Open AccessDETERMINING THE IMPACTS OF FREIGHT TRANSPORT MODE COMBINATIONS ON AIR POLLUTION USING ARTIFICIAL NEURAL NETWORKS
Author(s) -
Nikola Petrović,
Vesna Jovanović,
Marijana Petrović,
Boban Nikolić
Publication year - 2021
Publication title -
facta universitatis. series: automatic control and robotics
Language(s) - English
Resource type - Journals
eISSN - 1820-6425
pISSN - 1820-6417
DOI - 10.22190/fuacr2003191p
Subject(s) - air quality index , mode (computer interface) , quality (philosophy) , european union , artificial neural network , mode of transport , air transport , air pollution , order (exchange) , computer science , transport engineering , environmental science , operations research , environmental economics , business , engineering , artificial intelligence , meteorology , economics , public transport , international trade , geography , philosophy , chemistry , organic chemistry , epistemology , finance , operating system
Transport is one of the largest emitters of harmful substances that affect air quality. Each combination of freight transport modes has a different volume and at the same time has a differentiated negative impact on air quality. That is why the European Union has been making special efforts for many years to create and implement strategies aimed at improving air quality. The main goal of this paper is to present a methodology that enables quantification and analysis of the impact of each freight transport mode combination on air quality using feed-forward neural networks. The developed model uses the parameters of the EU member states in the period from 2000 to 2014. In addition to the scientific and practical contribution, the development of the model provides a good basis for the universal platform formation in order to create and develop strategies, i.e. measures to improve air quality on a global level.