Application of Neural Networks in Assessing the Impact Of Micromobility on Environmental Pollution

Abstract

Constant increase of passenger cars usage in urban area has numerous negative social and economic effects. More and more often, road traffic is the leading source of negative impacts on the environment. In order to solve the problems of environmental pollution caused by road traffic, without imposing mobility restrictions, alternatives to vehicles with combustion engines are often proposed. The results of numerous studies indicate that micromobility vehicles can have a significant impact on reducing environmental pollution, if, in addition to emissions of pollutants and noise, the source of electricity and the process of their production are taken into account. Bearing in mind the above, the aim of this paper is to analyze the influence of micromobility vehicles on environmental pollution, as well as to analyze the acceptability of the use of this type of mobility by the public. In addition to the descriptive statistical analysis, the methods of artificial neural networks and Naive Bayes were applied in order to evaluate the impact of micromobility vehicles on environmental pollution. The obtained results indicate that in addition to the fact that micromobility vehicles can have a significant impact on environmental pollution, they can also contribute to changing modal shift. A comprehensive analysis can be a useful basis for decision-making when defining strategies for reducing environmental pollution.