In this paper, the calculation of exhaust emission costs originating from aircraft and road vehicles in the base year 2017 and in the forecasting year 2032, in the Republic of Serbia, was carried out. The presented methodology includes a number of influential factors for air transport (airport capacity, number of operations, aircraft type, relevant engine, range) and for road transport (changing of traffic volumes, design and operating speeds, the quality of the pavement structure, type of terrain and category of road sections, dependence of exhaust emission from changes in vehicle speed). It was found that in the current operating conditions, the dominant costs in the total exhaust emission costs are the costs of nitrogen oxides (61%) in road transport, whilst carbon dioxide costs are dominant in air transport (52%). In the future, carbon dioxide costs will have a share of over 80% in the road transport sector and over 58% in the air transport sector in total exhaust emission costs. The average exhaust emission costs per one aircraft operation (international flights) will range from 141 to 145€. In road transport, the average exhaust emission costs at 100 km in 2032 will range from 1.8 to 2.2€.

To solve the problem of environmental pollution caused by road traffic, alternatives to vehicles with internal combustion engines are often proposed. As such, eco-mobility microvehicles have significant potential in the fight against environmental pollution, but only on the condition that they are widely accepted and that they replace the vehicles that predominantly pollute the environment. With this in mind, this study aims to elucidate the main variables that influence the acceptability of these vehicles, using prediction models based on binary logistic regression and a multilayer artificial neural network—a multilayer perceptron (ANN). The data of a random sample obtained via an online questionnaire, answered by 503 inhabitants of Belgrade (Serbia), were used for training and testing the model. A multilayer perceptron with 9 and 7 neurons in two hidden layers, a hyperbolic tangent activation function in the hidden layer, and an identity function in the output layer performed slightly better than the binary logistic regression model. With an accuracy of 85%, a precision of 79%, a recall of 81%, and an area under the ROC curve of 0.9, the multilayer perceptron model recognized the influential variables in predicting acceptability. The results of the model indicate that a respondent’s relationship to their current environmental pollution, the frequency of their use of modes of transport such as bicycles and motorcycles, their mileage for commuting, and their personal income have the greatest influence on the acceptability of using eco-mobility vehicles.

The development of alternative environmentally friendly modes of transportation is becoming an increasingly promising solution in traffic-congested and polluted urban areas. E-bikes, as one of them, are recognized as an ecologically sustainable means of transportation that has significant potential to replace motorized modes of transportation that can improve urban mobility. Relying on artificial intelligence and considering an ecological approach when considering the acceptability of e-bikes by setting a direct question for users influences the development of an innovative way of understanding and evaluating the use of more sustainable modes of transportation. In this regard, this study aims to elucidate the main variables influencing the acceptability of e-bike use using artificial neural network (ANN) models—multilayer perceptron (MLP) and radial basis function (RBF). For training and testing the models, data from a random sample obtained through an online questionnaire, which was answered by 626 residents of Belgrade (Serbia), were used. A multilayer perceptron with nine and seven neurons in two hidden layers, respectively, hyperbolic tangent activation function in the hidden layer and identity function in the output layer, gave better results than the radial basis function model. With an accuracy of 89%, a precision of 83%, a recall of 79%, and an area under the receiver operating characteristic (ROC) curve of 0.927, the multilayer perceptron model recognized the influential variables in predicting acceptability. The results of the model indicate that the mileage traveled, the frequency of motorcycle use, the respondents’ awareness of the pollution in Belgrade, and the age of the respondents have the greatest influence on the acceptability of using e-bikes. In addition to majority acceptability (69.8%), the results obtained by the model can represent a useful basis for decision-makers when defining strategies for the development and application of e-bikes while reducing traffic congestion and environmental pollution in Belgrade.

Parking charges are considered to be a powerful tool for managing transport demand, as parking price is one of the most important factors impacting the travel decision-making process. This paper examines and quantifies the parking price impact on parking demand due to an increase in initial parking price as well as due to the real price decrease (inflation-adjusted) over time. Revealed preference data were collected for a parking garage located in central Belgrade at three different times: before the price increase, after the price increase and after a certain period of time had passed since the price was increased. The results confirm that parking price impacts demand. However, initial price changes have become less effective in managing demand over time. Although such a pattern is typical and expected, its quantification could be of great importance when defining parking prices and predicting impacts of new prices, as well as in determining the periodicity of price changes in order to maintain desired parking occupancy levels.

Transport, especially road transport is one of the main sources of emissions of greenhouse gasses as well as numerous air pollutants that have harmful effects on human health, particularly in urban areas. The European Union, as well as many of its Member States are making huge efforts to reduce these emissions and their harmful effects. One possible solution is the application of green public procurement, which can be used as a powerful tool to stimulate the demand for zero and low emission vehicles and to promote the introduction and increase the share of renewable energy sources in the transport sector. The activities of the European Union related to encouraging greater use of green public procurement as an instrument for solving environmental problems and the experience of the countries with the longest tradition in their application are the subject of the analysis in this paper. At the end of the paper, special attention is paid to the challenges and possibilities of using green public procurement as a means to achieve the environmental goals of the transport sector of the Republic of Serbia.