This article explores the formal and legal framework governing the air transport of oversized cargo, emphasizing applicable regulations and available aircraft types. Drawing on insights from a semi-structured in-depth interview (IDI) with an industry expert, the study outlines the key stages of the transport process and identifies potential hazards that may arise at each step. Particular attention is given to operational challenges such as regulatory compliance, appropriate aircraft selection, and coordination of logistics. In response, the paper proposes targeted preventive and corrective measures aimed at minimizing disruptions and enhancing the safety and efficiency of operations. The results not only offer practical guidance for industry professionals but also serve as a foundation for future research focused on developing tailored risk assessment tools and methodologies suited to the specific demands for non-standard air cargo transport.

Road maintenance project delays present serious obstacles to the development of infrastructure, especially in nations where political and economic instability are prevalent. This study focuses on the key reasons why road repair projects in Syria took longer than expected between 2019 and 2022. Insufficient construction material supply and late contractor payments are the most important delay issues identified by the research, which is based on official records overseen by the Public Establishment of Road Communications. The study illustrates the intricate relationship between emergency repairs, fuel shortages, administrative inefficiencies, and governmental requirements through a correlation analysis of several projects. To reduce future delays in Syria's road infrastructure sector, the findings are intended to assist stakeholders in enhancing project planning, resource management, and execution techniques.

This study investigates the determinants of customer satisfaction with on-demand restaurant food delivery services, focusing on last-mile logistics factors. The research aims to identify which service characteristics most significantly influence customer satisfaction levels and usage patterns in the rapidly growing food delivery sector. An empirical study was conducted using a custom-designed online questionnaire distributed via the CAWI method through Google Forms. Data were collected from 335 respondents in Poland who actively use food delivery platforms to order restaurant meals. The findings revealed that key factors influencing platform selection include a wider restaurant choice, promotions and discounts, and lower delivery costs. Customer satisfaction levels remain generally positive, with delivery time and app usability receiving the highest satisfaction ratings. The findings provide actionable insights for food delivery platforms regarding service optimization priorities, particularly emphasizing the importance of delivery reliability, cost transparency, and restaurant variety. This research addresses a gap in empirical literature by examining the relationship between last-mile logistics performance and customer satisfaction in on-demand food delivery services.

The article presents an analytical model for optimizing the "grace period" of mooring and documentary clearance of ships in the port of Tianjin by introducing a centralized "single window" platform. The functional roles of the key documents - Forwarder's Cargo Receipt (FCR) and Shipping Order - regulating the readiness of cargo for loading and confirming the completion of customs clearance are investigated. Based on regulatory analysis, expert interviews, and simulation modeling, critical bottlenecks in the processes of verification and berth slot assignment are identified. A stochastic model of the total processing time was developed, where each stage is described as an independent, normally distributed random variable. Using this model, the probability of exceeding the permissible processing interval is estimated, and the feasibility of automating document flow is substantiated. Technical and organizational solutions for implementing a unified digital infrastructure, including standardized exchange formats, double data validation, electronic stamping, and integration with PCS/TOS, are proposed. This creates the basis for utilizing these results in the development of intelligent port systems and enhancing the efficiency of logistics operations.

In modern urban environments, the efficiency of public transportation systems plays a crucial role in ensuring sustainable mobility and reducing traffic congestion. Among various public transport services, bus systems remain the most flexible and widely used mode of transit. However, growing travel demand and increasing road traffic intensity often lead to delays, decreased service reliability, and reduced passenger satisfaction. One effective solution to these challenges is the implementation of express bus services, which offer limited-stop operations aimed at reducing travel time and improving overall system efficiency. This study focuses on the development of an algorithm for determining the rational parameters of the express mode of bus traffic in urban areas. The proposed approach considers factors such as passenger flow distribution, traffic conditions, and operational constraints to identify optimal solutions that enhance service performance and passenger convenience. The results are expected to contribute to the improvement of urban public transport systems by providing a methodological framework for efficient express service planning and management.

The aviation industry encompasses a variety of stakeholders. In recent years, the growing reliance of airlines on leased aircraft has elevated leasing companies to a critical position within the sector. Despite their importance, the capital structure of leasing companies remains an underexplored area in the aviation literature. This study is a pioneering effort to investigate both the theoretical and empirical aspects of leasing companies’ capital structure. Using panel data analysis, the research examines the capital structure behavior of these companies over the period from 2013 to 2023. Six different models are developed to provide a more in-depth analysis of the effects of short-term and long-term financing decisions on the capital structure. The findings generally indicate that the financing behavior of leasing companies is in line with the pecking order theory, which suggests seeking internal financing before seeking external debt or equity.

This study investigates lateral lane-keeping behavior among human-driven passenger vehicles on Jordanian multi-lane roads. Using overhead video footage collected at five sites, 500 vehicles traveling alone in the leftmost lane under free-flow, daylight conditions were manually annotated for centerline deviation. The lateral position was analyzed using descriptive statistics, temporal trends, and spectral frequency analysis. Results show that 61% to 83% of vehicles remained within a ±0.5 m “safe zone” from the lane center. No vehicle exceeded the ±1.75 m legal lane boundary, and wheel position plots confirmed consistent lateral margins. Sites 1 and 2 exhibited a slight rightward bias, while Sites 3 through 5 showed a leftward tendency, especially Site 3, which had the highest variability (std dev = 0.43 m). Spectral analysis revealed consistent low-frequency oscillations (~0.01-0.02 Hz), indicating slow, smooth steering adjustments with no erratic corrections. The study confirms that under ideal conditions, drivers maintain stable lateral control within 3.5 m lane widths. These results provide valuable reference data for autonomous vehicle calibration, infrastructure planning, and future research into lane-keeping behavior under variable traffic and environmental conditions.

Artificial intelligence approaches, especially those involving deep learning, have recently become integral to object detection, as they can autonomously identify relevant features in visual datasets. The identification of military equipment, including mechanized vehicles, is crucial for threat detection and minimizing the impact of enemy actions by enabling countermeasures to be taken as quickly as possible after the threat is detected. The application of deep learning, particularly convolutional neural networks (CNN), is a highly effective tool for image processing and pattern recognition in visual data. These networks utilize convolutional layers to automatically extract features from images, making them ideal for analyzing synthetic aperture radar (SAR) imagery. Active sensor technologies like SAR are essential for object recognition due to their capability to operate in all weather conditions, both day and night.

The article is devoted to the topic of passenger maritime transport in selected EU countries and the changes in the maritime passenger transport market resulting from the COVID-19 pandemic. The implemented restrictions on movement entailed a voluntary and temporary suspension of the activities of tourist companies using the maritime fleet. With the above in mind, the purpose of this article was to identify the consequences of changes in the mobility behavior of the population as a result of the COVID-19 pandemic on the operation of passenger maritime transport. The differences in the number of seaborne passengers served in selected EU countries between actual and projected values were indicated. Long-term forecasts were also made, which made it possible to develop scenarios of possible events, describing the potential development directions of the branch. We use a combined forecast method based on a weighted average of individual forecasts (weights inversely proportional to mean percentage absolute error (MAPE)). We use such forecasting methods as Fourier spectral analysis, exponential smoothing models, and seasonality indices. We used time series models to build long-term forecasts. Combined forecasts for selected EU countries were determined. They were used to supplement long-term forecasts. This made it possible to assign the obtained results to ambient scenarios. Combined forecasts showed that in all quarters of 2020-2021, the number of passengers transported by sea transport was lower than forecast values in all EU countries analyzed. This confirms the negative impact of the COVID-19 pandemic on this branch of transportation as well. Long-term forecasts, built on the basis of combined forecasts and assumptions about the annual growth rate of passenger numbers, indicate that in most of the countries analyzed, the most likely scenario is an annual increase of 10% in passenger numbers. This means that by 2026 only Germany and Denmark will see the number of maritime passengers return to pre-pandemic levels.

During the transportation of cargo on the type Slmmps wagon platform, significant attention is paid to securing the transported load on a given wagon platform, primarily based on the imposed loading instructions. In the subject literature, there are no direct computational analyses on the forces acting on a specific type of cargo with a given mass, particularly under critical conditions. This article presents an analysis of the security of an armored vehicle in a platform wagon under critical conditions. Securing analysis was conducted for two fastening methods: the cross method (with a large strap angle relative to the direction of travel) and the straight method (with a small strap angle relative to the direction of travel). On the basis of these fastening methods, an assessment was made of the security of the transported cargo in the form of an armored vehicle. Armored vehicles are most commonly secured using the cross-fastening method with four securing straps. This study compares the force magnitudes concentrated on the securing straps in the cross-fastening and straight-fastening methods.