{"title":"利用具有均值异质性的相关随机参数秩模型研究影响超速行驶相关碰撞严重程度的因素的空间异质性","authors":"","doi":"10.1080/19427867.2023.2262201","DOIUrl":null,"url":null,"abstract":"<div><div>Speeding has been acknowledged as a critical determinant in increasing the risk of crashes and their resulting injury severities. This paper employs Global Moran’s I coefficient and local Getis – Ord G* indexes to systematically account for the spatial distribution feature of speeding-related crashes, study the global spatial pattern of speeding-related crashes, and identify severe crash cluster districts. The findings demonstrate that severe speeding-related crashes within the state of Pennsylvania have a spatial clustering trend, where four crash datasets are extracted from four hotspot districts. Two log-likelihood ratio (LR) tests were conducted to determine whether speeding-related crashes classified by hotspot districts should be modeled separately. The results suggest that separate modeling is necessary. To capture the unobserved heterogeneity, four correlated random parameter order models with heterogeneity in means are employed to explore the factors contributing to crash severity involving at least one vehicle speeding.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigating the spatial heterogeneity of factors influencing speeding-related crash severities using correlated random parameter order models with heterogeneity-in-means\",\"authors\":\"\",\"doi\":\"10.1080/19427867.2023.2262201\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Speeding has been acknowledged as a critical determinant in increasing the risk of crashes and their resulting injury severities. This paper employs Global Moran’s I coefficient and local Getis – Ord G* indexes to systematically account for the spatial distribution feature of speeding-related crashes, study the global spatial pattern of speeding-related crashes, and identify severe crash cluster districts. The findings demonstrate that severe speeding-related crashes within the state of Pennsylvania have a spatial clustering trend, where four crash datasets are extracted from four hotspot districts. Two log-likelihood ratio (LR) tests were conducted to determine whether speeding-related crashes classified by hotspot districts should be modeled separately. The results suggest that separate modeling is necessary. To capture the unobserved heterogeneity, four correlated random parameter order models with heterogeneity in means are employed to explore the factors contributing to crash severity involving at least one vehicle speeding.</div></div>\",\"PeriodicalId\":48974,\"journal\":{\"name\":\"Transportation Letters-The International Journal of Transportation Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Letters-The International Journal of Transportation Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/org/science/article/pii/S1942786723002412\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TRANSPORTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Letters-The International Journal of Transportation Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/org/science/article/pii/S1942786723002412","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0
摘要
超速已被公认为是增加撞车风险及其造成的严重伤害的关键决定因素。本文采用全局莫兰 I 系数和局部 Getis - Ord G* 指数系统地解释了超速相关碰撞事故的空间分布特征,研究了超速相关碰撞事故的全局空间模式,并确定了严重碰撞事故集群区。研究结果表明,宾夕法尼亚州内与超速相关的严重碰撞事故具有空间集群趋势,其中从四个热点地区提取了四个碰撞事故数据集。为了确定按热点地区分类的超速相关碰撞事故是否应单独建模,进行了两次对数似然比(LR)检验。结果表明,单独建模是必要的。为了捕捉未观察到的异质性,我们采用了四个具有均值异质性的相关随机参数阶次模型,以探讨导致至少有一辆车超速的碰撞严重程度的因素。
Investigating the spatial heterogeneity of factors influencing speeding-related crash severities using correlated random parameter order models with heterogeneity-in-means
Speeding has been acknowledged as a critical determinant in increasing the risk of crashes and their resulting injury severities. This paper employs Global Moran’s I coefficient and local Getis – Ord G* indexes to systematically account for the spatial distribution feature of speeding-related crashes, study the global spatial pattern of speeding-related crashes, and identify severe crash cluster districts. The findings demonstrate that severe speeding-related crashes within the state of Pennsylvania have a spatial clustering trend, where four crash datasets are extracted from four hotspot districts. Two log-likelihood ratio (LR) tests were conducted to determine whether speeding-related crashes classified by hotspot districts should be modeled separately. The results suggest that separate modeling is necessary. To capture the unobserved heterogeneity, four correlated random parameter order models with heterogeneity in means are employed to explore the factors contributing to crash severity involving at least one vehicle speeding.
期刊介绍:
Transportation Letters: The International Journal of Transportation Research is a quarterly journal that publishes high-quality peer-reviewed and mini-review papers as well as technical notes and book reviews on the state-of-the-art in transportation research.
The focus of Transportation Letters is on analytical and empirical findings, methodological papers, and theoretical and conceptual insights across all areas of research. Review resource papers that merge descriptions of the state-of-the-art with innovative and new methodological, theoretical, and conceptual insights spanning all areas of transportation research are invited and of particular interest.