Measuring Transport System Efficiency
Planners are professional do-gooders. Our job is to improve the world by helping decision-makers choose optimal solutions. Specifically, we develop analysis frameworks that define problems and goals, and evaluate potential improvements. This process translates general, qualitative concepts into specific, quantitative analysis suitable for decision-making.
Qualitative analysis defines what is considered good or bad. Quantitative analysis measures their magnitude –how good or bad – which is important when decisions involve trade-offs between different goals. For example, qualitative analysis may indicate that economic development, safety and pollution reduction are desirable goals, but quantitative analysis is needed to determine whether a specific economic development strategy is justified if it reduces safety or increases pollution.
Quantitative analysis often defines goals in terms of efficiency, which refers to the ratio of inputs (costs) to outputs (benefits). Economic efficiency and cost effectivness are measured using monetized (measured in monetary units) indicators such as benefit/cost ratios, net present value or return on investment: higher values indicate greater efficiency. Such values are easy to understand and so can dominate decision-making. For example, if an analyst presents a graph indicating that one policy or project has a much higher benefit/cost ratio than others, that option will usually be selected.
Of course, the devil is in the details. Some impacts are easier to monetize than others, and so tend to receive greater weight in economic evaluation. This is particularly important for transport planning, which tends to have diverse impacts.
There are several possible ways to measure transport system efficiency, which can result in very different conclusions about what solutions are optimal:
A recent study published in the Journal of the American Planning Association highlights this issue. "Does Accessibility Require Density or Speed?" [PDF] analyzed of the number of destinations that can be reached within a given travel time by automobile and transit for about 30 US metropolitan areas. The results indicate that increased proximity from more compact and centralized development has about ten times more influential on overall accessibility than the same percentage increase in vehicle traffic speeds. This illustrates how different evaluation methods can result in very different conclusions about what solutions are best overall.
It is important that anybody involved in planning understand the omissions and biases of their analysis methods. When somebody claims that a particular option is efficient it is important to investigate exactly how efficiency is defined and measured. An option that seems good when evaluated in one way often turns out to be bad when evaluated using a more comprehensive and objective evaluation framework.
For More Information
Steve Abley, Paul Durdin and Malcolm Douglass (2010), Integrated Transport Assessment Guidelines, Report 422, Land Transport New Zealand (www.nzta.govt.nz); at www.nzta.govt.nz/resources/research/reports/422.
Richard Dowling, et al. (2008), Multimodal Level Of Service Analysis For Urban Streets, NCHRP Report 616, Transportation Research Board (www.trb.org); at http://trb.org/news/blurb_detail.asp?id=9470; User Guide at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w128.pdf.
Eric Dumbaugh (2012), Rethinking the Economics of Traffic Congestion, Atlantic Cities (www.theatlanticcities.com), 1 June 2012; at www.theatlanticcities.com/commute/2012/06/defense-congestion/2118.
GIZ (2011), Changing Course in Urban Transport- An Illustrated Guide, Sustainable Urban Transport Project (www.sutp.org) Asia and GIZ; at www.sutp.org/index.php?option=com_content&task=view&id=2825.
Jonathan Levine, Joe Grengs, Qingyun Shen and Qing Shen (2012), “Does Accessibility Require Density or Speed?” Journal of the American Planning Association, Vol. 78, No. 2, pp. 157-172, http://dx.doi.org/10.1080/01944363.2012.677119; at www.connectnorwalk.com/wp-content/uploads/JAPA-article-mobility-vs-proximity.pdf. Also see, Metropolitan Accessibility: Comparative Indicators for Policy Reform, at www.umich.edu/~umaccess/index.html.
Todd Litman (2001), What’s It Worth? Life Cycle and Benefit/Cost Analysis for Evaluating Economic Value, Presented at Internet Symposium on Benefit-Cost Analysis, Transportation Association of Canada (www.tac-atc.ca); at www.vtpi.org/worth.pdf.
Todd Litman (2011), Smart Congestion Relief: Comprehensive Analysis Of Traffic Congestion Costs and Congestion Reduction Benefits, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/cong_relief.pdf
John Poorman (2005), “A Holistic Transportation Planning Framework For Management And Operations,” ITE Journal, Vol. 75, No. 5 (www.ite.org), May, pp. 28-32; at www.ite.org/membersonly/itejournal/pdf/2005/JB05EA28.pdf.
UITP (2012), Better Urban Mobility in Developing Countries: Problems, Solutions and Good Practices, International Association of Public Transport (www.uitp.org); at www.uitp.org/publications/brochures/Dev-Countries-uk.pdf.
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