Accid Anal Prev [JOURNAL]
Sun
200 papers
On the analytical relationship between string stability and traffic safety.
Zhuo J, Zhu F
Accid Anal Prev · 2026 May · PMID 41637872 · Publisher ↗
String stability and traffic safety have both received considerable attention in transportation research. However, the analytical relationship between them remains insufficiently explored. This paper addresses this gap b...
String stability and traffic safety have both received considerable attention in transportation research. However, the analytical relationship between them remains insufficiently explored. This paper addresses this gap by examining four questions: (1) Does string stable traffic imply safe traffic? (2) Does string unstable traffic imply unsafe traffic? (3) Does unsafe traffic imply string unstable traffic? (4) Does safe traffic imply string stable traffic? To investigate these questions, various string stability criteria for both homogeneous traffic and heterogeneous traffic are revisited. Traffic safety is quantified using the Time-To-Collision (TTC) metric, and its connection to string stability is examined through linear stability analysis. The analytical relationships between string stability and safety are derived by incrementally applying conditions from progressing from heterogeneous to homogeneous traffic, providing theoretical answers to the posed questions. The derived relationships are further validated through extensive simulation experiments based on the car-following model calibrated with real-world trajectory data.
Alcohol-involved road safety trends and strategies: insights from U.S. road safety monitoring (2015-2024).
Delavary M, Lyon C, Barrett H … +4 more , Brown S, Wicklund C, Robertson RD, Vanlaar W
Accid Anal Prev · 2026 May · PMID 41633089 · Publisher ↗
Persistent risky behaviours continue to undermine progress reducing traffic fatalities in the United States. Despite the implementation of programmatic interventions combined with increasing awareness, alcohol-impaired d...
Persistent risky behaviours continue to undermine progress reducing traffic fatalities in the United States. Despite the implementation of programmatic interventions combined with increasing awareness, alcohol-impaired driving remains a contributing factor, accounting for nearly 32% of crash deaths in 2022. This study analyzed longitudinal trends in self-reported alcohol-involved driving behaviours, attitudes, and injury outcomes from 2015 to 2024 using Road Safety Monitoring survey data, supported by national fatality statistics from the Fatality Analysis Reporting System. The objective was to identify demographic and behavioural predictors of high-risk outcomes and track patterns over time, especially during periods of disruption. The application of logistic regression models to the survey data showed males were 2.1 times more likely to engage in impaired driving than females and drivers with multiple traffic tickets were more than tenfold likely to do so. The percentage of individuals reporting driving over the legal alcohol limit among those who consumed any level of alcohol in the past 12 months (19,173 out of 26,639 in total) increased from 8.83% in 2015 to 23.99% in 2024. Meanwhile, ride-share use to avoid impaired driving rose from 18.7% in 2016 to 46.4% in 2024. Results indicate a troubling pattern: self-reported alcohol-impaired driving increased significantly between 2015 and 2021 and remained elevated in subsequent years. High levels of public concern about alcohol- and cannabis-impaired driving, along with rising traffic-related injuries, underscore the urgent need for targeted prevention strategies that align with Vision Zero and Safe System goals by addressing the behaviours and groups most at risk.
Which approach better samples extreme traffic conflicts? Conventional- vs. machine learning-based sampling methods.
Hasanpour M, Chen Z, D'Agostino C … +2 more , Persaud B, Milligan C
Accid Anal Prev · 2026 May · PMID 41633088 · Publisher ↗
Extreme value theory has been receiving much attention of late for proactively estimating crash risk through a two-step procedure that first samples extreme traffic conflicts and then estimates crash risk based on those...
Extreme value theory has been receiving much attention of late for proactively estimating crash risk through a two-step procedure that first samples extreme traffic conflicts and then estimates crash risk based on those sampled extremes. Although the existing body of research has encapsulated sampling methods within a predominant conventional technique, there is no universally accepted practice on how to efficiently select threshold values, nor on how to evaluate the sampled extreme conflicts alignment with the conceptual crash severity level framework. This research aims to address these issues by employing machine learning-based sampling methods, which do not require predefined thresholds, and by comparing the sampled extremes with the conceptual severity levels, to assess their alignment. After a review of recent developments in machine learning techniques in transportation and other engineering fields, two promising machine learning sampling models, autoencoder neural network and isolation forest, were investigated using a database of vehicle-to-pedestrian conflicts at urban signalized intersections. Sampled extreme conflicts using the machine learning and conventional sampling techniques-as a baseline -were assessed and compared using two criteria: their visual alignment with the conceptual severity level framework, and their compatibility with the extreme value distribution. The results demonstrate that the extreme conflicts selected based on the machine learning methods better mirror the conceptual severity levels than the conventional sampling technique. Moreover, extremes classified by the isolation forest more closely preserve the characteristics of the empirical tail distributions, demonstrating a better contextual representation for modeling with the extreme value distribution compared to the autoencoder neural network and conventional sampling methods.
Identification of high-risk expressway segments using connected vehicle data: an empirical analysis.
Li X, Wang J, Fu T … +3 more , Shangguan Q, Fang S, Li X
Accid Anal Prev · 2026 May · PMID 41633087 · Publisher ↗
Traditional road safety analysis primarily relies on historical crash data, which require long accumulation periods and are constrained by limitations such as insufficient data volume, imprecise location information, and...
Traditional road safety analysis primarily relies on historical crash data, which require long accumulation periods and are constrained by limitations such as insufficient data volume, imprecise location information, and underreporting, potentially leading to biased or delayed assessments of road safety risks. The emergence of connected vehicle (CV) technology provides new opportunities for more timely safety analysis. CVs are equipped with onboard sensors that monitor driving behavior and issue critical warnings, including headway monitoring warnings (HMWs) and forward collision warnings (FCWs). This study aims to proactively identify high-risk expressway segments using CV warning data. Accordingly, an integrated framework is developed, combining spatial hotspot identification and statistical regression modeling. Based on CV data from nine expressways in Shanghai, warning hotspots are identified using Moran's I and Getis-Ord Gi*, indicating locations with spatial clustering of HMWs and FCWs. The relationship between warning frequency and the number of collisions is examined through Poisson and Negative Binomial models estimated with and without incorporating CV warning frequencies as explanatory variables. To address the potential endogeneity between traffic conflicts and collisions, an instrumental variable Poisson model is further employed. The results confirm that HMW and FCW frequencies are positively associated with collisions, and that accounting for endogeneity improves estimation robustness. In addition, hotspot co-occurrence analysis and statistical testing reveal that segments identified exclusively as CV warning hotspots still experience significantly more collisions compared to segments identified as neither warning nor collision hotspots. This suggests that CV warning data can support early detection of emerging safety risks. This study contributes a structured and empirically supported framework that advances the application of connected vehicle data in proactive traffic risk assessment.
Toward early warning of unsafe behavior of excavator operators under time pressure: experimental evidence and EEG-based detection via RCF-IncepLite model.
Cheng B, He X, Huang J … +3 more , Li H, Wu S, Chen H
Accid Anal Prev · 2026 May · PMID 41633086 · Publisher ↗
Time pressure can impair the cognitive functioning of excavator operators, thereby increasing unsafe behaviors and elevating the likelihood of accidents. This study uses a controlled excavator operation task with synchro...
Time pressure can impair the cognitive functioning of excavator operators, thereby increasing unsafe behaviors and elevating the likelihood of accidents. This study uses a controlled excavator operation task with synchronized behavioral observation and electroencephalography (EEG) recording to examine how escalating time pressure alters operators' cognitive states and safety performance. Results show that as the task deadline approaches, the frequency of unsafe behaviors increases significantly, accompanied by heightened beta-band power and elevated engagement index, reflecting potential cognitive overload under time pressure. To facilitate timely identification of these risk-related neural patterns, we develop RCF-IncepLite, a lightweight EEG-based classification model designed for resource-constrained environments. The model achieves 82.3% accuracy while maintaining minimal computational demands, underscoring its potential for future integration into wearable neuro-sensing systems for early warning of unsafe behaviors. This study provides empirical evidence of the cognitive pathways through which time pressure elevates behavioral risk in construction, and offers a practical methodological foundation for advancing proactive accident prevention in fast-paced construction environments.
When feeling safe becomes risky: A VR-EEG-computer vision framework for analyzing cyclist safety in dynamic traffic environment.
Xu L, Lin T, O'Hern S … +4 more , Delbosc A, Chen Z, Kim I, Luo S
Accid Anal Prev · 2026 May · PMID 41621145 · Publisher ↗
The mismatch between cyclists' perceived safety and actual crash risk in mixed-traffic environments is a critical yet underexplored issue in road safety research. While prior studies have focused on static environmental...
The mismatch between cyclists' perceived safety and actual crash risk in mixed-traffic environments is a critical yet underexplored issue in road safety research. While prior studies have focused on static environmental factors, they often overlook the real-time influence of dynamic visual stimuli on risk perception. To address this gap, this study developed a multisource-integrated virtual reality (VR) experimental platform that synchronously captured millisecond-level electroencephalography (EEG) signals from 72 participants, built environment (BE) features, and time-to-collision (TTC) data from VISSIM microsimulation. A Long Short-Term Memory (LSTM) model was used to examine how mismatches emerge between perceived safety and crash risk. Results reveal a 'perceptual relief period' after being overtaken, where cyclists exhibit higher perceived safety despite persistent threats from following vehicles, creating a potentially hazardous temporal window. This mismatch effect is further amplified in environments characterized by high spatial enclosure, complex visual textures, dense vegetation, and low visible vehicle density. These findings suggest that BE features intended to enhance aesthetic appeal or reduce stress may inadvertently impair cyclists' ability to perceive risk in high-conflict areas. This study offers empirical support for an integrated human-vehicle-environment safety framework and calls for interdisciplinary collaboration between neuroscience and transport engineering in the design of safer mobility systems.
Visual attention and driving behavior of male autistic individuals while encountering driving hazards: A driving simulator study.
Mamo WG, Alhajyaseen WKM, Dirix H … +5 more , Brijs K, Vanroelen G, Hussain Q, Wets G, Ross V
Accid Anal Prev · 2026 May · PMID 41616433 · Publisher ↗
Hazard perception is an important aspect of driving competence that significantly contributes to road safety. Allocating sufficient visual attention to hazards and responding accordingly can help reduce the likelihood of...
Hazard perception is an important aspect of driving competence that significantly contributes to road safety. Allocating sufficient visual attention to hazards and responding accordingly can help reduce the likelihood of road crashes. Although hazard perception has been investigated to some extent in autistic individuals, little attention is given to hazards for which attention has to be divided among different hazard sources. The current study assessed visual attention and driving behavior of autistic individuals to hazards, including dividing and focusing attention (DF), environmental prediction (EP), and behavioral prediction (BP) hazards. A total of 53, male participants, 19 autistic and 34 non-autistic individuals participated in the study. All participants completed a driving simulator scenario while wearing an eye-tracking system. The included eye-tracking measures were time to first fixation (TTFF), frequency count (FC), first fixation duration (FFD), and average fixation duration (AFD). The included driving measures were brake reaction time (BRT), minimum time-to-collision (minTTC), and speed change immediately before encountering the hazard. A self-reported appraisal regarding difficulty in managing hazards was also included. A series of Linear Mixed Models (LMM) were computed to assess the effects of participant group (autistic and non-autistic) and hazard types (DF, EP and BP) on the included measures. Comparisons of visual attention between autistic and non-autistic participants when responding to hazards yielded mixed results. For certain hazards, autistic participants demonstrated faster fixation (e.g., DF and BP). In contrast, for other hazards, non-autistic participants exhibited quicker fixation (e.g., EP) and longer average fixation duration (e.g., DF and EP). For some hazards, however, both groups displayed comparable levels of average fixation duration (e.g., BP). Although variations in visual attention to hazards were observed between autistic and non-autistic individuals, these differences did not manifest in driving performance metrics. This is evidenced by the absence of significant interactions between participant groups and hazard types concerning driving measures. However, autistic individuals were more likely to experience crashes involving BP hazards than non-autistic individuals. Notably, inexperienced autistic participants had a higher crash rate on BP hazards compared to non-licensed non-autistic participants. In contrast, the crash rates were comparable between licensed participants in both groups. The study may reflect that pre-driver autistic participants could benefit from hazard perception training, particularly in dealing with BP hazards.
Trajectory planning for traffic safety with dynamic ethical risk adjustment.
Liu C, Hu H, Ma T … +6 more , Zhang Y, Jia M, Li L, Gan J, Qu X, Ran B
Accid Anal Prev · 2026 May · PMID 41610753 · Publisher ↗
The integration of ethical principles into the trajectory planning of connected and automated vehicles remains a critical challenge, balancing technical efficacy with societal values. Current algorithms prioritize ego-ve...
The integration of ethical principles into the trajectory planning of connected and automated vehicles remains a critical challenge, balancing technical efficacy with societal values. Current algorithms prioritize ego-vehicle safety but inadequately address ethical risks for all road users and cultural variations in moral preferences. This study proposes a trajectory planning algorithm with dynamically adjustable ethical risks, introducing two key innovations: (1) an "ethical knob" mechanism that flexibly weights risks between ego vehicles and other road user, enabling region-specific ethical customization, and (2) a hybrid subjective-objective weighting method combining Analytic Hierarchy Process and coefficient of variation to dynamically allocate weights among four ethical principles-utilitarianism, justice theory, deontology, and responsibility ethics. The algorithm embeds these frameworks into a safety potential field model, translating abstract ethics into computable risk costs. Multi-scenario simulations demonstrate that neutral ethical knob settings minimize overall risk costs, while dynamic weighting automatically adapts to environmental changes compared to static approaches. Crucially, the framework avoids predefined "trolley problem" dilemmas by focusing on accident prevention rather than post-collision decisions. By enabling cultural adaptability and transparent ethical trade-offs, this work advances interdisciplinary solutions for socially acceptable autonomous systems.
Parents protect their children when travelling: Exploring traffic safety behavioral intentions through the lens of cognitive appraisal and protection motivation theories.
Vo NDQ, Nguyen-Phuoc DQ, Pervez A … +1 more , Lee JJ
Accid Anal Prev · 2026 Apr · PMID 41592356 · Publisher ↗
Child traffic safety remains a significant public health concern in developing countries such as Vietnam, where private vehicles are the primary mode of transport for children. Despite existing regulations, concerns pers...
Child traffic safety remains a significant public health concern in developing countries such as Vietnam, where private vehicles are the primary mode of transport for children. Despite existing regulations, concerns persist regarding parental adherence to traffic safety practices. This study examines the psychological factors influencing parents' intentions to adopt protective measures (i.e., using child safety equipment and safe driving practices). By integrating Cognitive Appraisal Theory and Protection Motivation Theory, the study develops a comprehensive model that includes perceived severity, perceived vulnerability, response efficacy, self-efficacy, and response cost, with fear acting as a key mediating variable. A structured survey was conducted with 775 parents (443 motorcyclists and 332 car drivers) in Da Nang, Vietnam, and the data were analyzed using PLS-SEM. Results show that fear significantly predicts both child-protective and safe driving behaviors. However, the key predictors of fear differ by vehicle type. For motorcyclists, fear is influenced by perceived vulnerability, response cost, and self-efficacy. For car drivers, response efficacy has the strongest effect, followed by perceived vulnerability and self-efficacy. These findings emphasize the central role of threat appraisal and coping appraisals in shaping parental coping intentions when transporting children and suggest that emotion particularly fear serves as a critical motivational force. The study contributes to traffic safety research by providing an integrated theoretical framework and offering practical implications for designing more effective public safety interventions and policies that target parental behavior in high-risk transportation environments.
Adaptive risk inversion with iterative exploration for high-risk AV-VRU interactions.
Zhao Y, Ni Y, Fan J … +4 more , Wang J, Sun J, Tian S, Wang Q
Accid Anal Prev · 2026 Apr · PMID 41579657 · Publisher ↗
The interaction between vulnerable road users (VRUs) and automated vehicles (AVs) represents a critical aspect of AV safety evaluation, especially under realistic high-risk scenarios. However, existing scenario-based tes...
The interaction between vulnerable road users (VRUs) and automated vehicles (AVs) represents a critical aspect of AV safety evaluation, especially under realistic high-risk scenarios. However, existing scenario-based testing methodologies often inadequately capture VRUs' behavioral complexity and struggle to systematically explore rare yet critical long-tail risks. This limitation diminishes the representativeness and rigor of safety validation, potentially underestimating the risks inherent in complex AV-VRU interactions. To address these shortcomings, this study proposes ARISE (Adaptive Risk Inversion for Scenario Exploration) specifically designed for generating long-tail, high-risk AV-VRU interaction scenarios. The framework integrates failure-driven feedback with interpretable behavioral modeling, systematically identifying, parameterizing, and refining high-risk regions within the scenario space. A multidimensional behavioral model capturing VRU decision-making variability is developed, enabling hierarchical extraction of critical single- and multi-parameter risk intervals with clear interpretability. The proposed ARISE is implemented on Open Natural Driving Intelligence Automotive Simulation Test Environment (OnSite), facilitating iterative scenario generation and systematic evaluation of AV safety performance. Experimental case studies validate that ARISE significantly improves both the diversity and severity of generated high-risk interactions compared to conventional methods. These findings underscore ARISE's capability to effectively bridge the gap between scenario generation and AV safety validation, providing a robust methodological foundation for assessing AVs in challenging and realistic VRU interactions.
From crash reports to safer roads: a multimodal framework integrating vision-language models and street view analysis.
Wu G, Yan X, Zou Y … +1 more , Xie Y
Accid Anal Prev · 2026 Apr · PMID 41579656 · Publisher ↗
Crash narratives and diagrams contain rich causal and contextual information that is often underutilized in traditional road safety analysis, which typically relies on aggregated crash counts and tabulated variables. Suc...
Crash narratives and diagrams contain rich causal and contextual information that is often underutilized in traditional road safety analysis, which typically relies on aggregated crash counts and tabulated variables. Such approaches have limited ability to explain the mechanisms of individual crashes. This study proposes a mechanism-oriented, multimodal framework for scalable and interpretable roadway risk diagnosis. The framework employs a vision-language model to jointly analyze each crash narrative and diagram, constructing an explicit reasoning chain that distinguishes immediate crash-triggering behaviors from underlying contextual constraints. Based on inferred root cause keywords, each crash is decomposed into proportional contributions of five predefined factors: human, vehicle, road, environment, and traffic signal. These attributions are spatially aggregated to identify hotspots with elevated road-related risk. For high-risk locations, street-view imagery is analyzed in the crash context to diagnose potential roadway design deficiencies and generate targeted recommendations. The framework is evaluated using 4,302 crash reports from Massachusetts with expert-annotated attributions. Vision-language models substantially outperform locally trained regression baselines, particularly under imbalanced factor distributions. Among all tested models, the Grok 2 multimodal model achieves the highest agreement with expert annotations. Further analysis shows that the distribution of inferred root cause keywords in the factor attribution space effectively explains how relative factor contributions are formed. The street-view-based diagnostic outputs align closely with real-world engineering issues, demonstrating that combining causal reasoning with multimodal crash data enables practical, mechanism-driven road safety diagnostics beyond frequency-based screening.
A 'Cluster-then-Estimate' Natural Language Processing (NLP) Approach for Classifying Maritime Incident Severity Based on Textual Descriptions.
Chen T, Liang M, Lee WS … +2 more , Cai Y, Meng Q
Accid Anal Prev · 2026 Apr · PMID 41576560 · Publisher ↗
Textual incident description is a vital source for understanding the severity of maritime incident. In the maritime industry, relevant authorities and companies typically rely on manual methods to estimate incident sever...
Textual incident description is a vital source for understanding the severity of maritime incident. In the maritime industry, relevant authorities and companies typically rely on manual methods to estimate incident severity based on textual descriptions. However, manual estimation is less efficient for assessing vessels' operational risk or managing historical incident archives, where a large volume of incidents is involved. Therefore, this study proposes a 'cluster-then-estimate' approach which uses Natural Language Processing (NLP) techniques to automatically estimate the severity level of incidents based upon their textual descriptions. In the proposed approach, Latent Dirichlet Allocation (LDA) is used to group the preprocessed textual descriptions into multiple clusters, with each cluster representing an incident type. Then, Bidirectional Encoder Representation from Transformers (BERT) model is fine-tuned for each cluster to estimate the incident severity based on the descriptions. This study introduces the detailed training schedule for the proposed approach. In the case study, a total of 22,458 incidents, categorized into three incident levels as per the extent of life loss and property damage, are used to train and validate the proposed approach. The proposed approach is compared to several state-of-the-art baseline models. The comparison demonstrates the superior performance of the proposed approach in accurately estimating the severity level of incidents. It manifests that the 'cluster-then-estimate' strategy effectively leverages the strengths of BERT to further enhance its estimation capability. To the best of our knowledge, the proposed approach is one of the first to adopt NLP techniques for incident severity estimation based on textual descriptions, which offers practical value for improving incident assessment and decision-making in the maritime industry.
Evaluation of the safety improvement effects and adaptability of speed limit measures on downhill curved sections of mountainous freeways.
Azati Y, Wang X, Yang X … +2 more , Zaidi SZ, Quddus M
Accid Anal Prev · 2026 Apr · PMID 41570352 · Publisher ↗
Downhill curved segments of mountainous freeways are high-risk locations due to the combined effects of longitudinal potential energy and lateral centrifugal forces, and speed limit optimization is a key measure for miti...
Downhill curved segments of mountainous freeways are high-risk locations due to the combined effects of longitudinal potential energy and lateral centrifugal forces, and speed limit optimization is a key measure for mitigating crash risk on such segments. However, conventional speed limit settings often rely on uniform standards and fail to adequately account for the combined influences of roadway geometry, slope, traffic conditions, and weather, resulting in heterogeneous safety outcomes across different road environments. This study employs a Causal Forest model with explicitly incorporated quarterly time variables, complemented by a Difference-in-Differences (DID) model, to evaluate the safety effectiveness of speed limit optimization on downhill curved segments of the Guidu Freeway. Empirical results indicate that speed limit measures significantly reduce crash risk, with the Causal Forest model estimating an average treatment effect (ATE) of -0.203 (95% confidence interval: -0.213 to -0.193), demonstrating high precision and robustness and outperforming the DID model in estimation stability. Heterogeneity analysis further reveals that the safety benefits are most pronounced on segments with relatively mild horizontal curvature, low curvature variability, and moderate downhill slopes, as well as under moderate traffic volumes (approximately 7,500-9,000 vehicles/day), while higher truck proportions weaken the effectiveness of the measures. In contrast, fog frequency exerts a relatively limited moderating effect, as treatment effects remain negative across different fog conditions. Overall, the findings confirm that speed limit optimization can substantially improve safety on downhill curved segments of mountainous freeways and highlight the importance of accounting for roadway geometry and traffic composition when designing targeted speed management strategies.
When does visual distraction become dangerous in car-following? Evidence from naturalistic driving study data with causal inference on time-to-collision and braking intensity.
Chun U, Abdel-Aty M, Wang Z
Accid Anal Prev · 2026 Apr · PMID 41564451 · Publisher ↗
Visual distraction is a major contributor to crash risk, particularly in car-following situations that demand continuous monitoring and rapid response. Although prior research using simulators and Naturalistic Driving St...
Visual distraction is a major contributor to crash risk, particularly in car-following situations that demand continuous monitoring and rapid response. Although prior research using simulators and Naturalistic Driving Study (NDS) data has advanced our understanding, evidence remains limited on how visual distraction increases risk in real-world contexts and under which conditions it is amplified. Visual distraction is not an isolated factor, but a context-dependent phenomenon shaped by roadway conditions, traffic dynamics, and external stimuli. Beyond measuring its overall effect, it is essential to identify the circumstances in which visual distraction becomes especially hazardous. To address this gap, this study applies causal inference methods to NDS data. A Causal Forest was used to estimate the causal effect of visual distraction on two safety indicators: time-to-collision (TTC) and braking intensity. Subsequently, mediation analysis using Double Machine Learning (DML) was applied to disentangle the extent to which visual distraction mediates driving risk from the portion attributable directly to roadway and traffic conditions, thereby clarifying the indirect behavioral pathways versus structural design effects. Results show that visual distraction significantly reduces TTC, indicating heightened conflict seriousness, whereas its effect on braking intensity was not statistically significant. Mediation analysis further revealed that the effect of visual distraction on TTC varied across contexts, with stronger effects under high traffic density, ADAS-equipped vehicles, wider sidewalks, and fewer lanes. These findings underscore the importance of integrated safety strategies that mitigate visual distraction while also accounting for roadway design, traffic environment, and vehicle technologies in shaping driver behavior and risk.
Classifying experienced male drivers' mental workload on freeway ramps based on heart rate and speed measurements: A real-vehicle experiment.
Wang J, Chen L, Zhu Q … +2 more , He S, Pervez A
Accid Anal Prev · 2026 Apr · PMID 41564450 · Publisher ↗
Freeway ramps are recognized as high-risk segments of the road network due to their geometric complexity and dynamic traffic demands. This study investigates drivers' mental workload in ramp areas by integrating psycho-p...
Freeway ramps are recognized as high-risk segments of the road network due to their geometric complexity and dynamic traffic demands. This study investigates drivers' mental workload in ramp areas by integrating psycho-physiological responses, specifically heart rate growth (HRG), with vehicle kinematic data, including speed and acceleration. Data were collected through real-world driving experiments from 32 experienced male drivers (aged 30-50 years) under both daytime and nighttime conditions. The findings revealed that HRG values were significantly higher at night, indicating increased cognitive stress in low-light conditions. In addition, the study identified a strong linear relationship between HRG and speed across all scenarios, indicating that increased speed is closely associated with higher mental workload. The relationship between HRG and acceleration followed a three-phase pattern, with sharp HRG changes at both low and high acceleration levels, and more stable responses within the mid-range. Based on these relationships, a classification framework was developed to categorize experienced male drivers' mental workload into three workload categories (Class 1, Class 2, and Class 3) using joint thresholds of HRG, speed, and acceleration. These findings provide a data-driven basis for identifying cognitively demanding ramp segments and inform the design of adaptive speed guidance systems, real-time driver monitoring technologies, and ramp infrastructure improvements.
Visual motion contrast thresholds in the periphery predict older drivers' behavior at intersections.
Francoeur V, Saber C, Henderson S … +3 more , Collin C, Yamin S, Gagnon S
Accid Anal Prev · 2026 Apr · PMID 41564449 · Publisher ↗
Peripheral motion contrast sensitivity decline is likely due to a progressive dysfunction of the magnocellular pathway in the aging brain. Previous research from our group had demonstrated that the Peripheral Motion Cont...
Peripheral motion contrast sensitivity decline is likely due to a progressive dysfunction of the magnocellular pathway in the aging brain. Previous research from our group had demonstrated that the Peripheral Motion Contrast Threshold 2-minute test version (PMCT-2) predicts older drivers' hazardous behaviors in simulated driving environments. This study extends this work by examining correlations between PMCT-2 scores and on road driving outcomes at intersections coded from video recordings of fifty older drivers (65-89) navigating predefined urban routes in their own vehicles. We found significant correlations between PMCT-2 and scanning errors at non-signalized and stop-signalized intersections. We also found significant PMCT-2 correlation with driving compliance errors, notably incomplete stops, which was further supported by single-predictor regression using heteroskedasticity-robust estimation. Multiple linear regression analyses further showed that PMCT-2 remained the only significant predictor of stop-sign compliance errors after adjusting for age, gender and scanning error rates at stop signs. In contrast, its relationship with scanning errors was attenuated in linear models, reflecting the very low frequency of scanning errors observed on the road. These findings build on prior evidence that the PMCT-2 predicts older drivers' performance outcomes and, for the first time, demonstrate its potential to predict actual on-road driving performance at intersections.
Sometimes right, sometimes wrong: Drivers' responses to inconsistently accurate automated vehicle system confidence information.
Lee M, Pitts BJ
Accid Anal Prev · 2026 Apr · PMID 41564448 · Publisher ↗
Automated vehicles (AVs) are becoming increasingly equipped with intelligent functions that support drivers' decision-making. Human-machine interfaces (HMIs) that communicate an AV's confidence in its ability to navigate...
Automated vehicles (AVs) are becoming increasingly equipped with intelligent functions that support drivers' decision-making. Human-machine interfaces (HMIs) that communicate an AV's confidence in its ability to navigate challenges in the driving environment are expected to become a pervasive feature. While this type of confidence display can enhance drivers' situation awareness, information presented to drivers may not always reflect accurate, real-world conditions, which can misguide perceptions and contribute to poor decision-making. Also, repeated exposure to inconsistently accurate information can reinforce negative biases. This study investigates how initial exposure to a series of both accurate and inaccurate information affects AV drivers' perceptions, behavior, and physiological responses in later interactions. Using a visual HMI displaying an AV's self-assessed confidence in avoiding a roadway obstacle, in a first phase, thirty participants were (unknowingly) assigned to two groups: one initially exposed to accurate confidence information, and the other to inaccurate confidence information. In the second phase, participants experienced the reversed information accuracy condition. The vehicle was highly reliable, but the AV confidence information was manipulated to either be aligned or misaligned with the system reliability. Across 12 takeover scenarios, drivers decided whether to take control of the vehicle, and their takeover decisions, trust levels, and physiological responses were collected. Overall, participants who were initially exposed to accurate information demonstrated heightened attention, faster voluntary takeovers, higher trust, and increased reliance on system information. In contrast, those initially exposed to inaccurate information spent more time monitoring the driving environment. Also, participants initially exposed to accurate information displayed higher cognitive workload (measured physiologically) and unchanged trust levels. This observation was also true when inaccurate information was presented later. The number of voluntary takeovers did not differ between the two groups. These findings highlight the role of initial information presentation in shaping drivers' perception and behavior, offering insights for designing AV systems that support effective human-AV interactions.
Proactive safety at CVIS-enabled intersections: a framework based on high-fidelity trajectory reconstruction and dynamic risk assessment.
Li Y, Wang S, Yue L … +3 more , Wei Z, Zheng W, Zhang L
Accid Anal Prev · 2026 Apr · PMID 41564447 · Publisher ↗
High-fidelity vehicle trajectories are critical for proactive safety at intersections, where traditional reconstruction methods based on linear models or interpolation fail to capture complex nonlinear dynamics like sudd...
High-fidelity vehicle trajectories are critical for proactive safety at intersections, where traditional reconstruction methods based on linear models or interpolation fail to capture complex nonlinear dynamics like sudden stops and sharp turns. While deep learning can model this complexity, its computational cost is prohibitive for real-time edge deployment. To address these challenges, this paper proposes an edge-computing-enhanced two-stage framework for high-fidelity trajectory reconstruction and dynamic risk assessment, specifically designed for Cooperative Vehicle-Infrastructure Systems (CVIS) at intersections. The first stage reconstructs accurate vehicle trajectories by applying physics-informed constraints derived from vehicle dynamics, combined with adaptive wavelet transforms and a hybrid thresholding strategy, enabling robust noise reduction from low-quality, multi-source sensor data. The second stage introduces a Vehicle Outline-based Conflict Algorithm (VOCA), which elevates traditional point-based conflict detection to outline-based spatial overlap analysis. By accurately modeling the real physical boundaries of vehicles, the proposed method significantly improves the sensitivity and timeliness of conflict detection, enabling more reliable proactive safety interventions in complex urban scenarios. Validated with real-world intersection data on an NVIDIA Jetson edge device, our method effectively suppresses high-frequency noise, reducing acceleration fluctuations by 98.66%. The outline-based VOCA proves vastly superior to traditional approaches, with center-point methods detecting only 22.53% of the conflicts identified by our algorithm. The entire framework achieves real-time performance, processing complex scenarios with delays under 100 ms per frame per vehicle. This work delivers an efficient solution for generating accurate, low-latency conflict warnings, advancing the practical application of CVIS for proactive safety management in urban environments.
Dynamic dilemma zone at signalized intersection: attention allocation patterns using cure survival analysis for male riders.
Gupta M, Velaga NR
Accid Anal Prev · 2026 Apr · PMID 41558258 · Publisher ↗
The design of the signal at the intersection considers the constant speed of the riders and the dilemma zone to be static. However, these assumptions may not hold true in complex environments with multiple users. This st...
The design of the signal at the intersection considers the constant speed of the riders and the dilemma zone to be static. However, these assumptions may not hold true in complex environments with multiple users. This study explores the dynamic dilemma zone by incorporating the time to detect the signal by analyzing the drivers' eye gaze movements and attention allocation patterns. The delay in detecting the amber phase of the signal can put drivers in a situation where they can neither safely cross the intersection nor stop before the stop line. The experiments were conducted in a virtual environment with 105 participants predominantly considering male riders. The image processing algorithms identified the first instance of riders noticing the amber phase. The parametric cure survival models were used to quantify the time to detect the signal as they incorporate the fact that some drivers may not look at the signal for the entire duration. This study further considered the complex decision-making of speeding and decelerating at the onset of amber phase at signalized intersections. The riders' choices to vary the speed and safely or unsafely crossing the signal were quantified across psychological constraints. The results revealed that the odds of unsafe crossing at signal increased by 3.3, even in situations where riders were talking to pillion riders. The results indicated that riders under time pressure were more focused on the road, and their time to detect the signal was 0.72 s more than the base conditions.
Safety-oriented facility design and operation management for transportation hub station.
Shen Y, Jia H, Ye X … +2 more , Lo SM, He B
Accid Anal Prev · 2026 Apr · PMID 41554194 · Publisher ↗
Given the high efficiency and punctuality, transportation hub station are widely used by citizens, travelers daily. The large volume of passengers tends to cause overcrowding in transportation hub stations. Therefore, pa...
Given the high efficiency and punctuality, transportation hub station are widely used by citizens, travelers daily. The large volume of passengers tends to cause overcrowding in transportation hub stations. Therefore, passenger movement efficiency has been a great concern for station designers, engineers, and facility managers. As the main facility connecting different floors in multilevel metro station, passengers' movement on the vertical pedestrian transit facilities including stairs and escalators are critical for passengers' safety and efficiency. To minimize passenger crowding and improve passenger movement efficiency, this study analysed the factors affecting passenger flow on vertical pedestrian transit facility and derived useful insights. By investigating the efficiency of passenger movement on the platform, influencing factors including the speed of escalator, passengers' willingness to choose the stairs to move up floor levels, the layout and length of the mills barrier were explored. Furthermore, a safety-oriented evacuation layout was also detailed in the study. The study of the mills barrier revealed that a mills barrier placed between a staircase and an escalator promoted passenger efficiency. Moreover, a mills barrier length of 1 or 1.5 m is recommended. For the guidance strategy on the metro platform, the effect of passengers' willingness to choose the stairs to move up on passenger efficiency was also investigated. Results indicated that passenger dwelling time decreased with an increasing proportion of passengers choosing the stairs. The suggested proportion of passengers choosing the stairs is 30%-40%, which effectively improve passenger efficiency. For the fire evacuation in transportation hub station, the removable facilities near the bottleneck point should be planned decently to be removed with the fastest speed, that will effectively speed up the evacuation process. The results are expected to be useful for designers, engineers, and facility managers.
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