Analytic Methods in Accident Research最新文献

{"title":"Autonomous vehicle sensor data and the estimation of network-wide spatiotemporal generalized extreme value models of rear-end injury-severity crash frequencies","authors":"Sunny Singh ,&nbsp;Yasir Ali ,&nbsp;Fred Mannering ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.amar.2025.100390","DOIUrl":"10.1016/j.amar.2025.100390","url":null,"abstract":"<div><div>Existing traffic conflict-based extreme value modeling applications are primarily restricted to a few concentrated locations due to the scarcity of network-wide vehicular trajectory data and the constraints associated with traditional network-wide modeling techniques. As such, this study develops a network-wide bivariate spatiotemporal non-stationarity generalized extreme value model to estimate rear-end crash frequency by injury severity level using Argo AI autonomous vehicle sensor data. Fusing this dataset with road network data from the Florida Department of Transportation, this paper studies a road network of 57 intersections and mid-blocks in Miami, Florida. Modified time-to-collision and the expected post-collision velocity difference (Delta-V) are used to estimate severe and non-severe rear-end crashes. Road geometry, road classification, and traffic state variables are used as covariates to address spatiotemporal heterogeneity in the generalized extreme value model estimation. Results show the significant impact of spatiotemporal variables such as lane width, median width, dedicated street parking, dedicated bike lane, vehicle class, and road class on rear-end crash frequency by injury severity levels. It is found that the bivariate spatiotemporal generalized extreme value model outperforms the bivariate random intercept generalized extreme value model and the univariate generalized extreme value model with conditional severity probability when benchmarked against observed annual crash frequency using root mean square error and the coefficient of determination (<em>R</em>-squared). Additionally, the bivariate spatiotemporal generalized extreme value model provides the closest estimate of observed severe crashes by roadway segments in the study area. The findings of this study underscore the importance of proactive network-wide safety management using spatiotemporal heterogeneity and autonomous vehicle sensor data to estimate crash frequency by severity for real-time decision-making.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"47 ","pages":"Article 100390"},"PeriodicalIF":12.5,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144194438","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 note from the new Editor-in-Chief of Analytic Methods in Accident Research","authors":"Shimul (Md Mazharul) Haque","doi":"10.1016/j.amar.2025.100389","DOIUrl":"10.1016/j.amar.2025.100389","url":null,"abstract":"","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"47 ","pages":"Article 100389"},"PeriodicalIF":12.5,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146948","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 the dynamic determinants of general aviation accidents across flight phases and time: A random parameter bivariate probit approach with heterogeneity in means","authors":"Qingli Liu ,&nbsp;Penglin Song ,&nbsp;Fan Li","doi":"10.1016/j.amar.2025.100386","DOIUrl":"10.1016/j.amar.2025.100386","url":null,"abstract":"<div><div>General aviation experiences significant variation in accident characteristics across flight phases. This study seeks to investigate the phase transferability and temporal stability of determinants influencing general aviation accidents, using the U.S. data (2008–2019) from the National Transportation Safety Board. To achieve this, a random parameter bivariate approach with heterogeneity in means was employed, focusing on two binary outcomes: injury severity (fatal/severe vs. minor/none) and aircraft damage (destroyed vs. non-destroyed). Four flight phases were analyzed: departure, enroute, maneuvering, and arrival. The data were divided into three time periods, 2008–2011, 2012–2015, and 2016–2019, to assess the determinants’ temporal stability. Likelihood ratio tests revealed that pilot injury and aircraft damage risks exhibit phase non-transferability and temporal instability. Out-of-sample predictions indicated a steady rise in fatal or severe injury risk, while aircraft damage risk initially increased before declining over time. A significant positive correlation between pilot injury and aircraft damage was observed through model estimation. Key factors, including pilot, aircraft, flight, and environmental conditions, significantly influenced both outcomes. Moreover, factors such as decision-making errors, adverse physiological conditions, fixed landing gear, and visual meteorological conditions showed both phase transferability and temporal stability. However, most factors were phase- and period-specific. Based on these findings, targeted measures, such as pilot escape and survival training, as well as phase-specific, scenario-based training, are proposed to mitigate general aviation risks.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"47 ","pages":"Article 100386"},"PeriodicalIF":12.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138288","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":"Grouped random parameters Poisson-Lindley model with spatial effects addressing crashes at intersections: Insights from visual environment features and spatiotemporal instability","authors":"Chenzhu Wang,&nbsp;Mohamed Abdel-Aty,&nbsp;Lei Han","doi":"10.1016/j.amar.2025.100387","DOIUrl":"10.1016/j.amar.2025.100387","url":null,"abstract":"<div><div>This study investigates the unobserved heterogeneity and spatiotemporal variations in the effects of visual environment features on intersection crash frequency. A Grouped Random Parameters Poisson-Lindley model with Spatial Effects is developed to account for spatial variations at both the macro (county) and micro (intersection) levels. The analysis utilizes crash data from 2,044 intersections across 12 Florida counties, collected between 2020 and 2022, along with explanatory variables including traffic flow, geometric design characteristics, and visual environment features (extracted from Google Street View images). Comparing to existing methods (e.g., Fixed, Random Parameters, and Grouped Random Parameters Poisson-Lindley models), the proposed approach, which incorporates both macro- and micro-level spatial effects, demonstrates significantly improved model performance. Additionally, the temporal variations of explanatory variables over the three-year period are clearly identified through out-of-sample predictions and marginal effects analysis. Two visual environment features, Vegetation and Grass, result in the identification of grouped random parameters, highlighting the varying impact of these features on intersection crash frequency across the 12 counties. The findings also reveal a strengthening of micro-level spatial effects, indicating heightened spatial correlations between adjacent intersections following the COVID-19 pandemic. Key factors influencing crash frequency include traffic volume, four-legged intersections, major roads with more than four lanes, wider minor roads, and a higher proportion of vehicles in the drivers’ field of vision. These results provide valuable insights into the influence of drivers’ visual environment on intersection safety and offer policy recommendations for enhancing traffic safety.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"47 ","pages":"Article 100387"},"PeriodicalIF":12.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088778","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":"Autonomous vehicle lane-changing dynamics and impact on the immediate follower","authors":"Yasir Ali","doi":"10.1016/j.amar.2025.100388","DOIUrl":"10.1016/j.amar.2025.100388","url":null,"abstract":"<div><div>Understanding and modelling lane-changing behaviour are critical aspects of microscopic traffic flow modelling, safety analyses, and microsimulation due to their significant impact on traffic flow characteristics and safety. Among the three aspects of lane-changing behaviour—decision-making, execution, and impact—the lane-changing impact has been comparatively underexplored in the literature, which is disproportionate to its importance. A lack of proper understanding of lane-changing impact may lead to inaccurate planning and interpretation of mixed traffic stream comprising both autonomous and human-driven vehicles. Motivated by this research gap, the current study investigates the lane-changing impact of autonomous vehicles on the immediate follower using the publicly available Waymo Open Dataset. Human-driven vehicle lane-changing data are also extracted from the same database and used for comparison. Lane-changing impact on traffic flow efficiency and safety is examined through the speed reduction of the follower in the target lane and deceleration rate to avoid a collision for the same follower, respectively. A correlated random parameters linear regression model is employed to assess the speed reduction of the follower as a function of lane-change duration, lag gap, lane-changer speed, and a dummy variable indicating whether the lane-changer is an autonomous vehicle or a human-driven vehicle. The results reveal that lane changes executed by autonomous vehicles may cause greater or lesser speed reductions for the follower compared to those executed by human-driven vehicles, which could be attributed to the heterogeneous behaviour of followers perceiving and responding differently to autonomous vehicle lane-changes compared to human-driven ones. Further, the block maxima and peak over threshold models are developed to estimate crash risk for the follower in the target lane using a deceleration rate to avoid a collision conflict measure. The results suggest that the risk of a collision increases substantially when the lane-changer is an autonomous vehicle. This elevated risk may be associated with drivers’ lack of trust in autonomous vehicles and traffic dynamics, reflecting self-inflicting hard deceleration to avoid potential collisions. Overall, this study highlights the heterogeneous impacts of lane-changing by autonomous vehicles on the immediate follower, emphasising the need for tailored models that accurately capture the dynamics of surrounding traffic behaviour. The findings will be helpful to road safety engineers and policymakers in planning mixed traffic with the safe integration of autonomous vehicles.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"46 ","pages":"Article 100388"},"PeriodicalIF":12.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069979","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":"Short-term conflict-based crash risk forecasting: A Bayesian conditional peak-over-threshold approach","authors":"Depeng Niu,&nbsp;Tarek Sayed","doi":"10.1016/j.amar.2025.100385","DOIUrl":"10.1016/j.amar.2025.100385","url":null,"abstract":"<div><div>Forecasting short-term crash risks is crucial for real-time road safety management, yet this research area remains largely underexplored. Classical Extreme Value Theory (EVT) models assume independent observations, limiting their ability to capture the clustering behavior in occurrence times and magnitudes of extreme traffic conflicts. To overcome this limitation, we introduce conditional peak-over-threshold (POT) models that incorporate time-varying parameters to simultaneously capture the dynamics of extreme traffic conflicts and enable forecasting for crash risk. Within the framework of marked point process (MPP) and EVT, we develop the conditional POT models based on two observation-driven approaches (self-exciting and score-driven) through Bayesian inference. A dynamic risk measure, Value-at-Risk (VaR), is employed to assess the performance of these conditional POT models for crash risk forecasting. Empirical analysis of rear-end conflict data collected from a signalized intersection across two separate days demonstrates that both self-exciting and score-driven POT models effectively characterize the clustering behavior of extreme traffic conflicts. Furthermore, backtesting confirms that conditional POT models provide more accurate crash risk forecasts than classical POT models, which tend to underestimate crash risk by ignoring temporal dependence in extreme traffic conflicts. Among the examined model specifications, score-driven POT models demonstrate superior forecasting performance. Our proposed Bayesian conditional POT approach provides probabilistic forecasting that enables direct uncertainty quantification and dynamic monitoring of crash risk, thereby supporting informed safety decisions.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"46 ","pages":"Article 100385"},"PeriodicalIF":12.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901998","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":"Modeling economic loss associated with fishing vessel accidents: A Bayesian random-parameter generalized beta of the second kind model with heterogeneity in means","authors":"Yun Ye ,&nbsp;Pengjun Zheng ,&nbsp;Qianfang Wang ,&nbsp;S.C. Wong ,&nbsp;Pengpeng Xu","doi":"10.1016/j.amar.2025.100384","DOIUrl":"10.1016/j.amar.2025.100384","url":null,"abstract":"<div><div>The distribution of economic loss associated with vessel accidents typically exhibits non-negative, continuous, positively skewed, and heavy-tailed characteristics. Another challenge in analyzing fishing vessel accidents is the absence of relevant factors. Ignoring such heterogeneity caused by unobserved factors potentially leads to inaccurate inferences. In the present study, a novel Bayesian random-parameter generalized beta of the second kind (GB2) model with possible heterogeneity in means and variances was developed. The flexible GB2 distribution was harnessed to model the skewed and heavy-tailed response variable, while the random parameters were specified to capture the unobserved heterogeneity. The proposed method was validated using an insurance claim dataset with 3448 fishing vessel accidents within Ningbo waters during 2018–2022. The proposed model successfully identified significant influential factors, including fixed parameters, random parameters, and covariates influencing the means of the random parameters. Specifically, offshore and inevitable accidents, fishing transport vessels, double-trawl vessels with mechanical failures, wide-hulled vessels, and favorable sea conditions were associated with greater economic loss. Special attention should also be paid to nighttime accidents involving steel-hulled fishing transport vessels, as this accident type emerged to result in greater loss during the pandemic lockdown period. Our approach can accommodate the abnormality, skewness, and heavy-tail of vessel accident loss data, adjust for the bias introduced by unobserved factors, and uncover the interactive relationship among covariates. Targeted countermeasures were proposed to mitigate economic loss resulting from fishing vessel accidents.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"46 ","pages":"Article 100384"},"PeriodicalIF":12.5,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844225","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":"Assessment of vehicle age as a contributor to temporal shifts in single-vehicle driver injury severities","authors":"Emmanuel Kofi Adanu ,&nbsp;Richard Dzinyela ,&nbsp;Dustin Wood ,&nbsp;Steven Jones","doi":"10.1016/j.amar.2025.100383","DOIUrl":"10.1016/j.amar.2025.100383","url":null,"abstract":"<div><div>Vehicle age plays a crucial role in crash occurrence and occupant injury severity, with older vehicles historically associated with more severe injury outcomes compared to newer models. This study investigates the temporal instability of specific injury-contributing factors for single-vehicle, single-occupant crashes involving vehicles equal or less than 3 years old at the time of the crash, using data from Alabama’s Critical Analysis Reporting Environment (CARE) system. The analysis spans four time points: 2010, 2014, 2018, and 2022. Preliminary data analysis indicates a reduction in new vehicle severe injury crashes from 7.25% in 2010 to 4.05% in 2022. Random parameters multinomial logit models with heterogeneity in means were developed to identify crash factors significantly related to injury outcomes. Key findings highlight the consistent trend of higher severity crashes in which drivers fail to use a seatbelt and airbags are deployed. However, there was a notable decrease in severe injuries for 3-year-old vehicles involved in crashes in 2022 compared to previous years. Model results revealed that this benefit is particularly evident in the reduced likelihood of severe injury among drivers older than 65 years where airbags were deployed over the years, except for 2010. The study indicates the importance of advancements in vehicle technology in enhancing occupant safety. It also emphasizes the need for ongoing research into driver behavior, road conditions, and the evolution of safety standards to fully leverage these technological improvements. The findings suggest that continuous updates to driver education and awareness programs are essential to reflect new technologies and changing driving environments, ensuring drivers can effectively utilize advanced safety features.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"46 ","pages":"Article 100383"},"PeriodicalIF":12.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776588","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 physics-informed risk force theory for estimating pedestrian crash risk by severity using artificial intelligence-based video analytics","authors":"Saransh Sahu ,&nbsp;Yasir Ali ,&nbsp;Sebastien Glaser ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.amar.2025.100382","DOIUrl":"10.1016/j.amar.2025.100382","url":null,"abstract":"<div><div>Pedestrians are a vulnerable road user group, and assessing their crash risk at critical locations, such as signalized intersections, is crucial for developing targeted countermeasures. While conflict-based safety assessments using traffic conflict measures effectively estimate crash risk, they often overlook the heterogeneity of different motorized and non-motorized road users. Conversely, field-based theories account for road user heterogeneity, yet their application in crash risk assessment, specifically evaluating pedestrian crash risk, and particularly by severity level using real-world data, remains underexplored. This study introduces a novel application of physics-informed risk force theory for assessing pedestrian crash risk by injury severity, utilizing facility-based video data at signalized intersections. The study derives risk forces that encompass pedestrian and vehicle heterogeneity as a nearness-to-collision component and vehicle impact speed as a severity component. Stationary and non-stationary extreme value models, incorporating exogenous traffic parameters at the signal cycle level, were applied to 72 h of video data collected from three signalized intersections in Queensland, Australia. The non-stationary univariate extreme value model with risk force as a measure of nearness-to-collision reliably estimated total crash frequency compared to historical crash records. In addition, the bivariate extreme value model with risk force and impact speed reasonably predicted pedestrian crashes by severity levels. The results also indicate that an increased volume of interacting pedestrians and left-turning vehicles elevates the likelihood of total and severe crashes. The proposed pedestrian crash risk assessment framework offers a unified and efficient proactive approach that can enhance automated safety analysis of traffic facilities, thereby assisting road authorities in real-time safety management.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"46 ","pages":"Article 100382"},"PeriodicalIF":12.5,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is there an emotional dimension to road safety? A spatial analysis for traffic crashes considering streetscape perception and built environment","authors":"Yiping Liu ,&nbsp;Tiantian Chen ,&nbsp;Hyungchul Chung ,&nbsp;Kitae Jang ,&nbsp;Pengpeng Xu","doi":"10.1016/j.amar.2025.100374","DOIUrl":"10.1016/j.amar.2025.100374","url":null,"abstract":"<div><div>Modern streetview image data provide two types of valuable information: the objective built environment and humans’ subjective perception of the streetscape. In the road safety domain, the built environment has been identified as playing a significant role while indicators of human perception are commonly used to evaluate street quality in urban planning. However, studies examining the association between humans’ perceptions of the streetscape and traffic crashes remain limited. This study aims to address this question and to inform safety considerations at the micro level in the planning process for the targeted streets. To answer the question, this study integrates databases on motor vehicle crashes, points of interest, street view images, and road networks for the urban area of Daejeon city in South Korea in 2019. A deep learning model was employed to calculate six perceptual indicators–wealthy, lively, boring, depressing, safety, and beautiful–based on a crowdsourcing dataset. Furthermore, a Bayesian multivariate Poisson-lognormal model with spatial-varying coefficients was introduced to simultaneously account for spatial random effect and the shared unobserved effect across crash severity levels. Results indicate that four of the six perceptual variables significantly affect the number of slight injury crashes, showing spatially heterogeneous effects. Based on the values of human perception indicators and their impacts on traffic crashes, we identified road segments which need special attention to objective safety performance when considering street renovation. Additionally, built environment factors such as the proportion of vegetation, the presence of sidewalks and fences, and points of interest (including educational, health service, and commercial establishments) were found to reduce the number of motor vehicle crashes. Overall, the findings are expected to facilitate the safety-enhanced street planning project, and contribute to the development of human-centric cities.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"46 ","pages":"Article 100374"},"PeriodicalIF":12.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}