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dc.contributor.authorYedla, S.-
dc.contributor.authorShrestha, R.M.-
dc.date.accessioned2025-01-23T09:14:31Z-
dc.date.available2025-01-23T09:14:31Z-
dc.date.issued2007-10-
dc.identifier.citation10.1504/WRSTSD.2012.044773en_US
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/2874-
dc.descriptionNITWen_US
dc.description.abstractThe 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.en_US
dc.language.isoenen_US
dc.publisherWorld Review of Science Technology and Sustainable Developmenten_US
dc.subjectAHPen_US
dc.subjectGMMen_US
dc.titleApplication of analytic hierarchy process to prioritize urban transport options: Comparative analysis of group aggregation methodsen_US
dc.typeReviewen_US
Appears in Collections:Civil Engineering

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