Search Results (537)

Traffic accidents significantly impact societies and economies. The risk of collision is highest in urban areas, leading to devastating loss of life and escalating socioeconomic costs. In this context, numerous studies have focused on accurately predicting accident risk, severity, and duration using various methodologies. This paper presents an overview of traditional statistical models for accident prediction and a comprehensive systematic review of the literature on statistical modeling, machine learning (ML), and deep learning (DL) techniques employed in this field. Different methodologies and techniques are compared by categorizing studies that adopt similar approaches and analyzing them comparatively. Furthermore, a distinction is made between temporal and spatiotemporal models to describe how these approaches influence the accuracy of future predictions regarding accident occurrence and the duration of impact. This review distinguishes itself from similar works by not only comparing models and approaches, but also by analyzing how external features, such as meteorological data, road geometric design, and land usage, affect the probability of accidents and the models’ accuracy in forecasting road safety. The study explores the performance levels and limitations associated with a set of forecasting approaches, offering an analytical discussion of their differences and similarities, and potential future developments in this research space, including the use of hybrid models and reinforcement learning (RL). The results of this review indicate that DL models tend to be better suited to complex forecasting problems due to their superior ability to represent features and extract non-linear spatiotemporal correlations. This article concludes by describing various directions for further research, ranging from optimizing model architectures to integrating real-time big data into proactive prediction systems. Full article

Accurate road-event detection and timely alert message dissemination are essential for the safety of connected and automated vehicles. In many scenarios, alert messages must reach not only nearby vehicles but also remote stakeholders, such as traffic management centers, cloud services, and infrastructure operators. This requirement motivates the adoption of cellular-based communication technologies in addition to short-range vehicle-to-everything (V2X) communications for data dissemination. In this work, we investigate vehicle-to-network-to-everything (V2N2X) communications for the dissemination of alert messages generated after the on-board detection of hazardous road events through machine learning (ML) algorithms. Although V2N2X connectivity is well suited for extending data dissemination beyond the local vehicular environment, its capability to guarantee prompt message delivery under strict latency constraints remains an open challenge, particularly when ML inference is integrated into the end-to-end processing pipeline. To address this issue, we develop and experimentally evaluate a proof-of-concept (PoC) platform that combines real-time road-event detection with relevant message dissemination towards both nearby and remote recipients. The proposed framework leverages 5G connectivity and publish/subscribe messaging protocols. The experimental results showcase that dissemination latency is highly influenced by both the adopted type of 5G deployment (private versus commercial networks) and the load conditions at the message broker. Full article

Introduction: Pedestrians lying on the road—collapsed through medical emergency, intoxication, or displacement following a prior collision—represent a disproportionately lethal and underaddressed category in road traffic safety. Forensic database analyses derived from Japan’s national police records document a fatality rate of 33.0% for collisions involving pedestrians lying on the road, more than double the rate for upright pedestrian collisions. Standard Advanced Driver-Assistance Systems (ADAS) yield a True Positive Rate (TPR) of only 21.4% for detecting pedestrians lying on the road under night conditions—a classification gap of 73.3 percentage points. Methods: In simulation trials, we evaluated the Advanced Falling Object Detection System (AFODS—where “falling object” denotes the low-profile human form at road level, distinguishing the prone pedestrian from the upright postures addressed by conventional ADAS) on a composite dataset of 3200 annotated fall events and 12,000 negative samples (training/validation), with 320 independent controlled simulation trials used for performance evaluation, spanning real-world, forensic-reconstruction, and Total Human Body Model for Safety (THUMS)-validated synthetic scenarios. No physical prototype has been evaluated; all performance data are derived from simulation, and 37.5% of positive samples are synthetically generated. These simulation conditions represent a first feasibility demonstration pending real-world hardware validation. This paper introduces three original contributions absent from prior work: a three-stage quantitative injury-risk model, a formal ISO 26262 Hazard Analysis and Risk Assessment (HARA), and a medicolegal SHAP interpretability framework. The injury-risk model translated detection latency via impact velocity to Head Injury Criterion (HIC) and estimated fatal injury probability (AIS ≥ 5); these model outputs should be interpreted as exploratory estimates pending ATD validation. Reporting follows principles consistent with the TRIPOD statement. Results: Under clear daytime conditions, AFODS demonstrated a TPR of 98.2% (95% CI: 97.4–98.8%) in simulation, decreasing to 95.6% under night dry-road conditions and 89.4% under night rain. The system achieved an AUC of 0.981 and a mean end-to-end latency of 46.5 ms, representing a 76.8 percentage-point improvement in simulation over the monocular RGB baseline (p < 0.001). The injury-risk model projects a reduction in estimated fatal head injury probability from 66.2% (Monte Carlo mean) (no detection, 50 km/h full-speed impact) to 0.7% under AFODS worst-case night/rain conditions, and to ≈0% under clear daytime simulation conditions. Conclusions: A 73.3 percentage-point classification gap places pedestrians lying on the road outside the effective detection envelope of current ADAS, compounded by the systematic exclusion of non-upright postures from regulatory test protocols and benchmark datasets. AFODS supports proof-of-concept feasibility under simulation conditions. Three translational steps are required: prototype validation on real-world hardware using instrumented Anthropomorphic Test Devices (ATDs); prone-posture biomechanical injury modelling using HIC and BrIC criteria; and regulatory extension of pedestrian AEB test standards to non-upright scenarios. Full article

Vulnerable Road Users (VRUs) are involved in a significant proportion of traffic fatalities, and they are highly exposed to severe injuries in urban traffic environments. For detecting and tracking VRUs, LiDAR technology offers precise 3D perception capabilities, overcoming challenges posed by their small size, dynamic behavior, and frequent presence in occluded or congested areas. This work aims to conduct a scoping review of LiDAR-based solutions for preventing and reducing accidents involving VRUs, synthesizing current methodologies, evaluating detection and tracking approaches, and identifying strategies to improve urban safety through data-driven interventions. An analysis of 49 publications indicates that effective monitoring of VRUs depends on a strategic balance between technological performance and practical limitations, such as system costs, calibration complexity, and hardware constraints. Privacy-preserving techniques, such as anonymization and LiDAR-based sensing, are essential to enable ethically responsible large-scale data collection. Full article

This paper presents a wearable system based on low-cost inertial sensors for the continuous monitoring of driver motion and behavior under controlled urban driving conditions. The system consists of distributed wearable units placed on the head, neck, and torso, each equipped with an inertial measurement unit (IMU) that measures linear acceleration and angular velocity. The acquired data are processed in real time to characterize the driver’s kinematic response during vehicle operation. The main contribution of this work is the definition of a novel Driving Aggressiveness Index (DAI) for quantitative driving style assessment. The proposed index integrates motion-derived features based on acceleration and angular velocity and combines information from multiple body segments through a normalization and weighting strategy, enabling a compact and interpretable representation of driver behavior. Experimental validation was conducted in an urban driving scenario under controlled traffic-free conditions, including typical maneuvers such as straight driving, braking, roundabout navigation, lane changes, and yielding, performed under both normal and aggressive driving styles. The results demonstrate that the monitoring system captures distinct kinematic patterns and that the proposed index provides a clear and consistent separation between driving behaviors. A data-driven threshold is also defined, enabling the quantitative classification of driving styles. Overall, the proposed approach offers an interpretable, scalable, and real-time solution for driver monitoring, with potential applications in road safety and sustainable mobility. Full article

Driver drowsiness detection has become an important application of sensor-based monitoring systems aimed at improving road safety. This review focuses on sensing technologies and physiological parameters used for real-time drowsiness detection in drivers. The surveyed approaches are categorized into physiological sensing methods, including electroencephalography (EEG), electrocardiography (ECG), galvanic skin response (GSR), and photoplethysmography (PPG), and mechanical sensing methods, including respiration rate, eye blinking, head movement, yawning, and steering wheel gripping force. Each method is analyzed from a sensor system perspective, considering signal acquisition principles, measurement location, and practical deployment constraints. In addition, the reviewed techniques are evaluated based on real-time capability, level of sensor attachment, cost, restriction of user movement, and suitability for standalone operation. The comparison highlights that mechanical sensing approaches provide non-invasive and cost-effective solutions; however, they are sensitive to environmental noise and behavioral variability. In contrast, physiological sensing methods offer more direct and earlier indicators of fatigue-related changes in biosignals, although they typically require wearable or contact-based sensors and more complex acquisition systems. The review further indicates that multimodal sensor fusion is increasingly being adopted to improve robustness and reliability in real-world driving conditions. Overall, this work provides a structured overview of sensing modalities and highlights key considerations for designing efficient, real-time driver monitoring systems. Full article

Managing surface subsidence caused by underground mining beneath critical infrastructure requires highly efficient and robust optimization models. This study presents a data-driven, multi-objective framework for the collaborative safety optimization of mine stopes and overlying roads, applied to a quartzite mine beneath a secondary highway in Yunnan Province. Based on 88 FLAC3D simulation samples, an XGBoost surrogate model was developed to predict geomechanical responses (Z-displacement and extraction volume), while a Bayesian-optimized Long Short-Term Memory (BO-LSTM) network was employed to forecast ore price signals. To ensure model generalizability and mitigate overfitting in the limited stope dataset, a 5-fold cross-validation (5-fold CV protocol was systematically incorporated into the model training process. Critically, the CV-supported predictive models underwent stress testing under varying training data sizes (40–90%), Gaussian noise intensities (1–13%), and outlier distributions (5–20% proportion; 10–50%amplitude) to define the boundaries of algorithmic reliability. The NSGA-II algorithm was used to map the Pareto-optimal frontier, which was deployed via a Tkinter graphical user interface for dynamic stope geometry adjustments driven by price fluctuations. Secondary FLAC3D validation of the recommended parameters (21.44 m pillar, 1.60 m sidewall) yielded minimal relative errors of 5.07% for displacement and 2.38% for extraction volume. This validated framework demonstrates numerical robustness while balancing geotechnical safety and dynamic market rewards. Full article

Accidents and vehicle damage caused by irregular road surfaces, such as potholes and cracks, remain a significant challenge in autonomous driving, particularly in terms of safety and trajectory reliability. Existing approaches often treat perception and control as separate processes, limiting their ability to respond effectively to road-surface anomalies in real time. In the proposed work, a unified framework for road-surface anomaly-aware control that integrates 3D point cloud perception with a Linear Parameter-Varying Model Predictive Controller (LPV-MPC) is presented. The proposed approach utilizes onboard sensors to capture detailed geometric information of the road surface and detect anomalies relevant to vehicle motion. The detected anomalies are represented in a control-oriented form and incorporated into the LPV-MPC framework, enabling adaptive trajectory planning and speed regulation. This integration allows the controller to proactively adjust vehicle behavior in response to surface irregularities, improving both safety and tracking performance. Experimental results demonstrate that the proposed method enhances robustness against road disturbances and improves trajectory tracking compared to conventional control approaches without anomaly awareness. These results highlight the effectiveness of tightly coupling perception and control for reliable autonomous driving in real-world conditions. Full article

As a complex structural form capable of simultaneously bearing upper-deck highway traffic, lower-deck highway traffic, and rail transit, the curved chord stiffened double-deck continuous steel truss bridge is distinct from traditional single-deck bridges. The spatial superposition of multiple traffic types within this structure may result in multiple components approaching their critical states concurrently. Despite prior research efforts on this structural type, the failure evolution process from local yielding to global collapse under mixed traffic loads remains ambiguous. This study addresses these questions through systematic numerical investigation of a nine-span bridge with a 300 m main span. A two-stage analytical approach is employed: a Midas/Civil analysis first identifies critically stressed regions, then ABAQUS multi-scale modeling enables refined analysis of critical components while maintaining computational efficiency. Twenty-nine combined traffic loading cases encompassing dual- and triple-category configurations are systematically analyzed. The results show that the ultimate load-carrying capacity coefficients range from approximately 7 to 18, with a minimum of 7.137, and the dual-level highway combinations exert greater influence than road–rail combinations. More importantly, three failure path convergence characteristics were discovered. First, the initial failure position under each working condition tends to be consistent, initiating at the lower chord near the top of the mid-span pier, which confirms that inherent structural defects exist at this location. Second, the gusset plate at the top of pier W6 appears as the second failure location in 48% of cases and ranks within the first four locations across all cases. Third, path similarity progressively increases with traffic diversity. Additionally, Q370qE steel exhibits 5–22% stress exceedance with variable critical locations depending on traffic conditions. Based on these convergence characteristics, a safety monitoring scheme is proposed: monitoring points need to be arranged symmetrically on both sides of the bridge on the top chords, bottom chords, web members, and wedge plates near the tops of the piers. Full article

High-speed train aerodynamics have mainly been improved by passive design methods, such as streamlined noses, local fairings, and surface smoothing. These methods have achieved clear benefits, but several important aerodynamic problems remain difficult to solve by geometry optimization alone. Open-air drag is still affected by tail flow separation, base-pressure recovery, and disturbances around bogies and the underbody; crosswind safety is influenced by unsteady leeward-side separation and wake asymmetry; slipstream behavior depends on wake vortices, boundary-layer development, and complex near-ground underbody flow; and tunnel-related pressure transients arise from compression-wave generation, propagation, and reflection. These coupled effects mean that one fixed train shape cannot perform optimally in all operating conditions. For this reason, this review proposes that active flow control (AFC) should not be regarded only as a drag-reduction or stability-improvement technique for high-speed trains. Instead, it should be understood as a mission-adaptive aerodynamic control framework, in which different control actions are used for different operating scenarios. This paper first clarifies that passive optimization is increasingly subject to diminishing returns under multi-objective and engineering constraints. It then reviews AFC studies on drag reduction, base-pressure recovery, wake and slipstream control, underbody flow conditioning, crosswind mitigation, and tunnel pressure-wave suppression. Related AFC studies on bluff bodies, road vehicles, and other separated flows are included only when their physical relevance to trains is clear. The review further distinguishes gross aerodynamic improvement from net energy gain and identifies actuator power, durability, maintainability, acoustic impact, validation level, and full-scale transferability as decisive feasibility factors. Current research is still dominated by open-loop numerical studies with simplified actuation. Future work should therefore move toward multi-objective, closed-loop, energy-aware, sensor–actuator-integrated, and explainable machine-learning-assisted AFC. The main message is that the next step in train aerodynamics is not simply a better fixed shape, but a control-enabled train that can selectively redistribute aerodynamic authority across its mission profile. Full article

Deep learning models are widely used in autonomous vehicle systems for perception, localization, and decision-making. However, their lack of transparency poses significant challenges in safety-critical environments. This systematic review presents a unified mathematical framework for explainable deep learning which integrates multimodal inputs, graph-theoretic road geometry, uncertainty modeling, and intrinsically interpretable representations. Road-structured priors that include lane topology and spatial constraints are incorporated into learning and optimization processes for ensuring model predictions and explanations to remain physically and semantically grounded. The review synthesizes methods across saliency-based, concept-based, causal, and intrinsic explainability, and extends them to vision-language models. This enables language-grounded, human-interpretable reasoning in autonomous vehicle systems. While vision-language models offer a new paradigm for semantic explainability, their limitations such as hallucinations, misgrounding, and reduced reliability under distribution shifts are also critically examined. Along with the role of road priors in improving alignment and robustness, another key contribution of this work is its quantitative evaluation metrics for road-aware explainability. These evaluation metrics link the explanations to spatial consistency, uncertainty alignment, and graph-structured reasoning. The overall framework connects latent representations, predictions, and explanations within a single formulation, enabling systematic comparison and analysis across models. Based on a PRISMA-guided review of 164 studies, this research identifies gaps in real-world reliability, temporal reasoning, and standardized evaluation, and outlines future directions including human-in-the-loop systems, regulatory readiness, and language-based auditing. Overall, this study advances a mathematically grounded and road-aware perspective on explainable vehicle AI which significantly bridges the gap between high-performance models and transparent, trustworthy autonomous systems. Full article

Traffic accidents pose a persistent challenge for urban mobility, public safety, and sustainable development in smart cities, particularly in rapidly growing urban environments. This study presents a data-driven spatiotemporal analysis of traffic accidents in Ecuador, aimed at supporting evidence-based urban traffic management and road safety planning. Using large-scale historical accident records, the proposed approach combines spatial clustering and temporal forecasting techniques to characterize accident concentration patterns and temporal dynamics at national and metropolitan scales. Spatial accident hotspots are identified using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling the detection of high-risk zones without imposing assumptions on cluster shape or size. This analysis reveals strong spatial concentration of accidents, with a limited number of clusters accounting for a substantial proportion of fatalities and injuries. Complementary temporal analysis is conducted using a multi-model ensemble framework to examine accident trends and seasonal patterns. This approach integrates SARIMA for linear stochastic modeling and Prophet for additive trend analysis, alongside two Long Short-Term Memory (LSTM) architectures: a direct 12-month vector output and a recursive horizon-3 model. By synthesizing these statistical and neural network-based methods through inverse-RMSE weighting, the study captures both stable seasonal cycles and non-linear, short-to-medium-term variations in accident frequency. Results show that traffic accidents in Ecuador exhibit stable diurnal and seasonal structures, alongside pronounced spatial heterogeneity across urban regions. The combined spatial and temporal insights provide a coherent representation of accident risk patterns, facilitating the prioritization of critical zones and high-risk periods. The resulting hotspot maps and multi-model forecasting horizons offer actionable information for smart city stakeholders, supporting targeted infrastructure interventions, adaptive enforcement strategies, and data-informed urban mobility policies. This work contributes to the broader understanding of traffic safety analytics as a core component of smart city decision-support systems. 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

The main target of street and road lighting is to ensure the safety and the well-being of pedestrians and drivers. Regulations and standards on lighting installations establish minimum photometric requirements to achieve it. Thus, the main parameters concern the average luminance or illuminance, overall uniformity, longitudinal uniformity, threshold increment, edge illuminance ratio, and minimum energy efficiency or its equivalent. Although they have well-defined minimum and maximum values, their compliance, especially in urban and peri-urban environments, strongly depends on the heterogeneous characteristics of the street and its surroundings, depends on human physiological and psychological aspects, and/or faces remarkable uncertainties and problems of definition. The coefficient of utilization, C_u, and energy efficiency, ε, are key quantifiers taking account of the installation capability to provide luminous flux on the visual work plane with respect to the flux emitted and the power consumed by the light sources, respectively. However, contradictions between accurate values of these coefficients and the real visual performance of people or the rational use of energy are frequent. This is a problem because the binomial safety–sustainability requires consideration of these parameters in the design to enhance pedestrian and driver safety, as well as energy efficiency for sustainability. This work highlights the uncertainties and limitations of C_u and ε, redefines them through a human-centered approach that opens new perspectives on other parameters and quantities involved in lighting, and optimizes the binomial safety–sustainability. 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