Road safety remains a pressing issue worldwide. In Europe, many countries are following the “Vision Zero” strategy, a concept developed in Sweden in the early 1990s, based on the conviction that road deaths can be systematically prevented. Speed is still one of the main causes of traffic accidents. Studies show that the risk of a fatal accident doubles with an increase in speed of just 15 km/h, which has led to numerous measures to reduce speed in various road categories. This article examines the effects of sectional speed control and dynamic speed management on road safety on motorways. Sectional speed control calculates the average speed of a vehicle over a given distance, while dynamic speed management adapts speed limits to traffic and environmental conditions in real time. Although these methods have been implemented successfully in several countries, Slovenia plans to introduce them in the second half of 2025. Based on a review of the relevant literature and existing case studies, this paper assesses the expected benefits for traffic safety on Slovenian motorways. Slovenia’s unique context, characterised by its transit location and significant daily interregional migration, results in strong and heterogeneous traffic flows on its motorways and deteriorated traffic safety on these roads.

Shuttle-based storage/retrieval systems (SBS/RS) require efficient order batching to optimise split-case picking. Original K-means clustering, which groups orders based on overlapping SKUs to minimise bin presentations, struggles with high-dimensional, sparse pharmaceutical data due to computational inefficiency, unsuitable distance metrics and unstable initialisation. We propose an enhanced K-means algorithm based on EIQ analysis. High-frequency SKUs are selected using IK frequency filtering, while Pearson correlation is applied to remove redundant features and reduce dimensionality. Cluster centre initialisation is improved using a roulette-based strategy, and cosine distance replaces Euclidean distance to better capture SKU similarity. Case studies using real data from Company A show that the proposed method outperforms both first-come-first-serve (FCFS) and standard K-means in reducing bin presentations and enhancing processing stability. The algorithm remains robust regardless of SKU popularity shifts. Sensitivity analysis confirms strong performance within appropriate thresholds for feature selection (n: 20–25) and correlation filtering (Pearson correlation: 0.8–0.9). Furthermore, as the number of item-lines per order increases, the improved algorithm yields greater efficiency gains. This algorithm can also be well applied to other industries.

Conventional vehicles rely on petrol and its derivatives to deliver strong performance and extended range. However, these vehicles significantly contribute to environmental pollution and the depletion of fossil fuels. Consequently, alternative-fuelled vehicle technologies have been developed, leading to a paradigm shift in the transportation sector toward electric vehicles (EVs) that reduce greenhouse gas emissions and lessen their environmental impact. For potential EV buyers, a selection problem exists due to the diverse range of alternatives. This study proposes an integrated model that selects and ranks EVs based on three main criteria: cost, driving performance and technical features. Hierarchical best worst method (HBWM), full consistency method (FUCOM), and step-wise weight assessment ratio analysis (SWARA) methods are employed to determine the importance weights for the main criteria and their sub-criteria. A sample EV set consisting of 10 potential alternatives was created for the study, and these EVs were ranked by combining the weights of the main criteria and sub-criteria obtained using the three aforementioned methods employing the technique of precise order preference (TPOP) method. Our experiments suggest that the most critical criterion among the 10 sub-criteria was price, and the least important criterion was high speed.

Dockless bicycle-sharing systems (DBSS) have emerged as critical solutions for first/last-mile connectivity in urban transportation networks. While demand forecasting is fundamental to optimising system deployment and improving service efficiency, existing models often fail to account for the spatial heterogeneity of urban functional zones. In this study, we utilise social network-based check-in data to characterise the functional composition of urban areas, offering a dynamic and data-driven perspective on interpreting travel demand patterns. This study proposes a methodological framework integrating demographic, land-use and built environment variables to model DBSS demand patterns. The geographic detector method is employed to quantify the contributions of multidimensional factors, enhancing variable selection efficiency and improving model robustness. To address spatial heterogeneity and nonlinear dynamics, a high-precision hybrid model, GWRBoost, is proposed by combining the spatial heterogeneity-capturing capability of geographically weighted regression (GWR) with the nonlinear learning strength of extreme gradient boosting (XGBoost). Benchmark testing against conventional approaches demonstrates enhanced predictive accuracy and improved spatial explicability. The findings provide practical guidance for urban transportation planning and the optimisation of shared mobility systems.

Regulatory approaches in intermodal transport are policy-driven frameworks and legal instruments that promote modal shift and sustainability by addressing barriers such as infrastructure deficiencies, pricing mechanisms, terminal accessibility, and coordination among public and private stakeholders. Contemporary research in intermodal transport is fragmented, as varied approaches and themes make it challenging for scholars and industry practitioners to form a clear vision of current regulatory approaches. To address this gap, this study conducts a bibliometric analysis of 84 articles regarding regulatory approaches in intermodal transport. The articles were published in 49 academic journals and authored by 185 scholars. The application of the bibliographic coupling methodology in the VOSviewer software enabled the identification of four clusters: (1) sustainable strategies in intermodal freight transport; (2) modal shift in intermodal freight transport; (3) integration and optimisation in intermodal freight transport; (4) decision-making and policy tools for intermodal transport. Content analysis results highlight infrastructure reforms, collaborative governance, and legal and regulatory measures as critical approaches to enhance modal shift efficiency and strengthen intermodal freight transport systems. The findings offer a scientifically robust foundation for scholars and industry practitioners aiming to improve their understanding of how regulatory approaches and possibilities can strengthen intermodal transport systems.

In this paper, a novel security framework for industrial internet of things (IIoT) and vehicular networks is proposed, integrating blockchain technology with advanced encryption and data classification mechanisms to enhance data integrity, confidentiality and trustworthiness. The work employed ChaCha20-Poly1305 encryption to safeguard the data transaction to local cluster nodes. A private blockchain gateway then processes the encrypted data, classifying it based on confidentiality levels, and directing storage either to cloud servers or the interplanetary file system (IPFS). To ensure data integrity, a proof of authority consensus mechanism within the blockchain is incorporated, while zero knowledge proof (ZKP) methods are used for authentication and secure data access. Empirical evaluations demonstrate that our framework achieves a data transmission security rate of 97.5%, with an average encryption and decryption latency of 150 milliseconds, significantly improving over traditional methods. The proof of authority consensus mechanism exhibits a transaction validation speed of 300 transactions per second, showcasing enhanced efficiency compared to standard blockchain models. Furthermore, the integration of ZKP challenges results in a 30% reduction in unauthorised access attempts, indicating a substantial improvement in overall security. This work emphasises the need for continuous innovation in addressing the various security issues in IoT, ultimately advancing the operational efficiency and security of these systems.

Despite a general decline in recent years, road accidents are still a major problem worldwide, including in Poland and Slovenia. Even if there is no denying that the COVID-19 epidemic has had an impact on accident rates, the statistics nonetheless show that urgent action is required to further lower their frequency. The purpose of this study is to project how many traffic accidents will occur in Slovenia and Poland between 2024 and 2030. This was accomplished by analysing historical data on yearly accident incidence from Eurostat and the Polish Police. To create the predicted numbers, certain neural network models were used, utilising these datasets. The results indicate that the number of road accidents is likely to stabilise soon. This forecast is influenced by a number of factors, including the continued increase in car ownership and the ongoing development of road infrastructure, including the construction of new motorways and roads. It is important to remember that the accuracy of the forecast is susceptible to inherent limitations due to the random sampling of the data used to test, validate and train the models.

Autonomous vehicles are capable of automatically cruising and parking, presenting an opportunity for these vehicles to be shared with others during idle periods. This study conducted a face to face stated preference survey on parking and shared use of privately owned autonomous vehicles based on a virtual-actual experience in Beijing. Utilizing 232 valid samples, a nested Logit model was established to analyse the hierarchical choice behaviour regarding parking modes, parking locations and shared use of autonomous vehicles. The research results show that travellers with a favourable initial understanding of autonomous vehicles and a significantly improved perception of them after the travel experience are more likely to choose the parking mode of ‘Platform agency (parking + paid sharing)’. Travellers tend to prefer remote parking places that offer lower parking fees and higher reliability in vehicle retrieval when needed. Additionally, travellers are more inclined to share their autonomous vehicles when the vehicle-sharing service platform offers lower agency fees and higher sharing earnings and allows temporary vehicle retrieval. Increasing public sharing attitude and perception of autonomous vehicles through travel experiences and advertising can encourage more people to accept car sharing. These findings offer key insights into the factors that affect the market penetration of private vehicle-sharing services in future.

Gesture control allows for reduced glance interaction compared to touchscreens when controlling secondary functions in cars. However, to date, there has been a lack of research investigating the influence of age and palm surface area (PSA) on performance during dual task applications, such as driving a car and using gestures with mid-air haptics feedback for controlling secondary functions. The following study investigates ways to introduce a characteristic feedback point on a virtual slider which differs from the adjacent points. This is either by changing the intensity or the distance to adjacent points. The time and the slider value set by the participant were saved. Participants gave ratings for each of the nine parameter sets by answering questions about the usability and the user experience questionnaire. The study consisted of two age groups: 30 younger participants with an average age of 31.0 years and 31 older participants with an average age of 63.0 years. Three of the older participants reported not feeling the mid-air haptics at all. Significant differences were found between the age groups in subjective and objective terms, and significant correlations for PSA and two subjective parameters. Further research will evaluate the findings in a more immersive static driving simulator.

Blockchain technology is increasingly proving to be an innovative solution in numerous spheres of the economy, including education, and by extension, institutions involved in Maritime Education and Training (MET). As MET institutions face increasing challenges, such as managing and maintaining large databases, extensive record-keeping, and exchanging and processing information, the application of this technology is emerging as a response to these challenges. In addition to the fact that it would lead to the digitalisation of documents, the application of the technology would, above all, improve the security and reliability of documentation. Owing to its decentralised, transparent and immutable nature, blockchain technology offers a comprehensive framework for overcoming these challenges within MET institutions. The objective of this paper is to propose a model for the application of blockchain technology in MET institutions in order to address the lack of a unified, secure and transparent data system shared by all stakeholders involved in maritime education and training. This paper provides a comprehensive review of the literature on the application of blockchain technology in the maritime industry, highlighting its potential for improving data management and operational efficiency. Furthermore, the paper proposes the implementation of blockchain technology, focusing on the creation of an integrated database that would be used by various users, including MET institutions, state administration (ministries, port authorities), seafarer training centres, maritime agencies, medical institutions and seafarers themselves. Moreover, the paper offers detailed recommendations and guidelines for implementing the hybrid model in MET institutions, with a special emphasis on its role in automating administrative processes through smart contracts, enhancing transparency, and improving overall document security.