Before the COVID-19 pandemic there had already been an increase in individual shipment transportation including inner-city areas. During the pandemic and implementation of adopted preventive measures, it has increased by more than 100% in some cities. This presents an unsustainable development, particularly in terms of urban environment. The above-mentioned development has accelerated the research related to optimal allocation of logistics centres considering the last-mile distribution. Unfortunately, the theoretical mathematical model that finds an optimal urban logistics centre location based on the matrix of distance, number, and weight of shipments is not applicable in most cities. Therefore, the following research methodology was chosen in accordance with the approved territorial plan. The authors considered those locations in Bratislava—the capital of Slovak Republic—which are designated, or suitable for building up of an urban logistics centre. These localities were afterwards evaluated in a real-world case study employing methods of mathematical programming (linear programming), the nearest neighbour method, and the Clarke-Wright method. The presented methodology can be applied not only when deciding on the appropriate location of the city logistics centre, but also at optimizing the vehicle routing problem. Taking into account the urban logistics sustainability and the e-commerce growth, it was analysed whether the suggested location of urban logistics centre is feasible to provision examined facilities using electric vehicles. The range of considered electric vehicles of N2 category present in the market tends to be at the limits of distribution routes length for the given case study. Therefore, the article also deals with the fast-charging possibilities of vehicles during handling operations and the use of hybrid freight vehicles in city logistics.

Prediction of electricity energy consumption plays a crucial role in the electric power industry. Accurate forecasting is essential for electricity supply policies. A characteristic feature of electrical energy is the need to ensure a constant balance between consumption and electricity production, whereas electricity cannot be stored in significant quantities, nor is it easy to transport. Electricity consumption generally has a stochastic behavior that makes it hard to predict. The main goal of this study is to propose the forecasting models to predict the maximum hourly electricity consumption per day that is more accurate than the official load prediction of the Slovak Distribution Company. Different models are proposed and compared. The first model group is based on the transverse set of Grey models and Nonlinear Grey Bernoulli models and the second approach is based on a multi-layer feed-forward back-propagation network. Moreover, a new potential hybrid model combining these different approaches is used to forecast the maximum hourly electricity consumption per day. Various performance metrics are adopted to evaluate the performance and effectiveness of models. All the proposed models achieved more accurate predictions than the official load prediction, while the hybrid model offered the best results according to performance metrics and supported the legitimacy of this research.

Car drivers have been suffering from road bottlenecks due to the constant increase in the number of vehicles that are not matched by the growth of the road network. Obstacles on a road or a road narrowed to one bidirectional are an accompanying phenomenon of a congested transport network. This paper solves a queueing system generated by such a road bottleneck managed by traffic lights. Generally, the problem can be classified as a Semi-Markov process with stochastic arrivals and deterministic service with a queue of an appropriate length. In order, to allow for an analytical solution, a simplifying assumption that the incoming flow of cars has a uniform distribution is made. A complex analysis of possible scenarios is done, the efficiency and the average waiting times are calculated. The results confirm the nature of the studied system and allow an optimization of the system settings.

Urban public transport companies worldwide are introducing environmentally friendly bus solutions, and in cities with an existing trolleybus network, battery-assisted trolleybuses are an efficient alternative. Equipped with a traction battery, these vehicles allow a short range outside of the trolley lines and on-board recuperation, and they combine the advantages of electric buses and trolleybuses without the need for costly infrastructure expansions. Two similar battery-assisted trolleybuses are in operation in Žilina, where the unitary traction energy consumption has been observed to decrease as a function of the battery-powered and on-trolley-line vehicle run ratio. This theory was confirmed by statistical regression analysis of real operational data for one year of operation in different situations. This research also includes an analysis of battery-assisted trolleybuses’ operation on conventional trolleybus lines, which brought surprising findings, as well as an analysis of other selected indicators that can be dependent on the battery run ratio. These findings can contribute to reducing traction energy consumption by simply modifying the operating technologies without the need for extensive investment in infrastructure or vehicle equipment. This research is pilot research ready for in-depth research.

The demand for public transport by tourists increases significantly in tourist-attractive destinations. This is in addition to regular passengers commuting to school and work. The level of irregular demand is influenced by several factors related to the characteristics of the day of the week, the period of the year, and the current weather. The main goal of the paper is to verify which factors most influence the irregular demand for transport in a tourist-attractive area to ensure operational planning of public passenger transport. Thanks to this, it is possible to ensure sufficient capacity and, at the same time, the efficiency of the operation of public passenger transport. The paper analyzes the main determinants of the irregular demand for regional public rail passenger transport in the High Tatras region of Slovakia. Multiple linear regressions were used to model the number of irregular passengers. The variables representing the day of the week, the attractiveness of the period, and the holiday were found to be the most significant. The variables describing the weather such as maximum daily temperature, precipitation, clouds, and wind had less influence. The obtained mathematical models for forecasting the irregular demand for public passenger transport can help optimize the timetable’s operational setting and the train sets’ size.