Assessing Spatial Synergy Between Integrated Urban Rail Transit System and Urban Form: A BULI-Based MCLSGA Model With the Wisdom of Crowds

• Published in: IEEE transactions on fuzzy systems Vol. 31; no. 2; pp. 434 - 448
• Main Authors: Chang, Jian-Peng, Chen, Zhen-Song, Wang, Zhu-Jun, Jin, LeSheng, Pedrycz, Witold, Martinez, Luis, Skibniewski, Miroslaw J.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Basic uncertain linguistic information (BULI); Cluster analysis; Clustering; Computational modeling; Costs; Linguistics; multicriteria large-scale group assessment (MCLSGA); Multiple criterion; Rails; Reliability; Reliability analysis; spatial synergy; Subjective assessment; Traffic congestion; Urban areas; Urban rail; urban rail transit (URT); Weighting; wisdom of crowds (WOC)
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2022.3185680

Spatially synergizing the urban rail transit (URT) network integrated with its feeder transit system and the urban form plays an important role in improving the effectiveness of URT in mitigating traffic congestion, reducing air pollution, optimizing urban spatial structure, etc. This article mainly focuses on assessing the spatial synergy between the two parts by specifying the assessment criteria that can effectively characterize the spatial synergic mechanism between the two parts and developing a novel multicriteria large-scale group assessment (MCLSGA) model, in which basic uncertain linguistic information (BULI), as an extended form of fuzzy linguistic approach, is used to model and process the subjective assessment information (Assess-Inf) elicited by experts. In order to alleviate the computing complexity, an agglomerative hierarchical clustering algorithm is introduced to cluster the Assess-Inf given by a huge number of experts as per the organizers' expected discrimination level on reliability-related Assess-Inf. A method for weighting the clusters is then proposed to control the roles played by the items of the Assess-Inf, representing each cluster and having different reliability levels in their fusing process to tap into wisdom of crowds (WOC) while accommodating the organizers' trust level in the reliability-related Assess-Inf given by experts. Afterward, the best-worst method is extended to the BULI-based large-scale group assessment context with the aim of accurately weighting the criteria by drawing on WOC. Finally, a case study on assessing the spatial synergy between Chongqing's integrated URT system and urban form is conducted to validate the validity of the proposed MCLSGA model.