Digital transformation driven by AI and IoT is reshaping transport systems and challenging traditional management frameworks. In this study is evaluated the effectiveness of innovation strategies through a system of composite indices. Using the secondary data from the European Commission JRC, Eurostat and Precedence Research, the economic modelling is applied - including a multilevel CAGR decomposition with logarithmic representation - to examine growth rates and structural coefficients. The Smart Transport sector (CAGR 15.3%) grows three times faster than the traditional ITS market (5.1%) and is projected to reach & euro;219.98 billion by 2028. Recommendations include diversifying investment sectors, raising R&D intensity, and integrating innovative management practices. Future research should focus on longitudinal monitoring of R&D transformation into practical transport innovations.
The paper is a contribution to the literature on digitalization barriers and digital readiness by offering an empirically grounded socio-technical interpretation of digital transformation constraints in a transition economy and by extending the discussion on the shift from Industry 4.0 to Industry 5.0. The purpose of the research was to develop a theoretical and methodological framework for the study of digitalization of industrial, transport and logistics enterprises based on a socio-technical approach. The work systematizes the limitations of digital transformation in the context of the evolution of industrial paradigms from Industry 4.0 to Industry 5.0. The theoretical provisions obtained from the conceptual basis for the subsequent empirical analysis of barriers to digitalization of enterprises in the Republic of Kazakhstan.
In this article is dealt with the ways of improving the diagnostic accuracy of braking systems of passenger cars, taking into account the operational factors. A diagnostic system that monitors the pneumatic processes and transmits the data in real time has been implemented. The method proposed has provided an increase of reliability from 83.1% to 87.6%.
In this article is addressed the optimization problem of assigning aircraft to parking stands on the apron of international regional airports. The goal was to position an aircraft on the airport apron in a way that complies with operational, capacity, and safety constraints while optimizing the value of the selected optimization criterion. In contrast to other commonly published approaches, problem considered here focuses on minimizing passenger transport between the parking stands and the terminal via airport buses. To solve this problem, a linear integer programming model is formulated and solved using the Xpress-IVE optimization software. The model's functionality is tested using real operational data from Ostrava International Regional Airport.
In this study is proposed and evaluated an adaptive traffic signal control system designed to synchronize the road and railway signaling, minimize collision risks, and improve traffic flow. Results show that adaptive control reduced average queue length for over 46%, cut vehicle delay for 34%, lowered stop frequency for 74%, and decreased CO2 emissions for nearly 46% compared to static control. Further analysis examined the effect of varying the distance between intersections and railway crossings, revealing significant safety and efficiency gains when the separation exceeded 80 m. The findings demonstrate that intelligent, sensor driven traffic management can substantially enhance both safety and throughput in these complex transport nodes, while delivering environmental benefits.
A machine learning framework for predicting the traffic accident severity under class imbalance conditions is predented in Amman, Jordan. The methodology began with a preprocessing pipeline consisting of IQR-based and Synthetic Minority Over-sampling Technique (SMOTE). Sixteen model configurations were considered, comprising four ensemble learning oversampling, with Optuna-based Bayesian hyperparameter optimisation. The result indicated that the class balancing using SMOTE had a greater influence on the predictive performance than hyperparameter optimisation, specifically for CatBoost model. Feature importance showed that vehicles involved, collision type, and speed are the strongest predictors of accident severity.
The presented research is aimed at assessment of issues relating to strength of a universal container of the standard size of 1CC while it is transported on the sea by a railway ferry. Further it is considered that the container is located on a flat wagon at which, it is fixed to the upper deck of a railway ferry. Three load scenarios were considered in the research. Each of these scenarios are characterized by loaded conditions defined in the study. The structure of the investigated container was modelled in a commercial simulation software. The dynamic loads acting to the container structure for all three considered schemes, were carried out using mathematical modelling. The finite element method was applied to the numerical analyses of strength of the container structure. It was revealed by means of simulation computations, that the maximal stress in the container structure was of 102.4 MPa within the permissible limits and the maximal displacements of 4.5 mm were achieved.
In the development of Global Navigation Satellite System (GNSS) Positioning, Navigation, and Timing (PNT), short-lived, fast-growing ionospheric storms (lasting up to three days) were assumed to constitute a single, uniform class of space weather events. To challenge this premise, here is presented a circular analysis of single-frequency commercial-grade Global Positioning System (GPS) horizontal positioning errors in the mid-latitude region during four observed storms (one in 2015 and three in 2017), revealing significant heterogeneity. Maximum positioning errors reached 7.495 m in March 2015, but peaked at 4.261 m, 4.387 m, and 3.948 m during storms in May, early September, and late September 2017, respectively. By demonstrating that short-duration, fast-growing storms require subclassification, in this study is offered a new perspective on disturbances.
The spatiotemporal redistribution of traffic flows under alternating blocking of channels at a signalized four-leg intersection is investigated using the case of Augustusplatz in Leipzig, Germany. Observations were conducted over three days, with flows recorded in 48 half-hour intervals per day across four directions and four categories of vehicles. For stochastic modelling, the negative binomial distribution, Taylor's dispersion index (VMR), Shannon entropy, and an approximation based on M/G/1 queueing theory were applied. A mathematically grounded framework for the real-time network control is proposed, highlighting the necessity of adaptive signal timing adjustments to accommodate stochastic fluctuations and spatial overcompensation effects during node closures.
Parametric transition curves designed to guarantee a smooth transition from tangent (straight) tracks to circular segments in the horizontal-plane projection of a railway track alignment. A rigorous theoretical framework was developed to express the functional dependence of curvature, deflection angle, and Cartesian coordinates on the curve's arc length. To validate the theoretical derivations, a detailed computational experiment was performed, fully confirming their correctness and accuracy. Furthermore, a methodology for constructing coordinate tables as a function of arc length - highly valuable for practical railway design and field layout - is provided. A robust mathematical apparatus for the curvature-based design of railway transition curves is established. The results demonstrate that the coordinates of the circular arc center were optimized and refined to the required level of precision.
A study of the vulnerability of emergency service systems leads to the problem of detecting elements (vertices and edges) of transportation networks that are critical to the functionality of these systems. In this paper, methods for identifying the most critical edges and vertices in networks with respect to emergency service systems are presented, and these methods were tested on real data. The primary results of this work are the development and testing of two algorithms that compute a measure referred to as change in transportation performance for edges and vertices in the network. A method for identifying the edges and vertices that are the most critical to the designed emergency service system is presented. Experiments on the transportation network of the Zilina region highlight the importance of probabilistic models of travel time elongation, based on appropriate probability density functions.
The purpose of this study was to determine the vibrations transmissibility characteristics of an air suspension system (ASS) with self-damping air bellows to assess the comfort and safety of the ride. The conceptual design of a pneumatic self-damping air bellow is presented, which involves forced airflow through a damping orifice between the air bellow and an auxiliary reservoir. Two dynamic models, classical and simple, of the analyzed air bellow were used for numerical modelling solutions. Vibrations transmissibility charts as a function of the frequency ratio and at different damping ratios were determined using the accepted excitation forces. The different vibrations transmissibility states and dynamic characteristics of the air bellow for the frequency ratios and individual damping (inherent, pneumatic self-damping, and hydraulic absorber) in the ASS range were examined.
Background - The decline in walking interest on campus is an unresolved issue. Through this study, factors that influence walking intention by integrating the Walkability Index and UI GreenMetric standards are identified. Methodology -The measuring instrument for data collection in the form of a questionnaire was distributed to 110 respondents at Padang State University. The data were analyzed using Exploratory Factor Analysis (EFA) and PCA with Varimax Rotation. Results - As a result, 5 factors were identified with their eigenvalues for pedestrian-friendly sidewalk design, namely: comfort (6.867), technical suitability (2.200), accessibility (1.662), safety (1.156), and sidewalk dimensions (1.142), with a KMO value of 0.823. Conclusion - Comfort is the most dominant factor that infrastructure planners can prioritize by focusing on elements that enhance it, such as the availability of supporting facilities, minimizing mode conflicts, and path cleanliness.
To remain competitive, authorities had to apply artificial intelligence, especially in logistics. This study's aim was to examine how the digital transformation and artificial intelligence create new opportunities for economic growth in the logistics industry, with a particular focus on Kyrgyzstan. Implementation remains low. Policies aim to develop the sector but are insufficient for international competitiveness. The AI can address logistical challenges, optimize routes, improve management, and reduce costs. The key issues - low digitalization, shortage of specialists, and weak infrastructure - can be mitigated through sound policies. The results provide insights for policymakers and industry stakeholders on leveraging AI and digital transformation to boost economic performance in logistics.
In this paper is analysed how the maritime freight costs fluctuations (2020-2024) affected the small and medium sized enterprises (SMEs) across the Visegrad Group using firm-level panel data and fixed-effects regressions. Four financial indicators were examined: turnover, gross margin, inventory stock, and cost of goods sold (COGS), with the Shanghai containerized freight index (SCFI) as the key explanatory variable. Higher freight costs are associated with increased turnover, inventories, and COGS, and with reduced gross margins, though the within-firm explanatory power is low. Overall, shipping price shocks leave measurable but modest effects on SME finances, indicating partial cost pass-through and strong resilience. Supporting supply-chain adaptability and digital logistics tools may further reinforce this resilience.
In this study was investigated the Technical Readiness Coefficient (TRC) of railway rolling stock and associated technical systems. The primary objective was to identify and analyze the key factors influencing TRC and to develop a structured methodology for the factor analysis of railway technical readiness. The research methodology includes the selection of principal determinants, construction of a descriptive analytical model, processing of empirical operational data, and application of regression and correlation analyses. Empirical and theoretical approaches were employed, using statistical data, maintenance schedules, normative tables, and computational tools. Findings can be used for monitoring the TRC, optimizing maintenance and modernization, improving operational efficiency, and supporting evidence-based management decisions. The methodology also provides a framework for evaluating the future railway systems and ensuring continuous monitoring of technical performance under operational conditions.
Lithium-ion batteries power light electric vehicles such as electric bicycles, but end users typically lack reliable information about true battery health and instead rely on misleading indicators of remaining charge. In this paper is introduced a novel external plug-and-play diagnostic module that connects between the standard charger and a battery of a light electric vehicle. The system uses a precise discrete-time energy integration method during standard charging. It captures dynamic current fluctuations and estimates battery health from the partial charging data. Experimental validation on multiple battery packs demonstrates that the proposed algorithm reliably distinguishes between healthy and degraded batteries. This non-invasive system adds diagnostics to conventional charging, enabling users to assess actual capacity without full discharge cycles.
Increasing disaster frequency intensifies the need for rapid, location-specific information in crisis management. A Virtual Operations Support Team (VOST) can enhance situational awareness by verifying open-source data, but these outputs are rarely transformed into resilience indicators. In this paper is presented a framework that maps verified VOST outputs and metadata to indicators for road critical infrastructure resilience and links them to the crisis decision cycle. A preliminary test on media data (1 Aug-31 Dec 2024; n = 2,045) confirmed a stable baseline and clear peaks usable for management-defined thresholds and trigger actions. The framework also outlines governance, supplier interfaces, and GIS-based operationalization of Exposure, Condition, Accessibility, and Consequence.
Rough roads negatively affect the ride quality and energy efficiency of autonomous vehicles (AVs) and demand adaptive speed control. To solve this, this research proposes a Deep Capsule Network integrated with reconstruct pavement conditions and adapt vehicle speed in real time. Fabric blockchain allows federated learning to be trained in a secure and decentralized manner without the transfer of raw sensor data. With the BDD100K dataset, supervised and reinforcement learning are used. According to the obtained results of the experiment, there is a 10.45% increase in ride comfort, 28.23% in energy efficiency, and 98.28% in computational efficiency. The framework attains 93.73% security, 96.46% throughput and 0.83 s operation time at 250 vehicles/km.
In this article are discussed the features of selecting the geometric parameters and calculating the strength of the horizontal lever in the brake lever transmission of a freight wagon bogie. The results of the research show that the greatest stresses are concentrated in the areas where the holes for the rollers are located. Since the design stresses are 22% lower than the permissible value, thus the strength of the horizontal lever is obeyed. This research results could be useful in the design of modern brake lever transmission systems.
