Search Results (707)

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

Road traffic accidents represent a critical road safety issue, the severity of which depends on the complex interplay of multiple factors. This issue directly impacts Target 3.6 of Sustainable Development Goal (SDG) 3, which aims to halve global deaths and injuries by 2030, and SDG 11, which focuses on safe and sustainable transport systems. The study of these factors and their interrelationships is important in the scientific literature. The objective of this study is to analyze the factors that determine the severity of road traffic accidents, identifying the most important ones and their correlations. A dataset containing variables such as infrastructure, location, time, and vehicle type, among others, was used to predict severity, applying Association Rules to identify latent correlations and the Classification and Regression Tree for hierarchical risk classification. The results reveal that the type of collision is the primary predictor of severity; the highest severity is associated with heavy traffic and head-on or side-impact collisions, involving critical scenarios, in the early morning hours and in rural areas, linked to trucks. The combined use of both tools provides a scientific basis for designing interventions on highly vulnerable road segments, contributing to the fulfillment of the 2030 Agenda for safe mobility. Full article

Crash severity prediction is critical for emergency response, infrastructure spending, and risk communication. Although machine learning has been widely applied to this problem, three gaps prevent practical deployment: uncalibrated probability scores, SHAP-based explanations whose faithfulness has not been verified, and models never tested outside their training jurisdiction. The proposed framework, SAE-XCrash (Safety-Aware and Explainable Crash Severity Prediction), addresses all three using two public datasets—US-Accidents (7.0 million records, 2016–2023) and UK STATS19 (approximately 1,010,000 records, 2016–2022)—with strict temporal splits throughout. Notably, the US-Accidents severity label measures traffic disruption duration, not injury outcome; results should be interpreted accordingly. Previously unknown label-schema drift led to a revised binary target with Severity 4 as the only positive class. Five classifiers are compared. Post hoc isotonic calibration reduces Expected Calibration Error by 97.3% at negligible discrimination cost. A four-step quantitative SHAP audit confirms statistically significant deletion faithfulness; however, explanation stability fails at realistic perturbation levels (54.3% low-stability fraction at sigma = 0.05), driven by spatial data sparsity in sparse geohash cells—a negative result that carries direct operational implications for deployment. A three-tier cross-dataset transfer experiment (zero-shot, recalibration, full retrain) shows that temporal features transfer robustly across jurisdictions, while spatial memorization is the primary generalization barrier. All code, split indices, and model artifacts are publicly available. Full article

Background/Objectives: Traumatic duodenal diverticulum perforation is a rare, potentially fatal consequence of blunt trauma. Nonspecific symptoms and diagnostic challenges often delay recognition. This systematic review characterizes its clinical features, management, and outcomes. Methods: A systematic literature search covering 1960 to December 2025 identified eligible cases. Inclusion criteria were blunt trauma-related duodenal diverticulum perforations confirmed by imaging or surgery. Data were analyzed according to PRISMA 2020 guidelines. Results: Twenty-one cases were identified (mean age 62.9 years; sex ratio (M:F) 8:13). Primary injury mechanisms were traffic accidents (10 of 21) and falls from height (8 of 21). Most injuries involved the descending duodenum (D2; 17 of 21). Common presenting signs included abdominal pain (19 of 21) and epigastric tenderness (16 of 21). Computed tomography confirmed findings consistent with perforation in all scanned patients (17 of 17). Surgical management was employed in 20 of 21 patients, predominantly via manual (11 of 20) or stapled (9 of 20) diverticulectomy, with drainage applied in 18 of 20 operated cases. Complications occurred in 13 of 21 patients. Overall mortality was 4 of 21. Conclusions: Traumatic duodenal diverticulum perforation remains a life-threatening event requiring high clinical vigilance. The data collected suggest that early CT scanning and prompt surgical intervention may be associated with better treatment outcomes, although these conclusions should be treated with caution due to the small sample size. The protocol was registered in PROSPERO (CRD420261285658). Full article

Taxi drivers are a group with high driving exposure and are involved in a significant number of urban traffic casualties. Using two modelling approaches, this study examines whether the intention to speed, as measured by the Theory of Planned Behaviour (TPB), can better fit a crash frequency model than errors or lapses as measured by the Driving Behaviour Questionnaire (DBQ). Data from 1000 drivers in Tehran was collected through questionnaires. The crash prediction model included a cross-sectional model using negative binomial (NB) regression methods and a tree regression model from a previous study. In the last three years, the drivers had been involved in 544 road crashes, and of those, 42 resulted in serious injuries. Due to the rare and random nature of crashes, the empirical Bayesian (EB) method was used for model testing. Comparing AIC and BIC showed that zero-inflated NB (ZINB) models performed better. The final selected model was the intention-based ZINB model without the age variable. The coefficients for intention, exposure, and driver experience were 0.205, 0.103, and −0.443, respectively. The high EB coefficients indicated strong reliance on predicted crash values. The conclusion is that road crashes are closely related to taxi drivers’ intention to speed rather than errors and lapses. This indicates that it can be described as a traffic violation, rather than a mistake. Therefore, significant efforts are required to increase compliance with speed limits and reduce road crashes. Further education and high-quality campaigns are essential elements to achieve this goal. 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

Road safety is a prerequisite for sustainable mobility, yet cyclists remain disproportionately exposed to severe outcomes in mixed traffic. Using police-reported bicycle–vehicle crashes from the national SEWIK registry in Poland (152,567 cyclist-involved records; 2015–2024), this study modeled five ordered injury-severity classes with a CatBoost gradient-boosting classifier, evaluated performance with quadratic weighted kappa and complementary class-sensitive metrics under extreme imbalance (including benchmark comparisons and calendar-based walk-forward stress tests), and interpreted predictions with SHAP to summarize transparent, feature-level association patterns. The results indicate modest overall ordinal discrimination (hold-out QWK ≈ 0.20), while highlighting elevated recall for rare fatal outcomes together with low precision, implying a substantial false-positive trade-off if outputs were used as deterministic classifiers. Global and local explanations point to stronger associations for cyclist age, shorter offender licensure tenure (a registry proxy for experience-related factors), regional context, and built-up versus non-built-up settings consistent with higher kinetic-energy environments; these variables should be interpreted cautiously because registry data are observational and omit key exposures (e.g., measured impact speed and cycling volume). Overall, the study contributes a nationwide, explainable severity-profiling workflow for prioritizing cyclist protection: combining benchmarked ML, multi-metric reporting, and XAI diagnostics can support monitoring and evaluation of speed management, infrastructure, and licensing-system improvements—without overstating causal effects from administrative records alone. Full article

Traumatic brain injury (TBI), mainly caused by road traffic accidents, is a serious global public health concern. Computed tomography (CT) is the best way to detect intracranial haemorrhage (ICH), but it is not always feasible because it is hard to access, exposes people to radiation, and is expensive, especially in low- and middle-income countries. Portable near-infrared spectroscopy (NIRS) devices offer a non-invasive, point-of-care option for early detection of ICH. The objectives of this study were to estimate the cost per case detected for patients with mild-to-moderate TBI, to estimate incremental cost and to perform a budget impact analysis to assess the financial feasibility of implementing this technology. This study employed a decision tree model from a health system perspective to calculate the cost per detected case and the incremental cost of NIRS across three tiers of care: ambulances, community health centres (CHCs), and tertiary hospitals. The cost per mild-to-moderate TBI case found was Rs. 2177.90 in ambulances, Rs. 748.09 in CHCs, and Rs. 628.14 in tertiary hospitals. The extra cost per patient was Rs. 984.15, Rs. 360.90, and Rs. 289.78, respectively. At the system level, NIRS raised the total costs for 264 ambulance patients from Rs. 37.71 lakh to Rs. 40.31 lakh and for 858 CHC patients from Rs. 115.64 lakh to Rs. 118.73 lakh. National extrapolation indicates a first-year budgetary impact of approximately Rs. 442 crores for ambulances and Rs. 187 crores for CHCs. These results support the strategic, phased implementation of NIRS to use resources better and improve early diagnosis of TBI. Full article

Road Traffic Accidents (RTAs) represent a significant public safety issue in rapidly urbanising nations, resulting in considerable fatalities, injuries, and economic losses. This research investigates the spatio-temporal distribution and hotspot dynamics of RTAs in Siliguri City, India, a principal transnational transport corridor connecting northeastern India with adjacent countries. A geocoded dataset comprising RTA incidents from 2021 to 2023 was analysed using integrated GIS-based machine learning and statistical methods. Temporal clusters were identified through Kulldorff’s purely temporal scan statistics, while Kernel Density Estimation (KDE) quantified accident density during morning peak, midday/off-peak, evening peak, and lean/night-time intervals. Spatial clustering was further assessed using LISA-Moran’s I, purely spatial scan statistics, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Emerging Hotspot Analysis (EHA) was employed to detect evolving hotspot patterns over time. The findings indicate that major accident hotspots are concentrated at key intersections and transport corridors, such as Hill Cart Road, Darjeeling More, Sevoke Road, Eastern Bypass, and Burdwan Road. Moran’s I (0.157; p = 0.007) demonstrates significant but moderate spatial autocorrelation, and spatial scan statistics identified three principal high-risk zones. HDBSCAN classified 81.90% of incidents within clustered areas. Lean/night-time periods exhibited the highest accident densities, reaching 14.21 accidents/km² at critical intersections. These results underscore the utility of integrating GIS and machine learning techniques for urban traffic safety planning and hotspot-focused intervention strategies. Full article

Road traffic crashes remain a global public health burden and a persistent resource allocation problem that undermines progress toward the sustainable development of safe, equitable mobility systems. Saudi Arabia’s Vision 2030 targets fewer than 10 fatalities per 100,000 population, a goal aligned with United Nations Sustainable Development Goal 3.6 (halving road traffic deaths) and SDG 11.2 (safe and sustainable transport), yet a gap persists between crash prediction research and how agencies deploy enforcement resources. This paper builds a closed-loop predict–optimize–evaluate framework connecting Long Short-Term Memory (LSTM) neural networks to a goal-distance gap metric and constrained optimization, feeding forecast outputs directly into enforcement scheduling decisions. Using monthly casualty data from official Saudi sources covering the entire kingdom (all 13 administrative regions) from 2010 through 2024 (N = 42,856 fatal and serious injuries across 180 monthly observations), we validate LSTM forecasting against five benchmarks plus a GRU and a Transformer baseline, apply gap analysis as a standardized goal-distance metric, optimize enforcement allocation with triangulated elasticity estimates, and evaluate past policy reforms through multi-method counterfactual analysis. A headline finding is that roughly 28% of fatal and serious injuries cluster within only about 6% of weekly hours, creating an unusually concentrated target for enforcement reallocation. The LSTM achieves RMSE = 2.47 with MASE = 0.83, beating ARIMA by 35% while maintaining robustness during COVID disruptions (RMSE = 2.38 in the post-acute period 2022–2024 versus 2.61 in the acute period 2020–2021). Temporal analysis confirms 28% of fatalities (95% CI: 26.0–30.0%) cluster within 6% of weekly hours. Enforcement elasticity triangulated from three independent sources converges at α ≈ 0.31 (90% CI: 0.25–0.40). The optimization model allocates 56% of enforcement resources to Thursday–Friday midnight-to-4 AM windows, projecting a 17.1% casualty reduction (90% CI: 13.5–20.6% under Monte Carlo uncertainty in α). Monte Carlo sensitivity analysis with 10,000 iterations confirms a median benefit-cost ratio of 1.88 (90% CI: 1.18–2.97), with P (BCR > 1.0) = 98.9%, using locally calibrated VSL = SAR 4.2 million (equivalent to approximately USD 1.12 million at the SAMA-pegged rate of 3.75 SAR/USD, in constant 2024 prices). Counterfactual evaluation finds that the post-2018-reform period was associated with a 22.1% casualty reduction (95% CI: 16.4–27.8%), with magnitude robust across four methods (LSTM counterfactual, Bayesian Structural Time-Series, Synthetic Control, and an inverse-variance-weighted synthesis of the three); we stress, however, that attribution to the driving reform itself cannot be cleanly separated from concurrent Saher camera expansion, public awareness campaigns, and trauma-care improvements. By translating prediction into evidence-based, resource-efficient enforcement, the framework supports sustainable road safety policy in middle-income and rapidly motorizing settings. Full article

Road intersection crashes remain a major contributor to injuries due to complex conflict patterns and multimodal interactions. Among the factors influencing intersection safety, inadequate intersection sight distance (ISD) attributed to roadside sight obstructions can limit drivers’ ability to respond to conflicting movements, potentially affecting crash injury outcomes. Despite its importance, visual obstruction has rarely been examined as a distinct context in traffic crash injury severity modeling. This study investigates crash injury severity at visually obstructed intersections using an ordinal probit modeling framework applied to 951 intersection crashes documented with sight obstruction as a contributing factor in Wyoming over the period 2014 through 2023. Crash data were analyzed to identify the effects of driver behavior, vehicle characteristics, roadway geometry, environmental conditions, and traffic control on ordered injury severity outcomes ranging from property damage only (PDO) to fatal and serious injury. Nonparametric bootstrap resampling with 1000 iterations was employed to assess parameter stability and construct empirical confidence intervals. Average marginal effects were estimated to quantify the change in probability of each injury severity level associated with key predictors. The results indicate that alcohol involvement produces the largest severity shift, reducing the probability of PDO outcomes by 51.2 percentage points while increasing the probability of fatal and serious injury by 34.2 percentage points. Hillcrest grade locations increase fatal and serious injury risk by 14.4 percentage points, while adverse road surface conditions, including snowy, icy, and wet pavements, consistently reduce fatal and serious injury probability by 12.5 to 15.1 percentage points, reflecting behavioral adaptation to visually salient hazard cues. Bootstrap validation confirms strong parameter stability across all estimates, with 94% of parameters showing bootstrap standard errors within 25% of their asymptotic counterparts. By formally establishing visually obstructed intersections as a dedicated severity modeling context and integrating systematic bootstrap validation, this study contributes both substantive and methodological insights to support evidence-based prioritization of intersection safety improvements. Full article

Background/Objectives: The purpose of this study was to analyze the demographics and injury patterns of children with transportation-related non-fatal injuries occurring on public roads, streets and highways using a nationwide emergency department (ED) database. Methods: Data from the National Electronic Injury Surveillance System (NEISS) All Injury Program (AIP) 2005–2021 was used. Five transportation methods (motor vehicle occupant, bicyclist, pedestrian, motorcyclist, other) occurring on a public highway, street, or road were analyzed. Statistical analyses were performed with SUDAAN 11.0.01™ software to obtain national estimates. Results: There were an estimated 8,188,810 ED visits for traffic-related injuries in children; the median age is 14.3 years. Sex distribution was equal; 93.4% were discharged from the ED, and the head/neck was the most injured area (51.9%). The most common diagnoses were contusion (35.7%), strain/sprain (28.0%), internal organ injuries (13.3%), fracture (8.4%), lacerations (7.4%) and concussions (4.1%). Predictor variable of not being discharged from the ED was the presence of a fracture (OR = 119.7 [71.3, 200.7], p < 0.0001), injury to the trunk (OR = 3.2 [2.7, 3.8], p < 0.0001), a pedestrian (OR = 3.9 [2.8, 5.3], p < 0.0001), those <1.5 years old (OR = 4.3 [2.8, 6.6], p < 0.001), and males (OR 1.5 [1.4, 1.6], p < 0.0001). The greatest prevalence of head/neck fractures was in motor vehicle occupants (23.3%), upper extremity fractures in bicyclists (73.1%) and motorcyclists (49.2%), and lower extremity fractures in pedestrians (56.6%). Conclusions: This detailed study can be used to compare/contrast these injuries to other countries regarding road traffic injuries in children. This data can be used to assess the outcomes of prevention strategies introduced in the future. Full article

Electric vehicles introduce new considerations for pedestrian safety because their lower operating noise at low speeds may reduce pedestrian detectability in urban traffic environments. This study proposes a simulation-based integrated active–passive pedestrian protection framework for electric vehicles by linking automatic emergency braking, active hood deployment, and post-crash head injury assessment. A total of 688 valid pedestrian–vehicle crash records from the National Highway Traffic Safety Administration database were analyzed, and 5 representative pedestrian crash scenarios were constructed through clustering-informed scenario screening and a benchmark pedestrian AEB scenario. The scenarios were reconstructed in a PreScan–Simulink co-simulation environment to evaluate a time-to-collision-based AEB strategy, while the active hood system was assessed using multi-body dynamics simulation and finite element head impact analysis. The AEB results showed that three scenarios were avoided before pedestrian contact, whereas two remained unavoidable, with residual impact speeds of approximately 31.5 km/h and 46 km/h. The hood reached a stable deployed posture within approximately 0.1 s under the modeled conditions. The HIC₁₅ results at eight selected impact points showed that speed reduction and hood deployment generally reduced head injury metrics, but full compliance with the reference HIC₁₅ threshold of 1000 was not achieved at all points. These findings suggest that the proposed strategy can improve simulated pedestrian head protection performance under selected electric vehicle crash scenarios, while further structural optimization, experimental validation, and cost–benefit assessments are still required. Full article

Alcohol and psychoactive substance use represent a major burden for global public health, increasing the risk of non-communicable diseases, violence, road traffic injuries, dependence, and mental disorders, and generating impacts on productivity and social welfare. This study aimed to estimate the burden of disease attributable to alcohol and other psychoactive substances in the departments of Colombia from 2016 to 2022. A burden-of-disease study was conducted using the Disability-Adjusted Life Years (DALYs) indicator, following the methodology of the World Health Organization Global Health Estimates. Official morbidity and mortality databases were used. An estimated 236,154.42 DALYs were attributable to alcohol and psychoactive substance use in Colombia during the study period, increasing from 14,158.7 DALYs in 2016 to 40,190.7 DALYs in 2022. The burden was heterogeneous across departments, with values above 1000 DALYs in Quindío (1779.5), Nariño (1624.3), and Norte de Santander (1008.0) and below 132 DALYs in La Guajira, Casanare, and Vaupés. Men accounted for 73.5% of total DALYs. The mean age of morbidity records associated with alcohol and psychoactive substance use disorders was 30.67 years in men and 32.37 years in women. The burden associated with psychoactive substance use is increasing in Colombia, with differences by sex and department of residence. 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