{"title":"An econometric framework for integrating aggregate and disaggregate level crash analysis","authors":"Shahrior Pervaz, Tanmoy Bhowmik, Naveen Eluru","doi":"10.1016/j.amar.2023.100280","DOIUrl":null,"url":null,"abstract":"<div><p>Traditionally, aggregate crash frequency by severity and disaggregate severity analysis have been conducted independently in the safety literature. The current research effort contributes to the safety literature by bridging the gap between these two different streams of research by using both aggregate and disaggregate level crash data simultaneously. To be specific, the study proposes a framework that integrates aggregate and disaggregate level models. The proposed framework allows for the influence of independent variables at the crash record level to be incorporated within the aggregate level propensity estimation. 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 20,204 crash records that contain crash specific variables, temporal characteristics, roadway, vehicle and driver factors, road environmental and weather information for each record. For aggregate level model analysis, the study aggregated the crash records by severity class over 300 traffic analysis zones. An exhaustive set of independent variables including roadway and traffic factors, land-use attributes, built environment, and sociodemographic characteristics are considered in this analysis. The empirical analysis is further augmented by employing several goodness of fit and predictive measures. A validation exercise is also performed using a holdout sample to highlight the superior performance of the proposed integrated model relative to the non-integrated crash count by severity model. The proposed model can also accommodate common unobserved spatial correlation among crash records within the same zone. The model results illustrate the benefits of developing an integrated model system for crash frequency and severity.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"39 ","pages":"Article 100280"},"PeriodicalIF":12.5000,"publicationDate":"2023-09-01","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/S2213665723000155","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
Traditionally, aggregate crash frequency by severity and disaggregate severity analysis have been conducted independently in the safety literature. The current research effort contributes to the safety literature by bridging the gap between these two different streams of research by using both aggregate and disaggregate level crash data simultaneously. To be specific, the study proposes a framework that integrates aggregate and disaggregate level models. The proposed framework allows for the influence of independent variables at the crash record level to be incorporated within the aggregate level propensity estimation. 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 20,204 crash records that contain crash specific variables, temporal characteristics, roadway, vehicle and driver factors, road environmental and weather information for each record. For aggregate level model analysis, the study aggregated the crash records by severity class over 300 traffic analysis zones. An exhaustive set of independent variables including roadway and traffic factors, land-use attributes, built environment, and sociodemographic characteristics are considered in this analysis. The empirical analysis is further augmented by employing several goodness of fit and predictive measures. A validation exercise is also performed using a holdout sample to highlight the superior performance of the proposed integrated model relative to the non-integrated crash count by severity model. The proposed model can also accommodate common unobserved spatial correlation among crash records within the same zone. The model results illustrate the benefits of developing an integrated model system for crash frequency and severity.
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.