{"title":"在面板数据中适应系统和未观察到的异质性:应用于宏观层面的崩溃建模","authors":"Tanmoy Bhowmik , Shamsunnahar Yasmin , Naveen Eluru","doi":"10.1016/j.amar.2021.100202","DOIUrl":null,"url":null,"abstract":"<div><p>The current research contributes to the burgeoning literature on multivariate models by proposing a hybrid model framework that (a) incorporates unobserved heterogeneity in a parsimonious framework and (b) allows for additional flexibility to accommodate for observed/systematic heterogeneity. Specifically, we estimate a Latent Segmentation Panel Mixed Negative Binomial (LPMNB) model to study the zonal level crash counts across different crash types. Further, we undertake a comparison exercise of the proposed hybrid LPMNB model with a Panel Mixed Negative Binomial model (PMNB) that accommodates for unobserved heterogeneity via a simulation setting. The analysis is conducted using the zonal level crash records by different crash types from Central Florida region for the year 2016 considering a comprehensive set of exogenous variables. The comparison exercise is further augmented by computing several goodness of fit measures along with elasticity analysis and the results offered by the LPMNB model highlight the value of the proposed model. Further, to offer insights on model selection incorporating computational complexity dimension along with other important attributes, we conduct a trade-off analysis considering four different attributes: (a) model fit, (b) prediction, (c) inference power and (d) computational complexity; across six different model strictures including traditional crash frequency models and our proposed LPMNB model.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.5000,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Accommodating for systematic and unobserved heterogeneity in panel data: Application to macro-level crash modeling\",\"authors\":\"Tanmoy Bhowmik , Shamsunnahar Yasmin , Naveen Eluru\",\"doi\":\"10.1016/j.amar.2021.100202\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The current research contributes to the burgeoning literature on multivariate models by proposing a hybrid model framework that (a) incorporates unobserved heterogeneity in a parsimonious framework and (b) allows for additional flexibility to accommodate for observed/systematic heterogeneity. Specifically, we estimate a Latent Segmentation Panel Mixed Negative Binomial (LPMNB) model to study the zonal level crash counts across different crash types. Further, we undertake a comparison exercise of the proposed hybrid LPMNB model with a Panel Mixed Negative Binomial model (PMNB) that accommodates for unobserved heterogeneity via a simulation setting. The analysis is conducted using the zonal level crash records by different crash types from Central Florida region for the year 2016 considering a comprehensive set of exogenous variables. The comparison exercise is further augmented by computing several goodness of fit measures along with elasticity analysis and the results offered by the LPMNB model highlight the value of the proposed model. Further, to offer insights on model selection incorporating computational complexity dimension along with other important attributes, we conduct a trade-off analysis considering four different attributes: (a) model fit, (b) prediction, (c) inference power and (d) computational complexity; across six different model strictures including traditional crash frequency models and our proposed LPMNB model.</p></div>\",\"PeriodicalId\":47520,\"journal\":{\"name\":\"Analytic Methods in Accident Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":12.5000,\"publicationDate\":\"2022-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Analytic Methods in Accident Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213665721000464\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytic Methods in Accident Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213665721000464","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}
Accommodating for systematic and unobserved heterogeneity in panel data: Application to macro-level crash modeling
The current research contributes to the burgeoning literature on multivariate models by proposing a hybrid model framework that (a) incorporates unobserved heterogeneity in a parsimonious framework and (b) allows for additional flexibility to accommodate for observed/systematic heterogeneity. Specifically, we estimate a Latent Segmentation Panel Mixed Negative Binomial (LPMNB) model to study the zonal level crash counts across different crash types. Further, we undertake a comparison exercise of the proposed hybrid LPMNB model with a Panel Mixed Negative Binomial model (PMNB) that accommodates for unobserved heterogeneity via a simulation setting. The analysis is conducted using the zonal level crash records by different crash types from Central Florida region for the year 2016 considering a comprehensive set of exogenous variables. The comparison exercise is further augmented by computing several goodness of fit measures along with elasticity analysis and the results offered by the LPMNB model highlight the value of the proposed model. Further, to offer insights on model selection incorporating computational complexity dimension along with other important attributes, we conduct a trade-off analysis considering four different attributes: (a) model fit, (b) prediction, (c) inference power and (d) computational complexity; across six different model strictures including traditional crash frequency models and our proposed LPMNB model.
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.