Freight transport by road is constantly increasing, therefore it is very important to ensure efficient distribution of flows by optimizing/reducing transport costs. With the recent increase in the demand for small loads, less-than-truckload transportation services are becoming increasingly popular among representatives of small and large businesses. This method of cargo transportation is more economically accessible and useful. The increasing competition between companies encourages the search for ways to improve the offered services, thereby reducing costs for customers. Digitization tools are among the most important in order to implement the principles of green logistics, ensure long-term competitive advantages by systematically improving logistics results and reducing costs. Automated service management, services based on customer data and intelligent solutions must help companies create a digital business model. The purpose of this article is the identification of problem areas in the service quality of companies carrying out less-than-truckload transportation on international routes and ways of solving them by applying digitization technologies. Research methods/methodology used: the work was carried out based on the analysis of scientific literature, statistical data analysis and survey methods, an expert survey was conducted by means of a questionnaire. The analysis of scientific literature made it possible to identify that the transportation of less-than-truckload shipments is not considered as a separate element, but included in the process of transportation of general loads. Considering the fact that less-than-truckload cargo transportation is becoming increasingly popular globally, this renders it possible to justify the novelty of the article and for a more detailed study. The result of the work is a conceptual model of service quality improvement based on digitization technologies.

Modern technology allows for the simplification of a number of functions in industry and business. Many companies have achieved a high level of robotisation and automation in the use of services, including companies operating in the transport sector, where smart systems help to control load planning, the issuing of documents, the tracking and transportation of shipments, etc. Drones can be exploited as smart assistants in delivering cargo in cities. Since it is a new technology capable of working autonomously, it presents various legal, psychological, and physical challenges. This article presents an analysis of the scientific literature on the development of small-cargo flows using drones and a research methodology on the development of the use of drones, presenting a model which helps to address the issue of cargo delivery in cities.

To ensure stability and transparency in the European logistics sector, in May 2017, the European Commission presented several proposals to change the regulation of the market—in particular, market access, driving and rest periods, and business trips. In the development of this package, several unfavourable decisions were made that go against Lithuanian transport companies, which will have a significant impact on the companies’ finances, as the frequent return of trucks will lead to additional fuel costs and is also in contradiction with the concept of green logistics. Thus, it is essential to study the Mobility Package’s pros and cons and compare researchers’ views. Accordingly, the subject of this article is the impact of the Mobility Package on Lithuanian logistics companies. This article employs various methods, including an analysis of the scientific literature and legislation, statistical data analysis, PEST analysis, and qualitative research based on expert interviews. The results allow us to identify that the content of the Mobility Package is driven by the goal of ensuring equivalent working conditions throughout the EU, which in this case is the most important object of the legal changes. Also, based on the results obtained, it can be stated that Lithuanian logistics companies that want to remain in the market have several solutions they can employ to achieve that goal, and to support their efforts, a competitiveness improvement model for Lithuanian logistics companies has been developed.

In transport and logistics systems, decision-making is increasingly influenced by uncertainty stemming from demand variability, technological disruptions, and systemic risks present in supply chains. In these contexts, organizations need approaches that are rooted in data and analysis to assess key elements affecting system resilience and performance. Although current studies widely utilize stochastic and fuzzy models for operational decision-making, there has been insufficient focus on the systematic assessment of human-centric system elements—especially competencies—as decision variables in intricate logistics systems. This research proposes an analytical framework for multi-criteria decision-making that is driven by data and aimed at evaluating the significance of various competencies that affect labor market competitiveness and the adaptability of supply chains. The approach combines expert assessment with statistical and information-theoretic metrics, utilizing Kendall’s coefficient of concordance for evaluating consistency, Shannon entropy for analyzing distributional uncertainty, and the Gini coefficient for measuring concentration. This integrated method allows for the measurement of both variability and inequality within decision frameworks in the face of uncertainty. The findings indicate that hands-on experience and professional skills play a crucial role in decision-making structures, whereas the ability to adapt to technological advancements and a commitment to ongoing learning greatly enhance system resilience. The entropy results reveal a significant degree of structural balance in the decision criteria, while the low Gini values affirm a lack of concentration, indicating a distributed and multi-dimensional decision-making environment. The study provides analytical insights into the structure and relative importance of competencies in decision-making contexts related to supply chain resilience.

Heavy-duty transit vehicles (N1–N3) (heavy vehicles) can generate disproportionate environmental and economic impacts in urban transport corridors despite representing a relatively small share of total traffic volume. This study develops an integrated probabilistic framework for assessing the relationships between traffic-flow variability, CO₂e emissions, particulate-matter-derived climate impacts, and external pollution costs associated with transit transport. The methodology combines traffic-flow modeling, emission estimation, PM-to-CO₂e transformation, probabilistic analysis, Monte Carlo simulation, sensitivity analysis, and scenario-based intervention assessment. Separate analyses were conducted for M1 passenger vehicles and heavy vehicles to evaluate differences in emission behavior, uncertainty, and economic impacts. The results indicate substantial structural differences between light-duty and heavy-vehicle regimes. Passenger-car traffic exhibited relatively stable emission distributions, whereas heavy vehicles demonstrated significantly greater variability, uncertainty, and emission intensity. Sensitivity analysis identified heavy-vehicle flow as the dominant factor influencing overall system emissions and pollution costs. Scenario analysis indicated that restrictions targeting heavy-vehicle traffic have the potential to generate considerably larger environmental benefits than generalized traffic-reduction measures. Probabilistic assessment further revealed that heavy vehicles contribute disproportionately to high-emission risk regimes and uncertainty propagation within the system. The proposed framework provides an integrated approach for evaluating climate impacts, uncertainty and economic externalities of transit transport. The results highlight the importance of heavy-vehicle management in reducing emissions and pollution costs while supporting risk-informed transport policy development.