Situation Model of the Transport, Transport Emissions and Meteorological Conditions
This work addresses air pollution reduction for urban planners and policymakers, but it is incremental as it applies an existing method (fuzzy inference systems) to a specific dataset.
The paper tackled the problem of predicting traffic emissions in cities by analyzing the effect of weather on emissions and dispersion, using fuzzy inference systems to develop a model based on data from Prague, Czech Republic, with results providing insights for urban planning and environmental management.
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.