Public transportation development decision-making under public participation: A large-scale group decision-making method based on fuzzy preference relations

Linling Zhang, Jinjian Yuan, Xinyu Gao and Dawei Jiang

Technological Forecasting and Social Change, 2021, vol. 172, issue C

Abstract: As an intensive travel mode, public transportation is an essential part of a circular economy construction. Scientific decision-making in a PT development plan is a fundamental prerequisite for ensuring its prioritization. In this study, based on the large-scale group decision-making of fuzzy preference relations, we examine PT development decision-making under public participation, to promote the effective use of PT resources. First, we cluster participants in the decision-making process based on the similarity method. Second, based on the clustering, an induced orderly weighting method is used to analyze the decision-making preferences of each sub-group. Finally, the decision-making preferences of each group are prioritized according to an analysis of group heterogeneity. We found that based on the similarity clustering method, when the threshold was set to 0.68, 85 participants were divided into 4 groups, and the demographic characteristics of the sub-groups presented some agglomeration. The four groups paid little attention to improving the quality of PT services. Moreover, the work unit had a significant impact on the decision-making preferences of participants, and public officials paid more attention to society and the government. Group heterogeneity significantly affects decision-making preferences and alternative priorities.

Keywords: Public transportation; Public participation; Fuzzy preference; Large-scale group decision-making (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:172:y:2021:i:c:s0040162521004522

DOI: 10.1016/j.techfore.2021.121020

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