Traffic Inj Prev [JOURNAL]
Sun
200 papers
Saccade dynamics in different spiral tunnels: An investigation of length and radius effects on driver visual load.
Liu B, Wang X, Han L … +1 more , Li L
Traffic Inj Prev · 2026 May · PMID 42085709 · Publisher ↗
OBJECTIVE: This study aims to systematically investigate how key geometric parameters of spiral tunnels, specifically tunnel length and radius and travel direction, influence drivers saccadic eye movements and visual loa...
OBJECTIVE: This study aims to systematically investigate how key geometric parameters of spiral tunnels, specifically tunnel length and radius and travel direction, influence drivers saccadic eye movements and visual load. METHODS: A field experiment was conducted using a wearable eye tracker to record saccadic behavior from 30 licensed drivers. Participants drove through 3spiral tunnels with varying lengths and radii under both uphill and downhill traversal conditions. Four saccade metrics (amplitude, duration, frequency, and velocity) were analyzed using descriptive statistics and ANOVA to evaluate visual workload. These metrics have been selected because they collectively reflect distinct aspects of visual scanning behavior: amplitude indicates the breadth of visual search, duration reflects the time required for processing fixated information, frequency represents the rate of gaze shifting, and velocity denotes the efficiency of oculomotor movement. RESULTS: The findings indicate that tunnel geometry and travel direction significantly affect saccadic dynamics. Longer tunnels and smaller radii resulted in increased saccade amplitude, prolonged duration elevated frequency, and reduced velocity, suggesting heightened visual processing demand. Furthermore uphill traversal consistently produced larger amplitudes, longer durations higher frequencies, and slower velocities than downhill traversal across all tunnels, revealing a directional asymmetry in visual load. CONCLUSIONS: This study demonstrates that spiral tunnel design, especially extended length and reduced radius, elevates drivers' visual cognitive load with uphill travel imposing greater demands. The results provide empirical evidence to inform geometry-based design guidelines for optimizing visual ergonomics and improving operational safety in spiral tunnels.
Assessing cognitive load in drivers during tunnel approach under combined fog and nighttime conditions based on fixation behavior.
Liu B, Wang X, Han L … +1 more , Li L
Traffic Inj Prev · 2026 Apr · PMID 42060340 · Publisher ↗
OBJECTIVE: This study aims to investigate the impact of combined fog and nighttime conditions on drivers' cognitive load during tunnel approach, as reflected through fixation behavior. Specifically, it examines how these...
OBJECTIVE: This study aims to investigate the impact of combined fog and nighttime conditions on drivers' cognitive load during tunnel approach, as reflected through fixation behavior. Specifically, it examines how these compounded adverse conditions influence visual attention patterns, including fixation duration, frequency, and spatial dispersion. METHODS: A real-world driving experiment was conducted with 30 licensed drivers on the Xinjin Expressway. Eye movement data were collected using a Dikablis Pro eye tracker across four environmental scenarios: clear-day, foggy-day, clear-night, and foggy-night. The analysis focused on the tunnel approach zone, defined as the 10-s travel distance preceding the tunnel portal. Dependent variables included fixation duration, fixation frequency, horizontal fixation deviation, and vertical fixation deviation. One-way ANOVA and Tukey HSD tests were employed to compare these metrics across scenarios. RESULTS: The results revealed systematic variations in fixation behavior with increasing environmental complexity. Fixation duration was longest under foggy-night conditions (689.82 ± 30.4 ms) and shortest under clear-day conditions (325.59 ± 34.52 ms). Fixation frequency decreased progressively, with the highest rate in clear-day conditions (2.85 ± 0.18 Hz) and the lowest in foggy-night conditions (1.55 ± 0.17 Hz). Horizontal fixation deviation was largest in clear-day conditions (18.93 ± 2.91°) and smallest in foggy-night conditions (6.08 ± 1.68°), indicating lateral gaze constriction. Conversely, vertical fixation deviation increased significantly under adverse conditions, peaking in foggy-night scenarios (26.21 ± 3.74°), suggesting compensatory vertical scanning. All pairwise comparisons between scenarios were statistically significant ( < 0.01). CONCLUSIONS: The combined effects of fog and nighttime conditions significantly elevate drivers' cognitive load during tunnel approaches, manifesting as prolonged information processing, reduced attentional shifting, lateral visual field narrowing, and compensatory vertical search. These findings confirm the sensitivity of fixation-based metrics as indicators of cognitive load under compounded environmental stressors. The study provides empirical evidence for developing context-aware safety interventions, such as optimized tunnel lighting, adaptive traffic management, and enhanced driver assistance systems, tailored to mitigate cognitive overload in high-risk driving scenarios.
Assessment models for driving comfort and fatigue status based on heart rate variability.
Jiang W, Zhou Q, Liu Z … +3 more , Ma C, Deng H, Zhang X
Traffic Inj Prev · 2026 Apr · PMID 42059879 · Publisher ↗
OBJECTIVES: This study, from an interdisciplinary perspective of human factors engineering and biomedical engineering, aims to develop a real-time assessment model for driver status to mitigate the risks associated with...
OBJECTIVES: This study, from an interdisciplinary perspective of human factors engineering and biomedical engineering, aims to develop a real-time assessment model for driver status to mitigate the risks associated with fatigue and discomfort during prolonged driving. The primary objective is to construct a machine learning model based on Heart Rate Variability (HRV) that enables continuous and objective monitoring of driver fatigue and comfort levels. METHODS: A total of 64 healthy drivers holding international Class B licenses with at least 2.5 years of driving experience were recruited. The experiment was conducted in a six-degree-of-freedom driving simulator employing a 2 (speed: low vs. high) × 2 (road type: smooth vs. rough) factorial design, simulating realistic driving conditions (low speed: 50 ± 5 km/h; high speed: 100 ± 10 km/h). Electrocardiogram (ECG) signals were continuously recorded using a BIOPAC MP150 system. Subjective ratings of comfort (on a 10-point scale) and fatigue scale (Multidimensional Fatigue Inventory, MFI) were collected synchronously. A comprehensive set of 85 HRV features was extracted from the ECG signals. Six machine learning regression algorithms were evaluated and optimized five-fold cross-validation. Model performance was assessed using Root Mean Square Error (RMSE) and the coefficient of determination (R). RESULTS: The Random Forest model outperformed others in predicting both fatigue (RMSE = 14.55) and comfort (RMSE = 1.56). Feature importance analysis identified six HRV features as most contributory: HRV Triangular Index (HTI), Shannon Entropy (ShanEn), Minimum NN interval (MinNN), Geometric Index (GI), Lorenz Plot Index (PI), and Power of Asymmetry Segment (PAS). These features are physiologically linked to autonomic nervous system activity, providing a mechanistic basis for the model's predictions. CONCLUSIONS: The developed HRV-based machine learning model demonstrates high reliability and potential for real-time application. It advances beyond traditional discrete classification by enabling continuous assessment of driver status. This research provides a quantitative tool for designing in-vehicle human-machine interfaces and early warning systems, contributing to the development of safer and more intelligent transportation systems. Future work should focus on validation in real-world driving conditions.
Uncovering direct and mediation pathways of autonomous vehicle crash types with augmented built environment factors.
Liang F, Xu M, Ren Q
Traffic Inj Prev · 2026 Apr · PMID 42059858 · Publisher ↗
OBJECTIVE: Autonomous vehicle (AV) crash types could be critically impacted by diverse contextual characteristics. However, their underlying relationships remain quantitatively underexplored. This study seeks to address...
OBJECTIVE: Autonomous vehicle (AV) crash types could be critically impacted by diverse contextual characteristics. However, their underlying relationships remain quantitatively underexplored. This study seeks to address this gap by examining the direct and indirect pathways through which contextual variables influence distinct AV crash types. METHODS: A total of 584 AV crashes reported to the California Department of Motor Vehicles (CA DMV) between April 1, 2018, and April 12, 2024, were collected for analysis. These data were further enriched with detailed built environment characteristics by geocoding each crash location and retrieving point of interest (POI) information. The AV crash types were regrouped into four categories: rear end, side swipe, angle, and other. Factors encompassing crash, road, temporal, vehicle, and environment characteristics were identified as independent variables. A Multinomial Logistic Regression (MNL) model integrated with a non-parametric bootstrap (1,000 resamples) mediation framework was employed to estimate the direct and indirect effects of significant factors on different AV crash types. RESULTS: Some variables in direct pathways were captured, such as AV movement preceding collision, vehicle number, VRU, intersection, driving mode, weather, and lighting. Additionally, the mediation pathways were also uncovered, with VRU and intersection emerging as key mediation variables. CONCLUSIONS: These findings contribute to a deeper understanding of AV crash patterns and provide a basis for advancing AV safety technologies.
Sharing the road with emergency vehicles: drivers' understanding of Queensland road rules.
Malone D, Irwin C, Wishart D … +2 more , MacQuarrie A, Stainer M
Traffic Inj Prev · 2026 Apr · PMID 42059818 · Publisher ↗
OBJECTIVE: For emergency vehicles traveling under lights and sirens to reach incidents safely and without delay, other road users must understand and comply with their legal obligation to give way and keep clear. However...
OBJECTIVE: For emergency vehicles traveling under lights and sirens to reach incidents safely and without delay, other road users must understand and comply with their legal obligation to give way and keep clear. However, reports of on-road behavior suggest many drivers may be uncertain of these requirements. This study examined drivers' understanding of the legislative requirements for sharing the road with emergency vehicles in Queensland, Australia, and whether drivers' self-rated understanding aligned with their actual knowledge of those rules. METHODS: A cross-sectional in-person survey was completed by 208 licensed drivers who answered 61 multiple-choice questions (25 emergency-vehicle items embedded among general road-rule items) and rated their understanding of the road rules. RESULTS: Participants generally believed they had a good understanding of emergency-vehicle road rules; however, survey performance indicated gaps in their knowledge. Across the 25 emergency-vehicle items, participants answered an average of 78.77% correctly. Knowledge of basic giving-way requirements was high, but drivers showed inconsistent understanding of the rules for passing stationary emergency vehicles, and many were unsure how to keep clear when no lane was available to their left. Understanding was also poor for situations where drivers may legally proceed through a red light, when safe, to make way for an emergency vehicle. Open-license drivers scored higher than learner drivers, but self-rated understanding did not align with actual understanding: lower-knowledge participants tended to rate their understanding more highly, while higher-knowledge participants rated it more conservatively. CONCLUSIONS: In the present sample, these findings highlight specific situations where drivers may misunderstand what they are required to do during encounters with emergency vehicles, creating the potential for obstructions, delays, and hazardous interactions.
Hazard perception and prediction model based on cognitive components of male BRT drivers.
Zarei AH, Asadamraji M
Traffic Inj Prev · 2026 Apr · PMID 42059813 · Publisher ↗
OBJECTIVES: Hazard perception is a crucial skill for drivers and is typically measured using computer-based hazard perception tests. In these tests, drivers identify potential hazards in video clips recorded from the dri...
OBJECTIVES: Hazard perception is a crucial skill for drivers and is typically measured using computer-based hazard perception tests. In these tests, drivers identify potential hazards in video clips recorded from the driver's perspective. Recently, researchers have also focused on another driver attribute called "hazard prediction." In hazard prediction tests, each scenario pauses just before a potentially dangerous event, and drivers must predict the subsequent events. Urban bus rapid transit (BRT) systems in Iran operate on dedicated routes that present specific hazards, such as sudden pedestrian crossings, motorcycle traffic, and emergency vehicles. Therefore, investigating the hazard perception and prediction abilities of BRT drivers can yield valuable insights to improve safety and reduce accidents. METHODS: This study was conducted in Tehran, Iran, involving 187 urban BRT drivers. Hazard perception and prediction tests were designed, and demographic as well as cognitive questionnaires were administered to assess driver characteristics. RESULTS: The data were analyzed using SmartPLS software and structural equation modeling. The final structural equation model for hazard perception indicated that social cognition, planning, and inhibitory control were the most influential factors. For hazard prediction, sustained attention, cognitive flexibility, inhibitory control, decision-making, and memory emerged as the most significant variables. CONCLUSIONS: The results of this research can inform the training, testing, and evaluation of urban BRT drivers.
Cluster analysis of major expressway traffic accidents: Characteristics and influential factors.
Zhang G, Yuan J, Liu D … +2 more , Liu W, Huang Y
Traffic Inj Prev · 2026 Apr · PMID 42047615 · Publisher ↗
OBJECTIVE: As China operates the world's most extensive expressway network, frequent traffic accidents undermine the sustainable development of its transportation system and negatively impact public travel safety. Variat...
OBJECTIVE: As China operates the world's most extensive expressway network, frequent traffic accidents undermine the sustainable development of its transportation system and negatively impact public travel safety. Variations in temporal and geographical conditions lead to differences in accident characteristics, distribution patterns, and contributing factors. This study aims to analyze the features, distribution, and influencing factors of expressway accidents in Guangdong Province from 2014 to 2023, and to propose evidence-based safety countermeasures. METHODS: A total of 131 expressway accidents recorded in Guangdong Province between 2014 and 2023 were statistically analyzed. Key accident characteristics, distribution patterns, and influencing factors were examined. Significant independent variables were standardized, and five determinant factors were extracted using factor analysis. These factors were subsequently classified into four distinct accident categories through K-means clustering to further explore underlying influences. RESULTS: Since 2020, the number of expressway accidents in Guangdong Province increased substantially, averaging approximately 16 incidents annually. Accidents occurred more frequently in summer (29.77%) and spring (27.48%). Within a 24-h period, 34.35% of accidents took place during early morning hours, representing the highest proportion by time segment. Heavy vehicles (45.80%) and passenger cars (29.77%) were involved in the majority of accidents. Improper driving behavior was a significant contributing factor, accounting for 18.32% of incidents. Secondary accidents were found to result in considerably more severe consequences than primary accidents. A significant correlation was identified between expressway accidents and five determinant factors: environmental (F1), cause of accident (F2), vehicle-related (F3), road-related (F4), and temporal (F5). Influencing factors such as season, time period, expressway type, secondary accident occurrence, vehicle type distribution, and city were significant only within specific accident categories. In contrast, weather conditions, road surface status, visibility, and accident causes were significant across multiple categories. CONCLUSIONS: The findings highlight distinct temporal, vehicular, and behavioral patterns associated with expressway accidents in Guangdong Province. The analysis of determinant factors and accident categories provides a nuanced understanding of contributing influences. Based on these results, targeted safety improvement strategies are proposed to support the development of expressway traffic safety measures and inform relevant policy-making.
Analyzing rear-end collision risks in foggy weather through a generalized extreme value model.
Pandit A, Budhkar AK
Traffic Inj Prev · 2026 Apr · PMID 42044075 · Publisher ↗
OBJECTIVES: Fog substantially impairs visibility and driver perception, leading to increased risk of crashes, especially rear-end collisions. Traditional crash-based safety studies are constrained by underreporting, low...
OBJECTIVES: Fog substantially impairs visibility and driver perception, leading to increased risk of crashes, especially rear-end collisions. Traditional crash-based safety studies are constrained by underreporting, low event frequency, and the inability to assess proactive risk under rare weather conditions. This study introduces a data-driven, proactive framework that integrates naturalistic vehicle trajectory data, anticipated collision time (ACT) as a surrogate safety measure, and extreme value theory (EVT) for estimating fog-related crash risks on multilane Indian highways. METHODS: Vehicle trajectory and fog data were collected from 8 highway sites in India, comprising both 4- and 6-lane divided segments. Visibility in fog was estimated using contrast-based image processing algorithms. Vehicle trajectories were extracted from traffic videos through advanced object detection and tracking algorithm, followed by smoothing using locally weighted polynomial regression. From over 165,000 trajectory points, 21,461 valid vehicle conflicts were identified and classified based on ACT profiles into rear-end, sideswipe, and angled interactions. Crash probabilities were estimated using extreme value theory (EVT) models under the block maxima framework, enabling risk. Stationary and nonstationary models were developed to examine how crash risk varies across visibility levels and road types using generalized extreme value distribution (GEV). RESULTS: The findings suggest rear-end conflicts pose a greater safety risk than sideswipe conflicts due to the car-following behavior of drivers during foggy weather. Results show that safety margin shrinks significantly during dense fog, especially for cars and 2-wheelers and on 4-lane highways, indicating elevated crash potential in mixed traffic. In 6-lane highways, crash risk of 2-wheelers increases sevenfold in dense fog compared to clear weather. Heavy commercial vehicles are observed to be safer during fog on 6-lane highways compared to other vehicle types. CONCLUSIONS: This research demonstrates the feasibility of ACT-based EVT modeling using field trajectory data for proactive safety evaluation in low-visibility environments where crash data are limited. The developed framework supports real-time risk estimation and can inform adaptive countermeasures such as variable speed limits, fog-responsive warning systems, and improved lane delineation for enhanced roadway safety.
Research on intelligent connected vehicles lane change trajectory optimization based on risk field in mixed traffic.
Shang T, Xu Y, Mao H
Traffic Inj Prev · 2026 Apr · PMID 42043971 · Publisher ↗
OBJECTIVE: This study investigates the optimization of lane-change trajectories for intelligent connected vehicles (ICVs) operating in mixed traffic environments. By integrating dynamic risk modeling with cooperative obs...
OBJECTIVE: This study investigates the optimization of lane-change trajectories for intelligent connected vehicles (ICVs) operating in mixed traffic environments. By integrating dynamic risk modeling with cooperative obstacle-avoidance planning, lane-change safety is enhanced while maintaining trajectory stability. METHODS: A comprehensive dynamic risk-field model was developed. The relative motion states of surrounding vehicles and their geometric occupancy were mapped into a continuous spatial risk distribution. This formulation characterizes the spatial propagation and attenuation of interaction risks and describes their spatiotemporal evolution during lane-change maneuvers. On this basis, a multi-objective trajectory optimization model was established within a path-velocity decoupling framework. Risk-field functions were incorporated together with vehicle dynamic feasibility constraints. Optimal lane-change trajectories were then generated through cost minimization. The effectiveness of the proposed risk-driven optimization approach was evaluated by comparing risk levels before and after optimization. RESULTS: The results indicate that the composite interaction risk during lane-change maneuvers follows the order: longitudinal following phase > lane-change execution phase > lane-change preparation phase > post-change adjustment phase. The proposed risk-driven optimization method achieves a balance between risk mitigation and dynamic responsiveness. Smooth and stable lane-change trajectories are generated for ICVs. Compared with the original trajectories, the comprehensive interaction risk decreased by 0.86%, 0.55%, 3.44%, and 5.62% across the four respective phases. In addition, high-risk regions contracted, and the distribution of risk gradients became more uniform. CONCLUSIONS: The proposed risk-field-driven multi-objective trajectory optimization method quantitatively characterizes the evolution of interaction risk during lane changes in mixed traffic. Interaction risk levels are effectively reduced through the proposed framework. Trajectory smoothness and control stability are improved while dynamic feasibility is strictly maintained. The method thus provides theoretical support for intelligent connected vehicles to execute safe lane-change maneuvers in complex multi-vehicle interaction environments.
Quantifying risk factor influences in autonomous vehicle collisions: a Bayesian network probabilistic analysis.
Yang L, Xu S, Du Z
Traffic Inj Prev · 2026 Apr · PMID 42043962 · Publisher ↗
OBJECTIVES: The frequent occurrence of Autonomous Vehicle (AV) collisions significantly impacts development and user trust. These collisions arise from a complex interaction of factors, but their interdependencies are no...
OBJECTIVES: The frequent occurrence of Autonomous Vehicle (AV) collisions significantly impacts development and user trust. These collisions arise from a complex interaction of factors, but their interdependencies are not fully understood. This study analyzed 776 publicly available AV -related collision reports from the California Department of Motor Vehicles, identifying key factors and their complex interactions. The identified risk factors are divided into three categories: vehicle basic information, collision details, and road and environmental characteristics. METHODS: The statistical analysis method of Chi-square test was used to evaluate the significance of single factors. Bayesian network analysis further exploratorily constructs causal chains and examines the impact of each factor on collision severity. RESULTS: Six variables, including vehicle mode, brand, collision type, pre-collision motion, weekday and roadway type, can independently affect the collision severity. Bayesian network exploration found that brand affects vehicle mode, vehicle mode affects pre-collision motion, and pre-collision motion affects collision type. Side swipe collisions, rear-end collisions, road sections, stationary or slow-moving conditions are the most likely to cause property damage. Casualties are most likely to occur in incidents involving broadside collisions and highways. Additionally, intersections are high-risk collision locations. The autonomous driving mode is similar to the conventional human driving mode in terms of collision risks, but there are still certain safety hazards, such as a higher probability of a broadside collision. CONCLUSIONS: These findings show that AV technology should be continuously improved in many aspects such as environmental perception, decision-making algorithms, and safety mechanism design to improve the overall safety and reliability of AV and make it better integrate into daily life.
Latent Class Logit Kernel framework for surrogate safety: identifying behavioral thresholds through conflict indicator profiles.
Al-Haideri R, Liu C, Ismail K … +2 more , Farooq B, Zhang C
Traffic Inj Prev · 2026 Apr · PMID 42024123 · Publisher ↗
OBJECTIVES: Crash data provide objective safety metrics but are rare and often unsuitable for proactive safety management. In contrast, traffic conflict indicators (e.g., time-to-collision, TTC) offer continuous measures...
OBJECTIVES: Crash data provide objective safety metrics but are rare and often unsuitable for proactive safety management. In contrast, traffic conflict indicators (e.g., time-to-collision, TTC) offer continuous measures of proximity to collision but require thresholds to separate routine from safety-critical events. Extreme Value Theory (EVT)-based approaches define statistically defensible thresholds from the tail behavior of conflict indicators, but these thresholds are not explicitly tied to observable maneuver adaptations. This study instead models drivers' discrete maneuver adjustments under varying conflict indicator levels and extracts candidate behavioral thresholds (CBTs) from the resulting maneuver-response probability profiles. METHODS: A Latent Class Logit Kernel (LC-LK) framework is proposed to identify CBTs by modeling drivers' maneuver choice under conflict. The LC-LK model distinguishes between low- and high-risk behavioral classes and allows each driver to express a probabilistic mixture of both states, capturing intra-driver heterogeneity. This heterogeneity refers to variation in the behavior of the same driver under different levels of conflict severity (as indexed by the indicator). The framework also incorporates correlation in alternatives through logit-kernel structured error components that represent shared unobserved influences among maneuvers involving similar kinematic adjustments (deceleration, acceleration, or turning). This design produces probability curves that describe how the likelihood of high-risk maneuvers changes with conflict indicator values. From these profiles, CBTs such as inflection points, crossovers, and tail-based thresholds can be derived. The framework is guided by four behavioral hypotheses: (i) drivers simultaneously exhibit varying degrees of membership in both low- and high-risk behavioral classes; (ii) class membership shifts systematically with conflict indicator values; (iii) this relationship often follows a logistic shape, with stable behavior across safe conditions and rapid transitions once critical values are reached; and (iv) even in free-flow conditions, drivers maintain a baseline level of caution. RESULTS: Application to naturalistic roundabout trajectories demonstrated the framework's diagnostic power. For TTC, stable behavioral inflections were observed between 0.8-1.1s, indicating clear transitions from low- to high-risk driving. In contrast, a modified variant of TTC (MTTC2) produced unstable and implausible thresholds (≈34s). This divergence suggested that not all indicators support identifiable behavioral transitions. One tentative interpretation, which we report cautiously, is that indicators that require more complex information (e.g., MTTC2) may be harder for drivers to process during routine driving. Simpler indicators such as TTC appear to yield more stable behavioral patterns, but further evidence is required to substantiate this explanation. CONCLUSIONS: The proposed framework is intended to complement and not replace EVT. The LC-LK model produces multiple CBTs, each capturing different aspects of behavioral transitions. Some CBTs showed consistency with EVT-derived thresholds and others diverged substantially. A systematic investigation is needed to determine which CBT should be used or how behavioral thresholds should be integrated with statistical and practice-based thresholds.
Drug-associated road traffic accidents: insights from an analysis of the FDA adverse event reporting system (FAERS) database.
Ma F, Shen N, Wu Y … +1 more , Yu Y
Traffic Inj Prev · 2026 Apr · PMID 42008598 · Publisher ↗
OBJECTIVE: Drug-related road traffic accidents (RTAs) represent a significant public health concern, yet large-scale real-world evidence on high-risk medications remains limited. This study aimed to characterize the land...
OBJECTIVE: Drug-related road traffic accidents (RTAs) represent a significant public health concern, yet large-scale real-world evidence on high-risk medications remains limited. This study aimed to characterize the landscape and quantify signals of drug-associated RTAs using the U.S. FDA Adverse Event Reporting System (FAERS). METHODS: A retrospective study was conducted on FAERS reports (Q1 2004-Q2 2025). Dispro-portionality analyses were performed using the Reporting Odds Ratio (ROR) and the Bayesian Confidence Propagation Neural Network (BCPNN) for the top 50 most frequently reported drugs. Gender differences and time-to-onset profiles were also analyzed. RESULTS: Among 22,775,812 total adverse event reports, we identified 38,760 RTA cases. The three most reported drugs were adalimumab ( = 2,278), oxycodone ( = 1,898), and etanercept ( = 1,087). Disproportionality analysis showed that 41 of the top 50 drugs (82.0%) generated positive safety signals for RTAs. Nervous system agents (36.6%) and antineoplastic/immunomodulating agents (34.1%) comprised the majority of signal-positive drugs. Zolpidem demonstrated the strongest signal (IC = 4.42), followed by alendronic acid (IC = 2.77) and buprenorphine/naloxone (IC = 2.64). Gender-stratified analysis of these 41 signal-positive drugs found that males had a significantly higher reporting risk for 26 drugs, whereas females had a higher risk for 6 drugs. CONCLUSION: This analysis identifies drugs most frequently associated with RTAs using real-world data, providing evidence for risk assessment and patient guidance. However, the FAERS database has inherent limitations, such as unadjusted confounders, missing data on alcohol or substance use, and incomplete accident outcomes. Thus, further clinical validation is warranted to confirm these associations.
Investigating driving behavior characteristics on freeway curves under accumulated sand environments: evidence from a driving simulator study.
Zhang J, Ma Y, Xing Y … +2 more , Xing G, Wang F
Traffic Inj Prev · 2026 Apr · PMID 42008578 · Publisher ↗
OBJECTIVE: To investigate how drivers respond and adapt their vehicle control strategies when encountering varying degrees of accumulated sand encroachment on desert freeway curves-a critical safety hazard. METHODS: A 3D...
OBJECTIVE: To investigate how drivers respond and adapt their vehicle control strategies when encountering varying degrees of accumulated sand encroachment on desert freeway curves-a critical safety hazard. METHODS: A 3D road framework replicating actual curve geometries was developed, and two sand encroachment scenarios (one-third and two-thirds coverage of the outside lane) were simulated. Fifty participants completed controlled driving simulator experiments. Data on driver decision-making and vehicle dynamics were collected and analyzed. Linear Mixed-effects Models (LMM) were applied to examine the influence of encroachment conditions and individual factors. RESULTS: When accumulated sand encroached one-third of the outside lane, 72.73% of drivers adopted a lane-keeping strategy with deceleration; when two-thirds was encroached, 81.82% chose a lateral avoidance strategy. Increased sand coverage significantly affected the effectiveness of avoidance strategies. For lane-keeping, higher encroachment led to decreased lateral control stability, earlier braking, and stronger deceleration; for lateral avoidance, it prompted more aggressive maneuvers. LMM analysis showed sand encroachment had significant effects on vehicle control, while gender and driving experience did not significantly influence behavior. CONCLUSIONS: Accumulated sand encroachment severity strongly influences driver decision-making and control strategies on desert freeway curves. These findings enrich the understanding of behavioral mechanisms in sand-affected scenarios and provide a scientific basis for safety management and proactive behavioral interventions on high-risk freeway segments.
Impact of land use on bus driver inattentiveness: Insight into crash risks.
Thakur DS, Chalumuri RS, Velmurugan S … +2 more , Arkatkar S, Parida M
Traffic Inj Prev · 2026 Apr · PMID 42008459 · Publisher ↗
OBJECTIVE: This study examines how roadside land-uses, namely commercial, residential, educational, and recreational establishments located on a typical Indian Highway, i.e., National Highway, influence the temporally di...
OBJECTIVE: This study examines how roadside land-uses, namely commercial, residential, educational, and recreational establishments located on a typical Indian Highway, i.e., National Highway, influence the temporally distributed bus driver inattention, such as distraction, drowsiness, and falling asleep behaviors and their associated influence on the crash severity occurring on the entire study corridor. METHODS: In this context, AI-powered Driver Monitoring Systems (DMS) were installed in a public bus fleet operated by 33 professional drivers over three months and generated multiple inattention events. These events were categorized into morning, afternoon, evening, and night periods. The above-mentioned roadside land-use features were extracted using high-resolution Google Earth imagery (0.5-1.0 m) by considering within a 100 m roadside buffer and validated through field surveys. Three years of inattention-related road crash data (2020-2022) were integrated. Analytical methods included descriptive statistics, structural equation modeling, correlation analysis, Generalized Linear Models (GLM) and Multiple Linear Regression (MLR) regression, and Artificial Neural Networks (ANN) modeling to estimate the associations among inattention events, land-use establishments, and crash risks. Model validity and robustness were assessed through sensitivity measures Receiver Operating Characteristic (ROC)-Area Under the Curve (AUC) metrics, and normalized importance variance values (NIVIA). ANN-based risk outputs were further visualized using spatial hotspots for crashes, fatalities, and injury severities. Spatial dependency and clustering were evaluated using Global Moran's I and Local Getis-Ord Gi* statistics. RESULTS: Inattention behaviors exhibited strong spatial dependence (Moran's = 0.32-0.48, < 0.001), whereas crash counts themselves were spatially random. Recreational, residential, and commercial land-use segments significantly increased distraction and asleep-related inattention. Morning distraction emerged as a strong predictor of crash occurrence, while nighttime asleep events showed the highest association with fatal and major-injury outcomes. ANN-based risk surfaces and Gi* clustering consistently identified intensive daytime distraction hotspots in activity-dense land-use clusters and severe nighttime hotspots driven by falling asleep. Sensitivity was highest for nighttime asleep (84.84%) and morning distraction (70.04%), with MLR models achieving strong discrimination (ROC-AUC = 0.94 to 1.00), but they have less confidence in test data. The predominantly non-linear crash risk relationships were better captured by ANN models, as reflected by synaptic weight patterns and variable importance scores. CONCLUSIONS: Driver inattention along the NH-65 corridor is shaped by both spatial context and circadian timing. Integrated evidence from statistical analyses, ANN risk estimation, and spatial hotspots indicates that daytime distraction and nighttime fatigue are the dominant contributors to crash severity. These findings highlight the need for time-specific and location-targeted interventions to enhance safety on this critical highway.
LTF-YOLO: an intelligent road defect detection model based on large-kernel enhancement and local multi-scale feature fusion.
Cao S, Li H, Zhao J
Traffic Inj Prev · 2026 Apr · PMID 42008447 · Publisher ↗
OBJECTIVE: To address the inefficiency and high cost of manual road inspections and the limited accuracy of existing computer vision methods, this study proposes a multi-scale road defect detection model, LTF-YOLO, based...
OBJECTIVE: To address the inefficiency and high cost of manual road inspections and the limited accuracy of existing computer vision methods, this study proposes a multi-scale road defect detection model, LTF-YOLO, based on YOLO11s. The model aims to enhance both the accuracy and robustness of automated road defect detection. METHODS: In the feature-extraction stage, an LMSFA module replaces the original C3k2 module in YOLO11s. This module employs a hierarchical large-kernel convolution combined with group convolution and block-concatenation strategies to achieve efficient multi-scale feature aggregation. During feature fusion, an improved SPPFMPF module introduces average pooling and channel attention branches, with learnable weights enabling adaptive fusion of multi-source features. In the detection head, the TADDH module separates classification and regression subspaces through a task-decoupling mechanism. The regression branch incorporates DyDCNv2 dynamic convolution for spatial adaptive alignment, while a confidence-guided mechanism enhances detection reliability. RESULTS: Experiments on the SVRDD dataset demonstrate that, compared with the baseline YOLO11s, LTF-YOLO improves Precision by 4.4%, rising from 70.2 to 74.6, mAP@50 by 3.3%, improving from 66.4 to 69.7, and mAP@50-95 by 3.8%, rising from 39.4 to 43.2. Cross-dataset experiments on the RDD2022 dataset further demonstrate the strong generalization ability of LTF-YOLO, with mAP@50 increasing from 47.2 to 49.2 while maintaining a lightweight architecture. The proposed model achieves more accurate identification of cracks and potholes under complex road conditions, effectively reducing missed and false detections. CONCLUSION: Through innovations in multi-scale feature extraction, adaptive feature fusion, and task-decoupled detection head design, LTF-YOLO significantly enhances detection accuracy and robustness. The model maintains high precision while remaining lightweight, providing an efficient and scalable solution for intelligent road maintenance and traffic safety monitoring.
Why the U.S. lags other countries in reducing traffic fatalities the past 25 years.
Viano DC
Traffic Inj Prev · 2026 Apr · PMID 41962029 · Publisher ↗
OBJECTIVE: Since 1979, traffic fatalities dropped 16.0% in the U.S. compared to 77.4 ± 5.9% for 14 other countries. The gap to other countries has grown and has been statistically significant since 1996. This study descr...
OBJECTIVE: Since 1979, traffic fatalities dropped 16.0% in the U.S. compared to 77.4 ± 5.9% for 14 other countries. The gap to other countries has grown and has been statistically significant since 1996. This study describes reasons for the gap in traffic fatality reductions in the U.S. METHODS: NHTSA's research, programs and activities were analyzed to identify causes for the lack of traffic fatality reductions in the U.S. This includes policy decisions, selection of research projects, meaningfulness of NCAP and other tests, and errors in field accident data on serious injury and death. RESULTS: There were three primary and nine secondary reasons identified. NHTSA has: 1) not set targets focusing activities on fatality reductions, 2) not pursued research with measurable reductions in fatalities, 3) no meaningful engagement with industry, IIHS and others on research, NCAP, and safety priorities, 4) not conducted critical analysis of projects, programs and research, 5) inherent problems managing research, regulations, investigations, and enforcement under one leadership, 6) not verified assumptions for field accident data collection, 7) not used correct sampling frequencies or case weights in NASS-CDS and CISS, 8) not terminated testing that does not measurably reduce fatalities, 9) not followed-up on useful research, 10) not pursued crash tests with relevance to traffic fatalities and wrongheaded focus on MAIS 2 injuries, 11) not required timely engineering reports on internal and external projects, and 12) inaccessible archives of many reports and findings. UNLABELLED: Fatalities in the U.S. would have increased the past 25 years if safety technologies had not been voluntarily introduced by automotive manufacturers, including ESC (electronic stability control), AEB (automatic emergency braking) and high retention seats. NHTSA's budget has increased 459% over 25 years, a 14.4% increase each year. NHTSA has little to show for the extremely large budget, except "we could do better with more money." CONCLUSION: NHTSA must set priorities and targets for research, programs, and activities that reduce traffic deaths. They must change their leadership, because the Agency has failed its core mission to reduce traffic deaths the past 25+ years. NHTSA focuses on new car technologies in crash tests with no relevance to fatal accidents. Most fatalities are in 10+ year old vehicles, where risky driver behavior is the main cause with no seatbelt use, alcohol-drug use, and aggressive, risk-taking speeding. NHTSA must prioritize risky driver behavior and align State activities to reduce traffic fatalities.
Spinal posture helps crash response evaluation of reclined occupant.
Shen W, Tang J, Tan P … +2 more , Zhuang Z, Zhou Q
Traffic Inj Prev · 2026 Apr · PMID 41941826 · Publisher ↗
OBJECTIVE: The growing adoption of self-driving vehicles and zero-gravity seats presents emerging challenges for occupant safety. With lumbar spine injury of reclined occupants receiving increasing research attention, th...
OBJECTIVE: The growing adoption of self-driving vehicles and zero-gravity seats presents emerging challenges for occupant safety. With lumbar spine injury of reclined occupants receiving increasing research attention, the influence of spinal postures on crash response warrants further investigation. METHODS: A precise adjustment method was employed to generate THUMS models in several specific reclined postures. Crash responses were evaluated against subjects in postmortem human subject (PMHS) tests in both average and subject levels. RESULTS: The average model aligned by angular spinal posture more accurately predicted the occupant kinematics than the model aligned by average vertebral location, yielding an average 47% improvement in predicting peak X-displacement (except at L3). Regarding injury response, the spine-specific models accurately predicted the lumbar and iliac injury locations observed in the PMHS tests. CONCLUSIONS: THUMS models incorporating subject-specific spinal postures provide improved prediction of Z-direction kinematics compared to generic models and enable preliminary evaluation of spinal injury risk. Additionally, correlations established between thoracolumbar spine kinematic and kinetic responses offer new insights into lumbar injury mechanisms for reclined occupants.
Diverging drivers' visual attention and search behavior in closely spaced tunnel-interchange structures: a field study.
Luo S, Chen K, Chen R … +1 more , Xu J
Traffic Inj Prev · 2026 Apr · PMID 41941775 · Publisher ↗
OBJECTIVE: Closely spaced tunnel-interchange (CSTI) structures are prevalent in mountain freeways, resulting from constraints imposed by topography, geological conditions, and cost considerations. The study aims to explo...
OBJECTIVE: Closely spaced tunnel-interchange (CSTI) structures are prevalent in mountain freeways, resulting from constraints imposed by topography, geological conditions, and cost considerations. The study aims to explore the visual attention and search behavior of drivers executing diverging maneuvers immediately after exiting tunnels. METHOD: An on-road experiment was conducted to collect eye movement data at four CSTI structures with varying net distances and at one conventional interchange. K-means++ and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) were utilized to segment drivers' areas of interest (AOIs). The proportion of fixation time allocated to each AOI was used to identify primary AOIs, while fixation duration and frequency were used to assess visual attention engagement. Saccade duration, saccade frequency, fixation frequency, one-step transition probability matrix, stationary gaze entropy, and gaze transition entropy were used to analyze visual search behavior. RESULTS: The near-forward, far-forward, and overhead roadway areas constituted the primary AOIs within the CSTI structures, upon which visual search of diverging drivers was predominantly concentrated. Compared with drivers in conventional interchanges, those in the CSTI structures exhibited faster but less efficient visual search behavior. As the net distance decreased, drivers' visual attention engagement declined, while visual search pace increased. CONCLUSIONS: This study reveals how diverging drivers navigate the challenges of CSTI structures by adjusting their visual attention engagement, visual attention allocation, and visual search strategies. The findings can offer a reference for designing and placing guide signs in CSTI structures.
A comparison of collision factors and toxicologic characteristics for rural and urban drivers presenting to the emergency department after a vehicular collision.
Besserer F, Simmons S, Erdelyi S … +20 more , Wells A, Atkinson P, Rowe BH, Vaillancourt C, Taylor J, Chan H, Davis P, Clarke DB, Daoust R, Eppler J, Emond M, Lee J, MacPherson A, Magee K, Mercier E, Ohle R, Parsons M, Rao J, Wishart I, Brubacher JR
Traffic Inj Prev · 2026 Apr · PMID 41941750 · Publisher ↗
OBJECTIVES: Impaired driving is a significant public health and safety issue. There is limited information about the differences in toxicologic characteristics and collision factors amongst rural and urban drivers. The a...
OBJECTIVES: Impaired driving is a significant public health and safety issue. There is limited information about the differences in toxicologic characteristics and collision factors amongst rural and urban drivers. The aim of this study is to compare demographic and toxicologic characteristics between urban and rural drivers involved in motor vehicle collisions (on and off-road vehicle use). METHODS: We prospectively analyzed blood samples for 13,792 injured drivers who presented to 17 Canadian emergency departments (ED) between 2018 and 2024 and had bloodwork drawn within six hours of their collision. Data extracted by patient record review included demographics, collision characteristics and disposition. A comparison of demographic, toxicologic and collision characteristics was conducted based on the de-identified injured drivers' residential postal code. RESULTS: Of 13,792 injured drivers,15.1% had a rural residential postal code. Rural drivers were more likely to be younger (6% vs 3.4% between 16-18 years of age), involved in a single vehicle crash (56.2% vs 39.4%) and more likely to be admitted to hospital (55.3% vs 33.4%). Blood alcohol content (BAC) > 0 (24.6% vs 14.8%; aPR = 1.21; 95%CI: 1.12-1.31) and BAC >0.08% (18.5% vs 11.3%; aPR = 1.23; 95%CI: 1.12-1.36) were both more prevalent amongst rural drivers. THC > 0 was more common amongst rural drivers (17.2% vs 14.6%; aPR = 1.14; 95%CI: 1.03-1.26) as was polysubstance use (28.8% vs 19.6%; PR = 1.24; 95%CI: 1.15-1.34). CONCLUSIONS: This study demonstrated a higher prevalence of elevated BAC and THC levels and increased polysubstance use amongst younger rural drivers. These findings should be used to target injury prevention initiatives for rural youth regarding the risks associated with substance misuse prior to driving.
Understanding bias in older drivers' self-reported driving styles using naturalistic driving data.
Zhu Y, Jiang M, Yamamoto T
Traffic Inj Prev · 2026 Apr · PMID 41941747 · Publisher ↗
OBJECTIVE: Accurate self-assessment of driving performance is critical for maintaining safety and mobility among older adults. However, many older drivers exhibit discrepancies between perceived and actual driving perfor...
OBJECTIVE: Accurate self-assessment of driving performance is critical for maintaining safety and mobility among older adults. However, many older drivers exhibit discrepancies between perceived and actual driving performance, which may lead either to unsafe driving in cases of overestimation or to premature driving cessation in cases of underestimation. This study aimed to examine the consistency between self-rated driving styles and objectively measured driving performance among older drivers, to identify drivers who overestimate or underestimate their performance, and to explore the factors associated with inaccurate self-assessments. METHODS: Naturalistic driving data were obtained from 58 older drivers aged 65 years and above who participated in the Data Repository for Human Life-Driving Anatomy project. The rate of harsh events-including sudden starts, braking, and steering-was computed as an objective indicator of risky driving behavior. Self-reported driving styles were assessed using the Driving Style Questionnaire, which measures several dimensions of perceived driving behavior including careful, risky, and anxious driving styles. An unsupervised machine learning method was developed to identify drivers who overestimated or underestimated their driving behaviors. Logistic regression models were then estimated to examine the associations between inaccurate self-assessment and explanatory variables such as age, gender, driving confidence, and personality traits. RESULTS: Weak correlations were found between self-reported driving styles and objectively measured performance, indicating inconsistencies between perceived and real-world behaviors. Self-confidence in driving ability and personality traits were significant predictors of inaccurate self-assessments. Over-estimators showed higher self-reported confidence, whereas under-estimators exhibited lower confidence. Impulsive drivers were more likely to overestimate their performance, while both sensation seeking and impulsivity were negatively associated with underestimation. Moreover, older age was linked to a greater likelihood of overestimation. CONCLUSIONS: These findings suggest that caution is warranted when relying on self-report data to assess older drivers' driving performance, and that the anxious driving style score, which reflects confidence in driving ability, may serve as a useful tool for identifying older drivers with inaccurate self-assessments of their driving behavior. Insights from this study can inform the development of tailored intervention strategies that enhance both road safety and sustained mobility among older adults.
About
- Frequency
- Sun
- Papers found
- 200
- RSS feed
- Subscribe