@ucla.edu

 

Overview

            This project began as a study of the effects of fare reductions on transit ridership, but during the course of the analysis was broadened considerably to analyze the array of factors  – including transit fares – which collectively explain both levels of and changes in transit patronage in the U.S.  For this study, we conducted both an extensive review and critique of the previous research on this topic, and we assembled an extensive data set on public transit systems nationwide drawing from the National Transit Database, U.S. census data, and a variety of other sources.

            We expanded our research on transit ridership beyond the effects of fare changes for two reasons.  First, analyses of the effects of transit fares on ridership at a transit system or metropolitan unit of analysis required that we control for the effects of many other factors on transit ridership, so we thought it appropriate to analyze the effects of these other factors as well.  And second, we were simply unable to secure from transit systems the kind of detailed passenger-level trip data required for a disaggregate analysis of transit fare elasticities we had hoped to conduct.

            We have produced two research papers from this study.  The first – The Factors Influencing Transit Ridership: An Analysis of the Literature – reviews and critiques the diverse bodies of research on the causes of transit ridership, emphasizing aggregate studies of transit patronage at the system or metropolitan unit of analysis.  The second paper produced from this study – Analyzing the Determinants of Transit Ridership Using a Two-Stage Least Squares Regression on a National Sample of Urbanized Areas – builds on the first to analyze an entirely original data set constructed for this study.  Each of these papers is discussed in turn below.

The Factors Influencing Transit Ridership

            What explains transit ridership?  Population density, levels of private vehicle ownership, topography, freeway network extent, parking availability and cost, transit network extent and service frequency, transit fares, transit system safety and cleanliness, and so on all surely play a role.  But the relatively importance of these various factors, and the interaction between them is not well understood.  Yet understanding the relative influence of these factors is central to public policy debates over transportation system investments and the pricing and deployment of transit services.  But the research literature on explaining transit ridership is surprisingly uneven, in some cases poorly conceived, and the results are often ambiguous or contradictory.

            To help close this gap between conventional wisdom and an ambiguous body of research, this paper develops a taxonomy of previous research on transit ridership (Figure 1), critiques the sometimes significant weaknesses in previous studies, draws conclusions from the more rigorous studies on the factors which most influence transit use, and recommends steps needed to better understand and explain transit ridership.

 

Figure 1.  Previous Studies of Transit Ridership

           

 

 

 

 

 

 

 

 

Principal Findings

•                     Transit ridership is largely, though not completely, a product of factors outside of the control of transit managers.  Among those factors that transit systems do control, the quality of transit service and adroit pricing of transit services to target particular travel markets have proven most effective.  The quantity of transit service is, of course, strongly related to transit use, but it is also determined by ambient levels of transit service demand. 

•                     While many of the factors which most affect transit ridership are outside of the control of transit managers, they are not beyond the bounds of public policy.  Policies which support private vehicle use (extensive arterial and freeway systems, relatively low motor fuel taxes, policies requiring parking to be provided to satisfy all demand at a price of zero, etc.) affect transit use more than policies (such as substantial public transit subsidies) which encourage transit use.  In addition, private vehicles provide travelers with spatially and temporal flexibility that traditional fixed-route transit services can rarely match.  Thus, the utility of private vehicles and the wide array of public policies in the U.S. which support their use explains more of the variation in public transit patronage than any other family of factors.

•                     Factors which directly or indirectly measure automobile access and utility (auto ownership, parking availability, etc.) explain more of the variation in transit ridership than any other family of factors.  Next are economic factors, such as unemployment levels, CDB employment levels, and income levels explain substantial portions of transit use.  Spatial factors, such as population and employment density, traffic congestion levels, and parking availability, are shown in many studies to explain much variation in transit ridership, but the colinearity among these variables, and with socio-economic variables related to transit use raise questions about both the direction of cause and effect, and the relative influence of the various factors measured on transit ridership.

•                     With respect to factors over which transit systems exercise some control, improvements in service supply – frequency, coverage, reliability, etc. – have been shown to be more important than price (fares) in determining ridership.  However, most research has measured service supply (vehicle hours, miles, etc.) rather than service quality (on-time performance, etc.).  Comparative measures of service and price elasticities find that responses to service changes are substantially more elastic than changes to fares.  However, focused fare programs that target populations (students, transit-dependents, etc.) with relatively high price-elasticities of demand have been very effective in attracting riders.

•                     With respect to the rigor and methods employed in this research, the findings of the descriptive, perceptional studies are rather consistent, but suffer from problems of self-selection bias and improperly implied causality.  On the other hand, the causal analyses tend to suffer from problems of aggregation (which masks important within group variance), lack of conceptual model specification, high levels of colinearity among the independent variables (and, importantly for policy, between the spatial and socio-economic variables), and endogenity problems between the service supply variables and service consumption.

Analyzing the Determinants of Transit Ridership

            This paper presents an analysis that attempts to address some of the shortcomings of previous studies of the determinants of transit patronage identified in The Factors Influencing Transit Ridership paper.  We begin by developing a simple causal model hypothesizing the collective influence of a wide range of factors on transit ridership.  Given this model, we briefly review and critique the previous research, emphasizing both the principal supportable findings and identifying many of the methodological problems plaguing previous research.  We then describe the national data set we developed from the National Transit Database (NTD) and several other sources to enable a cross-sectional regression analysis of transit ridership using a two-stage simultaneous equations model.  We use this approach to identify an array of factors thought to significantly influence transit ridership, while taking into account both the small samples sizes and the simultaneity conundrum of transit supply and transit demand common to many previous studies.  We then present our models results and conclude with a discussion of the implications for policy.

            Specifically, most previous aggregate analyses of the factors influencing transit ridership have examined one or just a few systems, have not included many of the external, control variables thought to influence transit use, and have not addressed the simultaneous relationship between transit service supply and transit patronage demand.  This study addresses each of these shortcomings in the previous research by (1) conducting a cross-sectional analysis of transit use in 265 urbanized areas, (2) testing a wide array of variables measuring transit system characteristics, auto/highway system characteristics, regional geography, metropolitan economy, and population characteristics, and (3) constructing two-stage simultaneous equation regression models to account for simultaneity between transit supply and demand.

Principal Findings

•                     In a nutshell, we find that most of the variation in transit ridership between urbanized areas – in both absolute and relative terms – can be explained by (1) the size (both population and area) of the metropolitan area, (2) the vitality of the regional economy (measured in terms of median housing costs), and (3) the share of the population with low levels of private vehicle access (measured in terms of zero-vehicle households).

•                     We find further that transit patronage is to a lesser, but still significant extent, explained by transit service levels and fares.

•                     Consistent with research on transit service elasticities, we find the relative influence of transit service levels on ridership to be greater than the relative influence of transit fares.

•                     Finally, by separating the service supply variable into an instrumental control variable and a residual policy variable, we estimate that large changes (particularly increases) in transit service are likely to have far less influence on transit ridership than many of the previous aggregate models of transit patronage would suggest.

Presentations

 

Taylor, Brian D.  (2003).  “The Factors Influencing Transit Ridership: What Has the Research Shown?” at the 82^nd Annual Meeting of the Transportation Research Board, Washington, D.C., January.  (Camille Fink, co-author)

 

Taylor, Brian D.  (submitted for 2004).  “Analyzing the Determinants of Transit Ridership Using a Two-Stage Least Squares Regression on a National Sample of Urbanized Areas,” to the 83^rd Annual Meeting of the Transportation Research Board, Washington, D.C.  (Douglas Miller, Hiroyuki Iseki, and Camille Fink, co-authors).

 

Publications

 

Taylor, Brian D. and Camille Fink. (2003).  “The Factors Influencing Transit Ridership: An Analysis of the Literature,” Working Paper, UCLA Institute of Transportation Studies, UCLA.

 

Taylor, Brian D. Taylor and Douglas Miller, with Hiroyuki Iseki and Camille Fink.  (submitted for review).  “Analyzing the Determinants of Transit Ridership Using a Two-Stage Least Squares Regression on a National Sample of Urbanized Areas,” Journal of the Transportation Research Board.