This study investigates the temporal dynamics and predictive modelling of fatal traffic accidents in Hatay province, Türkiye, using both classical time series approaches (ARIMA, SARIMA, Holt–Winters) and machine learning techniques (Random Forest, Gradient Boosting). Monthly accident data from 2017–2021 were analysed through seasonal decomposition, stationarity testing, and comparative model evaluation. Results revealed a distinct seasonal pattern, with accident counts peaking during summer months and declining in winter, and a long-term trend showing a notable reduction in fatalities after 2017. Among the tested models, the Enhanced Gradient Boosting approach demonstrated the highest predictive accuracy (R² = 0.97, RMSE = 1.59), outperforming both classical time series and other ensemble methods. Forecast results for 2021 indicated seasonal peaks in June and August, corresponding to increased traffic density during the holiday period. The COVID-19 pandemic was associated with a marked short-term reduction in fatalities, though the effect appeared to diminish post-lockdown. These findings highlight the value of integrating advanced ensemble learning methods into traffic safety forecasting and underscore the importance of seasonally targeted interventions.

Variable Speed Limit (VSL) control is essential for managing highway tunnel maintenance work, as it adjusts speed limits based on road conditions to regulate traffic flow. Developing a VSL control strategy that balances traffic efficiency and safety during maintenance can be challenging. This paper addresses this issue by proposing a VSL control strategy based on Model Predictive Control (MPC) that considers the spatial characteristics of traffic flow in a tunnel maintenance work zone. The strategy aims to minimise total travel time, reduce speed variance, and maximise traffic flow through a multi-objective optimisation approach using a Non-dominated Sorting Genetic Algorithm II (NSGA-II). With the Qinling Tiantai Mountain Tunnel selected as the experimental object, a simulation section is constructed based on the SUMO model with the measured data, and a comparative experiment of different speed limit control cycles in the maintenance work zone is designed. The results show that the method of this paper can effectively reduce the total travel time under the influence of maintenance operations by more than 17.5%, reduce the standard deviation of speed by about 22.1%, and enhance the traffic volume by about 7.8%, which can effectively improve the efficiency of road access and safety level.

This study examines the challenges and potential solutions associated with the retention of stormwater from road surfaces – a critical component of urban infrastructure in the face of climate change. The research highlights that intensified urbanisation and the increasing prevalence of extreme weather events have exacerbated issues related to rapid rainwater runoff, leading to urban flooding and infrastructural degradation. Employing quantitative empirical methods, a survey was conducted among 362 road infrastructure managers in Poland, assessing the technical condition of roads, drainage system performance, and the barriers to adopting modern retention and infiltration solutions. Findings reveal a mixed perception of current drainage performance, with many respondents reporting inadequate solutions that compromise both safety and sustainability. Key barriers include high implementation costs, technical and infrastructural challenges, resistance to change, and limited public awareness. The results underscore the necessity for modern, integrated stormwater management practices that not only protect infrastructure but also enhance urban water balance and sustainability.

Micromobility is gaining momentum in many countries, helping reduce congestion and pollution in the streets of major cities. The use of e-scooters has increased rapidly over the past three years, posing additional risks to road safety. Therefore, it is necessary to identify the key factors influencing the number of accidents and their consequences. The current article assesses eight e-scooter safety importance criteria, applying various methods and consulting highly qualified experts. It is noteworthy that the maximum weights of criteria determined by the ARTIW-L, ARTIW-N (Average Rank Transformation into Weight Linear and Non-Linear), and AHP (Analytic Hierarchy Process) methods are consistent. Evaluating the opinions of 15 experts using the ARTIW-L, ARTIW-N, DPW (Direct Percentage Weight), and AHP methods allowed for the determination of average criterion weights and the ranking of their priorities. Averaging the criteria weights calculated by the four expert evaluation methods yields the following overall priority order: Type and quality of road surface≻Road or street element≻Maximum power≻Speed limits≻Mandatory helmet use≻Seasonality≻Age of the road user≻Educational activities. The findings highlight that the type and quality of the road surface, as well as road or street design, have the greatest impact on e-scooter safety. These insights can guide urban planners and policymakers in prioritizing infrastructure improvements and developing evidence-based safety regulations for micromobility users.

This study investigates the stability of skeleton-reinforced concrete arch bridges during the concrete encasement process, employing a homogeneous generalized yield functions for extreme buckling load determination in nonlinear finite element analysis. Through an analysis of the stability of a stiff skeleton arch bridge with a 600 m span during the concrete wrapping stage, this study delves into and elucidates the mechanism by which the transverse brace enhances the out-of-plane stability capacity of the skeleton arch ribs. Additionally, a method for improving stability by controlling the lateral rotation angle of arch ribs is proposed. The results indicate that the lateral deflection angle of arch ribs serves as a crucial metric for assessing the out-of-plane stability of arch bridges. Transverse braces effectively coordinate and constrain the lateral deflections of two isolated arch ribs through their bending stiffness along the tangential direction of the arch axis. Notably, transverse braces within the range of L/8 to 3L/8 make the most substantial contribution to the lateral stiffness of arch ribs. Consequently, wrapping surrounding concrete on transverse braces within the L/8 to 3L/8 range proves advantageous for enhancing the stability of a stiff skeleton arch bridge under construction. Specifically, it is recommended to pour surrounding concrete on transverse braces at L/4 before the closure of the bottom plate’s concrete ring. After the ring of bottom plate’s concrete is closed, a symmetrical pouring of surrounding concrete on transverse braces from L/4 to the arch spring and vault is proposed.

This study investigates the seasonal structural behaviour of flexible pavement structures constructed on subgrades with varying types of treatment. Eight road sections in Lithuania, featuring natural subgrades or soils stabilised with lime, cement, or hydraulic road binder (HRB), were evaluated using Falling Weight Deflectometer (FWD) testing during thawed and recovered states. Structural condition was assessed using deflection-based indices: the Surface Curvature Index (SCI), Base Damage Index (BDI), and Base Curvature Index (BCI). Seasonal changes were quantified, and Wilcoxon signed-rank tests were applied to assess the statistical significance of deflection differences. The results revealed that the untreated subgrades experienced the largest seasonal softening, with BCI increases of up to 45%. Cement stabilization provided the most effective mitigation, limiting the BCI to 14% and preserving the stability of SCI. Lime-treated sections showed a dosage-dependent improvement, while HRB treatment yielded results comparable to high-percentage lime stabilisation. The study confirms that the type and dosage of subgrade treatment significantly influence pavement resistance under freeze-thaw conditions and highlights the importance of evaluating the geometry of the deflection bowl to correctly interpret structural indicators. These findings contribute to improved mechanistic understanding of seasonal load response in flexible pavements and inform best practices for subgrade stabilisation.

Research on the microscopic mechanisms of open-graded friction courses (OGFCs) is still in its early stages, and the specific effects of various factors on the fatigue performance of OGFCs have not been fully explored. This study investigates the effects of oil-stone ratios, void fractions, and maximum nominal particle sizes on the fatigue life of OGFCs at the macroscopic and microscopic scales. At the macroscopic level, indirect tensile fatigue tests were conducted on OGFC specimens. At the microscopic level, a three-dimensional (3D) reconstruction model of OGFC was developed using computed tomography (CT) and image processing techniques. Additionally, a 3D randomized aggregate model was developed using the Monte Carlo method and an aggregate random placement algorithm. Virtual splitting and fatigue tests were conducted to analyse the correlation between virtual and experimental macroscopic tests. The results showed that the splitting strength and fatigue life of the OGFC increased at higher oil-stone ratios but decreased at higher void fractions and larger nominal maximum particle sizes. The variation in the results of the virtual splitting fatigue tests derived from the CT reconstruction model and the experimental results was only 9–11%, indicating a strong correlation between the two approaches.

Road safety continues to be a major international concern, with approximately 1.19 million fatalities and up to 50 million injuries occurring annually. In response, the European Union and the United Nations have launched ambitious strategies like “Vision Zero” and the Decade of Action for Road Safety (2021–2030), aiming to drastically reduce or eliminate traffic-related deaths. Countries such as Poland have shown measurable progress, achieving a 35% reduction in fatalities and a 29% decline in total accidents since 2019. As human error remains the dominant cause of traffic accidents, autonomous vehicles (AVs) are being considered a transformative solution. AVs are expected to eliminate typical driver mistakes such as distraction, fatigue, and impaired judgment. However, their deployment requires substantial investment in digital infrastructure, such as V2X and C-V2X communication, HD maps, and 5G networks. This study estimates and compares the annual cost of road accidents caused by human error (EUR 11.13 billion) with the infrastructure investments needed to support AVs in Poland. Based on population density modelling, the number of intersections was estimated at 1.9 million, leading to a V2X deployment cost of EUR 140.1 billion. Adding HD mapping (EUR 1.84 billion) and 5G rollout (EUR 2 billion), the total estimated investment is approximately EUR 143.94 billion. Assuming AVs fully eliminate human error, the investment could be recouped in about 13 years through accident cost savings. While based on simplified assumptions, this initial analysis highlights the potential long-term value of AV implementation in achieving road safety objectives and reducing economic losses from traffic incidents.

Pavement must have the characteristics that allow safe, fast, comfortable, economical and reliable vehicle traffic. The normative documents introduce limit values and permissible deviations of the controlled parameters of the road pavement, for which penalty deductions are levied if they are not satisfied or exceeded. The asphalt pavement installation rules for the ĮT ASFALTAS 08 contain 10 such indicators, whose relevance in respect of road quality is investigated in this paper. The relative weights of each indicator (defect) have been determined using assessments from 91 experts. The methods used in the study are rank correlation, ARTIW-L, ARTIW-N (Average Rank Transformation into Weight Linear and Non-linear), and DPW (Direct Percentage Weight). The expert team’s opinions are consistent because the empirical concordance coefficient value of 0.715 is 34.6 times higher than the minimum concordance coefficient value of 0.021. The most important indicators for the experts are the lower degree of compaction, the lower thickness of the layer, and the lower amount of binder. A pilot project dedicated to determining the required number of experts has shown that the number of experts in a team, exceeding 20, has almost no effect on the average of ranks and percentage weights of the criteria.

The reasonable selection of soil layer parameters relates to the accurate prediction of the horizontal deformation of the foundation pit, which is the main problem of highway tunnel pit design. The aim of this paper is to obtain suitable soil layer parameters for finite element simulation of highway tunnel based on the particle swarm optimisation (PSO) and support vector machine (SVM). First, considering the overfitting problem of SVM in the inversion of soil parameters, the PSO was used to improve the SVM model. Second, the PSO- SVM model was trained with 25 groups of elastic modulus as input values and deformation as output values. Then, according to the monitored deformation data, the soil parameters were inverted by PSO-SVM model. Finally, the inversion parameters were substituted into the finite element model to predict the horizontal deformation of the foundation pit. The results showed that based on the inversion parameters of PSO-SVM model, the finite element method had a good accuracy in predicting the horizontal deformation of the foundation pit. The average relative error between the predicted value and monitored value was 2.95%. Therefore, the application of the parameter inversion method based on PSO-SVM had a reference value for tunnel pit design.