Analytic Methods in Accident Research最新文献

{"title":"Analyzing crash injury severities with deep learning and advanced statistical models: An assessment of methodological challenges","authors":"MohammadAli Seyfi ,&nbsp;Amir Mohammad Karimi Mamaghan ,&nbsp;Ali Behnood ,&nbsp;Fred Mannering","doi":"10.1016/j.amar.2025.100405","DOIUrl":"10.1016/j.amar.2025.100405","url":null,"abstract":"<div><div>In this research, statistical and deep learning models are applied to determine factors that affect motorcycle crash-injury severities. Four methodological challenges are considered: 1) imbalanced data (because fatal injuries are an exceedingly small portion of all resulting injury outcomes); 2) unobserved heterogeneity (because many unobserved factors will influence resulting injury severities); 3) quantification of variable effects; and 4) the possibility of temporally shifting relationships among variables. Convolutional neural networks and deep neural networks are the deep learning models considered, and random parameters logit models with heterogeneity in means and variances is the statistical model considered. Extensive experimentation indicated that data imbalance and unobserved heterogeneity could be best handled in deep learning models with a Bayesian deep neural network with a random generator and weighted loss function. With statistical modeling indicating significant shifts in model parameters over time, the data were segmented by year and both statistical and deep learning models were estimated. While techniques are available for deep learning to potentially handle data imbalance and unobserved heterogeneity, the quantification of variable effects and temporal shifts remains a challenge. For example, a comparison of variable effects show that the deep learning estimates of variable effects are generally inconsistent with the plausible values generated by the statistical models in terms of magnitudes and occasionally in terms of direction, indicating a need for improvements in deep-learning variable-effect extraction methods. The findings also show the need for future work to isolate the effect of complex temporal relationships which are currently imbedded in deep learning approaches, because the segmentation of data that has been used in statistical models to isolate temporal effects, and even the use of all data and defining new time-dependent variables, may not be a viable deep learning option due to the potential loss in predictive performance.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"48 ","pages":"Article 100405"},"PeriodicalIF":12.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120262","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 unified framework for modeling traffic crashes from hierarchical spatial resolutions","authors":"Shahrior Pervaz ,&nbsp;Tanmoy Bhowmik ,&nbsp;Naveen Eluru","doi":"10.1016/j.amar.2025.100398","DOIUrl":"10.1016/j.amar.2025.100398","url":null,"abstract":"<div><div>Independent traffic crash modeling approaches do not account for the embedded relationships related to the multi-resolution data structure, leading to mis-specified estimations. The recently developed integrated frameworks demonstrate the capability of addressing this drawback. The current study proposes an integrated framework that accommodates information from multiple spatial units and observation resolutions. Specifically, the study develops an integrated model system that allows for the influence of independent variables from disaggregate crash record, micro-facility (segment and intersection) and macro (traffic analysis zone) level simultaneously within the macro level propensity estimation. The empirical analysis considers disaggregate crash records of 1818 segments and 4184 intersections from 300 traffic analysis zones in the City of Orlando, Florida. These crash records contain crash-specific factors, driver and vehicle factors, roadway, road environmental and weather information of each crash record. For micro-facility and macro levels, an exhaustive set of independent variables including roadway and traffic factors, land-use and built environment attributes, and sociodemographic characteristics are considered. The proposed model system can also accommodate for hierarchical correlations among the data across observation resolutions and parameter variability across the system. The empirical analysis is augmented by employing several goodness of fit and predictive measures. The results clearly demonstrate the improved performance offered by the proposed integrated model system relative to the non-integrated model. A validation exercise also highlights the superiority of the proposed framework. The application of the proposed integrated framework can allow transportation professionals to adopt policy-based, site-specific, and outcome-specific solutions simultaneously.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"47 ","pages":"Article 100398"},"PeriodicalIF":12.5,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679617","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":"Assessing the impact of COVID-19 on driver injury severities in fixed-object passenger car crashes: Insights from temporal and partially constrained modeling analysis","authors":"Md. Moshiur Rahman ,&nbsp;Salvador Hernandez ,&nbsp;Rakan Mohammad Radwan Albatayneh","doi":"10.1016/j.amar.2025.100397","DOIUrl":"10.1016/j.amar.2025.100397","url":null,"abstract":"<div><div>The COVID-19 pandemic reshaped the global transportation sector, including in the U.S., creating an unprecedented shift in traffic patterns. Despite a reduction in vehicle miles traveled (VMT), crash severity, particularly fatalities, increased significantly. Among all crash types, fixed-object collisions have consistently posed a critical safety concern due to their disproportionately high fatality rates, a trend further exacerbated during the pandemic. This study examines the impact of COVID-19 on driver injury severity in fixed-object passenger car crashes in Oregon. The authors estimated separate unconstrained models of driver injury severity in fixed-object passenger car crashes across three distinct time periods: before pandemic (March 2019–February 2020), during pandemic (March 2020–February 2021), and after pandemic (March 2021–February 2022), as well as a partially constrained model utilizing a random parameters multinomial logit model that incorporates heterogeneity in both means and variances of the random parameters. The analysis utilized 22,522 crash records for the state of Oregon obtained from the Oregon Department of Transportation. Likelihood ratio tests were performed to assess the temporal instability of model parameter estimates throughout the three time periods and to compare the partially constrained and unconstrained models. The findings indicated notable temporal variations in the determinants of injury severity, encompassing driver attributes, crash circumstances, roadway characteristics, and environmental elements. While alcohol consumption, improper driving, and collisions with trees consistently influenced injury severity across all periods, factors such as gender, airbag deployment, speeding, seasonal variations, and road surface conditions exhibited changing effects. Out-of-sample predictions indicate that severe injuries in fixed-object crashes were consistently underestimated, highlighting growing concerns about increasing crash severity, particularly in the post-pandemic period.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"47 ","pages":"Article 100397"},"PeriodicalIF":12.5,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470157","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 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}