Abstract
This paper explores the relative influence of factors affecting light rail ridership on 57 light rail routes in Australia, Europe and North America through an empirical examination of route level data. Previous research suggests a wide range of possible ridership drivers but is mixed in clarifying major influences. A multiple-regression analysis of route level ridership (boardings per route km) and catchment residential and employment density, car ownership, service level, speed, stop spacing, share of accessible stops, share of segregated right of away and integrated fares was undertaken. This established a statistically significant model (99% level, R2 = 0.76) with five significant variables including service level, routes being in Europe, speed, integrated ticketing and employment density. In general these findings support selected results from previous research. A secondary analysis of service effectiveness measures (boardings/vehicle km, i.e. the relative ridership performance for a given level of service), established a statistically significant model (99% level, R2 = 0.67) with 6 significant explanatory variables including being in Europe, speed, employment density, integrated ticketing, track segregation and service level. The latter implies that a higher frequency results in higher service effectiveness. Overall the research findings stress the importance of providing a high level of service as a major driver of light rail ridership. The ‘European Factor’ is also an important though intriguing influence but its cause remains unclear and requires further research to elaborate its nature.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
A log transformation of Boardings/Route Km (and, later, boardings/vehicle km) resulted in a lower R2 so the untransformed variable was used.
References
Australian Bureau of Statistics (2006). Census of population and housing
Babalik-Sutcliffe, E.: Urban rail systems: analysis of the factors behind success. Transp. Rev. 22, 415–417 (2002)
Balcombe, R., Mackett, R., et al. The Demand for Public Transport; A Practical Guide. Transport Research Laboratory, Crowthorne (2004)
Barnett, V., Lewis, T.: Outliers in statistical data. New York, Wiley (1978)
Capineri, C., Kamann, D.: Synergy in networks: concepts. In: Button, K., Nijkamp, P., Priemus, H. (eds.) Transport Networks in Europe—Concepts, Analysis and Policies. Edward Elgar Publishers, Cheltenham, UK (1998)
Chen, C., Gong, H., et al.: Role of the built environment on mode choice decisions: additional evidence on the impact of density. Transportation 35(3), 285–299 (2008)
Cook, S.A., Weisberg, S.: Residuals and Influence in Regression. Chapman & Hall, New York (1982)
Crampton, G.: International Comparison of Urban Light Rail Systems; the Role of Integrated Ticketing, Pedestrianisation and Population Density. European Regional Science Association (ERSA) 2002. Dortmund, Germany (2002)
Currie, G., Delbosc, A.: Understanding ridership drivers for bus rapid transit systems in Australia. Australasian Transport Research Forum, Canberra, Australia (2010)
Currie, G., Shalaby, A.: Success and challenges in modernising streetcar systems—experience in Melbourne and Toronto. Transp. Res. Record J. Transp. Res. Board Wash. DC 2006, 31–39 (2007)
Denant Boemont, L., Mills, G.: Urban light rail: intermodal competition or coordination? Transp. Rev. 19, 241–253 (1999)
Durbin, J., Watson, G.S.: Testing for serial correlation in least squares regression, II. Biometrika 30, 159–178 (1951)
Edwards, M., Mackett, R.: Developing new urban public transport systems: an irrational decision-making process. Transp. Policy 3(4), 225–239 (1996)
Egis Semaly Ltd and Faber Maunsell. Comparative Performance Data from French Tramsway Systems—Final Report, South Yorkshire Passenger Transport Executive (2003)
Evans, J.E.: Traveler Response to Transportation System Changes Chapter 9—Transit Scheduling and Frequency. Transportation Research Board, Washington, DC (2004)
Fielding, G.J.: Managing Public Transit Strategically. Jossey-Bass Inc Publishers, San Francisco (1987)
FitzRoy, F., Smith, I.: Public transport demand in Freiburg: why did demand double in a decade? Transp. Policy 5, 163–173 (1998)
Green, S.B.: How many subjects does it take to do a regression analysis? Multivar. Behav. Res. 26(3), 499–510 (1991)
Hair, J., Black, N., et al.: Multivariate Data Analysis, 6th edn. Prentice Hall, New Jersey (2006)
Hass-Klau, C., Crampton, G.: Future of urban Transport; Learning from Success and Weakness; Light Rail. Environment and Transport Planning, Brighton, UK (2002)
Johnson, A.: Bus transit and land use: illuminating the interaction. J Public Transp. 6(4), 21–39 (2003)
Kain, J., Liu, Z.: Secrets of success: the large increases in transit ridership achieved by Houston and San Diego transit providers. Transp. Res. A 33, 601–624 (1999)
Kain, M., Barranda, A., et al.: Factors influencing light-rail station boardings in the United States. Transp. Res. A 38, 223–247 (2004)
Kelley, K., Maxwell, S.: Sample size for multiple regression: obtaining regression coefficients that are accurate, not simply significant. Psychol. Methods 8(3), 305–321 (2003)
Kenworthy, J., Laube, F.: The Millennium Cities Database for Sustainable Transport. International Union of Public Transport (UITP), Brussels (2001)
Knowles, R.: What future for light rail in the UK after ten year transport plan targets are scrapped? Transp. Policy 14, 81–93 (2007)
Mackett, R., Babalik-Sutcliffe, E.: New urban rail systems: a policy based technique to make them more successful. J. Transp. Geogr. 11, 151–164 (2003)
McCollom, B.E., Pratt, R.H.: Traveler Response to Transportation System Changes Chapter 12—Transit Pricing and Fares. Transit Cooperative Research Program. Washington DC, Transportation Research Board, TCRP Report 95 (2004)
Mees, P.: How dense are we? Another look at urban density and transport patterns in Australia, Canada and the USA. Road Transp. Res. 18(4), 58–67 (2009)
Myers, R.: Classical and modern regression with applications, 2nd edn. Duxbury, Boston, MA (1990)
Pratt, R.H., Evans, J.E.: Traveler Response to Transportation System Changes Chapter 10—Bus Routing and Coverage. Transit Cooperative Research Program, Washington, DC (2004)
Seskin, S., Cervero, R.: Transit and urban form. Federal Transit Administration, Washington, DC (1996)
Statistics Canada: Ontario: 2006 Community Profiles, 2006 Census, Ottawa (2007)
Stevens, J.P.: Applied Multivariate Statistics for the Social Sciences, 4th edn. Erlbaum, Hillsdale, NJ (2002)
Stopher, P.: Development of a route level patronage forecasting method. Transportation 19, 201–220 (1992)
U. S. Census Bureau: Profile of selected social characteristics (2000)
University of Toronto: Transportation Tomorrow Survey. Data Management Group, University of Toronto, Toronto, Canada (2006)
Acknowledgments
Parts of this research was conducted while Dr. Ahern was on sabbatical at the Institute of Transport Studies, Monash University. The authors would like to thank the Urban Institute in University College Dublin for part-funding this sabbatical. The authors would also like to acknowledge the help of Susanna Schmidt of the Urban Institute Dublin for help with processing data. In addition Karen Woo of the University of Toronto assisted collecting some of the North American data whilst on a visit to Monash from the University of Toronto. We would also like to thank Prof Patrick Bonnel Professor of Transport Economics, ENTPE France for advice on sources for collecting data from France.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Currie, G., Ahern, A. & Delbosc, A. Exploring the drivers of light rail ridership: an empirical route level analysis of selected Australian, North American and European systems. Transportation 38, 545–560 (2011). https://doi.org/10.1007/s11116-010-9314-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11116-010-9314-9