Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25565
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Vehicle dimensions based passenger car classification using fuzzy and non-fuzzy clustering methods
Authors: Niroomand, Naghmeh
Bach, Christian
Elser, Miriam
et. al: No
DOI: 10.1177/03611981211010795
10.21256/zhaw-25565
Published in: Transportation Research Record: Journal of the Transportation Research Board
Volume(Issue): 2675
Issue: 10
Page(s): 184
Pages to: 194
Issue Date: 2021
Publisher / Ed. Institution: Sage
ISSN: 0361-1981
2169-4052
Language: English
Subject (DDC): 510: Mathematics
629: Aeronautical, automotive engineering
Abstract: There has been globally continuous growth in passenger car sizes and types over the past few decades. To assess the development of vehicular specifications in this context and to evaluate changes in powertrain technologies depending on surrounding frame conditions, such as charging stations and vehicle taxation policy, we need a detailed understanding of the vehicle fleet composition. This paper aims therefore to introduce a novel mathematical approach to segment passenger vehicles based on dimensions features using a means fuzzy clustering algorithm, Fuzzy C-means (FCM), and a non-fuzzy clustering algorithm, K-means (KM). We analyze the performance of the proposed algorithms and compare them with Swiss expert segmentation. Experiments on the real data sets demonstrate that the FCM classifier has better correlation with the expert segmentation than KM. Furthermore, the outputs from FCM with five clusters show that the proposed algorithm has a superior performance for accurate vehicle categorization because of its capacity to recognize and consolidate dimension attributes from the unsupervised data set. Its performance in categorizing vehicles was promising with an average accuracy rate of 79% and an average positive predictive value of 75%.
URI: https://digitalcollection.zhaw.ch/handle/11475/25565
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Management and Law
Organisational Unit: Center for Economic Policy (FWP)
Appears in collections:Publikationen School of Management and Law

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