An integration model of DEA and RST for measuring transport sustainability

Abstract

This paper addresses the issue of measuring transport sustainability for decision-makers. The Pareto rule states that 80% of important information is concentrated in 20% of content. Simplifying useful information for decision-makers is becoming increasingly more important in a complicated society. This paper integrates data envelopment analysis (DEA) and rough set theory (RST) methods for measuring transport sustainability to obtain effective and clear decision-support information for decision-makers, including transport sustainability performance and its related knowledge base. The current work first compiles and summarizes transport sustainability indicators into five generalized efficiency indicators, termed ‘cost efficiency’, ‘cost effectiveness’, ‘service effectiveness’, ‘service reduction’, and ‘service impact’. Then, applies the layered DEA to classify the decision making units (DMUs: time series data of indicator systems) according to five generalized efficiency indicators. The RST application sets up the related knowledge base, including decision rules and core indicators. An empirical study demonstrates Taiwan's transportation sector during the 1993–2007 period. The empirical study results show core indicators of ‘cost efficiency’, ‘service reduction’, and ‘service impact’. Certain important decision rules indicate that if the ‘cost efficiency’ or ‘service reduction’ indicator performs well, the transport system will move toward more sustainable development. Simplified and important information provided to decision-makers introduces them to strategic tools for improving transport sustainability.

Keywords:

• data envelopment analysis (DEA)
• rough set theory (RST)
• knowledge base
• decision rule