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Application of analytic hierarchy process to prioritize urban transport options: Comparative analysis of group aggregation methods

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  • Sudhakar Yedla

    (Indira Gandhi Institute of Development Research)

  • Ram M. Shrestha

    (Asian Institute of Technology)

Abstract

The present study presents a comparative analysis of different group aggregation methods adopted in AHP by testing them against social choice axioms with a case study of Delhi transport system. The group aggregation (GA) methods and their correctness were tested while prioritizing the alternative options to achieve energy efficient and less polluting transport system in Delhi. It was observed that among all group aggregation methods, geometric mean method (GMM) - the most widely adopted GA method of AHP - showed poor performance and failed to satisfy the most popular "pareto optimality and non-dictatorship axiom" raising questions on its validity as GA method adopted in AHP. All other group aggregation methods viz. weighted arithmetic mean method with varying weights and equal weights (WAMM, WeAMM) and arithmetic mean of individual priorities (AMM) resulted in concurring results with the individual member priorities. This study demonstrates that WeAMM resulted in better aggregation of individual priorities compared to WAMM. Comparative analysis between individual and group priorities demonstrates that the arithmetic mean (AMM) of priorities by individual members of the group showed minimum deviation from the group consensus making it the most suitable and simple method to aggregate individual preferences to arrive at a group consensus.

Suggested Citation

  • Sudhakar Yedla & Ram M. Shrestha, 2007. "Application of analytic hierarchy process to prioritize urban transport options: Comparative analysis of group aggregation methods," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2007-011, Indira Gandhi Institute of Development Research, Mumbai, India.
    • Handle: RePEc:ind:igiwpp:2007-011
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    References listed on IDEAS

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