{"title":"Integrating macro and micro level crash frequency models considering spatial heterogeneity and random effects","authors":"Shahrior Pervaz , Tanmoy Bhowmik , Naveen Eluru","doi":"10.1016/j.amar.2022.100238","DOIUrl":null,"url":null,"abstract":"<div><p>Safety literature has traditionally developed independent model systems for macroscopic and microscopic level analysis. The current research effort contributes to the literature on crash frequency by building a bridge between these two divergent streams of crash frequency research. The study proposes an integrated micro–macro level model for crash frequency estimation. Specifically, the study develops an integrated model system that allows for the influence of independent variables at the microscopic level to be incorporated within the macroscopic propensity estimation. The empirical analysis is based on the data drawn from 300 traffic analysis zones, 1818 roadway segments, and 4184 intersections from the City of Orlando, Florida for the years 2018 and 2019. The study considers a host of exogenous variables including roadway and traffic factors, land-use, built environment, and sociodemographic characteristics for the model estimation. The proposed model system can also accommodate for hierarchical correlations such as correlation between all segments or intersections in a zone. The study findings highlight the presence of common spatial unobserved factors influencing crash frequency across segment level and intersection level as well as presence of significant parameter variability across both micro and macro level in the crash frequency. The empirical analysis is further augmented by employing several goodness of fit and predictive measures. The results clearly demonstrate the improved performance offered by the proposed integrated micro–macro model relative to the non-integrated macro model. The overall model fit measures and interpretations encourage the application of the proposed model for crash frequency analysis.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.5000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytic Methods in Accident Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213665722000276","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: 3
Abstract
Safety literature has traditionally developed independent model systems for macroscopic and microscopic level analysis. The current research effort contributes to the literature on crash frequency by building a bridge between these two divergent streams of crash frequency research. The study proposes an integrated micro–macro level model for crash frequency estimation. Specifically, the study develops an integrated model system that allows for the influence of independent variables at the microscopic level to be incorporated within the macroscopic propensity estimation. The empirical analysis is based on the data drawn from 300 traffic analysis zones, 1818 roadway segments, and 4184 intersections from the City of Orlando, Florida for the years 2018 and 2019. The study considers a host of exogenous variables including roadway and traffic factors, land-use, built environment, and sociodemographic characteristics for the model estimation. The proposed model system can also accommodate for hierarchical correlations such as correlation between all segments or intersections in a zone. The study findings highlight the presence of common spatial unobserved factors influencing crash frequency across segment level and intersection level as well as presence of significant parameter variability across both micro and macro level in the crash frequency. The empirical analysis is further augmented by employing several goodness of fit and predictive measures. The results clearly demonstrate the improved performance offered by the proposed integrated micro–macro model relative to the non-integrated macro model. The overall model fit measures and interpretations encourage the application of the proposed model for crash frequency analysis.
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.