Land use largely depends on the traffic policy of a city. The appearance of e-scooters can greatly affect the visual distribution of transportation, and thus the occupation of land, primarily in the central areas of cities. E-scooters as a shared micro-mobility service have become widespread worldwide since 2017. The advent of e-scooters has made changes in travel habits, especially in the central parts of big cities. However, many issues are focused on e-scooter shared mobility management policies. One of the important issues is the price of renting an e-scooter, on which the percentage of users who use e-scooters largely depend. In order to determine willingness to pay for e-scooter dockless shared mobility, a survey was conducted in the city of Belgrade (Serbia, Europe) on the willingness of participants to use this mode of transport for commuting and other travel purposes depending on the price of renting an e-scooter. The results showed that price plays an important role in the willingness of participants to use an e-scooter. The paper presents mathematical models, which include the cost of renting an e-scooter and the percentage of participants who would accept this type of transport. These mathematical models can help a decision maker to determine the pricing policy in order to maximize the profit from renting an e-scooter, as well as to influence modal shift in order to reduce car-dependent trips.

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.

Increased emissions of polluting materials and noise emissions by motor vehicles, contributing to the reduction of air quality and life of all residents. One of the possible solutions to the problem of environmental pollution in cities is the use of micromobility vehicles. Micromobility implies the use of small vehicles that have zero emissions and do not emit noise. The results of previous research indicate that the use of "micro" vehicles can significantly contribute to the reduction of environmental pollution, taking into account their characteristics and the type of drive they carry. Bearing in mind the above, the goal of this work is to analyze the impact of micromobility vehicles on the environment, as well as the analysis of factors that influence the acceptability of the use of this type of mobility by the public. The results of the research indicate that micromobility vehicles can have a significant potential in reducing environmental pollution, which can influence changes in modal shift. A comprehensive analysis of the impact of micromobility vehicles presented in the paper can be a useful basis for decision-making when defining a strategy for reducing environmental pollution.