Search Results (1,104)

Prevention of crossing collisions between cyclists and vehicles at nonsignalized intersections on community roads where walls and hedges limit visibility is required in Japan. Because available observation information in real-time is limited on community roads, the use of statistical information that represents the typical movement characteristics of cyclists is effective to compensate for the lack of observation information. From such a background, in our previous study, we proposed a method to construct “location-dependent statistical information” (LDSI) and a method to utilize it as “virtual observation” (VO) and “virtual control input” (VCI) in stochastic position estimation. Here, although LDSI was constructed for multiple clusters of cyclists, the classification method of the cyclists observed in real-time was not considered. In the real world, the limitation of the observation information causes classification uncertainty. Thus, in this study, we propose a position estimation method that utilizes soft classification results and considers classification uncertainty by integrating VO and VCI derived from LDSI of each cluster. To evaluate the proposed method in this study, we conduct a simulation and an experiment in the real world. Through the comparison with conventional methods, we confirm that our proposed method in this study improves the performance of the position estimation. The proposed method will contribute to a cooperative safety system. Full article

In recent years, global climate change has significantly increased the incidences of grassland fires, shifting their occurrence from seasonal events (primarily spring and autumn) to annual incidents. To enable a more accurate evaluation and zoning of grassland fire risk, this study established the Fire Source Hazard Index, Fire Fuel Hazard Index, and Fire Environmental Hazard Index based on multi-source data, employing the entropy weight method, random forest modeling, mathematical statistics, and spatial analysis. A comprehensive seasonal grassland fire hazard assessment model was constructed using these three indices and seasonal fire hazard zones were evaluated in Inner Mongolia. The results indicated that, among the fire source factors, the hazard weight of foreign fire sources was relatively high during spring (0.37) and summer (0.44). In autumn and winter, the hazard weights of road networks were higher, at 0.38 and 0.44, respectively. In the comprehensive hazard assessment, the fire environment hazard exhibited an objective existence with notable seasonal variation, whereas the hazard weight of fire source factors exceeded that of fuels across all seasons. The comprehensive grassland fire hazard in Inner Mongolia demonstrated distinct seasonality and regional heterogeneity. Temporally, fire hazards are widespread and intense in spring, limited and concentrated in summer, extensive yet dispersed in autumn, and lowest in winter. Spatially, grassland fire hazards decreased from east to west, with higher hazards concentrated in the eastern regions. Western Inner Mongolia had the lowest probability of fire occurrence. The validation results revealed a positive correlation between the proportion of fire points and hazard grades, confirming the rationality of the hazard classification and the accuracy of the assessment, which provides an important theoretical basis for the scientific management and effective prevention and control of grassland fires. Future research should further refine and explore more precise methods for grassland fire hazard assessment. Full article

In Europe, bitumen classification has traditionally relied on empirical tests, namely penetration and the Ring and Ball softening point, originally developed for unmodified binders and considered insufficient for modern modified binders. As an alternative, a rheology-based method, the Bitumen Typisierungs Schnell Verfahren (BTSV) rapid bitumen categorization method, has been developed in Germany to characterize high service temperature performance, with performance requirements introduced in 2025 in the German specifications. In this study, the performance of five bitumen types commonly used in Hungarian road construction was investigated using the BTSV method. During testing, the softening temperature corresponding to a rheological threshold value of G* = 15.0 kPa (T_BTSV) and the phase angle (δ_BTSV) were determined. T_BTSV is defined as the temperature corresponding to G* = 15 kPa, representing the softening state, while δ_BTSV reflects the viscoelastic balance between elastic and viscous behaviour. The objective of this study is to evaluate the high-temperature performance of commonly used Hungarian bituminous binders using the BTSV method and to compare the results with traditional empirical parameters and German classification systems. A total of 137 binder samples from production control were tested and analysed, including paving-grade, SBS-modified, and chemically stabilized rubber-modified binders. Statistical evaluation included mean values and 95% confidence intervals. For rubber-modified bitumens, the recoverable, insoluble rubber content was determined using the Soxhlet extraction method. Based on the results, it can be concluded that with increasing rubber content, the T_BTSV value shows an increasing trend, while the δ_BTSV value decreases. As discussed in the paper, a strong linear relationship was observed between the investigated parameters in the T_BTSV–δ_BTSV diagram, with a coefficient of determination of R² = 0.99. Full article

Pavement management systems (PMS) are essential for formulating a cost-effective capital improvement plan (CIP) that adheres to budget constraints. Optimization techniques are vital in enhancing the efficiency of these plans. Among the various methods available, genetic algorithms (GA) are particularly effective at identifying optimal solutions in complex scenarios. This study introduces a GA-based priority optimization model designed to select the most beneficial road improvement projects while staying within budgetary limits. The model was applied to the extensive road network of Fort Wayne, Indiana, considering critical factors such as budget allocation, roadway classification, PASERs, treatment options, and associated costs. The results demonstrate the model’s effectiveness in prioritizing projects, ensuring that available funds are utilized to achieve maximum impact on roadway conditions. By leveraging GA, this approach not only enhances decision-making processes but also provides a robust framework for future pavement management efforts. Overall, the integration of genetic algorithms into PMS can lead to more strategic and economically sound infrastructure improvements. Full article

Urban transportation intrusion detection is difficult because many compromised messages remain individually credible until they are checked against surrounding road, sensor, and signal states. This study investigated this problem by formulating it as a five-way message classification task over one benign class and four attack families, and by evaluating three detector families under matched access to transportation state: a local-rule baseline, a flat-feature multiclass logistic model, and a knowledge-graph detector with explicit graph reasoning. This study presents a two-part evaluation that combined a controlled simulator with a real-city analysis built from OpenStreetMap and Texas Department of Transportation (TxDOT) data for downtown Austin, Houston, and Dallas. In the fully observed configuration, both the flat-feature logistic and knowledge-graph detectors perform well, while the knowledge-graph detector preserves an explicit rule structure. In the three-city configuration, the knowledge-graph detector shows better portability across cities and lower inference latency. The ablation results further show that roadside sensing and topology account for most of the graph-based detector’s performance. Full article

Road icing is a complex and highly dynamic phenomenon that poses a serious risk to winter road safety. However, nonlinear evolution, multiscale temporal dependence, and rapid transitions between adjacent stages still make it difficult to accurately identify icing states from multivariate environmental time-series data. In this work, a road icing state classification model based on TimesNet is developed. We integrate wavelet transform convolution (WTConv) into the original TimesNet architecture to strengthen multiscale time–frequency feature extraction, enabling more effective capture of high-frequency dynamics and abrupt local variations. To address the hyperparameter sensitivity commonly observed in icing scenarios, an Improved Whale Optimization Algorithm (IWOA) is employed and uses Pearson correlation analysis to select informative and physically meaningful features from multi-source monitoring data. Experiments on a real-road dataset show that the proposed IWOA–TimesNet–WTConv model improves overall accuracy from 92.72% to 98.83% compared with the baseline TimesNet model. In addition, feature selection yields a further 1.04 percentage-point gain in overall accuracy and increases the Macro F1-score from 0.9691 to 0.9809, indicating reduced redundancy and more stable discrimination under transitional icing conditions. Overall, the proposed method provides a practical and effective data-driven solution for intelligent road icing monitoring and early warning in complex winter road environments. Full article

To address the complexities inherent in evaluating electric motorcycle ride comfort across diverse road profiles and operating speeds, this study establishes a systematic evaluation framework utilizing a multi-level fuzzy comprehensive assessment approach. Empirical investigations were conducted on asphalt, Belgian block, and speed-bump terrains at varying velocities. Triaxial acceleration data were acquired from the seat, footrest, and handlebar interfaces to compute weighted Root Mean Square (RMS) acceleration, Vibration Dose Value (VDV), and Power Spectral Density (PSD). By synthesizing subjective ratings, a correlation between tactile perception and objective metrics was derived to calibrate the two-level fuzzy model. Analysis reveals that vibration energy is predominantly concentrated in the vertical low-frequency domain (0–20 Hz) independent of test conditions. Notably, a 50% increase in velocity precipitated a 22.4% decrement in the comprehensive ride comfort index, degrading the classification from “Moderate” to “Fair.” The proposed framework provides a rigorous quantitative paradigm for vibration mitigation strategies and informed speed management in electric vehicle engineering. Full article

Reliable perception of roadway signals is critical for autonomous vehicles operating in complex urban environments, particularly when traffic lights convey safety-critical instructions through flashing and arrow indications that extend beyond conventional red, yellow, and green states. However, most existing vision-based approaches focus primarily on static traffic-light recognition and lack robust mechanisms for interpreting temporal behaviors such as flashing signals. To address this limitation, this paper proposes a unified real-time perception framework, termed HybridSignalNet, for multi-class recognition of traffic lights, road signs, and lane-related roadway elements. The framework combines spatial detection with temporal state reasoning to interpret both steady and flashing signal patterns in video streams. Experimental evaluation demonstrates strong performance across multiple object classes, achieving an average detection F1-score of 91.3%, while traffic-light state classification reaches 96.7%, including reliable identification of flashing and arrow-based signals. The proposed system operates in real-time and provides an interpretable and deployable solution for intelligent transportation systems and autonomous driving applications, particularly at signalized intersections where temporal signal understanding is essential for safe decision-making. Full article

The article addresses the challenges of modernizing Kazakhstan’s railway infrastructure under conditions of technological dependence on foreign automation systems and obsolete relay-based equipment. These factors pose significant risks to economic and information security and limit the throughput capacity of level crossings. A digital system, KZ-DALCS-AI, is proposed, based on a multi-level safety architecture and the integration of artificial intelligence into monitoring and control processes. A key component is an obstacle detection and classification algorithm that considers object types (vehicles, humans and animals, foreign objects, and environmental factors) and enables intelligent real-time decision making using the KZ-ODC-AI controller with data from video surveillance, microwave sensors, and inductive loops. The system architecture, operational logic, and level crossing control algorithm are developed, including optimization of closing time by minimizing the deviation between calculated and actual values. The results of the performed calculations confirm the effectiveness of the proposed notification algorithm, ensuring the required level of safety while reducing unnecessary delays for road traffic. The implementation of the system improves throughput, reduces operational costs, enhances reliability, and minimizes the impact of the human factor. Full article

Road safety barriers represent a core component of the road with relevant consequences on effective safety for users. Maintaining these components in adequate conditions, within the quality admissibility thresholds, in compliance with all economic and management constraints, is a primary need for road administrators. In this paper, the authors propose an original procedure to classify the state of efficiency of road safety barriers, at the network scale and relying on conventional administrative data, in an optimized BIM environment, to simplify evaluations and management procedures. Through purpose-built algorithms based on selected geometric and functional parameters of the different road barriers, the algorithm provides a preliminary classification of the various segments, evidencing attention class indicators, useful as preliminary alert signals and for anticipating detailed investigations that can ensure significant economic efficiencies. The method was tested on a 10 km long motorway segment in Italy, evidencing the potential advantages of such an innovative approach to support, as a final goal, a comprehensive infrastructure digital model for virtual inspections, evaluating road component “health” state and properly implementing maintenance strategies. This approach improves network-scale monitoring and maintenance-related activity prioritization phases for road safety barriers, leveraging administrative data. This methodology functions as a BIM-based asset screening tool, as it offers a digital decision support system that identifies critical segments, to optimize the allocation of physical resources and prioritize on-site inspections where they are most needed. Full article

Unsignalized intersections involve complex interactions among heterogeneous road users and are associated with elevated safety risks. Although surrogate safety measures derived from high-resolution trajectories enable proactive safety assessment, such data are not widely available in routine monitoring systems, which often provide only coarse-grained traffic observations. This study examines how the inferability of surrogate safety information changes as the available traffic data become progressively coarser. Using the high-resolution inD dataset, we implement a controlled feature degradation framework across three nested levels of data granularity and develop intersection-specific models for three tasks: critical conflict detection, dominant direction classification, and vulnerable road user (VRU) involvement identification. Model performance and changes in variable importance are evaluated using PR-AUC and SHAP analysis. The results show clear task-dependent degradation. Models based on high-granularity data achieve strong overall performance, with an average PR-AUC above 0.88. Dominant direction classification remains relatively robust as data granularity decreases, with PR-AUC declining from 0.970 to 0.893, whereas VRU involvement identification deteriorates substantially, from 0.991 to 0.697. The results further indicate that vehicle-based traffic variables retain meaningful predictive value for conflict detection and direction classification but are insufficient for reliable inference of VRU-related risk. Interpretability analysis shows a progressive shift in model reliance from kinematic interaction variables to coarser exposure-related and structural descriptors as observability decreases. These findings clarify the relationship between data granularity and task-dependent surrogate safety inference at unsignalized intersections. 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

Winter road snow significantly alters surface friction conditions and traffic capacity, serving as a critical factor contributing to traffic accidents, congestion, and temporary traffic control measures. Compared with sparsely deployed road sensors and labor-intensive field inspections, traffic surveillance cameras offer advantages such as dense spatial coverage, low deployment cost, and continuous observation capability, providing a feasible solution for segment-level winter road condition monitoring. To meet traffic management needs, this study categorizes the impact of road snow on passability into four classes: Clear, Light, Medium, and Heavy. A road snow coverage dataset containing 10,498 images under complex traffic scenarios was constructed and has been publicly released. Furthermore, nine representative deep learning models were systematically evaluated to compare their recognition performance and applicability for this task. Experimental results show that all models achieved over 89% classification accuracy on the test set. To further examine cross-regional generalization capability, 48 surveillance cameras from Canada and Norway were selected for real-world validation. Among all models, Swin Transformer achieved the highest accuracy of 81.2% under complex lighting conditions and varying viewpoints, demonstrating superior stability and transferability. The findings provide quantitative guidance for model selection and engineering deployment of camera-based winter road monitoring systems. Full article

Road traffic injuries represent one of the most critical public health challenges in the Gulf region. Predicting traffic accident severity is therefore a critical component of evidence-based road safety management. In this study, we develop machine learning frameworks for predicting traffic accident severity using Qatar’s national dataset (2020–2025), addressing extreme class imbalance and interpretability. A dataset of 588,023 accident records was systematically preprocessed from 1,000,500 raw reports. We compare three approaches: multi-class (four severity levels), binary (Safe vs. Severe), and cascaded two-stage (combining both). Six classifiers were evaluated across two encoding methods and three balancing strategies. Systematic hyperparameter tuning with 5-fold stratified cross-validation was performed for all models. The binary LightGBM classifier achieved BA = 71.04%, AUC-ROC = 0.772, Sensitivity = 61.03%, and Specificity = 81.05%, demonstrating superior performance over multi-class approaches. Temporal validation on 2025 data (trained on 2020–2024 data) supported good temporal generalization. Analysis of 10,000 test instances identified the time period as the dominant predictor of accident severity. The binary LightGBM framework provides an interpretable and effective approach for severe accident identification and risk prioritization, with SHAP findings supporting targeted temporal enforcement and pedestrian safety as evidence-based policy priorities. Full article

Unlike image-only systems that falter in shadows, glare, and low contrast, LiDAR directly records surface geometry and supports depth-aware quantification. This survey examines LiDAR-based road surface damage classification across the entire pipeline, encompassing acquisition with mobile and terrestrial laser scanning, preprocessing and representation choices, supervised, semi-supervised, and unsupervised learning techniques, as well as multisensor fusion at early, mid, and late stages. A consistent thread is measurement, not just detection: we describe how LiDAR damage classification maps to agency practices such as the Distress Identification Manual and the Pavement Condition Index. We summarize datasets and evaluation protocols for detection, segmentation, 3D reconstruction, and ride quality. We outline practical concerns for corridor-scale deployment: calibration and timing, intensity normalization, tiling/streaming, and runtime budgeting. The review concludes with open problems and outlines directions for robust, severity-aware, and scalable field systems. Full article