Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-25565Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Niroomand, Naghmeh | - |
| dc.contributor.author | Bach, Christian | - |
| dc.contributor.author | Elser, Miriam | - |
| dc.date.accessioned | 2022-09-08T08:45:41Z | - |
| dc.date.available | 2022-09-08T08:45:41Z | - |
| dc.date.issued | 2021 | - |
| dc.identifier.issn | 0361-1981 | de_CH |
| dc.identifier.issn | 2169-4052 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/25565 | - |
| dc.description.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%. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | Sage | de_CH |
| dc.relation.ispartof | Transportation Research Record: Journal of the Transportation Research Board | de_CH |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | de_CH |
| dc.subject.ddc | 510: Mathematik | de_CH |
| dc.subject.ddc | 629: Luftfahrt- und Fahrzeugtechnik | de_CH |
| dc.title | Vehicle dimensions based passenger car classification using fuzzy and non-fuzzy clustering methods | de_CH |
| dc.type | Beitrag in wissenschaftlicher Zeitschrift | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Management and Law | de_CH |
| zhaw.organisationalunit | Zentrum für Arbeitsmärkte, Digitalisierung und Regionalökonomie (CLDR) | de_CH |
| dc.identifier.doi | 10.1177/03611981211010795 | de_CH |
| dc.identifier.doi | 10.21256/zhaw-25565 | - |
| zhaw.funding.eu | No | de_CH |
| zhaw.issue | 10 | de_CH |
| zhaw.originated.zhaw | No | de_CH |
| zhaw.pages.end | 194 | de_CH |
| zhaw.pages.start | 184 | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.volume | 2675 | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.webfeed | W: Spitzenpublikation | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Management and Law | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2021_Niroomand-etal_Vehicle-dimensions-based-passenger-car-classification.pdf | 1.73 MB | Adobe PDF |  View/Open |
Show simple item record
Niroomand, N., Bach, C., & Elser, M. (2021). Vehicle dimensions based passenger car classification using fuzzy and non-fuzzy clustering methods. Transportation Research Record: Journal of the Transportation Research Board, 2675(10), 184–194. https://doi.org/10.1177/03611981211010795
Niroomand, N., Bach, C. and Elser, M. (2021) ‘Vehicle dimensions based passenger car classification using fuzzy and non-fuzzy clustering methods’, Transportation Research Record: Journal of the Transportation Research Board, 2675(10), pp. 184–194. Available at: https://doi.org/10.1177/03611981211010795.
N. Niroomand, C. Bach, and M. Elser, “Vehicle dimensions based passenger car classification using fuzzy and non-fuzzy clustering methods,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2675, no. 10, pp. 184–194, 2021, doi: 10.1177/03611981211010795.
NIROOMAND, Naghmeh, Christian BACH und Miriam ELSER, 2021. Vehicle dimensions based passenger car classification using fuzzy and non-fuzzy clustering methods. Transportation Research Record: Journal of the Transportation Research Board. 2021. Bd. 2675, Nr. 10, S. 184–194. DOI 10.1177/03611981211010795
Niroomand, Naghmeh, Christian Bach, and Miriam Elser. 2021. “Vehicle Dimensions Based Passenger Car Classification Using Fuzzy and Non-Fuzzy Clustering Methods.” Transportation Research Record: Journal of the Transportation Research Board 2675 (10): 184–94. https://doi.org/10.1177/03611981211010795.
Niroomand, Naghmeh, et al. “Vehicle Dimensions Based Passenger Car Classification Using Fuzzy and Non-Fuzzy Clustering Methods.” Transportation Research Record: Journal of the Transportation Research Board, vol. 2675, no. 10, 2021, pp. 184–94, https://doi.org/10.1177/03611981211010795.
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