Traffic Injury Prevention最新文献

{"title":"Serious injury prediction in motor vehicle crashes: a nonlinear modeling approach using trainable B-spline functions.","authors":"Yimeng Mei, Fusako Sato, Yusuke Miyazaki","doi":"10.1080/15389588.2026.2655342","DOIUrl":"https://doi.org/10.1080/15389588.2026.2655342","url":null,"abstract":"<p><strong>Objective: </strong>Advanced Automatic Collision Notification (AACN) systems rely on accurate prediction of serious occupant injuries to guide emergency response decisions. Current injury severity prediction (ISP) algorithms predominantly use logistic regression models that assume linear relationships in the log-odds space, potentially overlooking complex nonlinear patterns between crash characteristics and injury outcomes. This study aims to develop an improved ISP algorithm that can capture and explicitly represent these nonlinear relationships while maintaining model interpretability comparable to traditional approaches.</p><p><strong>Methods: </strong>We developed a prediction model based on trainable B-spline functions using crash data from the US National Automotive Sampling System-Crashworthiness Data System (NASS-CDS, 2010-2015) and Crash Investigation Sampling System (CISS, 2017-2023). The final complete dataset comprised 17,045 crash-involved occupants representing 9,225,347 weighted occupants nationwide. In addition to developing the predictive model using the complete dataset, we also conducted imputation and resampling experiments to demonstrate the distribution of potential model outcomes. Beyond predictors commonly employed in existing AACN algorithms, we incorporated underutilized information related to collision objects, including crash type and hit object type. Model performance was evaluated using both traditional classification metrics and triage-specific measures designed for AACN applications.</p><p><strong>Results: </strong>The proposed model outperformed existing AACN ISP algorithms across all evaluation metrics. Analysis of the trained model revealed that continuous risk factors exhibit distinct nonlinear relationships with serious injury in the log-odds space: delta-V follows an arctangent-like relationship, principal directions of force (PDOF) exhibit a distinct bimodal pattern, and both occupant age and BMI show a Gaussian-like relationship. Among categorical predictors, crash type and hit object type were identified as influential categorical predictors.</p><p><strong>Conclusions: </strong>Trainable B-spline functions enable effective modeling of complex nonlinear relationships in crash injury prediction while providing explicit mathematical formulations similar to traditional logistic regression. The identification of specific functional patterns for key risk factors enhances understanding of injury mechanisms and provides a foundation for more accurate AACN systems. These findings, including the importance of previously underutilized predictors such as crash type and hit object type, provide a reference for the future development of AACN prediction systems.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147857627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal analysis of urban traffic crash risk using a bagging-optimized dynamic mode decomposition framework.","authors":"Xuguang Ma, Yijun Zhang, Ninghao Hou, Hui Zhang, Jieling Jin","doi":"10.1080/15389588.2026.2654184","DOIUrl":"https://doi.org/10.1080/15389588.2026.2654184","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to analyze the spatiotemporal evolution of traffic crash risk in Manhattan and to improve one- to seven-day-ahead crash prediction through a Bagging Optimized Dynamic Mode Decomposition (BOPDMD) framework. By examining two full years of daily crash data (2019-2020), the study further investigates how latent crash patterns differ between a typical pre-pandemic year and a disruption-dominated pandemic year.</p><p><strong>Methods: </strong>Daily crash counts from 69 Manhattan neighborhoods were aggregated into zone-day matrices for 2019 and 2020, as the daily scale provides a practical compromise between short-term responsiveness and data stability for neighborhood-level crash modeling. A unified preprocessing pipeline was applied, including square-root variance stabilization and zone-wise standardization. For prediction experiments, a fixed 212-day window from April 1 to October 30 was used in each year to ensure identical sample length. Within this window, the first 160 days were used for training and the remaining 52 days were used for testing. For interpretation, BOPDMD was applied to the complete 2019 and 2020 matrices, with 365 and 366 days respectively, to extract spatial modes, temporal coefficients, and modal frequencies that characterize underlying crash dynamics. Its forecasting performance was compared with standard Dynamic Mode Decomposition (DMD) and several representative baseline models under one- to seven-day prediction horizons.</p><p><strong>Results: </strong>BOPDMD achieved the lowest or near-lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) across most prediction horizons in both years and exhibited the slowest error accumulation in multi-step forecasting. The spatiotemporal mode analysis revealed clear cross-year differences. In 2019, dominant modes captured stable high-risk corridors and regular weekly and seasonal oscillations, indicating a mobility-driven and quasi-periodic crash regime. In contrast, the 2020 modes exhibited abrupt spatial reconfiguration, rapid temporal decay, and weakened periodic structure, reflecting pandemic-induced disruptions in travel demand and risk allocation. Eigenvalue patterns confirmed that 2020 dynamics were more transient and less cyclic than those of 2019.</p><p><strong>Conclusions: </strong>The findings demonstrate that BOPDMD provides both accurate one- to seven-day-ahead crash forecasts and interpretable representations of underlying risk dynamics. The revealed modal structures highlight how urban crash risk shifts between stable mobility patterns and externally driven disruptions. These insights can support proactive safety management by enabling zone level risk monitoring, prioritization of high risk areas, and the design of targeted interventions for both persistent hotspots and disruption induced risk shifts in complex urban environments.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-10"},"PeriodicalIF":1.9,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147857665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on driving fatigue detection and arousal based on brain functional connectivity networks.","authors":"Huoping Lu, Bangbei Tang, Yan Li, Mingxin Zhu","doi":"10.1080/15389588.2026.2650664","DOIUrl":"https://doi.org/10.1080/15389588.2026.2650664","url":null,"abstract":"<p><strong>Objective: </strong>Driver fatigue poses a serious threat to road safety. This study presents a method for detecting driving fatigue and initiating wakefulness based on electroencephalogram (EEG) signals.</p><p><strong>Methods: </strong>A total of 1,230 EEG samples were collected from 30 drivers during simulated driving. These samples were decomposed into θ, α, and β frequency bands using Discrete Wavelet Transform (DWT). A brain functional connectivity network was constructed based on the Phase-Lag Index (PLI) to extract features. CNN-LSTM, Transformer, and logistic regression models were trained to evaluate arousal effects under visual, olfactory, auditory single-modality, and multimodal conditions.</p><p><strong>Results: </strong>Results showed that the β frequency-band dataset achieved the highest average accuracy (0.69), with the Transformer temporal classification model performing best (accuracy 0.76). All arousal protocols effectively alleviated fatigue (<i>p</i> < 0.05), with the multimodal visual,auditory,and olfactory approach yielding the strongest effect, reducing fatigue levels by an average of 2.633 points. The arousal effect was more pronounced at higher fatigue levels.</p><p><strong>Conclusions: </strong>This study provides a theoretical foundation for fatigue monitoring and intervention in intelligent cockpits and autonomous driving systems.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of high-contact sport on driving behavior in automated vehicles: A study involving ice hockey athletes.","authors":"Fabiha Islam, Vipul Lugade, Stanley Hunter, Max Kammerman, Karyssa Bowron, Michael Dulas, Mark Fuchs, Clayton Battle, David Howell, Chao Shi","doi":"10.1080/15389588.2026.2651947","DOIUrl":"https://doi.org/10.1080/15389588.2026.2651947","url":null,"abstract":"<p><strong>Objectives: </strong>Conditionally Automated Vehicles (CAVs) can operate autonomously under specific conditions, requiring the human driver to be cognitively prepared to intervene when the system reaches its operational limits. This reliance on human intervention raises concerns about the drivers' cognitive readiness during takeover. Individuals, including athletes in high-contact sports, may frequently experience concussions, which may lead to cognitive impairments affecting their driving. This study examined the differences in cognitive and driving performance between groups with and without a history of concussion.</p><p><strong>Methods: </strong>Seventeen high-contact sports athletes and seventeen control participants completed takeover tasks in CAV simulator. The takeover tasks required the driver to regain vehicle control when the ADS is particularly unlikely to operate as intended, necessitating cognitive responses within limited timeframe. Mental workload, situational awareness (SA), takeover success, takeover time, manual driving success, and manual driving duration were measured.</p><p><strong>Results: </strong>Results indicated that high-contact sports athletes exhibited longer response time to future oriented SA queries and shorter manual driving duration than control group.</p><p><strong>Conclusion: </strong>These findings may reflect group differences potentially related to concussion history. This study highlights the need for further research into CAV design improvement and clinical guidelines for safe return-to-driving timelines for cognitively impaired drivers.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-8"},"PeriodicalIF":1.9,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Saccade dynamics in different spiral tunnels: An investigation of length and radius effects on driver visual load.","authors":"Bohang Liu, Xingju Wang, Lei Han, Long Li","doi":"10.1080/15389588.2026.2653672","DOIUrl":"https://doi.org/10.1080/15389588.2026.2653672","url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-11"},"PeriodicalIF":1.9,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations of crash, penalty, and demographic factors with hazard perception among Iranian professional drivers.","authors":"Mina Mahmoudi, Emad Heidarian, Ali Hadianfar, Payam Moeini, Maryam Jaberi, Seifollah Gharib","doi":"10.1080/15389588.2026.2648759","DOIUrl":"https://doi.org/10.1080/15389588.2026.2648759","url":null,"abstract":"<p><strong>Objective: </strong>Hazard perception plays a pivotal role in preventing road accidents. Despite control measures in developing countries, crash and injury rates remain high, indicating limitations in current strategies. Framed within Endsley's Situational Awareness Theory, this study examined associations between crash history, traffic penalties, and demographic factors (age and education) with hazard perception among Iranian professional drivers.</p><p><strong>Methods: </strong>This cross-sectional study included 220 Iranian professional car drivers (age range: 23-75 years; <i>M</i> = 48.3, SD = 10.4). Participants completed a demographic questionnaire and the standardized Hazard Perception Test (HPT) validated for the Iranian context. Data were analyzed using univariate tests and a General Linear Model (GLM).</p><p><strong>Results: </strong>The mean hazard perception score was remarkably low at 35.60 ± 15.68 (out of 100), with an average error rate of 3.68 ± 1.91 missed hazards. Drivers with no crash history in the past three years scored 11.68 points higher on average than those with crash involvement (<i>p</i> < 0.001). Higher penalty frequency was associated with lower hazard perception scores (<i>p</i> < 0.001). In the GLM, crash history (β = 11.68, 95% CI: 8.14-15.21, <i>p</i> < 0.001) and penalty frequency (β = -3.62, <i>p</i> < 0.001) remained significant predictors, while age, education, gender, and driving experience showed no independent association (all <i>p</i> > 0.05). This performance level is substantially lower than scores typically reported in high-income countries with mandatory HPT in licensing (often >50-60% among experienced drivers).</p><p><strong>Conclusions: </strong>Crash and penalty history were strongly linked to poorer hazard perception, highlighting behavioral factors as key risk markers. The HPT effectively distinguished high- from low-risk drivers, supporting its use as a screening tool for targeted interventions in settings with limited systemic protections. These findings extend Situational Awareness theory to low- and middle-income contexts and emphasize the need for context-specific training.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maxillofacial trauma secondary to airbag deployment: A scoping review.","authors":"Edson Tadamitsu Tokashiki, Laís Albuquerque Fernandes, Leandro Bottura, Natacha Kalline de Oliveira, Fernando Melhem-Elias, Ricardo Grillo","doi":"10.1080/15389588.2026.2642122","DOIUrl":"https://doi.org/10.1080/15389588.2026.2642122","url":null,"abstract":"<p><strong>Objectives: </strong>Motor vehicle accidents remain a leading cause of craniofacial trauma, with injury severity evolving alongside automotive safety advancements. While airbags and seatbelts have revolutionized trauma prevention, reducing worldwide mortality by over 70,000 lives in five years, their mechanics can paradoxically modify or exacerbate facial injuries due to occupant positioning, chemical factors, and collision dynamics. This study examines injury patterns, mechanisms, and trauma prevention strategies related to airbag-related maxillofacial trauma.</p><p><strong>Methods: </strong>A scoping review was conducted across PubMed, Google Scholar, and Scopus (up to October 2025). Search terms included \"airbag,\" \"maxillofacial injuries,\" and \"occupant restraint system injuries.\" Inclusion criteria focused on human studies reporting airbag-related facial trauma. Two reviewers independently screened literature, resolving discrepancies via consensus.</p><p><strong>Results: </strong>Orbital fractures (particularly blow-out fractures) and ocular trauma dominated reported injuries, attributed to blunt force distribution during a car crash with airbag deployment. Soft tissue lesions, chemical burns, and atypical fractures were also documented. Case analyses revealed that injury severity and pattern were highly variable, significantly influenced by risk factors such as pre-impact braking, seatbelt nonuse, and close occupant proximity to the steering wheel. These findings underscore that trauma prevention strategies must extend beyond the presence of safety devices to include public education on optimal occupant positioning and restraint system interactions. Furthermore, continued technological refinements aimed at mitigating deployment kinetics and chemical risks remain critical.</p><p><strong>Conclusion: </strong>Airbags provide indispensable protection in motor vehicle collisions, yet a balance between their lifesaving benefits and potential for injury requires multidisciplinary collaboration. Future efforts should integrate biomechanical research, clinical findings, and policy updates to improve occupant safety and optimize protective outcomes.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-7"},"PeriodicalIF":1.9,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sharing the road with emergency vehicles: drivers' understanding of Queensland road rules.","authors":"Daniel Malone, Chris Irwin, Darren Wishart, Alexander MacQuarrie, Matthew Stainer","doi":"10.1080/15389588.2026.2648057","DOIUrl":"https://doi.org/10.1080/15389588.2026.2648057","url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-8"},"PeriodicalIF":1.9,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing cognitive load in drivers during tunnel approach under combined fog and nighttime conditions based on fixation behavior.","authors":"Bohang Liu, Xingju Wang, Lei Han, Long Li","doi":"10.1080/15389588.2026.2650661","DOIUrl":"https://doi.org/10.1080/15389588.2026.2650661","url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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 <i>post-hoc</i> tests were employed to compare these metrics across scenarios.</p><p><strong>Results: </strong>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 (<i>p</i> < 0.01).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-13"},"PeriodicalIF":1.9,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hazard perception and prediction model based on cognitive components of male BRT drivers.","authors":"Amir Hossein Zarei, Morteza Asadamraji","doi":"10.1080/15389588.2026.2635562","DOIUrl":"https://doi.org/10.1080/15389588.2026.2635562","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>The results of this research can inform the training, testing, and evaluation of urban BRT drivers.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}