Vehicle Dimensions Based Passenger Car Classification using Fuzzy and Non-Fuzzy Clustering Methods

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%.

• Record URL:
  https://doi.org/10.1177/03611981211010795
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • © National Academy of Sciences: Transportation Research Board 2021.
• Authors:
  • Niroomand, Naghmeh
  • Bach, Christian
  • Elser, Miriam
• Publication Date: 2021-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 184-194
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2675
  • Issue Number: 10
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Automobiles; Cluster analysis; Fuzzy logic; Passenger vehicles; Vehicle classification; Vehicle size
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01771506
• Record Type: Publication
• Files: TRIS, TRB, ATRI
• Created Date: May 12 2021 4:45PM