Situation Model of the Transport, Transport Emissions and Meteorological Conditions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866916948694007808 |
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| author | Benes, V. Svitek, M. Michalikova, A. Melicherik, M. |
| author_facet | Benes, V. Svitek, M. Michalikova, A. Melicherik, M. |
| contents | Air pollution in cities and the possibilities of reducing this pollution represents one of the most important factors that today's society has to deal with. This paper focuses on a systemic approach to traffic emissions with their relation to meteorological conditions, analyzing the effect of weather on the quantity and dispersion of traffic emissions in a city. Using fuzzy inference systems (FIS) the model for prediction of changes in emissions depending on various conditions is developed. The proposed model is based on traffic, meteorology and emission data measured in Prague, Czech Republic. The main objective of the work is to provide insight into how urban planners and policymakers can plan and manage urban transport more effectively with environmental protection in mind. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10541 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Situation Model of the Transport, Transport Emissions and Meteorological Conditions Benes, V. Svitek, M. Michalikova, A. Melicherik, M. Artificial Intelligence Machine Learning Air pollution in cities and the possibilities of reducing this pollution represents one of the most important factors that today's society has to deal with. This paper focuses on a systemic approach to traffic emissions with their relation to meteorological conditions, analyzing the effect of weather on the quantity and dispersion of traffic emissions in a city. Using fuzzy inference systems (FIS) the model for prediction of changes in emissions depending on various conditions is developed. The proposed model is based on traffic, meteorology and emission data measured in Prague, Czech Republic. The main objective of the work is to provide insight into how urban planners and policymakers can plan and manage urban transport more effectively with environmental protection in mind. |
| title | Situation Model of the Transport, Transport Emissions and Meteorological Conditions |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.10541 |