Accident; analysis and prevention最新文献

{"title":"Experimental study on the following behavior of pedestrians encountering those who go against the flow","authors":"Jianlin Li ,&nbsp;Jun Zhang ,&nbsp;Shuchao Cao ,&nbsp;Xiangxia Ren ,&nbsp;Weiguo Song ,&nbsp;Eric Wai Ming Lee","doi":"10.1016/j.aap.2025.108337","DOIUrl":"10.1016/j.aap.2025.108337","url":null,"abstract":"<div><div>Pedestrian following behavior has a significant impact on crowd dynamics within transportation hubs, where dense and heterogeneous passenger flows pose substantial traffic risk challenges. However, empirical research on this behavior in such complex environments remains scarce, and many existing models still rely on subjective assumptions. This study bridges this gap through controlled experiments to investigate pedestrian following behavior during walking and running, which are typical movement states in transportation hub scenarios.<!--> <!-->The<!--> <!-->results demonstrate that pedestrians exhibit<!--> <!-->a<!--> <!-->lower willingness to follow others when running compared to walking. After ceasing one following behavior, more than 70% of pedestrians will initiate the next following within 0.5 s. Walking pedestrians tend to follow the individual within 2.49 m ahead with an angle rang of −53.77° to 50.25°, while the running pedestrians prefer to follow the one within 1.99 m ahead with an angle range of −80.83° to 57.21°, parameters that can inform spatial risk assessment in hub functional zones. Notably, no clear evidence shows that followers prefer pedestrians with larger speed differences or those whose movement directions align closely with their desired velocity direction. Additionally, during the following process, followers may switch their targets or cease following them. The study finds that “the distance between the follower and the leader” and “the number of pedestrians within the rectangular area formed by the positions of the follower and the leader” are the main driving factors for changes in the following behavior of followers in both walking and running states. Specifically, as these two factors increase, the probability of followers changing their following behavior also rises, which is vital for developing safety control strategies during passenger transfers. These findings are further compared with the assumptions about following behavior in previous models. This study enhances the understanding of pedestrian dynamics and aims to facilitate the integration of following behavior into crowd dynamics models, thereby improving the accuracy of evacuation models and supporting traffic risk prevention in hub systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108337"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145676145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SET-DGCN: An end-to-end electroencephalography-based fatigue detection method for young drivers","authors":"Yang Cao ,&nbsp;Tiantian Chen ,&nbsp;Ke Han ,&nbsp;Hyungchul Chung ,&nbsp;Zhaoguo Huang ,&nbsp;Hongliang Ding","doi":"10.1016/j.aap.2025.108311","DOIUrl":"10.1016/j.aap.2025.108311","url":null,"abstract":"<div><div>Driver fatigue poses a critical threat to global road safety, particularly among young drivers. Nevertheless, policy-level interventions remain fragmented due to the lack of reliable and deployable detection technologies. Bridging this gap requires accurate, interpretable, and real-time fatigue monitoring systems capable of informing practical decision-making in transportation safety management. To address this challenge, we propose an end-to-end EEG-based fatigue detection model, Scale-Enhanced Transformer and Dynamic Graph Convolutional Network (SET-DGCN). The model captures multi-scale temporal dependencies and spatial brain-region interactions by integrating convolutional embeddings, attention mechanisms, and learnable graph structures. Extensive evaluations on both a driving simulation dataset and the publicly available SEED-VIG dataset confirm that SET-DGCN outperforms mainstream convolutional neural network (CNN)-based, graph convolutional network (GCN)-based, and Transformer-based models in terms of accuracy and F1-score, while maintaining strong cross-subject generalization. To enhance both interpretability and application relevance, a component-level attribution method (COAR) is employed to evaluate the functional contribution of model modules, while SHapley Additive exPlanations (SHAP) analysis is used to uncover brain region–specific patterns across fatigue stages. Based on these neural insights, a set of multi-level policy and design recommendations is proposed, ranging from infrastructure enhancements to adaptive in-vehicle systems and individualized interventions, to provide a comprehensive framework for mitigating fatigue among young drivers in real-world transportation contexts.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108311"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crash typology of professional cycling crashes","authors":"Benjamin P. Krbavac ,&nbsp;Rory England ,&nbsp;Séan Mitchell ,&nbsp;Paul Sherratt ,&nbsp;Kevin Gildea ,&nbsp;Jon Farmer","doi":"10.1016/j.aap.2025.108332","DOIUrl":"10.1016/j.aap.2025.108332","url":null,"abstract":"<div><div>Mild traumatic brain injury (mTBI) is a frequent but underreported consequence of professional cycling crashes, yet current helmet testing standards primarily simulate head-first impacts, and their representation of real-world head impact scenarios is unclear. This study explores crash typology of professional cycling crashes involving head-ground contact through systematic video analysis of 128 head impacts occurring between 2012 and 2024. Most head impacts occurred during road races (113/128, 88 %) and were associated with multi-cyclist collisions rather than single-cyclist crashes, with topple-over crashes representing the most common mechanism (49 %), followed by skid-outs. Riders predominantly landed front or front-side relative to their direction of travel, with 66 % of impacts occurring in a sideways body posture, and head contact most frequently involved the helmet’s side and rim regions (&gt;50 % of impacts). Notably, body-first head impacts dominated the crash profiles (92 %), with the torso or arms contacting the ground before the head, while direct head-first impacts comprised 8 % of cases. Impact severity was distributed relatively evenly across low (30 %), medium (33 %), and high (36 %) categories, with collision-related crashes being more likely to result in high-severity outcomes than non-contact crashes. These findings reveal a potential mismatch between current helmet testing protocols and the predominant mechanisms observed in professional cycling crashes. Video-based analysis provides critical insights into impact mechanisms that are overlooked by traditional injury reporting methods, particularly highlighting the prevalence of body-first impacts and side-rim head impacts. This crash typology may provide a foundation for future biomechanical studies and could support the development of helmet testing methods that better represent real-world cycling impact scenarios.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108332"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145676103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Why do drivers brake later than AEB in rear-end collisions? An analysis based on drive recorder videos","authors":"Yuqing Zhao,&nbsp;Yifan Lin","doi":"10.1016/j.aap.2025.108275","DOIUrl":"10.1016/j.aap.2025.108275","url":null,"abstract":"<div><div>Rear-end collisions are among the most common crash types in Japan. However, due to their relatively low fatality and injury severity, research on them remains limited. Although previous studies have shown the effectiveness of autonomous emergency braking (AEB), further studies is needed to improve understanding of driver-related factors and enhance system performance to reduce collision risk.</div><div>This study investigated the effectiveness of AEB and explored factors contributing to braking delays by reconstructing 52 real-world taxi collisions exceeding 10 km/h using PC-Crash. Simulations were conducted with time-to-collision (TTC) and TTC_2nd-based (considering relative deceleration) AEB to evaluate collision-avoidance performance. Additionally, a decision tree was used to examine environmental, vehicular, and human factors affecting the time difference between driver-initiated braking and AEB activation.</div><div>The result indicates that AEB installed in taxis effectively reduced rear-end collisions. TTC_2nd-based AEB could avoid more collisions involving decelerating lead vehicles. Reducing AEB delay times could further enhance prevention. However, the AEB simulation showed limited effectiveness in collisions involving high speeds, wet roads, or sudden deceleration of the lead vehicle. Moreover, the primary reason drivers braked later than the simulated AEB was their failure to maintain a forward gaze before collisions. Drivers were more likely to divert their gaze from forward driving-related areas in non-critical situations, such as low-speed driving or red traffic signals ahead.</div><div>This study provides quantitative insights into human factors and AEB technology, which may inform the optimization of AEB systems and the development of driver monitoring systems, contributing to collision prevention and traffic safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108275"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145562149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A vine copula-based analysis of spatial dependence of traffic conflict risk at highway ramp areas","authors":"Ruifeng Gu,&nbsp;N.N. Sze","doi":"10.1016/j.aap.2025.108330","DOIUrl":"10.1016/j.aap.2025.108330","url":null,"abstract":"<div><div>Understanding spatial dependence on real-time safety risk is crucial for developing effective traffic management and control strategies at highway ramp areas. Prevalent statistical models for spatial analysis of safety risk are limited in their capability to capture complex, non-linear, and asymmetric dependence patterns across multiple locations. In this study, a novel vine copula approach is proposed for the spatial analysis of traffic conflict risk at highway ramp area, allowing for more flexible specification and control of heterogeneous spatial dependence patterns. Specifically, spatial correlations in traffic conflict risk, measured by time-integrated time-to-collision (TIT) across road segments and traffic lanes, are estimated using high-resolution vehicle trajectory data collected from unmanned aerial units. The results reveal significant variations in spatial dependence patterns of traffic conflict risk, particularly asymmetric tail dependencies in high-risk scenarios. These findings underscore the need for advanced statistical models to accommodate spatial dependence in traffic safety analysis, thereby supporting the development and implementation of more effective real-time traffic safety management systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108330"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145615716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a bus real-time crash risk prediction framework by using a self-attention-based bidirectional long and short-term memory network with anomaly detection learning and mixed sequence features","authors":"Ning Zhang ,&nbsp;Yiping Wu ,&nbsp;Aoran Yang ,&nbsp;Tao Liu","doi":"10.1016/j.aap.2025.108306","DOIUrl":"10.1016/j.aap.2025.108306","url":null,"abstract":"<div><div>The accuracy of real-time crash risk prediction and the reasonableness of crash warning time are decisive factors in enabling bus drivers to perform effective crash avoidance maneuvers. However, existing research based on microscopic time-series data often suffers from insufficient crash samples and neglects the historical behavioral patterns of driving behavior. Meanwhile, the bus crash risk warning time needs to be further optimized and selected to satisfy the crash risk response time requirements for drivers. To address these issues, this study proposes a novel framework, a self-attention-based bidirectional long and short-term memory network with anomaly detection learning and mixed sequence features (ADL-Mi-Att-Bi-LSTM), for real-time bus crash risk prediction using bus operational data from Beijing. Specifically, the framework reframes the prediction task as an anomaly detection problem, extracts mixed sequence features through a dual nested sliding time window to learn normal driving patterns and predict rare crash risk, and systematically compares various observation and interval time window combinations to determine the optimal crash warning time that accommodates driver response time. The results demonstrate that the model achieves optimal performance (Accuracy, F1-score, and AUC all exceeding 96.5 %) with a 6 s observation time window and a 1 s interval time window (i.e., crash warning time of 3 s). The ADL-Mi-Att-Bi-LSTM model significantly outperforms the other five kinds of models obtained from the ablation test as well as the traditional LSTM, XGBoost and RF models, confirming the efficacy of the integrated ADL, mixed sequence features, bidirectional LSTM layers, and the self-attention layer. Feature importance analysis further reveals that transient sequence features proximal to the crash warning, the cumulative and lagged effects of jerk, and the statistical features of driver operations are key factors influencing crash risk. This research provides a robust basis for optimizing bus crash warning mechanisms and developing more targeted driver safety training programs.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108306"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do drivers understand cyclists’ communication cues?","authors":"Christine M. Petersen,&nbsp;Patricia R. DeLucia","doi":"10.1016/j.aap.2025.108333","DOIUrl":"10.1016/j.aap.2025.108333","url":null,"abstract":"<div><div>In the last decade, cyclist fatalities in traffic collisions have increased. Therefore, the current study aimed to identify which cues drivers use to make quick and accurate judgments of cyclists’ intent and whether cell phone use affects prediction accuracy. Sixty drivers viewed video clips of a cyclist performing combinations of different cues (position on the road, head movement, arm signals) and reported the cyclist’s intended action (turn left, turn right, stop, or go straight) as indicated by arm signals defined by U.S. transportation laws. Half of the participants completed a last letter task simulating a cell phone conversation. Drivers predicted cyclists’ intentions with above-chance accuracy; however, only arm signals significantly influenced correct predictions (<em>p</em> &lt; 0.001), whereas head movement and position on the road had no significant effect. Simulated phone conversations reduced prediction accuracy, highlighting the dangers of distracted driving. Eye tracking revealed that drivers first fixated on the cyclist’s back before shifting attention to the signaling arm. Post-study questionnaire data showed that although all drivers correctly defined the straight-arm turn signals, less than a quarter understood the bent-arm right turn signal. Over three quarters correctly defined the stop/slowing signal, but half indicated they would use a halt-motion instead. These findings suggest that cyclists should use straight-arm signals and consider adapting the stop/slowing signal to better align with drivers’ expectations. Using communication cues that match drivers’ mental models may reduce confusion, improve situational awareness, and help prevent collisions—especially when driver attention is compromised.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108333"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145653096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of N-back-induced mental workload and time budget on takeover performance","authors":"Courtney M. Goodridge ,&nbsp;Rafael C. Gonçalves ,&nbsp;Ali Arabian ,&nbsp;Anthony Horrobin ,&nbsp;Albert Solernou ,&nbsp;Yee Thung Lee ,&nbsp;Audrey Bruneau ,&nbsp;Yee Mun Lee ,&nbsp;Natasha Merat","doi":"10.1016/j.aap.2025.108327","DOIUrl":"10.1016/j.aap.2025.108327","url":null,"abstract":"<div><div>Mental Workload (MWL) refers to the specification of information processing capacity used for maintaining task performance. Some studies find no effects of high MWL on the timing and quality of takeovers, whilst others have found increases in crash risk and delayed response times. The effect of time budget – the time between event onset and an impending crash − is much clearer; drivers react faster when time budgets are smaller. However, no study has investigated whether the effects of a pure MWL interact with the effects of time budget during critical takeovers from a hands-off Level 2 (L2) driving system. A Bayesian multilevel modelling approach was used to quantify the direction, size, and uncertainty of the effects that MWL and time budget have on driver performance. Drivers (N = 37) used a hands-off L2 driving system: once while completing a pure MWL task (2-back) and another while monitoring the road. Rear-end conflicts were generated via lead vehicles decelerating with short (TTC = 3 s) or long (TTC = 5 s) time budgets. 2-back-induced MWL had no consistent or substantial impact on the timing or quality of takeovers. Conversely, drivers were faster to respond but more erratic in their post-takeover lateral control following events with smaller time budgets. We discuss the reasons for the absence of effects from the 2-back-induced MWL on takeover performance. One suggestion is that rear-end scenarios elicit automatised behaviours that do not rely upon cognitive control and thus remained unaffected by MWL. Conversely, scenarios that require cognitive control (e.g., lane change manoeuvres or hazard perception tasks) may be more susceptible to the detrimental effects of MWL during transitions of control.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108327"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145615714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pedestrian modeling with realistic dynamic behaviors and its application in virtual safety testing for autonomous vehicles","authors":"Xin Gao ,&nbsp;Quan Li ,&nbsp;Siyuan Liu ,&nbsp;Yiran Luo ,&nbsp;Bo Zhang ,&nbsp;Wei Lu ,&nbsp;Zheng Wang ,&nbsp;Qing Zhou ,&nbsp;Bingbing Nie","doi":"10.1016/j.aap.2025.108328","DOIUrl":"10.1016/j.aap.2025.108328","url":null,"abstract":"<div><div>Pedestrians, as vulnerable road users, face significant safety risks in traffic environments. In hazardous traffic scenarios, they often exhibit collision-avoidance behaviors that significantly influence vehicle interactions and potential injury risks. However, the inherent uncertainty and complexity of influencing factors make accurate modeling of pedestrian behavior in safety–critical scenarios particularly challenging. To address this, we propose a comprehensive model for pedestrian dynamic behaviors in such scenarios, which is subsequently implemented in a virtual vehicle safety testing platform to enable realistic pedestrian-vehicle interactions. The developed model systematically captures the process of pedestrian risk perception, avoidance decision-making, and kinematic response, ensuring high behavioral fidelity by leveraging real pedestrian data collected in a virtual reality experiment. Utilizing actual accident data, the constructed platform is capable of generating imminent yet realistic pedestrian-vehicle pre-crash scenarios. Furthermore, it integrates pedestrian injury risk into a unified safety evaluation framework that synergizes active and passive safety assessments. Validation with real-world data demonstrates the effectiveness of the proposed pedestrian model and testing platform. Across 4,200 simulated pedestrian-vehicle interactions, pedestrians’ active avoidance of oncoming vehicles reduced collisions by 27.77 % relative to a no-avoidance baseline, highlighting the necessity of incorporating dynamic behavior modeling into virtual testing. The virtual testing framework proposed herein enables practical implementation of the high-fidelity pedestrian model, offering a pathway for autonomous vehicles to better interpret pedestrian behaviors and enhance interactive safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108328"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145676090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling cognitive load using drift-diffusion models in pedestrian street-crossing: a method supported by neural evidence","authors":"Siwei Ma ,&nbsp;Xuedong Yan ,&nbsp;Lu Ma ,&nbsp;Jac Billington ,&nbsp;Natasha Merat ,&nbsp;Gustav Markkula","doi":"10.1016/j.aap.2025.108310","DOIUrl":"10.1016/j.aap.2025.108310","url":null,"abstract":"<div><div>When pedestrians are cognitively loaded, this influences their street-crossing behaviour, leading to negative impact on overall road safety. However, the mechanisms underpinning this impact remain debated, and this study seeks to further investigate them through modelling and electroencephalography. We conducted a computer-based pedestrian crossing experiment, and employed drift-diffusion models to quantitatively analyse how cognitive load impacts pedestrian decision-making. To further test the models’ validity, we analysed centro-parietal positive potential (CPP), a neural signal associated with evidence accumulation, to investigate whether this neural evidence aligned with the evidence accumulation predicted by the models. In our experiment, participants encountered a simulated scenario with a car approaching under four different time-to-arrival (TTA) conditions. In half the trials, participants performed cognitive tasks while deciding when to cross the street. Results showed that cognitive load weakened the effect of TTA on the probability of crossing before the car, increased response times, raised the probability of collision, and attenuated CPP amplitude. The best-performing model, which captured all of these effects, accumulated evidence based on utility estimates, but with a lower responsiveness to these utilities during cognitive load. This model also showed the strongest correlation between its evidence traces and the CPP amplitude, both with and without cognitive load. These findings support the hypothesis that cognitive load reduces responsiveness to perceptual evidence (at least in non-automatised tasks), making it a strong candidate for explaining both our results and existing research on the effects of cognitive load in other tasks.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108310"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145538191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}