Dawei Li , Mustafa F.M. Al-Mahamda , Yuchen Song , Siqi Feng , N.N. Sze
{"title":"一种具有非对称特性的备用碰撞严重性多类别建模方法","authors":"Dawei Li , Mustafa F.M. Al-Mahamda , Yuchen Song , Siqi Feng , N.N. Sze","doi":"10.1016/j.amar.2022.100218","DOIUrl":null,"url":null,"abstract":"<div><p>The logit model and its variations have been used extensively in the field of traffic safety in general, and crash severity analysis in particular. Attempts were made to overcome the logit's shortcomings and limitations by generalizing its binary form to a more relaxed and unconstrained setting. Such attempts include the addition of shape parameters in order to add more flexibility to the probability distribution, while maintaining the straightforwardness provided in the logit-type models, with the least computational effort. A well-known form that provides an extra parameter to the base logit is the scobit model. In this study, we explore several generalizations of the binary scobit model by applying the same conventional methods associated with the generalized logit forms, principally to cover the multinomial nature of crash severity outcomes. Those are the multinomial and the ordinal forms. Furtherly, we utilize mixed distributions to provide crash-specific random parameters with heterogeneity in means and variances. Crash severity dataset taken from Guangdong province, China, was used to compare the different forms. The multinomial scobit models provided better results in terms of sample and out-of-sample fit, with the cost of some complexity in the heterogeneous forms. Other forms did not show a substantial or consistent advantage over their logit counterparts. All models exhibit temporal instability when applied to multiple time periods.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.5000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An alternate crash severity multicategory modeling approach with asymmetric property\",\"authors\":\"Dawei Li , Mustafa F.M. Al-Mahamda , Yuchen Song , Siqi Feng , N.N. Sze\",\"doi\":\"10.1016/j.amar.2022.100218\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The logit model and its variations have been used extensively in the field of traffic safety in general, and crash severity analysis in particular. Attempts were made to overcome the logit's shortcomings and limitations by generalizing its binary form to a more relaxed and unconstrained setting. Such attempts include the addition of shape parameters in order to add more flexibility to the probability distribution, while maintaining the straightforwardness provided in the logit-type models, with the least computational effort. A well-known form that provides an extra parameter to the base logit is the scobit model. In this study, we explore several generalizations of the binary scobit model by applying the same conventional methods associated with the generalized logit forms, principally to cover the multinomial nature of crash severity outcomes. Those are the multinomial and the ordinal forms. Furtherly, we utilize mixed distributions to provide crash-specific random parameters with heterogeneity in means and variances. Crash severity dataset taken from Guangdong province, China, was used to compare the different forms. The multinomial scobit models provided better results in terms of sample and out-of-sample fit, with the cost of some complexity in the heterogeneous forms. Other forms did not show a substantial or consistent advantage over their logit counterparts. All models exhibit temporal instability when applied to multiple time periods.</p></div>\",\"PeriodicalId\":47520,\"journal\":{\"name\":\"Analytic Methods in Accident Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":12.5000,\"publicationDate\":\"2022-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Analytic Methods in Accident Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213665722000070\",\"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/S2213665722000070","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}
An alternate crash severity multicategory modeling approach with asymmetric property
The logit model and its variations have been used extensively in the field of traffic safety in general, and crash severity analysis in particular. Attempts were made to overcome the logit's shortcomings and limitations by generalizing its binary form to a more relaxed and unconstrained setting. Such attempts include the addition of shape parameters in order to add more flexibility to the probability distribution, while maintaining the straightforwardness provided in the logit-type models, with the least computational effort. A well-known form that provides an extra parameter to the base logit is the scobit model. In this study, we explore several generalizations of the binary scobit model by applying the same conventional methods associated with the generalized logit forms, principally to cover the multinomial nature of crash severity outcomes. Those are the multinomial and the ordinal forms. Furtherly, we utilize mixed distributions to provide crash-specific random parameters with heterogeneity in means and variances. Crash severity dataset taken from Guangdong province, China, was used to compare the different forms. The multinomial scobit models provided better results in terms of sample and out-of-sample fit, with the cost of some complexity in the heterogeneous forms. Other forms did not show a substantial or consistent advantage over their logit counterparts. All models exhibit temporal instability when applied to multiple time periods.
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.