Unsafe behaviors among construction workers remain a leading cause of accidents in the construction industry. Previous studies have primarily relied on structural equation modeling and causal inference approaches to investigate the determinants of workers’ safety behavior. However, these methods are often limited in their ability to address confounding bias inherent in observational data and tend to focus on isolated effects of individual variables, thereby overlooking the complex interactions between organizational and individual factors. To overcome these limitations, this study applies the Categorical Boosting (CatBoost) algorithm to examine the joint organizational and individual mechanisms underlying construction workers’ safety behavior. CatBoost is particularly suitable for small- to medium-sized datasets and is capable of automatically capturing complex, nonlinear relationships among variables. Leveraging the SHAP interpretability framework, both main-effect and interaction analyses are conducted to systematically identify the most influential determinants. The results demonstrate that CatBoost outperforms eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) models in predicting safety-related outcomes. Prosociality (PSO) is identified as the most influential predictor, followed by personal proactivity (PAC). Interaction analyses further reveal that organizational attributes—such as prosociality, loyalty, and mutual assistance—play a critical role in cultivating a safety-oriented organizational climate, while an optimistic personal attitude further enhances safety performance on construction sites. Overall, these findings provide meaningful theoretical insights and practical implications for improving safety management in the construction sector.
The application of 3D data in pavement inspection represents an emerging trend. Acquiring and measuring the 3D information of pavement distress enables a more comprehensive assessment of severity, thereby allowing for accurate monitoring and evaluation of the pavement’s technical condition. Existing methods face challenges in high-cost pavement scanning and insufficient research on automated 3D distress segmentation. This study employed a consumer-grade action camera for data acquisition and constructed an engineering-aligned 3D point cloud dataset of pavements. Then a long-tail class imbalance mitigation strategy was introduced, integrating adaptive re-sampling with a weighted fusion loss function, effectively balancing minority class representation. The proposed network, named PointPaveSeg, was a dedicated point cloud processing architecture. A dual-stream feature fusion module was designed for the encoder layer, which decoupled geometric and semantic features to improve distress extraction capability. The network incorporated a hierarchical feature propagation structure enhanced by edge reinforcement, global interaction, and residual connections. Experimental results demonstrated that PointPaveSeg achieved an mIoU of 78.45% and an accuracy of 95.43%. In the field evaluation, post-processing and geometric information extraction were performed on the segmented point clouds. The results showed high consistency with manual measurements. Testing confirmed the method’s practical applicability in real-world projects, offering a new lightweight alternative for intelligent pavement monitoring and maintenance systems.
Understanding the interface shear behavior between clay and structures is crucial in geotechnical engineering. The mechanism of the roughness effect in the shear process between the clay and structures was studied to reveal the macroscopic and microscopic interface shear behavior. The different surface protrusion shapes of the structures were produced using a three-dimensional (3D) printer. Direct shear tests were conducted to analyze the shear failure modes and peak and residual strengths under different conditions. Subsequently, a discrete element method (DEM) numerical analysis was employed to study the contact network, soil fabric evolution, shear zone, coordination number, and void ratio variations in the interface shear. The test results indicated that the shear interfaces exhibited the same failure mode under various conditions, and the peak and residual strengths showed a strong positive correlation with roughness. The results obtained from numerical calculations match the experimental findings. The contact orientations and principal stresses shifted during the shear process, and the shear zone, coordination number, and void ratio also showed regular changes with the change of roughness. The evolution of microscopic parameters in DEM can effectively help explain the macroscopic interface shear behavior.
This paper presents a priori knowledge-based low-light image enhancement framework, termed Priori DCE (Priori Deep Curve Estimation). The priori knowledge consists of two key aspects: (1) enhancing a low-light image is an ill-posed task, as the brightness of the enhanced image corresponding to a low-light image is uncertain. To resolve this issue, we incorporate priori channels into the model to guide the brightness of the enhanced image; (2) during the enhancement of a low-light image, the brightness of pixels may increase or decrease. This paper explores the probability of a pixel’s brightness increasing/decreasing as its prior enhancement/suppression probability. Intuitively, pixels with higher brightness should have a higher priori suppression probability, while pixels with lower brightness should have a higher priori enhancement probability. Inspired by this, we propose an enhancement function that adaptively adjusts the priori enhancement probability based on variations in pixel brightness. In addition, we propose the Global-Attention Block (GA Block). The GA Block ensures that, during the low-light image enhancement process, each pixel in the enhanced image is computed based on all the pixels in the low-light image. This approach facilitates interactions between all pixels in the enhanced image, thereby achieving visual balance. The experimental results on the LOLv2-Synthetic dataset demonstrate that Priori DCE has a significant advantage. Specifically, compared to the SOTA Retinexformer, the Priori DCE improves the PSNR index and SSIM index from 25.67 and 92.82 to 29.49 and 93.6, respectively, while the NIQE index decreases from 3.94 to 3.91.
This study examined underground roads to evaluate the effects of traffic congestion prevention strategies. A specific framework, called the traffic congestion judgment criteria and process (TJCAP), was developed for underground road application. Using this framework, the study analyzed congestion relief effects by applying traffic strategies commonly used on surface roads. A real underground road in Seoul was used as a testbed. Microscopic traffic simulation was conducted using the VISSIM to create a realistic simulation network. The model was calibrated using observed traffic volume and speed data, both on the underground and adjacent surface roads. This approach enabled the analysis of traffic strategies aimed at reducing congestion. Results showed that the effectiveness of the strategies depends on the type of surface road (interrupted or uninterrupted flow) and its traffic conditions. In particular, the strategies were effective when the connected surface road had a level of service (LOS) of D or better.
Transportation engineering, Transportation and communications
Using point cloud sequences is a popular way to harness the additional information represented in the time domain in order to enhance the performance of 3D object detector neural networks. However, it is not trivial to decide which abstraction level should the additional information presented to the network, or what is the point in the architecture, where aggregating the additional information is most beneficial. In this article, the authors propose various voxel-based networks and analyze their performance in relation to the abstraction level of the time series data. During the evaluation, the authors examine the object detection performance of a popular voxel-based neural network with its original architecture and several variants where the time domain related features were propagated through the network and aggregated at different stages of processing. Based on the evaluation results, a conclusion is drawn regarding the abstraction level at which the time-series aggregation step is performed in order to improve the performance of the baseline voxel-based detector.
For trips to the suburban area, passengers who do not own a vehicle typically use either a bus or a suburban railway. The main purposes of such trips are usually work, education, tourism and leisure, medical treatment or healthcare services, visiting relatives and friends, and similar activities. In most cases, passengers plan their trip before departure, which includes choosing the mode of transportation, departure and arrival times, trip duration, transportation costs, and other factors. All these factors differently influence the passenger's final choice of transportation mode.
This study investigates the impact of tariffs on the passengers' choice of suburban transportation mode under conditions with an equivalent road and railway connection from the departure point to the destination. During the study, the tariffs were compared for transportation by road and railway suburban transport for different travel distances within the suburban area.
The main task of the research is to identify the primary factors that passengers consider when selecting a mode of transport and to establish the conditions under which passengers are more likely to choose suburban railway transport.
The article also determines the ratio of travel costs by road and rail modes of transport within suburban traffic and calculates the change in this ratio with increasing travel distance. Furthermore, the influence of other factors on passengers' choices was determined through surveys. The research results allow for more effective setting of transportation tariffs, rational use of strategies for the developing the transportation enterprise, and more accurate forecasting of transportation revenue. Choosing the optimal tariff promotes an increase in demand for railway transport, helps maintain competitiveness in the transportation market, and provides an opportunity to attract new users. The organization of a rational pricing policy ensures effective management of the carrier's income and considers the opportunities and interests of passengers and the transportation company
Urbanization has profoundly reshaped the patterns and forms of modern urban landscapes. Understanding how urban transportation and mobility are affected by spatial planning is vital. Urban vibrancy, as a crucial metric for monitoring urban development, contributes to data-driven planning and sustainable growth. However, empirical studies on the relationship between urban vibrancy and the built environment in European cities remain limited, lacking consensus on the contribution of the built environment. This study employs Munich as a case study, utilizing night-time light, housing prices, social media, points of interest (POIs), and NDVI data to measure various aspects of urban vibrancy while constructing a comprehensive assessment framework. Firstly, the spatial distribution patterns and spatial correlation of various types of urban vibrancy are revealed. Concurrently, based on the 5Ds built environment indicator system, the multi-dimensional influence on urban vibrancy is investigated. Subsequently, the Geodetector model explores the heterogeneity between built environment indicators and comprehensive vibrancy along with its economic, social, cultural, and environmental dimensions, elucidating their influence mechanism. The results show the following: (1) The comprehensive vibrancy in Munich exhibits a pronounced uneven distribution, with a higher vibrancy in central and western areas and lower vibrancy in northern and western areas. High-vibrancy areas are concentrated along major roads and metro lines located in commercial and educational centers. (2) Among multiple models, the geographically weighted regression (GWR) model demonstrates the highest explanatory efficacy on the relationship between the built environment and vibrancy. (3) Economic, social, and comprehensive vibrancy are significantly influenced by the built environment, with substantial positive effects from the POI density, building density, and road intersection density, while mixed land use shows little impact. (4) Interactions among built environment factors significantly impact comprehensive vibrancy, with synergistic interactions among the population density, building density, and POI density generating positive effects. These findings provide valuable insights for optimizing the resource allocation and functional layout in Munich, emphasizing the complex spatiotemporal relationship between the built environment and urban vibrancy while offering crucial guidance for planning.
Owing to their excellent physical characteristics of lightweightiness, compactness and rapid deployment, the inflated membrane structures satisfy the demands of maritime salvage and military transportation for long-distance delivery and rapid response. Exploring the failure behaviour of inflated membrane structures can greatly contribute to their widespread applications in ocean engineering. In this research, the main objective is to comprehensively investigate the bending and failure behaviour of inflated membrane structures. Thus, the Surface-Based Fluid Cavity method is employed to set up the finite element model (<i>FEM</i>) which is compared to the experimental results to verify its reliability. In parallel, the effects of internal pressure and wrinkles are discussed. An empirical expression of the ultimate bending loading was fitted by face-centred composite designs of the Response Surface Methodology. The results of experiments and <i>FEM</i> show that the bearing capacity of the inflated membrane structure is positively correlated with the internal pressure but decreased obviously with the occurrence and propagation of wrinkles. The deformation behaviour and the stress distribution are similar to those of the traditional four-point bending beam, and the local instability induced by wrinkles will cause structural failure. In addition, the numerical model and the proposed expression showed deviations below 5% in relation to the experimental measures. Therefore, the <i>FEM</i> and proposed expression are high of reliability and have important engineering guiding significance for the application of inflated membrane structures in ocean engineering.
Jiayue Shen, Korkut Bekiroglu, Ali Tekeoglu
et al.
This paper studies the performance of Polyvinylidene fluoride (PVDF)–based strain sensor subject to dynamic loads with different load-moving velocities and the strain sensor’s performance for bottom-up crack detection of an asphalt pavement subject to dynamic loads. The core of the strain sensor is a metalized PVDF sensing film packaged with three protection layers. The encapsulated strain sensor adopts an H-shape to optimize the overall performance. Two numerical models are built in this paper and validate that the voltage output of the PVDF-based strain sensor can well capture the peak lateral strain with the propagation of the bottom-up cracks or the variation of a load moving velocity. Additionally, the sensor has better performance when it is in its lateral alignment position.
Efficient and reliable path planning is crucial for smart ships when avoiding collisions with static and dynamic obstacles in complex marine environments. This research proposes a novel path planning method based on the fast marching method to specifically assist with safe navigation for autonomous ships. At the very beginning, a unified representation is specially produced to describe the path planning space based on the parametric fast marching speed function. In addition, the spatial–temporal interaction effects of dynamic obstacles are considered and integrated into the construction of planning space. Subsequently, a path optimization strategy is put forward based on the trajectory prediction of dynamic objects. Particularly, the effectiveness of the method has been validated and evaluated through a number of simulations, which proves that such a method is practical in narrow and crowded waterways.
Uncertainty is usually perceived as having negative effects on transportation systems, such as increasing operation cost, decreasing resource utility, and reducing customer satisfaction. However, it is unclear whether this perception is universally true or is true only under certain conditions. This research compares the performance of transportation systems with uncertain parameters with the performance of the same systems in which the uncertain parameters are replaced by their expectations. The analyses prove that uncertainty can have negative, negligible, and positive impact on the performance of transportation systems under different conditions.
In this paper some selected results related to motor vehicle dynamics have been presented basing on the computer simulations of a sports two-seater performing a power-off straight line maneuver with different road conditions and the lack of a straight-line motion control having been included. All simulations have been performed in the MSC Ad-ams/Car environment and the adopted maneuver was performed at the instant speed of 100km∙h-1. The selected phe-nomena have therefore been observed along the road long enough to relate them to different aspects of vehicle dynam-ics and the road traffic safety research. The adopted vehicle’s model moved along the flat and the randomly uneven road with the almost similar and the almost different profiles for the left and the right wheels. Additionally, two values of the coefficient determining the maximum amplitude of road irregularities have been selected, i.e., 0.3 for the lower and 0.9 for the higher irregularities. This meant that the road conditions have been considered as one of the main factors possibly affecting disturbances of the motor vehicle’s motion. Such research seems valuable from the point of view of the road safety and the vehicles’ maintenance.
A power-off straight maneuver is not very often performed during the normal road traffic and might seem useless. However, in this case it seemed essential to test the response of a vehicle’s model to such factors as, e.g., the uneven loading, suspension characteristics, etc. This in turn might prove valuable when considering, e.g., the additional con-centration of a driver to overcome the external disturbances acting on a moving vehicle. The presented research is the second part of the paper (Kisilowski, 2019) where the power-off maneuver was considered but with the straightforward motion control. Here, the straight-line control has been switched off to examine an untypical situation where, for example a driver loses consciousness, and the vehicle moves freely along the road.
Betina Weber, Manfred Dangelmaier, Frederik Diederichs
et al.
Abstract Current statistics show that distraction is a central cause of traffic accidents. Safety systems with distance control currently available on the market have great potential for preventing accidents and significantly reducing their severity. However, depending on the driver’s level of attention, the systems warn too early or too late, which impairs use acceptance. Adaptive systems allow for personalization according to driver’s attention level. Studies were carried out in a driving simulator in order to compare the system adaptations with regard to acceptance for attentive and distracted driving phases. Seventy-two participants took part in the study, with a between-subjects test design. Acceptance ratings shows highest acceptability for the adaptive systems in distractive situations. We conclude that personalization of attention-adaptive systems shall be implemented in case safety benefits are proven.
Transportation engineering, Transportation and communications
Abstract In this paper, the fundamental characteristics of PaveM—the pavement management system for the California Department of Transportation (Caltrans) is introduced first, together with the various beta-testing efforts to validate and customize the engineering configuration of the software. The implementation of PaveM to support the development of the pavement asset ten-year management plan is then described. The tools and methods implemented to facilitate total investment determination and individual project evaluation and selection are presented in detail. In the end, lessons learned and future initiatives are presented.
<p>Road networks are complex interconnected systems. Any sudden disruption can
result in debilitating impacts on human life or the economy. In particular,
road systems in mountain areas are highly vulnerable, because they often do
not feature redundant elements at comparable efficiencies.</p>
<p>This paper addresses the impacts of network interruptions caused by
landslide events on the (rural) road network system in Vorarlberg, Austria.</p>
<p>Based on a landslide susceptibility map we demonstrate the performance of
agent-based traffic modelling using disaggregated agent data. This allows
us to gain comprehensive insights into the impacts of road network
interruptions on the mobility behaviour of affected people. Choosing an
agent-based activity-chain model enables us to integrate the individual
behavioural decision-making processes into the traffic flow model. The
detailed representation of individual agents in the transport model allows
optimisation of certain characteristics of agents and including their
social learning effects into the system.</p>
<p>Depending on the location of the interruption, our findings reveal median
deviation times ranging between several minutes and more than half an hour,
with effects being more severe for employed people than for unemployed
individuals.</p>
<p>Moreover, results show the benefits of using agent-based traffic modelling
for assessing the impacts of road network interruptions on rural
communities by providing insights into the characteristics of the population
affected, as well as the effects on daily routines in terms of detour
costs. This allows hazard managers and policymakers to increase the
resilience of rural road network systems in remote areas.</p>