Search Results (2,966)

This article draws on public opinion surveys conducted as part of the AV-PL-ROAD project, “Polish Road to Automation of Road Transport”. Although selected findings from this survey material were published in 2023, the earlier study was limited to descriptive statistical analysis. The present paper re-examines the same empirical dataset through a different analytical framework focused on latent-structure reconstruction, using a different analytical framework focused on latent-structure reconstruction, providing a more structured and informative interpretation of perceptions of autonomous vehicles in Poland. The study combines within-respondent standardization, Principal Component Analysis (PCA), and k-means clustering to identify the dominant dimensions of perception and recurring perception profiles, complemented by qualitative insights from focus group interviews (FGI) used to support interpretation. The results indicate that perceptions of autonomous vehicles are not one-dimensional, but are organized around three main axes: systemic benefits versus implementation barriers, technological trust and information security, and regulatory-ethical readiness linked to deployment conditions. The analysis also reveals four recurring perception profiles that do not map directly onto simple demographic divisions and are better understood in relation to operational and institutional context. In addition, statistically significant differences between clusters were confirmed using nonparametric tests (Kruskal–Wallis with Dunn–Šidák post hoc analysis). The main contribution of the paper is methodological: it illustrates that previously analyzed survey data can yield structurally informative insights, including the identification of latent dimensions, perception profiles, and statistically significant differences between clusters when reinterpreted through a latent-space approach rather than conventional descriptive methods. The findings provide additional evidence on the social and institutional conditions of transport automation in Poland and provide a more robust analytical basis for future mobility policy and implementation strategies. Full article

Urbanization increases pluvial flood risk by expanding impermeable surfaces, which is a trend likely to intensify with climate change. Permeable pavement (PePav) made from industrial byproducts, in accordance with circular economy principles, may improve soil permeability. Public acceptance remains a critical barrier to its implementation. Existing measures of willingness to accept (WtA) new technologies are inconsistent, limiting interdisciplinary collaboration. Therefore, a concise WtA scale was adapted from the Bogardus Social Distance Scale to assess acceptance of PePav at varying levels of proximity in residential contexts, from public flood-prone roads to private yards. The scale was evaluated across three studies: Study 1 (N = 195) and Study 2 (N = 187) utilized mixed student samples, while Study 3 (N = 625) involved a non-student sample. The 5-item solution, identified through factor analysis in Study 1, consistently demonstrated a unidimensional and cumulative structure and satisfactory reliability, even after the proposed PePav ingredient modification in subsequent studies. The scale correlated with recycling experience and professional background, indicating convergent validity, but not with flooding or informal construction experience, across all samples. Study 3 provided evidence of external validity by incorporating empirically well-established Theory of Planned Behavior (TPB) constructs and showing that WtA predicted PePav use beyond TPB variables and demographics. The scale also showed measurement invariance across sample type (student vs. general population) and different levels of construction experience. The constructed WtA scale is suitable for efficiently assessing professional and public acceptance of circular building materials and may have broad cross-disciplinary relevance. This enables timely, targeted interventions and informed policy decisions to advance sustainable technologies in the built environment, with substantial implications for education, professional policy, and sustainable engineering. Nevertheless, further validation is required. Full article

The construction industry remains among the most hazardous, with workers in horizontal transportation infrastructure facing additional risks from dynamic work zones, live traffic exposure, and variable environmental conditions. Immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) offer new approaches to accident analysis and prevention, yet their applications toward improving occupational safety in transportation construction have not been comprehensively reviewed. This paper presents a systematic review of 54 studies published between 2016 and 2025 collected from two online databases (Transportation Research International Documentation and Web of Science). This review synthesizes how immersive technologies contribute to occupational risk assessment, safety training, and real-time hazard monitoring in the construction of roads, bridges, tunnels, and work zones. Each study is classified across two dimensions: the immersive medium (VR, AR, etc.) and the operational context within the construction lifecycle (onsite tools, offsite monitoring and planning, simulation-based analysis, and workforce education). This dual classification is the first to systematically map immersive technology applications for occupational safety, specifically within horizontal transportation infrastructure. The findings of this review demonstrate the unique use cases of each immersive medium, revealing that VR is primarily used for controlled experimentation and full-immersion remote analysis, whereas AR and handheld devices are preferred for field-deployed applications. Despite these promising capabilities, widespread adoption remains limited by hardware constraints, challenging field conditions, and organizational resistance. This suggests that future work should focus on safety systems tested in real-world settings and rigorously evaluated by domain experts to enable their integration into standard workplace risk management practices. Full article

In response to the influence of extreme disasters, damage to distribution lines and user outages, a parallel implementation strategy is proposed for emergency repair of disaster-damaged distribution networks and rapid restoration of power supply for users, considering the collaboration of “human–vehicle–road–pile” resources. This strategy constructs a hierarchical optimization framework, with the upper-level model aiming to minimize the repair time for disaster damage. It adopts a collaborative optimization approach between repair resources and transportation routes to quickly repair the connection between the distribution network and the main power network. In the lower-level model, a model predictive control mechanism is adopted to schedule electric vehicles (EVs) in Real-time as mobile energy storage systems, and vehicle-to-grid (V2G) service technology is used to provide an emergency power supply for key loads during the repair period, achieving parallel optimization of “repair–restoration”. Considering constraints such as emergency repair resources, time-varying transportation, electric vehicle scheduling and power management, charging pile capacity, power flow safety of the distribution network, and topology of the distribution network, second-order cone relaxation technology is adopted to improve solving efficiency. The simulation results show that compared with the traditional serial restoration strategy, the proposed strategy delivers a dual benefit: it significantly eliminates the power supply vacuum period without compromising the efficiency of emergency repair operations. Specifically, it increases weighted load restoration by 57.2% compared with traditional sequential methods and reduces the average outage time for key loads from 3.22 h to 0.5 h, effectively enhancing the resilience and restoration ability of the power supply guarantee of the distribution network. Full article

Speed is a key determinant of crash risk and injury severity, particularly on urban and secondary roads with frequent interactions between vulnerable road users. Traffic calming measures (TCMs) encompass physical, regulatory, perceptual, and technological interventions and aim to reduce operating speeds and improve safety and liveability. This study systematically evaluates the effectiveness of TCMs in reducing speed and improving safety outcomes on urban roads, following PRISMA 2020 guidelines. It encompasses the identification, screening, and synthesis of articles from the Scopus, ScienceDirect, and SpringerLink databases, published between January 2020 and February 2026. Risk of bias in the included studies was assessed qualitatively by the co-authors. The assessment was conducted independently, with discrepancies resolved through discussion. A total of 91 studies were included in the review. Evidence from field studies, driving simulator experiments, and analytical, simulation, and computation-based evaluations is reviewed and structured within a three-cluster taxonomy comprising physical and geometrical measures, regulatory and perceptual interventions, and digital and technological approaches. The synthesis indicates that physically self-enforcing measures yield the most consistent reductions in speed. At the same time, regulatory and digital interventions can deliver meaningful safety benefits when implemented at scale with credible governance. Perceptual and advisory measures show more varying and context-dependent effects. The evidence base is limited by heterogeneity in study designs, short-term evaluations, and inconsistent reporting across studies. Full article

The article provides a comprehensive overview of the role of hydrogen as a key vector in the decarbonization of the global transport sector. The study situates hydrogen within the broader context of energy transition and climate neutrality targets, emphasizing its potential to replace fossil fuels in road, rail, maritime, and aviation applications. The analysis integrates a review of current technological, infrastructural, and policy developments, covering both combustion-based and fuel-cell hydrogen propulsion systems. Quantitative and qualitative data were assessed from international reports, scientific publications, and ongoing industrial projects to evaluate performance, efficiency, safety, and cost parameters such as Levelized Cost of Hydrogen (LCOH) and Total Cost of Ownership (TCO). The results indicate that while hydrogen remains economically challenging, technological progress in electrolysis, fuel cells, and refueling infrastructure significantly improves its competitiveness, particularly in heavy-duty and long-range transport. The paper highlights the critical role of international strategies, including the European Hydrogen Strategy and Fit for 55 package, in driving market adoption and regulatory alignment. The conclusions suggest that by 2050, hydrogen could contribute up to one-quarter of total transport energy demand, positioning it as a cornerstone of sustainable mobility and a bridge toward a fully decarbonized transport ecosystem. Full article

In people-centered urban planning, enhancing the well-being of residents and tourists is one of the core objectives. Tourist emotion serves not only as a key indicator of the tourism experience but also indirectly reflects the quality of a city’s public spaces and built environment. In recent years, user-generated content has provided abundant data for understanding human emotional responses in urban environments, while deep learning models offer new technological pathways for extracting spatial–emotional associations from such data. However, existing research lacks a systematic evaluation of emotion analysis models from an urban spatial perspective and their application to uncover the relationship between emotional distribution and spatial characteristics in specific urban contexts. Based on a dataset of 9419 manually annotated travel reviews from Harbin, this study developed a multi-level evaluation framework and conducted a systematic comparison of seven emotion analysis models. This study then screened for the optimal model combinations based on two dimensions—spatial location and emotion polarity—to create a model matching matrix for mapping Harbin’s emotion map. Subsequently, a regression analysis was performed to examine the relationship between emotions and built environment elements. The results show that the ERNIE model demonstrated the best overall performance. Road density, green space density, and accommodation facility density were positively correlated with emotion, while POI diversity showed a negative correlation. This study demonstrates that emotion analysis technology can serve as a valuable analytical tool for identifying spatial patterns of sentiment, thereby offering empirical support for optimizing spatial design parameters and advancing a more people-centered approach to urban development. Full article

Complex environments, such as underground pipe galleries, subway tunnels, and bridge piles, seriously affect the construction of underground passages. Reducing the disruption of the surrounding environment and road traffic during the construction of underground passages in urban transportation hubs is very important for underground passages. Overcoming these difficulties is a problem that we constantly strive to solve. In this paper, we innovatively propose an open-shield construction method (OSM) without a support structure. It simplifies the construction process, is very adaptable to low soil cover depth and complex construction environments, and has minimal impact on traffic disruption during construction. We first analyze the main problems in the construction of urban underground passages and then elaborate on the OSM in detail. Then, an example of an actual project is used to explain the requirements for prefabricated pipe segments and the waterproof structure. Finally, the effect of applying this method is evaluated by using numerical simulation technology and actual monitoring data. This method provides practical engineering application references for the construction of urban underground passages. Full article

This study evaluates the landslide susceptibility of Zonguldak Province, Türkiye, by integrating the Analytical Hierarchy Process (AHP), artificial intelligence (AI) algorithms, and SBAS-InSAR deformation data. Eight environmental and geological parameters—elevation, slope, aspect, lithology, hydrogeology, land use, and distances to rivers and roads—were weighted using AHP and analyzed through 25 AI models. Among them, the Ensemble Bagged Trees (EBT) algorithm achieved the highest predictive accuracy (84%), demonstrating strong adaptability to complex geological datasets. The resulting susceptibility maps were validated using both traditional landslide inventories and InSAR-derived deformation maps, achieving an overall agreement of 83.05%. This dual-validation approach allows for the identification of unrecorded or active slope movements not captured in existing inventories. The combined use of AHP and AI significantly improves model reliability by incorporating both expert judgment and data-driven learning. The study introduces a novel hybrid framework for landslide susceptibility mapping and provides a valuable reference for disaster risk management and spatial planning in regions with complex topography. This study also contributes to sustainability by supporting risk-informed land-use planning, reducing potential economic losses, and enhancing environmental resilience in landslide-prone regions. The proposed framework aligns with sustainable development goals by integrating geospatial technologies and data-driven approaches for long-term hazard mitigation. Full article

With the rapid development of society, the number of road vehicles has increased significantly, leading to a growing severity of traffic accident issues. Timely and accurate detection of road traffic anomalies or accidents is crucial for reducing fatalities and alleviating traffic congestion. Consequently, the detection of road traffic anomalies has become a focal point of research in recent years. With the assistance of computer technologies such as deep learning, researchers have developed more accurate and effective methods for detecting road traffic anomalies. However, the small proportion of anomaly-prone areas in surveillance video frames, combined with the complex and difficult-to-capture patterns of accidents, presents new challenges for the application of deep models to traffic anomaly detection from a surveillance perspective. In light of this, this paper annotates the TADS dataset we previously proposed, a popular text-assisted video representation learning method, to develop a more efficient detection method. Utilizing the well-known video-text model CLIP, we have constructed a detection model that leverages unique text and eye-gaze annotation data from the TADS dataset to learn anomaly representations more effectively, thereby improving the detection of road traffic anomalies from a surveillance perspective. Experimental results demonstrate the superiority of our model for detecting traffic anomalies from a surveillance perspective, as well as the utility of the text and eye-gaze data included in the dataset. Full article

The planning of trajectories for Connected Autonomous Vehicles (CAVs) represents a pivotal aspect of autonomous driving technologies, enabling secure navigation within traffic environments. Traditional models for trajectory control primarily focus on the efficiency and safety of individual vehicles but often overlook the dynamics involved in vehicle-to-vehicle and vehicle-to-infrastructure interactions. This study introduces a novel concept, the “driving risk field,” which imposes constraints on vehicular movement within designated road spaces to enhance safety. A vehicle dynamics model is developed, employing a non-linear fifth-degree polynomial to approximate the trajectory curves, with optimization performed using the Sequential Quadratic Programming (SQP) method. The efficacy of the optimized model is validated through simulations on the Prescan/Simulink plat Full article

Small and medium-sized ports are currently underutilised within supply and logistics chains, yet many can be successfully integrated through optimisation. A significant share is located near large cities and industrial zones, a situation that can be exploited not only to make better use of the ports themselves but also to develop nearby cities and regions. The “integration” of small and medium-sized ports into the Motorways of the Sea (MoS) system encompasses technical, technological, organisational, and legal aspects. This article primarily analyses the adaptation of small and medium-sized ports to the MoS objectives from a technical and technological perspective. The adaptation of the technical capabilities of small and medium-sized ports, linking them with major ports, focuses on bypassing “overloading” in land transport systems, optimising the costs of transporting goods by up to 25–30%, and reducing environmental impact compared with road transport by up to 50%. The article presents a mathematical model for adapting small and medium-sized ports to the MoS system, assessing the cost of cargo transportation, the reduction in environmental impact, and the technical and technological utilisation of these ports. Full article

Warm-mix asphalt (WMA) technology and basalt fiber modification have been increasingly applied in road engineering. However, conventional basalt fibers often disperse unevenly and tend to agglomerate. In this study, basalt fiber powder (BFP) was incorporated into a Sasobit-based WMA system and systematically compared with matrix asphalt, Sasobit-modified WMA, conventional basalt fiber-modified WMA, and styrene butadiene styrene (SBS)-modified asphalt. Multiscale characterization—including dynamic shear rheometry (DSR), bending beam rheometry (BBR), scanning electron microscopy (SEM), and nanoindentation—was conducted to elucidate rheological behavior and interfacial micromechanical responses. The corresponding Asphalt Concrete-13 (AC-13) mixtures were further evaluated through rutting tests, low-temperature bending tests, and moisture susceptibility tests. Results demonstrate that micronized BFP achieves more homogeneous dispersion within the asphalt matrix and may promote a more effective reinforcing morphology, significantly enhancing high-temperature deformation resistance while partially mitigating the low-temperature stiffness increase induced by Sasobit. Compared with conventional basalt fiber systems, BFP shows better stress relaxation capacity and interfacial mechanical response under the tested conditions. At the mixture level, the BFP–Sasobit system showed the best overall performance, with the dynamic stability increasing by 242.2% relative to the base asphalt mixture and the residual Marshall stability reaching 92.3%, while the low-temperature flexural strain increased by 33.3%. Overall, the findings suggest that morphology-controlled micronization provides a morphology-guided enhancement strategy for Sasobit-based warm-mix asphalt by promoting coordinated improvements across the rheological, micromechanical, and mixture scales. Full article

Road pavement rehabilitation increasingly incorporates innovative surface technologies aimed at improving pavement performance while reducing environmental impacts. In addition to conventional recycled asphalt pavement (RAP) maintenance strategies, advanced pavement surface systems such as reflective coatings, rejuvenator-based self-healing mixtures, and thin low-noise asphalt layers have been developed to enhance durability and functional performance. This study presents a comparative Life Cycle Assessment (LCA) of four pavement surface technologies using primary inventory data obtained from full-scale road sections. The systems evaluated include a conventional maintenance mixture and three alternative surface solutions: reflective pavement coatings, RAP mixtures incorporating rejuvenator-based self-healing systems, and thin low-noise asphalt layers. The assessment follows ISO 14040 and ISO 14044 standards and applies the ILCD 2011 midpoint+ (EF 2.0) method. To enable comparability between technologies with different durability, the functional unit was defined as 1 m² of rehabilitated pavement per year of service life. The results indicate that thin low-noise asphalt layers provide the highest environmental benefits across most impact categories due to significant material savings associated with reduced layer thickness. Reflective pavement coatings decrease several impacts, particularly fossil resource depletion and atmospheric emissions, although higher burdens are observed in some categories due to synthetic binder production. RAP mixtures incorporating rejuvenator-based self-healing systems improve resource efficiency and extend pavement durability but may increase impacts associated with binder manufacturing. Overall, the findings highlight relevant environmental trade-offs between different pavement surface technologies and demonstrate that parameters such as layer thickness, binder composition, recycled material content, and service life strongly influence environmental performance. The study illustrates how comparative Life Cycle Assessment supports the development and selection of more sustainable pavement surface systems. Full article

Road accidents related to long-distance driving remain a major safety concern, primarily driven by fatigue and musculoskeletal discomfort. This study investigates the acceptance of driver assistance technologies, specifically seat adjustment and fatigue warning applications using the Technology Acceptance Model (TAM). Data were collected from 1600 drivers in Thailand. Participants were categorized into low-discomfort (n = 785) and high-discomfort (n = 815) groups based on the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ). Multi-group Structural Equation Modeling (SEM) demonstrated that the TAM framework adequately explains technology acceptance in both groups. However, significant differences in the underlying mechanisms were observed. For drivers with lower discomfort levels, attitude toward technology played a more prominent role in shaping usage intention. In contrast, perceived usefulness emerged as the dominant factor for drivers experiencing higher musculoskeletal discomfort. These findings indicate that physical discomfort functions as a critical moderator in technology acceptance. Practically, the results highlight the need for driver assistance systems that prioritize ergonomic effectiveness to enhance adoption among physically strained users. Full article