Accident; analysis and prevention最新文献

{"title":"Street vitality and traffic risk: a multiscale analysis of Barcelona and Warsaw","authors":"Anastasiia Galaktionova ,&nbsp;Aura-Luciana Istrate ,&nbsp;Tiago Tamagusko ,&nbsp;Páraic Carroll","doi":"10.1016/j.aap.2026.108393","DOIUrl":"10.1016/j.aap.2026.108393","url":null,"abstract":"<div><div>This study examines the relationship between street vitality and traffic crash risk in Barcelona and Warsaw using street-level spatial regression models and group-based temporal clustering. Street vitality, operationalised as functional density and computer-vision-derived streetscape safety scores, shows a positive association with crash frequency across the street network in both cities, supporting the hypothesis that lively streets increase exposure and possibilities for conflict. However, street vitality explains only part of this risk dynamic: street length remains the strongest predictor overall, while building density produces mixed effects. In hotspot models, street vitality effects weaken substantially; functional density becomes insignificant, and visual safety effects diminish, suggesting that once crash concentrations form, risk is shaped more by localised design and behavioural factors than by land-use intensity. These findings underscore the importance of combining system-wide and site-specific perspectives in street safety planning, highlighting the need for design interventions that reconcile lively public spaces with traffic safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108393"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923363","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":"Injury severity analysis of e-bike crashes: An age-stratified study of riders aged 40 and above","authors":"Jingchun Jia ,&nbsp;Hao Yue ,&nbsp;Shanglin Yang ,&nbsp;Xiaolu Jia ,&nbsp;Yushuang Qiu","doi":"10.1016/j.aap.2026.108392","DOIUrl":"10.1016/j.aap.2026.108392","url":null,"abstract":"<div><div>As electric bikes (e-bikes) gain popularity, traffic safety concerns have intensified, particularly for riders aged 40 and above, who face heightened risks due to declining physiological capabilities. However, research analyzing crash injury severity factors for this demographic remains limited. This study examined 2452 e-bike crashes involving riders aged 40 and above in Jiaozhou, China, divided into three groups: 40–50 years, 50–60 years, and 60 years and above. A hybrid methodological framework combining the eXtreme Gradient Boosting (XGBoost) algorithm with Shapley Additive exPlanations (SHAP) and a Random Parameters Binary Logit model with Heterogeneity in Means (RPBL-HM) was constructed. Results showed that rural areas, primary/secondary roads, and holidays increase severe injury likelihood across all riders aged 40 and above. Each age group exhibited distinct risk patterns. The 40–50 age group showed higher severe injury probability with sub-zero temperatures and truck-involved crashes. The 50–60 age group faced elevated risks during nighttime, dawn, rainy or snowy weather, sub-zero temperatures, unhealthy air quality, and weekday nights. The 60 and above age group demonstrated higher risks when riders were farmers, unhealthy air quality, off-peak hours, motorcycle/truck involvement, rural autumn, and autumn crashes involving trucks. These findings provide evidence for developing age-targeted traffic safety interventions, offering significant implications for improving e-bike safety among elderly riders in an increasingly aging society.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108392"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923362","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":"Dynamic dilemma zone at signalized intersection: attention allocation patterns using cure survival analysis for male riders","authors":"Monik Gupta,&nbsp;Nagendra R. Velaga","doi":"10.1016/j.aap.2026.108408","DOIUrl":"10.1016/j.aap.2026.108408","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108408"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146008747","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 role of ADHD in aggressive driving behavior among young adult drivers: effects of traffic aggressiveness and roadway environments","authors":"John M. Duany,&nbsp;Mustapha Mouloua,&nbsp;P.A. Hancock","doi":"10.1016/j.aap.2026.108403","DOIUrl":"10.1016/j.aap.2026.108403","url":null,"abstract":"<div><div>This study examined the effects of Attention-Deficit Hyperactivity Disorder (ADHD), traffic aggressiveness, and roadway environment on driving behavior. Fifty-seven participants (26 ADHD, 31 non-ADHD; <em>M</em>_age = 20.75, <em>SD</em> = 5.19; 33 males, 24 females) completed questionnaires related to driving behavior. Participants then completed a series of simulated aggressive and non-aggressive drives in both city and freeway environments. Prior to the experimental drives, all participants completed a baseline control drive. Driving performance metrics (i.e., steering angle, acceleration pressure, brake pressure, and speed) and mental workload were recorded across all simulated drives. It was hypothesized that ADHD diagnosis, traffic aggressiveness, and roadway environment would each affect driving performance. Results showed that drivers with ADHD exhibited higher driving speed, while traffic aggressiveness and roadway environment exerted significant effects on steering angle and braking. Notably, ADHD drivers exhibited lower HRV (RMSSD), and NASA-TLX scores tended to be higher under aggressive city driving. The implications of these results for driver assessment, traffic safety, and public health are discussed.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108403"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974325","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":"Inferring the structure of pedestrian flows at a transportation hub","authors":"Xiaolu Jia ,&nbsp;Claudio Feliciani ,&nbsp;Hisashi Murakami ,&nbsp;Sakurako Tanida ,&nbsp;Liang Chen ,&nbsp;Hao Yue ,&nbsp;Daichi Yanagisawa ,&nbsp;Katsuhiro Nishinari","doi":"10.1016/j.aap.2025.108391","DOIUrl":"10.1016/j.aap.2025.108391","url":null,"abstract":"<div><div>In transportation hubs, pedestrian flows form complex network structures, leading to serious congestion at peak hours. Understanding their dynamics is crucial to managing safe and efficient transportation. Although many experimental and theoretical studies have investigated pedestrian interactions at the microscopic level, computational models that account for pedestrians’ macroscopic origin and destination (OD) demands and mesoscopic route choices in large walking facilities are rare and lack empirical validation. In other words, pedestrians’ decision-making at strategic (macroscopic) and tactical (mesoscopic) levels, other than the operational (microscopic) level, has remained largely unexplored. Here, we propose an integrated Strategic–Tactical–Operational model for transportation hub (STO-Hub model), and validate it using 0.87 million pedestrian trajectories collected over three days by means of 11 LiDAR sensors at JR Shinjuku station in Japan. Based on an abstracted graph of the main concourse with directed links between different platform entrances and gates, we employ the gravity model at the strategic layer to estimate time-varying OD demand, a logit route-choice model at the tactical layer to capture route choice behavior, and an agent-based model to reproduce interactions with the surrounding environment and pedestrians. The STO-Hub model accurately reconstructs OD demand and route-choice behavior, achieving high agreement with directed flow counts, and the simulation delineates local congested areas evident in the sensing data. By estimating OD demand and route splits and by reproducing local interactions at any selected section, the STO-Hub model captures pedestrian dynamics across all three levels, including at congested locations. We further propose a STO-Hub framework that integrates sensing, the STO-Hub model, and management plans, providing a practical 10-min-resolution basis for OD-informed pedestrian guidance and control in transportation hubs. The study fills a gap in strategic modeling and management for large transportation hubs and supports congestion prevention, improved safety, and higher operational efficiency.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108391"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974324","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":"Enhancing vision-based traffic crash detection performance consistency across day-night scenes: A depth-aware and domain-adaptive network","authors":"Yang Yang ,&nbsp;Xiantian Chen ,&nbsp;Jianyu Wang ,&nbsp;Yue Dong ,&nbsp;Kun Qie ,&nbsp;Zhenzhou Yuan","doi":"10.1016/j.aap.2026.108405","DOIUrl":"10.1016/j.aap.2026.108405","url":null,"abstract":"<div><div>Closed-circuit television (CCTV)-based traffic video crash detection systems require stable and consistent cross-scene performance to support all-day crash response and rescue efficiency. However, due to the substantial domain discrepancies between daytime and nighttime scenes—particularly in illumination and imaging quality—traffic crash detection still suffers from significant performance degradation when transferred across heterogeneous lighting conditions. To address this issue, this research proposed a depth-aware and domain-adaptive network built upon the Visual State Space Model (VSSM) to achieve robust crash detection across heterogeneous lighting environments. The proposed model employed a two-stream architecture that integrated appearance, motion and 3D depth information, in which the depth enhancement module captured fine-grained spatial geometry to provide complementary structural constraints, while the domain adaptation constraint effectively mitigated domain shift, thereby improving the overall robustness and reliability of crash detection. Experimental results demonstrated that the proposed model achieved a recall of 96.043 %, a miss rate of only 2.507 %, and an F1-score of 97.003 %, significantly outperforming several widely used baseline models. Ablation experiments further confirmed the critical roles of optical flow representation, 3D depth features, and the dual-level domain adaptation mechanism in enhancing spatiotemporal consistency. Moreover, the model required only 0.623 GFLOPs and achieved a real-time inference speed of 118 frames per second (FPS), demonstrating high computational efficiency. The proposed framework effectively mitigates the performance discrepancy between daytime and nighttime crash detection, and its high inference speed can contribute to faster emergency response and reduced casualty risk, offering a practical foundation for developing stable and transferable intelligent traffic safety monitoring systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108405"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974481","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":"Exploring novel surrogate safety indicators measuring conflict riskiness and severity: a case study in Sacramento, United States","authors":"Yikai Chen ,&nbsp;Yujie Bian ,&nbsp;Quan Yuan ,&nbsp;Mark King ,&nbsp;Jie He ,&nbsp;Xudong Cao ,&nbsp;Xiaobo Ruan ,&nbsp;Yubing Zheng","doi":"10.1016/j.aap.2026.108400","DOIUrl":"10.1016/j.aap.2026.108400","url":null,"abstract":"<div><div>Determining appropriate traffic conflict indicators is essential for accurately conducting road safety evaluations. However, existing studies often fail to comprehensively address key factors such as the presence or absence of collision courses, the riskiness before and after the conflict point, and the mobility of the conflict point when selecting conflict riskiness indicators. Moreover, current conflict severity indicators are based on fully inelastic collision theory, which lacks sufficient modeling accuracy. This study delves into the characterization of collision and crossing courses in both angle and straight-line conflicts, providing a comprehensive analysis of the risks faced by road users both before reaching a conflict point and after one has passed it. Based on this analysis, a new combination scheme of conflict riskiness indicators is proposed. A two-dimensional, six-degrees-of-freedom potential collision model is then developed based on the theory of partially elastic collisions, and a new indicator, Extended Delta-E, is introduced. Finally, correlation analysis based on crash and conflict data is performed to compare the proposed indicators with traditional riskiness and severity indicators, evaluating their accuracy in road safety evaluation. The results demonstrate that the conflict rates and severity rates derived from the proposed indicators exhibit the strongest correlation with actual crash rates and crash severity rates, respectively, compared to other indicators. The conflict riskiness and severity indicators proposed in this study offer a more nuanced characterization of conflict events, and can serve as highly accurate alternative measures for crash-based road safety evaluations.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108400"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923364","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":"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","authors":"Uibeom Chun,&nbsp;Mohamed Abdel-Aty,&nbsp;Zijin Wang","doi":"10.1016/j.aap.2026.108404","DOIUrl":"10.1016/j.aap.2026.108404","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108404"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146017066","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":"Sometimes right, sometimes wrong: Drivers’ responses to inconsistently accurate automated vehicle system confidence information","authors":"Myeongkyu Lee,&nbsp;Brandon J. Pitts","doi":"10.1016/j.aap.2026.108412","DOIUrl":"10.1016/j.aap.2026.108412","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108412"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146017086","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 doubly robust estimation framework to quantify potential bias in linked crash-EMS-trauma data with multi-cohort overlap","authors":"Sajjad Karimi,&nbsp;Robert Kluger","doi":"10.1016/j.aap.2025.108380","DOIUrl":"10.1016/j.aap.2025.108380","url":null,"abstract":"<div><div>Reliable estimation of injury severity is essential for informing trauma care, evaluating crash interventions, and guiding EMS resource allocation; however, analyses based on linked administrative datasets are often compromised by incomplete linkage and selection bias. This study employs a doubly robust estimation framework to address potential bias in injury severity estimation when integrating multiple datasets. Using Augmented Inverse Probability Weighting (AIPW), we adjust for selection bias introduced by incomplete linkage while improving robustness to misspecification in either the selection or outcome model. Using data from a multi-source linkage of crash, EMS, and trauma records, we estimate the Injury Severity Score (ISS) under three approaches: naïve complete-case analysis, inverse probability weighting (IPW), and AIPW. The naïve approach yielded a mean ISS of 13.52, while both IPW (10.86) and AIPW (10.93) provided adjusted estimates accounting for selection. Subgroup analyses revealed substantial differences in effect size and direction between models. For instance, the impact of male gender on ISS was estimated at 3.98 in AIPW versus 2.22 in naïve analysis. Similarly, secondary collisions and frontage-road crashes showed ISS increases exceeding 10 points under AIPW, compared to considerably lower naïve estimates. Several protective factors, including airbag deployment and crash setting, also demonstrated stronger effects when adjusted for bias. Our results demonstrate that traditional analyses of linked data may underestimate or misstate key risk and protective associations. The proposed AIPW framework offers a practical, statistically rigorous solution for producing population-level inferences in injury severity research using linked administrative data.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108380"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145951060","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}