Sustainable Urban Transportation: Performance Indicators and Some Analytical Approaches

TLDR

Urban governments are increasingly focused on sustainable transportation, yet in Australia progress on sustainability indicators and analytical techniques remains limited despite numerous recommendations. This review seeks to define sustainable urban transportation and land use systems and to identify objectives that underpin suitable performance indicators. The authors employ decision‑theory hierarchical diagrams to link policy objectives to actions, attributes, and indicators, and apply a suite of analytical techniques—including descriptive statistics, spatial mapping, spatial statistics, regression, travel‑preference models, and linear programming—illustrated with Sydney census data. The study highlights the necessity of geographically based indicators and integrated transportation‑land‑use policies.

Abstract

Urban governments show considerable interest in formulating policies for a more sustainable transportation sector. In Australia, despite the Commonwealth Government Ecologically Sustainable Development (ESD) Transport Working Group making over 40 recommendations for more sustainable urban transportation a decade ago, a recent Institution of Engineers Australia, Transport Panel found little progress with transportation indicators of sustainability and appropriate analytical techniques. A review of the international literature is made to determine definitions of a sustainable urban transportation and land use system, and objectives that would form the basis for determining suitable indicators of performance. Drawing on hierarchical diagrams from decision theory, we show the link between higher-level policy objectives for sustainability and lower-order actions, measurable attributes, and performance indicators. The analytical framework for sustainable urban transportation analysis includes descriptive statistics—exploratory and graphical methods, spatial mapping, spatial statistics (to identify geographical patterns and to identify outliers in the data), regression analysis, travel preference functions based on Stouffer's intervening opportunity model, and linear programming. These analytical techniques are illustrated with examples of travel and urban form in Sydney using data from the Census of Population and Housing, 1961–1996. The need for geographically based indicators and transportation and land use policies is emphasized.