{"title":"An integrated multi-resolution framework for jointly estimating crash type and crash severity","authors":"Shahrior Pervaz , Tanmoy Bhowmik , Naveen Eluru","doi":"10.1016/j.amar.2024.100321","DOIUrl":null,"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":12.5000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytic Methods in Accident Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213665724000058","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Analytic Methods in Accident Research is a journal that publishes articles related to the development and application of advanced statistical and econometric methods in studying vehicle crashes and other accidents. The journal aims to demonstrate how these innovative approaches can provide new insights into the factors influencing the occurrence and severity of accidents, thereby offering guidance for implementing appropriate preventive measures. While the journal primarily focuses on the analytic approach, it also accepts articles covering various aspects of transportation safety (such as road, pedestrian, air, rail, and water safety), construction safety, and other areas where human behavior, machine failures, or system failures lead to property damage or bodily harm.