Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-29785Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Niroomand, Naghmeh | - |
| dc.contributor.author | Bach, Christian | - |
| dc.contributor.author | Elser, Miriam | - |
| dc.date.accessioned | 2024-02-02T11:11:55Z | - |
| dc.date.available | 2024-02-02T11:11:55Z | - |
| dc.date.issued | 2023-09-12 | - |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/29785 | - |
| dc.description.abstract | The overall level of emissions from the Swiss passenger cars is strongly dependent on the fleet composition. Despite technology improvements, the Swiss passenger cars fleet remains emissions intensive. To analyze the root of this problem and evaluate potential solutions, this study applies deep learning techniques to evaluate the inter-class (namely micro, small, middle, upper middle, large and luxury class) and intra-class (namely sport utility vehicle and non-sport utility vehicle) differences in CO2 emissions. Since the division of vehicles into segments by experts is not standardized and therefore not always uniform, and some vehicle models have recently positioned themselves as "crossovers" between established vehicle categories, it has become increasingly difficult and inaccurate to segment the vehicle population using conventional classification methods. The development of a mathematical approach to accurately segment passenger vehicles is essential for determining the real CO2 emissions from road traffic in the future. While road traffic has so far had its own energy system, which was comparatively easy to assess in terms of CO2 emissions, increasing electrification of road traffic will difficult the distinction of energy consumption from road traffic and other stationary energy uses. Based on this novel approach, we can then predict accurate segment-based CO2 emissions, which allows for detailed analyses of the main factors influencing the average fleet CO2 emissions. Our results show that the proposed method is a viable and effective to categorize vehicles based on their technical, emission and dimensional features. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | ZHAW Zürcher Hochschule für Angewandte Wissenschaften | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.subject.ddc | 380: Verkehr | de_CH |
| dc.title | Deep learning techniques utilized for assessing CO2 emissions of Swiss passenger cars | de_CH |
| dc.type | Konferenz: Poster | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Management and Law | de_CH |
| dc.identifier.doi | 10.21256/zhaw-29785 | - |
| zhaw.conference.details | Mobility Research and Innovation in Switzerland Workshop, Biel, Switzerland, 12 September 2023 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.publication.review | Not specified | 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 | |
|---|---|---|---|---|
| 2023_Niroomand-etal_Deep-learning-techniques-CO2-emissions.pdf | 2.04 MB | Adobe PDF |  View/Open |
Show simple item record
Niroomand, N., Bach, C., & Elser, M. (2023, September 12). Deep learning techniques utilized for assessing CO2 emissions of Swiss passenger cars. Mobility Research and Innovation in Switzerland Workshop, Biel, Switzerland, 12 September 2023. https://doi.org/10.21256/zhaw-29785
Niroomand, N., Bach, C. and Elser, M. (2023) ‘Deep learning techniques utilized for assessing CO2 emissions of Swiss passenger cars’, in Mobility Research and Innovation in Switzerland Workshop, Biel, Switzerland, 12 September 2023. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-29785.
N. Niroomand, C. Bach, and M. Elser, “Deep learning techniques utilized for assessing CO2 emissions of Swiss passenger cars,” in Mobility Research and Innovation in Switzerland Workshop, Biel, Switzerland, 12 September 2023, Sep. 2023. doi: 10.21256/zhaw-29785.
NIROOMAND, Naghmeh, Christian BACH und Miriam ELSER, 2023. Deep learning techniques utilized for assessing CO2 emissions of Swiss passenger cars. In: Mobility Research and Innovation in Switzerland Workshop, Biel, Switzerland, 12 September 2023. Conference poster. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. 12 September 2023
Niroomand, Naghmeh, Christian Bach, and Miriam Elser. 2023. “Deep Learning Techniques Utilized for Assessing CO2 Emissions of Swiss Passenger Cars.” Conference poster. In Mobility Research and Innovation in Switzerland Workshop, Biel, Switzerland, 12 September 2023. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-29785.
Niroomand, Naghmeh, et al. “Deep Learning Techniques Utilized for Assessing CO2 Emissions of Swiss Passenger Cars.” Mobility Research and Innovation in Switzerland Workshop, Biel, Switzerland, 12 September 2023, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2023, https://doi.org/10.21256/zhaw-29785.
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