Search Results (210)

Highway–rail grade crossing (HRGC) incidents remain a persistent safety concern due to repeated interactions between roadway users and rail operations under varying environmental and operational conditions. Existing studies rely on raw incident counts or partial exposure measures that can be influenced by differences in infrastructure exposure and do not account for spatial dependence, limiting consistent comparison across locations. This study developed an exposure-normalized framework to model incident intensity at the county level using accumulated incidents per crossing (AIPC), which normalizes cumulative incidents by crossing exposure. The analysis integrated statistical distribution modeling, spatial clustering, and supervised machine learning. The study combined county-level HRGC data for the contiguous United States from 1975 to 2025 with infrastructure, traffic, environmental, and accessibility variables. Results showed that AIPC was consistent with a gamma distribution, indicating a continuous representation of incident intensity without discrete risk regimes. Local Moran’s I identified statistically significant high-intensity clusters in specific regions, confirming spatial dependence in incident intensity. Machine learning models achieved strong predictive performance, with the extra trees model reaching AUC = 0.907 (F1 = 0.528) and ensemble methods consistently outperforming linear and kernel approaches. SHAP and permutation-based feature importance analysis identified temperature, train frequency, and accessibility measures as the most influential predictors, while aggregate density measures contributed the least. The results provided consistent evidence that incident intensity was associated with environmental conditions, operational exposure, and network structure. The proposed framework supports exposure-based risk assessment and enables identification of high-intensity counties for targeted intervention. This approach provides a transparent and transferable method for improving HRGC safety analysis and prioritizing resource allocation across large geographic areas. Full article

Road safety is a global priority, and driver behavioral factors are among its most critical determinants. Although the literature has advanced in characterizing driving styles using psychometric instruments such as the Multidimensional Driving Style Inventory (MDSI), an empirical gap persists in the simultaneous integration of latent behavioral profiles, driving exposure, and road safety outcomes, particularly in Latin American contexts and across generational extremes. This study examined the relationship between latent driving style profiles and road safety outcomes among young (18–25 years) and older (≥65 years) licensed drivers in Ecuador, while evaluating the moderating role of driving exposure. A structured survey based on the MDSI was administered to 833 active drivers, and data were analyzed using Latent Profile Analysis (LPA) and binary logistic regression. The six-profile solution was selected according to the Bayesian Information Criterion (BIC = 11,655.07), with acceptable classification quality (entropy = 0.860; minimum posterior probability = 0.808); for inferential parsimony, these profiles were subsequently consolidated into three analytically interpretable categories: Predominantly Careful, Predominantly Risky, and Distress-Reduction. The Predominantly Risky profile was significantly associated with higher odds of traffic accident involvement (OR = 2.76, 95% CI [1.55, 4.93]), whereas the Distress-Reduction profile showed substantially higher odds of receiving traffic infraction fines (OR = 4.74, 95% CI [1.69, 13.34]). The composite driving exposure index was a robust predictor across both models (accident model: OR = 2.82, 95% CI [1.60, 5.14]; fine model: OR = 1.87, 95% CI [1.29, 2.74]). In addition, a significant interaction was observed between the Predominantly Risky profile and driving exposure in the model predicting traffic infraction fines, suggesting that exposure amplified sanction risk within this behavioral category. Older drivers showed a substantially higher representation of the Distress-Reduction profile than young drivers. These findings underscore the utility of person-centered approaches for identifying heterogeneous driver configurations and for designing profile-differentiated road safety interventions; from a practical perspective, these results support the development of targeted road safety programs that integrate behavioral profile screening with exposure-based risk management for young and older drivers. 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 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

Identifying and optimizing core factor configurations for anchorage operational safety under typhoon scenarios is critical to enhancing anchorage operational resilience and sustainable port development. This study develops a complementary hybrid SEM–fsQCA framework: key factors are identified via literature review and expert interviews; SEM quantifies factor correlations and contribution weights and corrects expert-evaluated anchorage capacity; six core factors are extracted, three typhoon types (heavy-rainfall, strong-wind, complex-track) are defined, and a coupled anchorage–typhoon case dataset is constructed. Subsequently, fsQCA performs necessary condition analysis and identifies causal configurations driving safety effectiveness. Based on these configurations, we establish a dynamic three-tier risk classification framework for refined anchorage management. Validated using 36 coupled cases (12 anchorages × 3 typhoon types) from Huizhou Port, a core hub in the Guangdong–Hong Kong–Macao Greater Bay Area, this framework enables adaptive vessel traffic scheduling throughout the entire typhoon cycle through dynamic tiered management. The proposed “identification-intervention-feedback” closed-loop governance model delivers theoretical rigor and operational implementation ability for coastal port typhoon risk mitigation. Full article

While multilane roundabouts follow geometric design standards, they often overlook motorcycle-dominated traffic behavior. This study evaluates lane-reduction strategies to create safer and more inclusive urban corridors in mixed-traffic conditions, focusing on a case study in Southern Thailand. High-resolution unmanned aerial vehicle (UAV) trajectory data were analyzed using the Macroscopic Fundamental Diagram (MFD), Cell Transmission Model (CTM), and Time-To-Collision (TTC) frameworks under three configurations: full lane availability, partial inner-lane closure, and full inner-lane closure. Results indicate progressive deterioration in performance under restricted-lane conditions. Under full closure, total flow decreased by 31%, and average travel time increased by 43%. The MFD curve shifted toward higher critical densities, indicating earlier congestion onset, while CTM results revealed longer discharge times, queue spillback, and increased merging friction. Conversely, safety outcomes (TTC) improved significantly: extreme rear-end conflicts were reduced by 48%, and severe lane-change conflicts were nearly eliminated (99%). Behavioral evidence suggests that full closure constrains motorcycles to a single circulating path, reducing erratic filtering and promoting more stable interactions. Overall, this study identifies a systemic trade-off between safety and efficiency, highlighting how geometric interventions catalyze behavioral adaptation. The findings highlight how geometric constraints shape collective behavior in motorcycle-dominated roundabouts and demonstrate the value of an integrated UAV-based framework as a vital tool for inclusive urban management, providing the granular data needed to balance safety and mobility in complex traffic landscapes. 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

Automated Speed Enforcement (ASE), a widely known speed management strategy, extends beyond its safety benefits and is shaped by public trust, broader governance, and policy frameworks. This study evaluated public opinions of the ASE program in school zones in Georgia, United States, which has recently undergone multiple policy changes. An online survey was conducted targeting Georgia drivers aged 18 years or older, which gathered 502 responses from a representative sample based on exposure, direct school connections, and sociodemographic factors. Respondents indicated their agreement levels on a Likert scale across multiple statements about ASE and their thoughts on enhancing the program’s transparency, trustworthiness, and fairness. Data analysis was conducted using descriptive statistical techniques and cross-classification. Among all respondents, 71 percent supported the program, and among individuals who had driven through speed-enforced school zones, 81 percent reported that ASE led them to reduce speeds. Issuing the citation to the actual driver at the time of violation, publicizing revenue allocation and utilization, publicizing safety benefits, and clearly posting the speed limits and the hours under evaluation were among the key concerns. These findings highlight the significance of integrating public perceptions into ASE policy, identifying areas needing improvement, and promoting community-endorsed traffic safety interventions. Full article

Despite extensive research on traffic safety, the complex, non-linear spatial discrepancy between risky driving and actual accidents remains a significant challenge to quantify within diverse urban contexts. This study investigates the non-linear relationship between grid-level risky driving patterns and traffic accident occurrence in Daejeon, Korea, examining how these associations vary across different urban contexts. Using data collected from July 2023 to June 2024, the analysis incorporates GPS-based risky driving indicators, including rapid acceleration, deceleration, and sudden maneuvers from general passenger vehicles, thereby overcoming the limitations of previous studies reliant on commercial vehicle data. By adopting an H3-based spatial grid system, the study classifies areas into four quadrants based on median values of risky behaviors and accident counts, further categorizing them into “Matched” and “Mismatched” types to identify spatial discrepancies. Furthermore, the Explainable Artificial Intelligence (XAI) technique is employed to integrate regional variables—including population density, land use, and transport infrastructure—to uncover the key drivers of accident risks. Providing a significant methodological improvement over traditional linear models, the findings demonstrate that identical driving behaviors can yield different safety outcomes depending on local environmental interactions. Specifically, while driver behavioral factors directly explain accident frequency in matched regions, accident risks in mismatched regions are more significantly shaped by spatial environmental factors, such as green spaces and commercial land use, which override direct behavioral impacts. This study provides a robust framework for developing data-driven, region-specific traffic intervention strategies, including context-aware advanced driver assistance systems (ADAS) and spatially tailored traffic calming, to enhance urban safety. Full article

In January 2025, New York City became the first major U.S. city to implement a cordon-based congestion pricing policy via the Central Business District Tolling Program. While the policy’s effects on traffic volume are well-documented, its impact on road safety remains underexplored. This study evaluates the short-term effects of the program on two distinct metrics: total crash counts (frequency) and injury rates (severity, defined as the number of persons injured per 10,000 residents), using a monthly panel dataset of ZIP code-level data from January 2024 to December 2025. We employ a rigorous multi-method causal inference framework—including difference-in-differences, matched difference-in-differences, and generalized synthetic control—to estimate changes in injury rates and total crash counts independently. Across all empirical specifications, we find no statistically significant reduction in either traffic injuries or collisions following the policy’s implementation. Event study analyses confirm a consistent null effect month-over-month, with no transient or sustained safety dividend. Subject to short-term methodological constraints, our findings suggest that congestion pricing functions primarily as a demand management tool; realizing immediate road safety benefits in complex urban grid networks likely requires complementary physical infrastructure interventions. Full article

Advanced Driver Assistance Systems (ADAS) are increasingly shaping urban mobility and road safety, yet their benefits depend not only on technical performance, but also on driver acceptance. This study examines how Hungarian drivers perceive and evaluate key ADAS functions, Adaptive Cruise Control (ACC), Lane Keeping/Centering Assist (LKA/LCA), and Forward Cross Traffic Alert (FCTA), in urban driving contexts. The research is based on qualitative focus group discussions conducted in Győr, Hungary, involving drivers aged 20–50 from different age cohorts. Data were analyzed using thematic analysis. The findings show that the acceptance of ADAS is strongly context-dependent and function specific. ACC was perceived primarily as a comfort-enhancing tool, especially on longer or more monotonous routes, while LCA was often regarded intrusive and less reliable in urban conditions due to poor road markings, potholes, and frequent stop-and-go situations. On the contrary, blind spot and cross-traffic-related functions were evaluated more positively due to their direct safety benefits. Trust, perceived risk, and control emerged as key dimensions of acceptance, with many participants emphasising the importance of warning-based support rather than a strong autonomous intervention. In general, the study concludes that urban acceptance of ADAS is shaped by the interaction of infrastructure conditions, perceived usefulness, and driver trust, highlighting the need for more transparent, context sensitive, and user-centered system design in support of safer urban mobility. Full article

This paper presents a novel system-oriented counterfactual deep learning framework, termed Hybrid Prediction–Intervention Neural Architecture (HPINA) for intelligent traffic accident risk prediction and proactive safety intervention in smart transportation systems. Unlike conventional data-driven models that rely solely on observational correlations, the proposed system integrates multi-domain data fusion, temporal deep representation learning, a continuous spatio-temporal risk field, and a latent-space counterfactual reasoning module within a unified decision-support architecture. The framework enables accurate prediction of traffic accident risk and simulation of “what-if” intervention scenarios to support real-time safety optimization in intelligent transportation environments. By leveraging heterogeneous inputs, including traffic dynamics, environmental conditions, road attributes, and temporal patterns, the system constructs a high-dimensional representation that captures complex nonlinear dependencies and evolving risk propagation across the network. A key innovation lies in the integration of a causal intervention mechanism and policy-guided decision layer, which jointly quantify intervention impact and identify optimal strategies for minimizing risk. The experimental results demonstrate that HPINA achieves a Test F1-score of 0.958 and an AUC of 0.989, outperforming strong baselines by up to 5.0% and 3.4%, while achieving a relative risk reduction of 0.091 and improved convergence stability with a validation loss of 0.042. These findings highlight the effectiveness of the proposed framework as an intelligent, scalable, and deployable system for real-world traffic safety management and smart city applications. Full article

Objectives: Management of unstable pelvic fractures during pregnancy presents a major therapeutic challenge, requiring careful multidisciplinary evaluation to balance maternal benefits and fetal radiation risks. Methods: We report the case of a 32-year-old patient who presented with a pelvic fracture due to a road traffic accident at three months of pregnancy. A left sacroiliac osteosynthesis was performed to treat a left sacroiliac diastasis with pelvic osteosynthesis using a trans-iliosacral approach under cone-beam CT (CBCT) guidance using a very-low-dose protocol. Radiation parameters and fetal dose estimates were calculated in advance in collaboration with a medical physicist. Tight beam collimation, a reduced field of view, and minimization of fluoroscopic checks were applied to keep fetal exposure as low as reasonably achievable. This article aims to demonstrate the feasibility of managing a complex pelvic fracture using interventional radiology and to review the literature on management options and gestational age-dependent fetal risks. Results: The estimated cumulative fetal dose from initial imaging, open surgery, and CBCT-guided osteosynthesis remained below 70 mGy using a pregnant phantom (Duke Organ Dose–Dosewatch–General Electric system), which is below thresholds associated with deterministic effects. The procedure achieved optimal screw positioning with less than 40 s of fluoroscopy. Maternal postoperative recovery was favorable, and follow-up revealed normal fetal development. Conclusions: This case demonstrates that CBCT-guided percutaneous iliosacral screw fixation can be safely performed during pregnancy with meticulous planning, dose-reduction strategies, and multidisciplinary collaboration, maintaining fetal radiation exposure below accepted safety thresholds. 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

Accurately estimating the severity of crash injuries among older drivers is paramount for enhancing traffic safety, a task challenged by class imbalance and label noise. Traditional predictive paradigms often struggle to identify rare severe cases, as they tend to prioritize global accuracy, thereby compromising sensitivity to high-risk outcomes. To overcome these limitations, this study develops a Log-Loss Cleaned and Probability-Calibrated Cascade (L-CSC) framework by strategically integrating existing advanced algorithmic components for robust and reliable severity prediction. Initially, a Log-Loss-based noise filtering mechanism is implemented to purge outliers and ambiguous samples from the training data, thereby enabling higher-quality representation learning. Subsequently, a two-stage cascade architecture is designed to decouple the classification task. Stage I employs a Preliminary Screening Model, optimized via Bayesian optimization for F2-score, to specifically maximize the recall for severe and fatal cases. In Stage II, a Stacking ensemble classifier is deployed to achieve a fine-grained classification of injury levels among the cases identified in the initial screening. Finally, Isotonic Regression is employed to calibrate the output probabilities from both stages, ensuring that the resulting risk estimations are statistically sound and reliable. Empirical evaluations demonstrate that the L-CSC framework effectively balances overall performance with critical risk detection, achieving a robust Macro-F1 of 0.7296. Specifically, compared to the best-performing baseline, the recall and F1-score for the critical severe and fatal category showed relative improvements of over 82% and 62%, respectively. Ablation analyses further substantiate the vital contributions of both the data cleaning and calibration modules. This research demonstrates that the cascaded framework effectively mitigates the biases inherent in imbalanced datasets, providing a robust algorithmic foundation to potentially support future traffic safety interventions. Full article