The intense growth of cities affects their inhabitants to a considerable extent. The issues facing the traveling population include congestion and growing harmful emissions. Urban transport requires changes towards eco-friendly solutions. However, even though new forms of traveling (sharing services) are being implemented, their integration with public transport remains problematic. On account of the large number of available services combined with the absence of their integration, city inhabitants are faced with the dilemma of choosing between one or several transport modes which would enable them to make the given trip. The main goal of this article is to propose a model for integration of different transport services which could support those who intend to travel in the decision-making process. Therefore, the parameters of a model of urban sharing services were identified and classified. The parameters discussed in the paper with reference to an extensive literature review describe how individual sharing services are functioning. What has also been identified is the location-specific factors as well as those related to the potential area of operation which affect the integration with public transport. In order to take all the relevant parameters into account and find a solution to the problem at hand, a multi-criteria decision-making approach has been proposed. To this end, scores and weights determining their impact on the model have been established. For purposes of the solution in question, the relevant calculations were conducted by referring to an actual need to travel between selected locations.

Minimization of negative environmental impact of transport cannot be pursued through mobility limiting, but rather through efficient utilization of natural resources. Some of the ways to reduce harmful pollution and noise include increasing the use of electric energy for transportation and developing electromobility. However, municipalities face difficult decisions connected with the siting of charging stations, for example, budget limitations are an important factor. The presented method allows for selecting a subset of existing parking lots where the charging devices will be sited. As the inputs, only easily accessible data is required. Applying a genetic algorithm combined with fuzzy logic and the Pareto front analysis, one could establish a set of optimal solutions for multiple pre-defined restrictive and partially contradictory criteria. The method has been discussed using a real example of a medium-sized city in southern Poland. Its results have also made it possible to verify whether a budget required for the planned investment is substantiated.

Development of electromobility in urban areas requires an appropriate level of vehicle charging infrastructure. Numerous methods for siting of charging stations have been developed to date, and they appear to be delivering diverse outcomes for the same area, which is why local authorities face the problem of choosing the right station layout. The solution proposed in this article is to use a travel planner to evaluate the distribution of charging stations over the area of a metropolis. The decision making support is achieved by determining optimal travel routes for electric vehicles according to their initial state of charge for the three selected station siting methods. The evaluation focused on the following three aspects: (1) number of travels that cannot be made (due to the lack of a charging station at a certain distance around the start point), (2) extension of the travel caused by the need to recharge the vehicle on-route, and (3) additional energy consumption by electric vehicles required to reach the charging station (necessity of departing from the optimal route). An analysis of the results has made it possible to determine a solution which is superior to others. For the case study analysed in the paper, i.e., the territory of the Metropolis of Upper Silesia and Dabrową Basin (Górnośląsko-Zagłębiowska Metropolia, GZM), the distribution of charging stations established in line with method I has returned the best results. What the method in question also makes possible is to indicate a safe minimum energy reserve to complete the travel by eliminating situations of unexpected vehicle immobilisation due to on-route energy depletion and by minimising the phenomenon referred to as range anxiety.

The article addresses a solution based on an empirical example proposed for planning of transport system elements through application of a sufficiently extensive trip planner. The tool described, i.e. GTPlanner, developed under implementation of the international program of ERANET Transport III, was provided with an additional feature allowing for the queries submitted by travelling persons to be archived and individual needs of travellers to be profiled within selected areas. The added value connected with the foregoing is also that the problem of transport system planning was perceived from a different perspective, assuming that actual preferences of travelling persons (rather than those imposed by the existing system) should provide the grounds for establishing orientations in terms of organizational and technical changes to be introduced into the transport system of the chosen territory. Besides the information function, GTPlanner also performs an educational role, as it highlights the impact of individual travelling modes on the environment, consequently fostering desirable travel behaviors

The determinants of children’s independent school mobility and the contextual discrepancies between these determinants have not been comprehensively investigated in previous studies. It is important to examine these determinants because independent school mobility is associated with children’s physical activity, according to the literature. This paper examined the associations of different groups of variables such as household, mobility, perceptions, and the built environment with independent school mobility of children between 9 and 12 years using a sample of 1304 girls (50.9%) and boys (49.1%) in seven European countries. The sample was analyzed by Multinomial Logistic Regression, Chi-square test of independence, and Proportional Reduction in Error methods. According to the findings, father’s and mother’s commute mode choice, child’s mode choice of commute to school, child’s bike ownership, parent’s perception of safety, parent’s evaluation of bike lane and sidewalk quality, child’s commute distance, number of driving licenses in the household, accessibility of public transport, and population density in the neighborhood and around the school proved to be very strong and significant determinants of children’s independent school mobility in the Europe-wide sample. The comparison of the levels of independent school mobility did not show any significant differences between high-income countries such as Germany, Italy, and the Netherlands, and emerging economies and developing countries like Poland, Greece, Turkey, and Croatia. However, a direct comparison between Poland (emerging economy) (33.6%) and the Netherlands (high-income) (31.7%) revealed significant differences in the level of independent school mobility. This study found the motives for this discrepancy due to the significant difference in bike ownership, the number of household members working outside of the house, household size, commute distances of parents, and driving license possession.