Traffic Inj Prev [JOURNAL]
Sun
200 papers
Alcohol- and cannabis-impaired driving in Chile, 2012-2022: trends in the context of alcohol policy changes and substitution theory.
Nazif-Munoz JI, Ouimet MC
Traffic Inj Prev · 2026 May · PMID 42201309 · Publisher ↗
OBJECTIVE: To examine temporal patterns in self-reported driving under the influence of alcohol (DUIA) and cannabis (DUIC) in Chile between 2012 and 2022, and to assess whether trends are consistent with substitution or...
OBJECTIVE: To examine temporal patterns in self-reported driving under the influence of alcohol (DUIA) and cannabis (DUIC) in Chile between 2012 and 2022, and to assess whether trends are consistent with substitution or complementarity between substances in the context of strengthened alcohol-impaired driving sanctions introduced by the law (2014), and comparatively weaker cannabis enforcement. METHODS: We analyzed six waves of repeated cross-sectional data (2012-2022) of the nationally representative Chilean Drugs Population Survey ( = 26,242 licensed drivers). Outcomes were self-reported DUIA and DUIC, analyzed separately and jointly. Survey year served as a proxy for exposure to Chile's traffic laws. Descriptive prevalence estimates were calculated across waves. Temporal patterns were examined using survey-weighted logistic regression models for DUIA and DUIC and multinomial logistic regression models comparing DUIA only, DUIC only, and combined DUIA and DUIC (DUIA&C) relative to no impaired driving. Models were adjusted for sex, age, age at alcohol onset, household income, and administrative region. RESULTS: Most respondents reported no impaired driving, ranging from 84.6% in 2012 to 88.8% in 2022. DUIA alone was reported at 12.9% in 2012 and 7.9% in 2022, DUIC alone was measured at 0.8% to 1.6% respectively, and DUIA&C remained rare (1.2-1.9%). Adjusted logistic regression showed that the odds of DUIA, when compared to 2012, went from 0.897 (95% CI: 0.798-1.008) in 2014 to 0.600 (95% CI: 0.526-0.685) in 2022 with the difference between these two years being statistically significant = 4.46, < .05). In contrast, the odds of DUIC increased from 1.345 (95% CI: 1.043-1.736) in 2014 to 1.673 (95% CI: 1.290-2.172) in 2022, with this difference also reaching statistical significance ( = 1.97, < .05). Multinomial analyses revealed a statistically significant divergence between trends in DUIA and DUIC (difference in slope = 0.132; 95% CI: 0.101-0.164; = 8.16, < .001), indicating that while alcohol-impaired driving decreased, cannabis-impaired driving rose. CONCLUSIONS: Following the 2014 laws, DUIA in Chile declined substantially, suggesting the contribution of strengthened alcohol-focused interventions. In contrast, DUIC increased modestly, suggesting potential substitution behaviors in response to stricter alcohol regulations and weaker cannabis enforcement. These findings highlight the importance of considering cross-substance dynamics in road safety policies and underscore the need for integrated strategies that target both alcohol and cannabis, to prevent unintended behavioral adaptations and enhance overall traffic safety.
Evaluation of the effectiveness of a road safety education program implemented in primary schools in Bouaké, Côte d'Ivoire, West Africa.
Bonnet E, Ikpo D, Kodo M … +4 more , Ikpo S, Azongnibo M, Monnier V, Ridde V
Traffic Inj Prev · 2026 May · PMID 42201265 · Publisher ↗
OBJECTIVE: Road traffic injuries are a leading cause of death among children in Africa, especially in urban areas with inadequate infrastructure. Despite this, few scientifically taught educational initiatives to address...
OBJECTIVE: Road traffic injuries are a leading cause of death among children in Africa, especially in urban areas with inadequate infrastructure. Despite this, few scientifically taught educational initiatives to address this issue at the school level have been implemented in West Africa. METHODS: This quasi-experimental study evaluated a road safety education program conducted in 11 primary schools in Bouaké, Côte d'Ivoire. The initiative targeted 509 students in Grade 4 (CM1) and was implemented in three formats: (1) printed textbooks, (2) animated videos, and (3) a combined textbook and video approach. Data were collected before and after the intervention using a 59-item questionnaire measuring knowledge, behaviors, and self-efficacy related to road safety. A difference-in-differences analysis using generalized linear models targeting the program's effectiveness. RESULTS: During the 7 month study period, a decrease in the number of crashes involving students was observed near the intervention schools. The combined textbook and video group achieved the greatest improvement across most outcomes, including road sign knowledge (+200%) and safe crossing behaviors. The textbook-only group also generated significant gains. The video-only group had limited impact. No significant change was found in self-efficacy. CONCLUSION: A hybrid educational intervention using printed and audio-visual materials improved road safety knowledge and behaviors among primary school children in an urban African setting. These results support the integration of context-adapted, multimodal road safety programs into national education strategies.
Stochastic simulation of an adaptive cruise control (ACC) system in electric vehicles to investigate human perceptions of safety, consistency, and comfort.
Peng J, Kang Z, Park BB
Traffic Inj Prev · 2026 May · PMID 42201255 · Publisher ↗
OBJECTIVE: This study evaluates and compares the performance of adaptive cruise control (ACC) systems in an internal combustion engine vehicle (ICEV) and an electric vehicle (EV) using stochastic simulation, based on mea...
OBJECTIVE: This study evaluates and compares the performance of adaptive cruise control (ACC) systems in an internal combustion engine vehicle (ICEV) and an electric vehicle (EV) using stochastic simulation, based on measures of safety, consistency, and comfort. METHODS: The safety, consistency and comfort measures were mapped to ACC system performance measures such as spacing distance and acceleration. Next, a high-fidelity and validated ACC simulation model was adapted to create two ACC systems: One system, called ACC-EV system, was composed of an EV automatically following an ICEV, and the other system, called ACC-ICEV system, was composed of an ICEV automatically following an ICEV. In addition, two scenarios were developed to evaluate whether and how much the three measures might be different between the two ACC systems. The first scenario simulated an extreme deceleration event, and the second scenario represented a repeated stop-and-go traffic event. RESULTS: For both scenarios, the ACC-EV system, when compared with the ACC-ICEV system, showed significantly higher safety, consistency, and comfort, with < 0.001 for all measures. In addition, the magnitude of the differences between the ACC-EV system and the ACC-ICEV system was substantially higher for the extreme deceleration event scenario than for the repeated stop-and-go traffic event scenario across all three measures. CONCLUSIONS: We were able to show corroborating evidence that ACC-EV system can provide safer, more stable, and more comfortable driving behavior compared to those of the ACC-ICEV system for the two distinctive scenarios. Furthermore, the results imply that a human driver using the ACC-EV system might feel less stressed and require less cognitive effort to maintain constant vigilance due to the enhanced safety, consistency, and comfort, contributing to traffic accident prevention.
EEG-based cognitive load assessment of eHMIs in pedestrian-automated vehicle interactions: A real-world experiment.
Zhao S, Xue X, Luo B … +2 more , Tang T, Xu C
Traffic Inj Prev · 2026 May · PMID 42172547 · Publisher ↗
OBJECTIVE: External human-machine interfaces (eHMIs) are an emerging technology designed to facilitate communication between road users and highly automated vehicles (HAVs). This study conducted a real-world experiment t...
OBJECTIVE: External human-machine interfaces (eHMIs) are an emerging technology designed to facilitate communication between road users and highly automated vehicles (HAVs). This study conducted a real-world experiment to evaluate and quantify the cognitive load of pedestrians crossing in front of an eHMI-equipped HAV, utilizing electroencephalography (EEG) data, an objective measure that addresses limitations of prior self-report methods. METHODS: A real-world experiment with 24 participants employed a 4x2 repeated-measures design. The independent variables were four eHMI types (no eHMI, light-band, text-based and symbol-based) and two vehicle kinematic conditions (yielding vs. non-yielding). Participants performed road-crossing tasks in front of a HAV. EEG data were recorded and analyzed using microstate analysis. A cognitive load quantification model was developed by integrating multiple microstate temporal parameters (average duration, occurrence frequency, coverage) the entropy weight method. RESULTS: In the simple one-on-one interaction scenario, the symbol-based eHMI consistently showed the lowest cognitive load values. Nevertheless, cognitive load did not differ significantly across eHMI types in either the yielding or non-yielding condition. Overall, pedestrians experienced lower cognitive load in non-yielding conditions than in yielding conditions. CONCLUSIONS: In low-complexity interaction scenarios, intuitive eHMI designs impose only minimal additional cognitive demand, while vehicle kinematics remain the primary determinant of pedestrians' cognitive load. The proposed EEG-based quantitative framework offers methodological support for the evaluation and optimization of more complex centralized eHMI designs that convey the behavioral intentions of multiple surrounding HAVs.
The dynamics of driving performance in non-optimal human states: Drowsiness, high cognitive load, and acute stress.
Bakhchina AV, Arutyunova KR, Varenov MV … +3 more , Vlasov RA, Filimonov AV, Shishalov IS
Traffic Inj Prev · 2026 May · PMID 42172069 · Publisher ↗
OBJECTIVES: Non-optimal driver states, such as drowsiness, high cognitive load, and stress, constitute significant factors contributing to risks on the road and traffic accidents. In this work, we aimed to describe the d...
OBJECTIVES: Non-optimal driver states, such as drowsiness, high cognitive load, and stress, constitute significant factors contributing to risks on the road and traffic accidents. In this work, we aimed to describe the dynamics of driving performance on highway and urban roads across non-optimal states viewed on the unified arousal scale, from drowsiness to stress. METHODS: Overall, 1240 (44% female, age = 41, SD = 14.40) drivers took part in experiments modeling states of decreased (fatigue, drowsiness) or increased (high cognitive load, stress) arousal in a simulated driving task. A set of metrics was selected to evaluate driving performance in highway and urban environments in terms of decline and improvement, which included measures of lateral control, longitudinal control, and response properties. RESULTS: Our analyses have shown that different non-optimal driver states can have similar negative effects on performance measures, and the results mostly fit the predictions of the Yerkes-Dodson Law. At the same time, we observed that changes in performance can be multidirectional: a drop in one metric may be accompanied by an improvement of another. Thus, patterns of driving performance in non-optimal states vary and can be adaptive, allowing a driver to maintain safe and efficient driving behavior. However, overall, low arousal on highway was associated with declines in lateral control measures and response properties. Additional tasks increasing arousal on highway improved lane keeping performance but were associated with more over speeding events and reduced response measures. Increased cognitive load and acute stress on urban roads reduced lane keeping performance. CONCLUSIONS: Our results highlight the importance of looking at combinations of metrics and their patterns that are characteristic for different non-optimal driver states and road types. Identifying maladaptive driving patterns that can be observed across human states on the arousal scale and specific for different road environments is pivotal for developing and validating driver monitoring systems.
Fifty year trends in U.S. societal costs and HARM from motor vehicle crashes.
Viano DC
Traffic Inj Prev · 2026 May · PMID 42160709 · Publisher ↗
OBJECTIVE: The 50 year trends were determined for societal costs and injury HARM from motor vehicle crashes in the U.S. The injury and fatality HARM provides a basis to set priorities and for cost-benefit analyses in aut...
OBJECTIVE: The 50 year trends were determined for societal costs and injury HARM from motor vehicle crashes in the U.S. The injury and fatality HARM provides a basis to set priorities and for cost-benefit analyses in automotive safety. The over- and under-counting of injuries and deaths was analyzed, and the costs for injury fraud, malingering, victim psychology, and product liability were evaluated. METHODS: NHTSA published eight studies since 1970 showing societal costs for motor vehicle crashes, including medical costs, property damage, productivity losses, insurance administration, losses to other individuals, employer losses, funeral costs, community service losses, pain and suffering, and miscellaneous accident costs. The trend in costs was determined for: (1) total societal costs, (2) injury HARM, (3) fatalities, (4) nonfatal injuries, (5) MAIS 4 + F, and (6) MAIS 1-3 injuries. The costs were adjusted for inflation to 1971 dollars ('71$) based on the annual CPI (Consumer Price Index). A linear trend line was fit to the various costs with the goodness of fit given by the correlation coefficient (R). RESULTS: The societal cost (C) of motor vehicle crashes was $339.8B in 2019 up from $46.0B in 1971. This was a 7.39-fold increase, or 13.0%/yr annual increase. The linear fit gave = $6.19B (yr - 1,967), = 0.978. The compound increase was 4.14%/yr. The injury HARM cost (H) from fatal and nonfatal injuries was $309.1B in 2019 up from $38.1B in 1971. This was an 8.01-fold increase, or 14.3%/yr annual increase. The linear fit gave = $5.63B (yr - 1,968), = 0.972. The compound increase was 4.64%/yr. After adjusting for inflation, the societal cost was '71$52.5B in 2019. This was a 1.142-fold increase or 0.291%/yr annual increase. The linear fit gave ('71$) = $0.283B (yr - 521), = 0.554. The '71$ compound increase was 0.200%/yr. HARM cost increased to '71$47.8B in 2019. This was a 1.238-fold increase or 0.486%/yr annual increase. The linear fit gave ('71$) = $0.333B (yr - 625), = 0.62. The '71$ compound increase was 0.644%/yr. The greatest increase in injury costs was for MAIS 2-4 injuries with only a modest increase in a fatality cost from $200,700 in 1971 to '71$247,442 in 2019. NHTSA added quality of life costs to the economic costs giving a high value for a statistical life (VSL), increasing the value of life in cost-benefit analyses. CONCLUSIONS: The societal cost and injury HARM from motor vehicle crashes increased above inflation over the past 50 years. NHTSA should set 5-year targets and track long-term trends for fatalities and HARM. Cost-benefit analyses depend on VSL, which is based on a willingness to pay. VSL over-values a life. NHTSA over- and under-counts injuries and deaths. The societal costs do not adequately address criminal and insurance fraud, like the Queens and helpful wave schemes, malingering, manipulation of medical imaging and treatments, victim psychology, Lithuanian experiences, and product liability.
Conflict angle analysis at multilane roundabouts using naturalistic vehicle trajectory data from UAVs.
Anagnostopoulos A, Ziakopoulos A, Kehagia F … +3 more , Theofilatos A, Kopelias P, Eliou N
Traffic Inj Prev · 2026 May · PMID 42160702 · Publisher ↗
OBJECTIVE: Roundabouts are crucial for improving traffic safety by reducing crashes, driving speeds, and conflict points during navigation. In Greece, the growing number of modern roundabouts necessitates a comprehensive...
OBJECTIVE: Roundabouts are crucial for improving traffic safety by reducing crashes, driving speeds, and conflict points during navigation. In Greece, the growing number of modern roundabouts necessitates a comprehensive assessment of their safety performance. This study aims to enhance current knowledge by employing Surrogate Safety Measures (SSMs) to investigate conflict angles at modern multilane roundabouts in Greece using naturalistic vehicle trajectory data. To the extent of the authors' knowledge, this is the first study that explicitly analyzes conflict angle as the response variable using naturalistic UAV data. METHODS: Detailed vehicle trajectory data from four modern multilane roundabouts were collected through Unmanned Aerial Vehicle (UAV) video recordings and processed using vehicle tracking software. The SSM Post-Encroachment Time (PET) was calculated, and a dataset incorporating driving behavior variables was developed. A mixed-effects Beta-regression Model (BRM) with site-varying random intercepts was applied to model conflict angles and identify the geometric and vehicular parameters influencing them. RESULTS: The modeling results revealed that the location of conflicts within the roundabout significantly affects the conflict angle. Furthermore, site-specific characteristics were found to have a substantial impact, emphasizing that roundabout safety performance can vary by location. CONCLUSIONS: The findings provide new insights into the factors affecting conflict angles at multilane roundabouts. These insights have important implications for both vehicle dynamics and infrastructure design, supporting policymakers and engineers in developing targeted strategies to further improve safety at modern roundabouts.
Facilitators and barriers to reducing motorcyclist casualties in Thailand: A qualitative study of stakeholder perspectives.
Jongwiriyanurak N, Christie N, Tanaksaranond G … +2 more , Haworth J, Anvari B
Traffic Inj Prev · 2026 May · PMID 42138992 · Publisher ↗
OBJECTIVE: Motorcycle-related fatalities account for most road deaths in Thailand, yet systemic barriers hinder progress. This study explores stakeholders' perspectives on challenges and opportunities for improving motor...
OBJECTIVE: Motorcycle-related fatalities account for most road deaths in Thailand, yet systemic barriers hinder progress. This study explores stakeholders' perspectives on challenges and opportunities for improving motorcycle safety. METHODS: Ten semi-structured interviews were conducted with stakeholders from government, law enforcement, health, NGOs, and academia. Data were analyzed using template analysis guided by Haddon's Matrix. RESULTS: Key barriers included inadequate infrastructure, weak enforcement, sociocultural norms, fragmented institutional coordination, and underutilization of data and technology. Suggested strategies included speed management, enforcement of drink-driving laws, helmet detection systems, improved licensing, and national-level coordination. CONCLUSIONS: A locally adapted Safe System approach integrating infrastructure, enforcement, education, and technology is essential. Findings offer hypothesis-generating insights for policy and future research in LMICs.
New modelling of equations for Delta-V calculation in low-speed rear-end collisions between vehicles.
Garrote-Márquez Á, Balboa-Vega S, Fernández-Cabrera D … +3 more , de-la-Prada-Rus R, Pro-Martín JL, Zabala-Ibáñez I
Traffic Inj Prev · 2026 May · PMID 42138990 · Publisher ↗
OBJECTIVE: Rear-end vehicle collisions at low speeds are among the most common types of traffic accidents in Europe, often leading to whiplash injuries. Precise estimation of the change in velocity (Delta-V, DV) is essen...
OBJECTIVE: Rear-end vehicle collisions at low speeds are among the most common types of traffic accidents in Europe, often leading to whiplash injuries. Precise estimation of the change in velocity (Delta-V, DV) is essential for accident reconstruction, particularly when evaluating injury thresholds. Traditional momentum-based methods assume conservation of linear momentum and neglect energy losses, which may produce significant inaccuracies when the striking vehicle brakes before impact. This study aimed to evaluate the discrepancies between theoretical and empirical DV under braking conditions and to develop a practical correction methodology to enhance the accuracy of accident reconstructions. METHODS: Crash test data were extracted from the AGU Zürich database in collaboration with the Dynamic Test Center (DTC) and insurance partners. Ninety-seven low-speed rear-end collisions (≤21 km/h) involving contemporary vehicles (manufactured post-1998) were selected. Two groups were defined: 21 tests without braking and 76 tests with braking applied by the striking vehicle. Empirical and theoretical DVs were calculated using classical restitution-momentum equations. Errors were analyzed for both groups. Linear correlation analyses were conducted to identify predictors of error, and multiple regression models were developed using vehicle mass and differences in momentum (). Cross-validation techniques were employed to minimize bias given the relatively small dataset. RESULTS: Predictive regression lines were then generated to correct DV estimates in braking scenarios because for the unbraked group, empirical and theoretical DVs were closely aligned, with mean errors below 4% for nearly all cases. In contrast, collisions with braking exhibited substantially greater discrepancies, with mean errors exceeding 11%. Strong correlations were observed between and DV errors (). Multiple linear regression using the mass of the striking vehicle and achieved higher predictive accuracy ( for errors in DV of the striking vehicle and for DV of struck vehicle). Application of the corrective regression models reduced DV errors to approximately 3%, representing an improvement in accuracy. CONCLUSIONS: This study confirms that braking in low-speed rear-end impacts disrupts momentum conservation, leading to systematic under- or over-estimation of DV using traditional methods. By incorporating vehicle mass and momentum differences into regression-based corrections, DV estimates can be substantially improved using minimal and easily obtainable parameters. The proposed workflow offers accident reconstructionists a practical, empirically validated method to enhance the reliability of DV calculations in braking scenarios. While results are robust across diverse makes and models, future research should expand to other impact types, such as lateral and oblique collisions.
Explainable modeling of lane-changing risk assessment incorporating driving-behavior uncertainty.
Yao W, Yang X, Xiang J … +5 more , Jin S, Bai C, Yang C, Wang W, Zhang C
Traffic Inj Prev · 2026 May · PMID 42138711 · Publisher ↗
OBJECTIVE: Lane-changing is one of the most common driving maneuvers. Nevertheless, dangerous lane-changing can pose a serious threat to surrounding innocent vehicles. Modeling lane-changing risks is crucial for understa...
OBJECTIVE: Lane-changing is one of the most common driving maneuvers. Nevertheless, dangerous lane-changing can pose a serious threat to surrounding innocent vehicles. Modeling lane-changing risks is crucial for understanding dangerous driving behaviors and preventing hazardous accidents. METHODS: When assessing lane-changing risk based on the physical processes of lane-changing behavior, there are certain limitations in considering the uncertainty of driving behavior. This study proposes a lane-changing risk assessment model that explicitly incorporates the uncertainty of driving behavior. By integrating a risk attenuation function and computing the definite integral of risk values across varying acceleration scenarios, the model comprehensively accounts for diverse driving behaviors to evaluate lane-changing risk. A subset of lane-changing samples is extracted from the NGSIM and highD datasets for model analysis and evaluation. RESULTS: The results show that, when compared with four commonly used lane-changing risk assessment indicators or models, the model proposed in this study performs better in identifying instantaneous lane-changing risks and predicting the deceleration behavior of the adjacent following vehicles. CONCLUSIONS: The risk model proposed in this study provides new ideas for lane-changing risk estimation and can effectively support the construction of human-like autonomous driving systems.
An examination of real-world disengagement patterns of automated driving systems in autonomous-mode: A deep-learning method.
Garakani G, Avetisyan L, Zhou F … +2 more , Guo H, Bao S
Traffic Inj Prev · 2026 May · PMID 42138693 · Publisher ↗
OBJECTIVE: Widespread adoption of automated driving systems (ADS) depends fundamentally on their operational reliability. Frequent or unexpected disengagements pose a significant barrier by undermining user trust. Althou...
OBJECTIVE: Widespread adoption of automated driving systems (ADS) depends fundamentally on their operational reliability. Frequent or unexpected disengagements pose a significant barrier by undermining user trust. Although human-automation interaction has been studied, key gaps remain in understanding contemporary disengagement trends, their underlying causes, and the implications of disengagement type (ADS-initiated versus driver-initiated). This paper addresses these gaps to provide insights for future technical development and policy frameworks. METHODS: This analysis utilized 23,505 disengagement reports from the California DMV's Autonomous Vehicle Tester program, covering Automated Driving Systems (ADS) at SAE Levels 3-5 for the period 2019-2022. A deep learning-based natural language processing (NLP) pipeline was developed to extract causal factors from unstructured disengagement text descriptions, followed by manual validation to classify events into seven root-cause categories: Control, Planning, Perception, Software/Hardware, Localization/Mapping, Prediction, and Other Road Users. Temporal trends and associations between disengagement types and causal factors were evaluated using statistical analyses, including logistic regression. RESULTS: Three key findings were identified. First, a significant temporal decline was observed in the proportion of disengagements initiated by the ADS ( < 0.01). Second, the root causes of disengagements exhibited a strong dependence on the initiator. Driver-initiated disengagements were predominantly associated with deficiencies in Perception, Planning, and Localization/Mapping. Conversely, ADS-initiated disengagements were overwhelmingly linked to Control-related failures. Finally, the failure profile of ADS-initiated disengagements evolved significantly between 2019 and 2022. Control-related issues, initially the dominant cause, became rare, while Software/Hardware issues emerged as the new leading cause, occurring at a rate three times that of driver-initiated disengagements by 2022. DISCUSSION: While declining ADS-initiated disengagements signal improved system robustness, persistent failures in Control and emerging Software/Hardware issues indicate unresolved challenges. The divergent causes of driver- versus ADS-initiated events-suggesting preemptive human intervention versus forced system handovers-reveal distinct operational failure modes. The increasing prominence of Software/Hardware problems likely stems from growing system complexity. Consequently, targeted edge-case validation and adaptive trust calibration are critical for future ADS development.
Assessment of road accessibility to medical demand points under the influence of specific speed reduction parameter settings.
Huang Y, Dong Y, Liu D … +3 more , Xiong L, Liu F, Liu W
Traffic Inj Prev · 2026 May · PMID 42138687 · Publisher ↗
OBJECTIVE: To address the dual constraints of icy-snowy conditions in winter and traffic congestion on expressway medical rescue efficiency, this study constructs an accessibility assessment model integrating multiple ro...
OBJECTIVE: To address the dual constraints of icy-snowy conditions in winter and traffic congestion on expressway medical rescue efficiency, this study constructs an accessibility assessment model integrating multiple road condition scenarios. It reveals the spatiotemporal accessibility characteristics of rescue services along expressways in cold regions, providing a basis for optimizing resource allocation and improving emergency response time. METHODS: Taking Shenyang's expressway network as the study area, this study constructs a medical rescue accessibility assessment model using 307 hospital POIs and 9,240 accident demand points with a time-weighted method. Real-time travel times and distances at 120 assessment time points are obtained via the Baidu Map API. An "ice-snow coefficient" () is introduced to quantify the impact of varying icy-snowy road conditions on vehicle speed, enabling multiscenario simulation by coupling normal congestion with icy-snowy conditions. RESULTS: The overall accessibility of expressway medical services in Shenyang is 45.51%, rated as "medium." As icy-snowy conditions worsen (decrease from 1.0 to 0.6), accessibility drops to 28.83%, average travel time increases from 1,618 s to 2,351 s, and speed decreases from 16.46 m/s to 11.33 m/s. Accessibility shows a spatial pattern of "high in the center, low in the periphery." Under icy-snowy conditions, accessibility during evening rush hours, in mountainous areas, and along provincial boundary sections deteriorates sharply, with most sections failing to meet the golden rescue time when λ ≤ 0.8. CONCLUSIONS: Icy-snowy road conditions significantly weaken expressway medical rescue accessibility in cold regions, and existing resource allocation struggles to meet emergency needs under combined congestion and icy-snowy conditions. This indicates inadequate expressway medical rescue services in China's cold regions, with emergency response capabilities compromised by seasonal icy-snowy weather. This study elucidates the spatiotemporal evolution of accessibility under the joint influence of traffic congestion and icy-snowy conditions, providing insights for proactive resource allocation and optimized scheduling.
LLM-driven causal chain extraction: An interpretable framework for autonomous vehicle crash narrative analysis.
Su H, Cao J, Li Z … +2 more , Tian S, Deng Y
Traffic Inj Prev · 2026 May · PMID 42127411 · Publisher ↗
OBJECTIVE: This study aims to establish an interpretable framework for analyzing root causes of autonomous vehicle (AV) crashes by leveraging unstructured crash narratives. It addresses critical gaps in existing research...
OBJECTIVE: This study aims to establish an interpretable framework for analyzing root causes of autonomous vehicle (AV) crashes by leveraging unstructured crash narratives. It addresses critical gaps in existing research, including fragmented causal attribution and limited utilization of textual data for mechanistic insights. METHOD: We propose an integrated framework that combines Large Language Model (LLM) and Chain-of-Thought (CoT) reasoning to analyze the causal mechanisms of AV crashes using original crash narratives. First, this study employs a sentence-level resampling method to oversample the labeled data. Second, the instruction-tuned LLM is used to extract structured Crash Causality Frames (CCFs), quintuple encoding Movement, Impact, Damage, Effect and Location, from 931 California DMV crash reports. Then, a system-theoretic taxonomy maps CCF elements to 64 causal indicators across five domains. Finally, CoT reasoning generates stepwise natural-language explanations to enhance interpretability. RESULTS: The optimized LLaMA-70B + LoRA model achieved 86.64% Accuracy in CCF extraction, while Data_sCR resampling further improved metrics to 97.93%. Analysis revealed five dominant causation patterns: Pattern 1 (30.5%, pure CV anomalies), Pattern 2 (51.9%, AV-CV interaction failures), and Patterns 3-5 (17.7%, integrating human/environment/, and infrastructure factors). Critical cross-domain couplings were identified (A1 and B2), with rear-end collisions (82.06%) predominating in Pattern 2 scenarios. Moreover, the CoT module generates auditable, step-by-step causal chains to enhance interpretability. Under a practical balance between reliability and computational cost, the accuracy of the generated CoT causal chains reaches 91.04%. CONCLUSION: The framework transforms unstructured crash narratives into interpretable causal models, demonstrating that systemic interactions (particularly AV-CV behavioral mismatches) constitute primary crash catalysts. Practical implications include: (1) Enhancing real-time CV intention prediction V2X, (2) Developing context-aware sensor fusion for adverse environments, and (3) Implementing standardized tester training protocols for takeover scenarios.
Fatbike crashes and severity in a level II trauma center: a 3-month observational study.
Snijders LI, Prins JTH, Barvelink B … +5 more , Ten Kate CA, De Swart ME, Bloem P, Wijers O, van Buijtenen JM
Traffic Inj Prev · 2026 May · PMID 42127383 · Publisher ↗
OBJECTIVE: Fatbikes are rapidly gaining popularity, but data on the severity and characteristics of fatbike-related injuries are lacking. This study compares fatbike-related crashes with those involving city bikes, e-bik...
OBJECTIVE: Fatbikes are rapidly gaining popularity, but data on the severity and characteristics of fatbike-related injuries are lacking. This study compares fatbike-related crashes with those involving city bikes, e-bikes and road bikes. METHODS: This is a retrospective single-center study conducted at a Dutch level II trauma center. Between July and September 2024, all Emergency Department visits due to bicycle crashes were included. The primary outcome was the Injury Severity Score (ISS), with secondary outcomes including demographic characteristics, specific injuries, helmet use and the mechanism of the crash. RESULTS: Of the 468 patients, 24 (5%) were involved in a fatbike crash. They were younger, less likely to wear a helmet (0% vs. 11%; = 0.028) and more frequently involved in a multi-vehicle crash (54% vs. 26%; = 0.008). The median ISS was similar between groups (4.0). CONCLUSIONS: Despite the small proportion of total bicycle crashes, fatbike riders constitute a vulnerable group due to their young age, lack of helmet use and higher involvement in multi-vehicle crashes.
Spatiotemporal analysis of urban traffic crash risk using a bagging-optimized dynamic mode decomposition framework.
Ma X, Zhang Y, Hou N … +2 more , Zhang H, Jin J
Traffic Inj Prev · 2026 May · PMID 42102301 · Publisher ↗
OBJECTIVE: 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...
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Serious injury prediction in motor vehicle crashes: a nonlinear modeling approach using trainable B-spline functions.
Mei Y, Sato F, Miyazaki Y
Traffic Inj Prev · 2026 May · PMID 42102299 · Publisher ↗
OBJECTIVE: 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 pred...
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Research on driving fatigue detection and arousal based on brain functional connectivity networks.
Lu H, Tang B, Li Y … +1 more , Zhu M
Traffic Inj Prev · 2026 May · PMID 42096296 · Publisher ↗
OBJECTIVE: 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. METHODS: A total of 1,23...
OBJECTIVE: 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. METHODS: 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. RESULTS: 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 ( < 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. CONCLUSIONS: This study provides a theoretical foundation for fatigue monitoring and intervention in intelligent cockpits and autonomous driving systems.
Impact of high-contact sport on driving behavior in automated vehicles: A study involving ice hockey athletes.
Islam F, Lugade V, Hunter S … +7 more , Kammerman M, Bowron K, Dulas M, Fuchs M, Battle C, Howell D, Shi C
Traffic Inj Prev · 2026 May · PMID 42090643 · Publisher ↗
OBJECTIVES: 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. T...
OBJECTIVES: 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. METHODS: 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. RESULTS: Results indicated that high-contact sports athletes exhibited longer response time to future oriented SA queries and shorter manual driving duration than control group. CONCLUSION: 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.
Associations of crash, penalty, and demographic factors with hazard perception among Iranian professional drivers.
Mahmoudi M, Heidarian E, Hadianfar A … +3 more , Moeini P, Jaberi M, Gharib S
Traffic Inj Prev · 2026 May · PMID 42085711 · Publisher ↗
OBJECTIVE: 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 w...
OBJECTIVE: 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. METHODS: This cross-sectional study included 220 Iranian professional car drivers (age range: 23-75 years; = 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). RESULTS: 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 ( < 0.001). Higher penalty frequency was associated with lower hazard perception scores ( < 0.001). In the GLM, crash history (β = 11.68, 95% CI: 8.14-15.21, < 0.001) and penalty frequency (β = -3.62, < 0.001) remained significant predictors, while age, education, gender, and driving experience showed no independent association (all > 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). CONCLUSIONS: 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.
Maxillofacial trauma secondary to airbag deployment: A scoping review.
Tokashiki ET, Albuquerque Fernandes L, Bottura L … +3 more , de Oliveira NK, Melhem-Elias F, Grillo R
Traffic Inj Prev · 2026 May · PMID 42085710 · Publisher ↗
OBJECTIVES: 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...
OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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