Search Results (985)

In this pilot study, the crossing behavior of elementary school students commuting on bicycles was investigated with the objective of enhancing safety around pedestrian crossings within school zones. With a noticeable increase in crashes involving young cyclists near schools, this research assessed the effectiveness of visual nudges in the form of red strips displaying “CYCLISTS DISMOUNT” instructions. Initial observations indicated a lack of compliance with dismounting regulations. After the initial observations, a specific elementary school was selected for the implementation of the nudging intervention and additional pre- (N = 91) and post-intervention (N = 71) observations. The pre-intervention observations again revealed poor adherence to the regulations requiring cyclists to dismount at specific points. Following our targeted intervention, the post-intervention observations marked an improvement in compliance. Indeed, the visual nudge effectively communicated the necessity of dismounting at a critical location, leading to a higher rate of adherence among cyclists (52.74% pre-intervention, 97.18% post-intervention). Although it also indirectly affected the behavior of the accompanying adult, who more often held hands with their children while crossing, this effect was weaker than the direct effect on dismounting behavior (20.88% pre-intervention, 39.44% post-intervention). The findings of the current pilot study underscore the possible impact of nudging on behavior and advocate for a combined approach utilizing physical nudges to bolster safety within school zones. Follow-up research, including, for instance, multiple sites, long-term effects, or children traveling alone, is called for. Full article

The stability of the intermediate rock wall in the blasting construction of bifurcated small-spacing tunnels directly affects the construction safety of the tunnel structure. Clarifying the damage characteristics of the intermediate rock wall has significant engineering value for ensuring the safe and efficient construction of bifurcated tunnels. Based on the Tashan North Road Expressway Tunnel Project, this paper investigated the damage characteristics of the intermediate rock wall in bifurcated tunnels under different blasting construction schemes, using numerical simulation methods to account for the combined effects of in situ stress and blasting loads. The results were validated using comparisons with the measured damage depth of the surrounding rock in the ramp tunnels. The results indicate that the closer the location is to the starting point of the bifurcated tunnel, the thinner the intermediate rock wall and the more severe the damage to the surrounding rock. When the thickness of the intermediate rock wall exceeds 4.2 m, the damage zone does not penetrate through the wall. The damage to the intermediate rock wall exhibits an asymmetric “U”-shaped distribution, with greater damage on the side of the trailing tunnel at the section of the haunch and sidewall, while the opposite is true at the section of the springing. During each excavation step of the ramp and main-line tunnels, the damage to the intermediate rock wall is primarily induced by blasting loads. As construction progresses, the damage to the rock wall increases progressively under the combined effects of blasting loads and the excavation space effect. In the construction of bifurcated tunnels, the greater the distance between the headings of the leading and trailing tunnels is, the less damage will be inflicted on the intermediate rock wall. Constructing the tunnel with a larger cross-sectional area first will cause more damage to the intermediate rock wall. When the bench method is employed, an increase in the bench length leads to a reduction in the damage to the intermediate rock wall. The findings provide valuable insights for the selection of construction schemes and the protection of the intermediate rock wall when applying the bench method in the construction of bifurcated small-spacing tunnels. Full article

Wildlife–vehicle collisions (WVCs) remain a significant cause of animal mortality worldwide, particularly in regions experiencing rapid road network expansion. During the COVID-19 pandemic, a number of studies reported decreased WVC rates, attributing this trend to reduced traffic volumes. However, the validity of the simplified assumption that “fewer vehicles means fewer collisions” remains underexplored from a mechanistic perspective. This study aims to reevaluate that assumption using two simulation-based models that incorporate both the physics of vehicle movement and behavioral parameters of road-crossing animals. Employing an inverse modeling approach with quasi-realistic traffic scenarios, we quantify how vehicle speed, spacing, and animal hesitation affect collision likelihood. The results indicate that approximately 10% of modeled cases contradict the prevailing assumption, with collision risk peaking at intermediate traffic densities. These findings challenge common interpretations of WVC dynamics and underscore the need for more refined, behaviorally informed mitigation strategies. We suggest that integrating such approaches into road planning and conservation policy—particularly under the European Union’s ‘Vision Zero’ framework—could help reduce wildlife mortality more effectively in future scenarios, including potential pandemics or mobility disruptions. Full article

The aim of this study was to evaluate the relationship between visual functions and road traffic accidents (RTAs) by meta-analysis of observational studies. The analysis included all drivers of motor vehicles, regardless of age, and those using private or public transport. Self-reported visual outcomes were excluded. The risk of RTA in patients with reduced visual acuity was observed in commercial drivers in cross-sectional studies (PR 1.54, 95% CI 1.26–1.88), but not in private drivers in cohort (RR 1.04, 95% CI 0.74–1.46) or case–control studies (OR 1.04, 95% CI 0.78–1.40). A non-statistically significant association between colour vision defects and RTA was observed in cross-sectional studies (PR 1.50, 95% CI 0.91–2.45). No evidence was found for an increased risk of accidents in people with reduced stereopsis. In older adults with abnormal contrast sensitivity, a weak risk of RTA was observed in cohort studies. Evidence from low-quality cross-sectional studies suggests an increased risk of RTAs among commercial drivers with reduced visual acuity. The few case–control and cohort studies identified did not show an association between accident occurrence and visual function. Attention needs to be paid to this issue to facilitate the conduct of high-quality research that can support the development of road safety policies. Full article

To overcome the limitations of traditional image alignment methods in capturing deep semantic features, a deep feature information image alignment network (DFA-Net) is proposed. This network aims to enhance image alignment performance through multi-level feature learning. DFA-Net is based on the deep residual architecture and introduces spatial pyramid pooling to achieve cross-scalar feature fusion, effectively enhancing the feature’s adaptability to scale. A feature enhancement module based on the self-attention mechanism is designed, with key features that exhibit geometric invariance and high discriminative power, achieved through a dynamic weight allocation strategy. This improves the network’s robustness to multimodal image deformation. Experiments on two public datasets, MSRS and RoadScene, show that the method performs well in terms of alignment accuracy, with the RMSE metrics being reduced by 0.661 and 0.473, and the SSIM, MI, and NCC improved by 0.155, 0.163, and 0.211; and 0.108, 0.226, and 0.114, respectively, compared with the benchmark model. The visualization results validate the significant improvement in the features’ visual quality and confirm the method’s advantages in terms of stability and discriminative properties of deep feature extraction. Full article

Background/Objectives: Aging is often accompanied by physical and cognitive decline, affecting older adults’ mobility. Virtual reality (VR) offers innovative opportunities to safely practice everyday tasks, such as street crossing. This study was designed as a feasibility and pilot study to explore acceptance, usability, and preliminary effects of a VR-based road-crossing intervention for older adults. It investigates the use of virtual reality (VR) as an innovative training tool to support senior citizens in safely navigating everyday challenges such as crossing roads. By providing an immersive environment with realistic traffic scenarios, VR enables participants to practice in a safe and controlled setting, minimizing the risks associated with real-world road traffic. Methods: A VR training application called “Wegfest” was developed to facilitate targeted road-crossing practice. The application simulates various scenarios commonly encountered by older adults, such as crossing busy streets or waiting at traffic lights. The study applied a single-group pre-post design. Outcomes included the Timed Up and Go test (TUG), Falls Efficacy Scale-International (FES-I), and Montreal Cognitive Assessment (MoCA). Results: The development process of “Wegfest” demonstrates how a highly realistic street environment can be created for VR-based road-crossing training. Significant improvements were found in the Timed Up and Go test (p = 0.002, d = 0.784) and fall-related self-efficacy (FES-I, p = 0.005). No change was observed in cognitive function (MoCA, p = 0.56). Participants reported increased subjective safety (p < 0.001). Discussion: The development of the VR training application “Wegfest” highlights the feasibility of creating realistic virtual environments for skill development. By leveraging immersive technology, both physical and cognitive skills required for road-crossing can be effectively trained. The findings suggest that “Wegfest” has the potential to enhance the mobility and safety of older adults in road traffic through immersive experiences and targeted training interventions. Conclusions: As an innovative training tool, the VR application not only provides an engaging and enjoyable learning environment but also fosters self-confidence and independence among older adults in traffic settings. Regular training within the virtual world enables senior citizens to continuously refine their skills, ultimately improving their quality of life. Full article

To inform the development of effective prevention strategies for reducing pedestrian fatalities in an ageing society, a retrospective analysis was conducted on fatal pedestrian–vehicle collisions in Japan. All pedestrian fatalities caused by motor vehicle collisions between 2013 and 2022 in Shiga Prefecture were reviewed. Among the 164 pedestrian fatalities (involving 92 males and 72 females), the most common scenario involved a pedestrian crossing the road (57.3%). In 61 cases (64.9%), pedestrians crossed from the oncoming traffic lane side to the vehicle’s lane side (i.e., crossing from right to left from the driver’s perspective, as vehicles drive on the left in Japan). In 33 cases (35.1%), pedestrians crossed from the vehicle’s lane side to the oncoming traffic lane side. Among cases of pedestrians crossing from the vehicle’s lane side, 54.5% were struck by the near side of the vehicle’s front, whereas 39.7% of those crossing from the oncoming traffic lane side were hit by the far side of the vehicle’s front (p = 0.02). Therefore, for both crossing directions, collisions frequently involved the front left of the vehicle. When pedestrians were struck by the front centre or front right of the vehicle, the collision speeds were higher when pedestrians crossed from the oncoming traffic lane side to the vehicle’s lane side rather than crossing from the vehicle’s lane side to the oncoming traffic lane side. A significant difference in collision speed was observed for impacts with the vehicle’s front centre (p = 0.048). The findings suggest that increasing awareness that older pedestrians may cross roads from the oncoming traffic lane side may help drivers anticipate and avoid potential collisions. Full article

Non-Newtonian debris flows represent a critical challenge for hydraulic infrastructure in mountainous regions, often causing significant damage and service disruption. However, current models typically simplify these flows as Newtonian, leading to inaccurate design assumptions. This study addresses this gap by comparing the hydraulic behavior of Newtonian and non-Newtonian flows in an alluvial fan, using the Amoray Gully in Apurímac, Peru, as a case study. This gully intersects the Interoceánica Sur national highway via a low-water crossing (baden), making it a relevant site for evaluating debris flow impacts on critical road infrastructure. The methodology integrates hydrological analysis, rheological characterization, and hydraulic modeling. QGIS 3.16 was used for watershed delineation and extraction of physiographic parameters, while a high-resolution topographic survey was conducted using an RTK drone. Rainfall-runoff modeling was performed in HEC-HMS 4.7 using 25 years of precipitation data, and hydraulic simulations were executed in HEC-RAS 6.6, incorporating rheological parameters and calibrated with the footprint of a historical event (5-year return period). Results show that traditional Newtonian models underestimate flow depth by 17% and overestimate velocity by 54%, primarily due to unaccounted particle-collision effects. Based on these findings, a multi-barrel circular culvert was designed to improve debris flow management. This study provides a replicable modeling framework for debris-prone watersheds and contributes to improving design standards in complex terrain. The proposed methodology and findings offer practical guidance for hydraulic design in mountainous terrain affected by debris flows, especially where infrastructure intersects active alluvial fans. Full article

The accurate early-stage estimation of environmental load (EL) and construction cost (CC) in road infrastructure projects remains a significant challenge, constrained by limited data and the complexity of construction activities. To address this, our study proposes a machine learning-based predictive framework utilizing artificial neural networks (ANNs) and deep neural networks (DNNs), enhanced by autoencoder-driven feature selection. A structured dataset of 150 completed national road projects in South Korea was compiled, covering both planning and design phases. The database focused on 19 high-impact sub-work types to reduce noise and improve prediction precision. A hybrid imputation approach—combining mean substitution with random forest regression—was applied to handle 4.47% missing data in the design-phase inputs, reducing variance by up to 5% and improving data stability. Dimensionality reduction via autoencoder retained 16 core variables, preserving 97% of explanatory power while minimizing redundancy. ANN models benefited from cross-validation and hyperparameter tuning, achieving consistent performance across training and validation sets without overfitting (MSE = 0.06, RMSE = 0.24). The optimal ANN yielded average error rates of 29.8% for EL and 21.0% for CC at the design stage. DNN models, with their deeper architectures and dropout regularization, further improved performance—achieving 27.1% (EL) and 17.0% (CC) average error rates at the planning stage and 24.0% (EL) and 14.6% (CC) at the design stage. These results met all predefined accuracy thresholds, underscoring the DNN’s advantage in handling complex, high-variance data while the ANN excelled in structured cost prediction. Overall, the synergy between deep learning and autoencoder-based feature selection offers a scalable and data-informed approach for enhancing early-stage environmental and economic assessments in road infrastructure planning—supporting more sustainable and efficient project management. Full article

The remote sensing monitoring of land use changes and future scenario simulation hold crucial significance for accurately characterizing urban expansion patterns, optimizing urban land use configurations, and thereby promoting coordinated regional development. Through the integration of multi-source data, this study systematically analyzes the spatiotemporal trajectories and driving forces of land use changes in the Pearl River Delta urban agglomeration (PRD) from 1990 to 2020 and further simulates the spatial patterns of urban land use under diverse development scenarios from 2025 to 2035. The results indicate the following: (1) During 1990–2020, urban expansion in the Pearl River Delta urban agglomeration exhibited a “stepwise growth” pattern, with an annual expansion rate of 3.7%. Regional land use remained dominated by forest (accounting for over 50%), while construction land surged from 6.5% to 21.8% of total land cover. The gravity center trajectory shifted southeastward. Concurrently, cropland fragmentation has intensified, accompanied by deteriorating connectivity of ecological lands. (2) Urban expansion in the PRD arises from synergistic interactions between natural and socioeconomic drivers. The Geographically and Temporally Weighted Regression (GTWR) model revealed that natural constraints—elevation (regression coefficients ranging −0.35 to −0.05) and river network density (−0.47 to −0.15)—exhibited significant spatial heterogeneity. Socioeconomic drivers dominated by year-end paved road area (0.26–0.28) and foreign direct investment (0.03–0.11) emerged as core expansion catalysts. Geographic detector analysis demonstrated pronounced interaction effects: all factor pairs exhibited either two-factor enhancement or nonlinear enhancement effects, with interaction explanatory power surpassing individual factors. (3) Validation of the Patch-generating Land Use Simulation (PLUS) model showed high reliability (Kappa coefficient = 0.9205, overall accuracy = 95.9%). Under the Natural Development Scenario, construction land would exceed the ecological security baseline, causing 408.60 km² of ecological space loss; Under the Ecological Protection Scenario, mandatory control boundaries could reduce cropland and forest loss by 3.04%, albeit with unused land development intensity rising to 24.09%; Under the Economic Development Scenario, cross-city contiguous development zones along the Pearl River Estuary would emerge, with land development intensity peaking in Guangzhou–Foshan and Shenzhen–Dongguan border areas. This study deciphers the spatiotemporal dynamics, driving mechanisms, and scenario outcomes of urban agglomeration expansion, providing critical insights for formulating regionally differentiated policies. Full article

Infrared and visible image fusion combines infrared and visible images of the same scene to produce a more informative and comprehensive fused image. Existing deep learning-based fusion methods fail to establish dependencies between global and local information during feature extraction. This results in unclear scene texture details and low contrast of the infrared thermal targets in the fused image. This paper proposes an infrared and visible image fusion network to address this issue via the use of a residual interactive transformer and cross-attention fusion. The network first introduces a residual dense module to extract shallow features from the input infrared and visible images. Next, the residual interactive transformer extracts global and local features from the source images and establishes interactions between them. Two identical residual interactive transformers are used for further feature extraction. A cross-attention fusion module is also designed to fuse the infrared and visible feature maps extracted by the residual interactive transformer. Finally, an image reconstruction network generates the fused image. The proposed method is evaluated on the RoadScene, TNO, and M3FD datasets. The experimental results show that the fused images produced by the proposed method contain more visible texture details and infrared thermal information. Compared to nine other methods, the proposed approach achieves superior fusion performance. Full article

This paper presents a novel system designed to enhance pedestrian safety in urban environments by utilizing real-time video analysis and machine learning techniques. With a focus on the bustling streets of Macao, known for its high pedestrian traffic and complex road conditions, the proposed model alerts drivers to the presence of pedestrians, significantly reducing the risk of accidents. Leveraging the You Only Look Once algorithm, this research demonstrates how timely alerts can be generated based on risk assessments derived from video footage. The model is rigorously tested against diverse driving scenarios, providing robust accuracy in detecting potential hazards. A comparative analysis of various machine learning algorithms, including Gradient Boosting and Logistic Regression, underscores the effectiveness and reliability of the system. The key finding of this research indicates that dataset refinement and enhanced feature differentiation could lead to improved model performance. Ultimately, this work seeks to contribute to the development of smart city initiatives that prioritize safety through advanced technological solutions. This approach exemplifies a vision for more responsive and responsible urban transport systems. Full article

Field data, as the basis for planning and designing forest roads, must have high spatial accuracy. Classical (using a theodolite and a level) and modern (based on total stations and GNSSs) surveying methods are used in current field data survey for forest road design. This study analyzed the spatial accuracy of classical and modern surveying methods, the accuracy of spatial data recorded using a UAV equipped with an RGB camera at different flight altitudes, and the accuracy of lidar data of the Republic of Croatia. This study was conducted on a forest area where salvage logging was carried out, which enabled the use of a GNSS receiver in RTK mode as a reference method. The highest RMSE values of the spatial coordinates were recorded for measurements obtained with the classical surveying method (0.89 m) and a total station (0.33 m). The flight altitude of the UAV did not significantly affect the spatial error of the collected data, which ranged between 0.07 and 0.09 m. The cross-terrain slope, as one of the factors that significantly affect the amount of earthworks, did not differ statistically significantly between the methods. The ALS error was strongly influenced by the cross-terrain slope. The authors conclude that the new survey methods (SfM and lidar data) provide high-accuracy data but also draw attention to challenges in their use, such as vegetation and biomass on the ground. Full article

The complex geological environment in western Sichuan, China, leads to frequent debris flow disasters, posing significant threats to the lives and property of local residents. In this study, debris flow susceptibility models were developed using three machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The models were trained with data in Songpan County and used for debris flow susceptibility prediction in Mao County, using small watersheds as assessment units. Seventeen key feature factors based on multi-source remote sensing data encompassing topography and geomorphology, geological structures, environmental elements, and human activities were selected as input parameters after assessment with Pearson correlation analysis. Model performance was rigorously evaluated through ten-fold cross-validation, and hyperparameter optimization was employed to enhance predictive accuracy. To assess the models’ robustness, the trained models were applied to the neighboring Mao County for cross-regional validation. The results consistently indicate that elevation, seismic nucleation density, population density, and distance to roads are the primary controlling factors influencing susceptibility. Comparative analysis between the Songpan and Mao County reveals that the RF model significantly outperforms SVM and XGBoost in accuracy and robustness. Therefore, the RF model is better suited for debris flow susceptibility assessment in western Sichuan. Although the effectiveness of this model may be limited by the relatively small sample size of debris flow events in the dataset and potential variations in environmental conditions across different regions, it still holds promise for providing a scientific basis and decision-making support for disaster mitigation in comparable areas of western Sichuan. Full article

The complex climate in permafrost regions poses severe challenges to infrastructure, and freeze-thaw cycles accelerate the deformation and damage of road embankments. Conventional thermosyphon technology, though effective in lowering permafrost temperatures, has a limited range of effect, making it hard to meet the demand for large-scale temperature regulation. This paper proposes a V-shaped transverse thermosyphon design with bidirectional heat conduction. It connects at the embankment centerline and transversely penetrates the entire cross-section to expand the temperature regulation range. Using a hydro-thermal coupling model, the temperature regulation effects of vertical, inclined, and V-shaped thermosyphons were calculated. Results show that the V-shaped design outperforms the other two in temperature control across different embankment areas. Transverse temperature analysis indicates uniform cooling around the embankment center, while depth temperature analysis reveals more stable temperature control with lower and less fluctuating temperatures at greater depths. Long-term temperature analysis demonstrates superior annual temperature regulation, providing consistent cooling. This research offers a scientific basis for embankment temperature regulation design in permafrost regions and is crucial for ensuring long-term embankment stability and safety. Full article