Analytic Methods in Accident Research最新文献

{"title":"An integrated multi-resolution framework for jointly estimating crash type and crash severity","authors":"Shahrior Pervaz ,&nbsp;Tanmoy Bhowmik ,&nbsp;Naveen Eluru","doi":"10.1016/j.amar.2024.100321","DOIUrl":"https://doi.org/10.1016/j.amar.2024.100321","url":null,"abstract":"<div><p>The current research effort contributes to safety literature by developing an integrated framework that allows for the influence of independent variables from crash type and severity components at the disaggregate level to be incorporated within the aggregate level propensity to estimate crash frequency by crash type and severity. The empirical analysis is based on the crash data drawn from the city of Orlando, Florida for the year 2019. The disaggregate level analysis uses 15,518 crash records of three crash types including rear end, angular and sideswipe. Each crash record contains crash specific factors, driver and vehicle factors, roadway attributes, road environmental and weather information. For aggregate level model analysis, the study aggregates the crash records by crash type over 300 traffic analysis zones. An exhaustive set of independent variables including roadway and traffic characteristics, land-use attributes, built environment and sociodemographic factors are considered in this level. The empirical analysis is further augmented by employing several goodness of fit and predictive measures. A validation exercise is also conducted using a holdout sample to highlight the superiority of the proposed integrated model relative to the non-integrated model system. The proposed framework can also incorporate unobserved heterogeneity in the model system. The findings of the study indicate that the proposed framework is advantageous for capturing the variable effects simultaneously across the aggregate and disaggregate levels.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"42 ","pages":"Article 100321"},"PeriodicalIF":12.9,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140191623","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 non-motorist safety in motor vehicle crashes – a copula-based approach to jointly estimate crash location type and injury severity","authors":"Robert Marcoux,&nbsp;Shahrior Pervaz,&nbsp;Naveen Eluru","doi":"10.1016/j.amar.2024.100322","DOIUrl":"https://doi.org/10.1016/j.amar.2024.100322","url":null,"abstract":"<div><p>Non-motorist injury severity can be affected by various observed and unobserved attributes related to the crash location type (segment or intersection). Recognizing the distinct non-motorist injury severity profiles by crash location type, we propose a joint modeling framework to study crash location type and non-motorist injury severity as two dimensions of the severity process. We employ a copula-based joint framework that ties the crash location type (represented as a binary logit model) and injury severity (represented as a generalized ordered logit model) through a closed form flexible dependency structure to study the injury severity process. The proposed approach also accommodates the potential heterogeneity (across non-motorists) in the dependency structure. The data for our analysis is drawn from the Central Florida region for the years of 2015 to 2021. The model system explicitly accounts for temporal heterogeneity across the two dimensions. A comprehensive set of independent variables including non-motorist user characteristics, driver and vehicle characteristics, roadway attributes, weather and environmental factors, temporal and socio-demographic factors are considered for the analysis. We also conducted an elasticity analysis to show the actual magnitude of the independent variables on non-motorist injury severity for the two locations. The results highlight the importance of examining the effect of various independent variables on non-motorist injury severity outcome by crash location type.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"42 ","pages":"Article 100322"},"PeriodicalIF":12.9,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140187614","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":"Effects of speed difference on injury severity of freeway rear-end crashes: Insights from correlated joint random parameters bivariate probit models and temporal instability","authors":"Chenzhu Wang,&nbsp;Mohamed Abdel-Aty,&nbsp;Lei Han","doi":"10.1016/j.amar.2024.100320","DOIUrl":"https://doi.org/10.1016/j.amar.2024.100320","url":null,"abstract":"<div><p>Rear-end crashes particularly on freeways are the most frequent type of collisions causing many injuries, damage and congestion. This paper investigates the impact of varying speed differences between following and leading vehicles on injury severity in two-vehicle rear-end crashes. It develops three groups of correlated joint random parameters bivariate probit models with heterogeneity in means. The rear-end crash data from 2019 to 2021 on Interstate freeways in Florida are utilized, and categorized into periods before, during, and after the COVID-19 pandemic. The study considers two potential injury severity outcomes: no injury and injury/fatality, for both drivers involved in these crashes. The findings indicate that a range of variables, including driver, vehicle, roadway, environmental, crash, and temporal attributes, significantly influence the injury severity outcomes for drivers in both following and leading vehicles. Demonstrating superior goodness-of-fit, the proposed approach sheds light on interactive unobserved heterogeneity, captured through heterogeneity in means and significant correlations among random parameters. The study observes critical influences on the injury severity outcomes of both drivers, with significant factors such as gender, age, vehicle type, weather conditions, lighting, and time of day. Furthermore, the results substantiate the heightened risk outcomes associated with greater speed differences and the period of the COVID-19 pandemic. These findings yield further insights into the risk mechanisms of two-vehicle rear-end crashes and offer guidance for the development of effective safety countermeasures.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"42 ","pages":"Article 100320"},"PeriodicalIF":12.9,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139993615","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 novel integrated approach to modeling and predicting crash frequency by crash event state","authors":"Angela Haddad ,&nbsp;Aupal Mondal ,&nbsp;Naveen Eluru ,&nbsp;Chandra R. Bhat","doi":"10.1016/j.amar.2024.100319","DOIUrl":"10.1016/j.amar.2024.100319","url":null,"abstract":"<div><p>In this study, we propose a novel integrated parametric framework for analyzing multivariate crash count data based on linking a univariate count model for the total count of motor vehicle crashes across all possible crash states with a discrete choice model for crash event state given a crash. In doing so, we are able to use information at the disaggregate crash-level from an unordered model structure in analyzing the aggregate level crash count. To our knowledge, this is the first such model proposed in the econometric literature. We apply this approach in a demonstration exercise to examine the number of motor vehicle crashes in Census Block Groups (CBGs) in Austin, Texas, considering four injury severity levels. At the disaggregate level, we incorporate several explanatory variables such as the characteristics of the most severely injured individual and at-fault vehicle’s parties, crash time variables (time of day, weather), crash location variables, and CBG level variables. At the aggregate level, we consider CBG level variables, including road design factors, land-use variables, crash exposure factors, aggregate sociodemographic attributes, and crime and traffic violations related measures. Importantly, our results indicate a significant and positive linkage between the disaggregate crash event state dimensions and the total crash count. Through the use of elasticity measures, our results also clearly highlight the improved policy sensitivity of the integrated model framework.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"41 ","pages":"Article 100319"},"PeriodicalIF":12.9,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213665724000034/pdfft?md5=3dc98bb8dced6fd4dab9a0b82b2486e1&pid=1-s2.0-S2213665724000034-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139880295","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":"Estimating the effect of proximity to school on cyclist safety using a simultaneous-equations model with heterogeneity in covariance to address potential endogeneity","authors":"Shahram Heydari,&nbsp;Michael Forrest","doi":"10.1016/j.amar.2024.100318","DOIUrl":"10.1016/j.amar.2024.100318","url":null,"abstract":"<div><p>Traffic safety around schools is a major concern for policy makers and as such safety interventions are often targeted near schools. This paper shows the importance of accounting for the potential endogeneity of proximity to school when attempting to estimate its impact on traffic safety. In this research, we use a Bayesian simultaneous econometric approach with heterogeneity in covariance to disentangle the true effect of proximity to school on cyclist injury frequencies at signalised intersections in an urban setting. We assess the robustness of the bivariate normal assumption, using a scale mixing approach. Notably, we found that proximity to school was associated with an increase in cyclist injuries and this association was stronger when endogeneity was accounted for in the model, confirming the importance of considering endogeneity in studies of traffic safety near schools. Our heterogeneity in covariance specification revealed systematic variations in the covariance structure, which would otherwise go unobserved, providing further insights into sources of heterogeneity with the same set of variables available in the data. A safety-in-numbers effect is also found for cyclists in the study area and period. This research offers policy implications based on the findings of the analysis including the need for safety interventions at intersections with high vehicle turning counts and those in proximity to public transport stops, and better informing decision-makers regarding the magnitude of the impact of proximity to school on cyclist safety at intersections.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"41 ","pages":"Article 100318"},"PeriodicalIF":12.9,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213665724000022/pdfft?md5=b9f11e4f15aeb626452e0d2feeba9602&pid=1-s2.0-S2213665724000022-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139667445","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":"A Prospective Study on the Anatomical Variations of the Frontal Recess and its Association with Computer Tomographic Signs of Sinusitis.","authors":"Snigdha Girish Koliyote, Rohit Singh, Neethu Mary Mathew, Prakashini K","doi":"10.1007/s12070-023-04193-3","DOIUrl":"10.1007/s12070-023-04193-3","url":null,"abstract":"<p><p>The frontal recess region has a complex anatomy and HRCT scans of the paranasal sinuses (PNS) are the gold standard in evaluating it. Classification systems have been established to identify the frontal recess cells. The objectives of this study are to describe the incidence of anatomical variations, classify the anatomy of the frontal recess using the IFAC & Kuhn's classification systems, find the association between the anatomical variations and the incidence of CT signs of sinusitis. A prospective study of patients undergoing HRCT-PNS was carried out. The frontal recess region was evaluated and classified as per both classification systems. The prevalence of each frontal cell was identified; presence of CT signs of sinusitis was noted and the correlation between the two was evaluated. 272 sides of HRCT scans were evaluated. Prevalence of cells as per IFAC classification showed ANC - 98.2%, SAC-43.4%, SBC-33.1%, SAFC- 28.3%, FSC -25%, SBFC- 3.7% and SOEC- 2.2%. Prevalence of cells as per Kuhn's classification showed ANC - 98.2%, Type 1- 38.2%, SBC-32.7%, FSC -24.3%, Type 3- 16.9%, Type 2- 12.9%, Type 4- 4.8%, FBC- 2.6% and SOEC-2.2%. Sinusitis was seen in 27.2% cases. A significant association was noted between the presence of SOEC, FSC and sinusitis as per both classification systems. (P=0.049 and P<0.001 respectively). In conclusion the cells which lead to an anteriorly based drainage pathway are more common, but the presence of posteriorly based SOEC and medially based FSC have a higher association with sinusitis.</p>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"32 1","pages":"495-502"},"PeriodicalIF":0.6,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10908951/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75295074","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":"A multi-year statistical analysis of driver injury severities in single-vehicle freeway crashes with and without airbags deployed","authors":"Richard Dzinyela ,&nbsp;Nawaf Alnawmasi ,&nbsp;Emmanuel Kofi Adanu ,&nbsp;Bahar Dadashova ,&nbsp;Dominique Lord ,&nbsp;Fred Mannering","doi":"10.1016/j.amar.2024.100317","DOIUrl":"10.1016/j.amar.2024.100317","url":null,"abstract":"<div><p>This paper seeks to identify factors that influence driver injury severities in single-vehicle freeway crashes when airbags deployed and when airbags did not deploy. Injury-severity models were estimated using random parameters logit models with consideration given to possible heterogeneity in means and variances of the random parameters to account for unobserved heterogeneity. Three years of pre-COVID pandemic crash data (2016, 2017 and 2018) from the state of Alabama were used in the model estimations. Models were estimated with data from all years, but the model formulation allowed the estimated parameters to vary by year. The model estimation results show that there are fundamental differences in crashes where airbags deployed (which tend to be crashes associated with greater energy transfers and variance in such transfers across crashes) relative to crashes where airbags did not deploy (which tend to be crashes associated with lower-speed impacts with less variance in energy transfers across crash observations). Moreover, the effects of most of the explanatory variables on resulting injury severities were found to vary significantly over time. However, explanatory variables such as shoulder and lap belt use, driver gender, newer model year vehicles, passenger car vehicle types, urban-located crashes, collisions with deer, collisions with trees and collisions with cable barriers did not vary significantly over time in either the airbag or non-airbag deployed models, or both. The findings of this study suggest that there is a potential for advances airbag systems to substantially improve safety by closing the injury-severity gap observed between men and women in particular, and that there is a need to further explore the evolution of driver behavior over time, which ultimately determines the effectiveness of ongoing improvements in vehicle and highway safety systems.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"41 ","pages":"Article 100317"},"PeriodicalIF":12.9,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213665724000010/pdfft?md5=472fca9a18a0bfd8d8a7b5f27fa5b6d2&pid=1-s2.0-S2213665724000010-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139508997","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":"A Poisson Lognormal-Lindley model for simultaneous estimation of multiple crash-types: Application of multivariate and pooled univariate models","authors":"Hassan Bin Tahir ,&nbsp;Shamsunnahar Yasmin ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.amar.2023.100315","DOIUrl":"10.1016/j.amar.2023.100315","url":null,"abstract":"<div><p>Challenges addressing overdispersion, unobserved heterogeneity, the preponderance of zeros, and correlation in the dependent variables of crash count models are of significant interest. Accounting for all these data issues simultaneously is few and far between. This study proposes a new mixing distribution model that accounts for overdispersion and the preponderance of zeros in crash count models. The proposed mixing distribution model extends to the multivariate structure to account for correlations between dependent variables and unobserved heterogeneity. The empirical analysis is conducted on crash data of Bruce highway involving single-vehicle and multi-vehicle crash types by “fatal and severe injury” and “moderate and minor injury” severity levels on aggregated data over three analysis years (2016, 2017, and 2018). The study demonstrates superior goodness of fit of the proposed multivariate random parameters Poisson lognormal-Lindley model compared to its restricted models. Moreover, pooling the crash data as repeated measures of crash types helped formulate a pooled-univariate random parameters Poisson-Lindley model to estimate multiple crash types by severity. The results showed the pooled-univariate model offers comparable goodness of fit and averaged marginal effects as the complex multivariate modeling structure. Moreover, the proposed pooled-univariate model reduced the model complexity to a one-dimensional integral and offered more efficient parameter estimates. In the empirical context, the modeling results showed that single-vehicle and multi-vehicle crashes by severity are linked with different causality.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"41 ","pages":"Article 100315"},"PeriodicalIF":12.9,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213665723000507/pdfft?md5=e161889941e1c64dcd7951caa77c2e70&pid=1-s2.0-S2213665723000507-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139062197","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":"Multi-dimensional unobserved heterogeneities: Modeling likelihood of speeding behaviors in different patterns for taxi speeders with mixed distributions, multivariate errors, and jointly correlated random parameters","authors":"Yue Zhou ,&nbsp;Chuanyun Fu ,&nbsp;Xinguo Jiang","doi":"10.1016/j.amar.2023.100316","DOIUrl":"10.1016/j.amar.2023.100316","url":null,"abstract":"<div><p>Speeding behaviors can be classified into different patterns according to both speeding-range and speeding-distance. Among the speeding patterns, some are more frequently observed in specific traffic scenarios, implying that the likelihood of speeding behaviors may vary across the speeding patterns due to the inconsistent impact of temporal, road, environmental, and other traffic factors. Additionally, the trigger of speeding is a complex process so the researchers may not have access to all the critical information associated with the speeding behaviors. This issue may bring about not only independent heterogeneity but also multi-dimensional heterogeneities that are mutually correlated when modeling speeding behaviors by patterns. However, the joint solution to the above challenges is rarely seen in past studies. To fill the knowledge gaps, this study uses taxi GPS trajectories to extract speeding behaviors and classify them into four patterns. The speeder’s percent of speeding distance for each speeding pattern is respectively measured to represent the likelihood of speeding behaviors by patterns. Afterwards, we compare the data-fitting between the models combined with different beta-gamma mixture distributions and a multivariate Gaussian error in modeling the four patterns of speeding likelihood. The combination with the best fitness is used to incorporate jointly correlated random parameters. The results show that the model with beta-gamma-gamma-gamma mixed distributions performs better than other combinations. The model with jointly correlated random parameters outperforms models with other random parameters. The factor analysis reveals that percent of driving at night, percent of driving on roads with a low-speed limit (≤30 km/h), average delays in junctions along the trips, and hourly income have consistent effects on the likelihood of speeding behaviors in all patterns, while the effects of the remaining factors are inconsistent across the speeding patterns. Furthermore, the heterogeneity unveiled by the model parameters is discussed. The study highlights the necessity of considering mixed distributions and multi-dimensional heterogeneities in modeling speeding likelihood by different patterns.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"41 ","pages":"Article 100316"},"PeriodicalIF":12.9,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213665723000519/pdfft?md5=ce9030f5389cb04b225dad2b9f21b051&pid=1-s2.0-S2213665723000519-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139062133","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":"On the need to address fixed-parameter issues before applying random parameters: A simulation-based study","authors":"Numan Ahmad ,&nbsp;Tanmoy Bhowmik ,&nbsp;Vikash V. Gayah ,&nbsp;Naveen Eluru","doi":"10.1016/j.amar.2023.100314","DOIUrl":"10.1016/j.amar.2023.100314","url":null,"abstract":"<div><p>Count regression models have been applied to model expected crash frequency at individual roadway locations. Random parameters have been increasingly integrated into these models to account for unobserved heterogeneity. However, the introduction of random parameters might also mask issues in the model specification, leading to inaccurate relationships and model interpretation. Two of these specification-related issues are: (1) not considering the appropriate functional form of explanatory variables; and, (2) ignoring the best set of significant explanatory variables. To better examine the need for careful model specification, this study uses synthetic data to demonstrate that the consideration of random parameters does not address the two model specification issues identified. The results from the simulation study illustrate that (a) model specification issues cannot be circumvented by random parameters alone and (b) random parameter models including the exhaustive set of explanatory variables available offer significant model improvements.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"41 ","pages":"Article 100314"},"PeriodicalIF":12.9,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213665723000490/pdfft?md5=758e1de36f599120beb557e28428c58c&pid=1-s2.0-S2213665723000490-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138538682","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}