Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model
This study is intended to investigate the influencing factors of injury severity by considering the heterogeneity issue of unobserved factors at different arterials and the spatial attributes in geographically weighted regression models. To achieve the objectives, geographically weighted panel logis...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/8521649 |
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| author | Daiquan Xiao Xuecai Xu Li Duan |
| author_facet | Daiquan Xiao Xuecai Xu Li Duan |
| author_sort | Daiquan Xiao |
| collection | DOAJ |
| description | This study is intended to investigate the influencing factors of injury severity by considering the heterogeneity issue of unobserved factors at different arterials and the spatial attributes in geographically weighted regression models. To achieve the objectives, geographically weighted panel logistic regression model was developed, in which the geographically weighted logistic regression model addressed the injury severity from the spatial perspective, while the panel data model accommodated the heterogeneity attributed to unobserved factors from the temporal perspective. The geo-crash data of Las Vegas metropolitan area from 2014 to 2016 was collected, involving 27 arterials with 25,029 injury samples. By comparing the conventional logistic regression model and geographically weighted logistic regression models, the geographically weighted panel logistic regression model showed preference to the other models. Results revealed that four main factors, human-beings (drivers/pedestrians/cyclists), vehicles, roadway, and environment, were potentially significant factors of increasing the injury severity. The findings provide useful insights for practitioners and policy makers to improve safety along arterials. |
| format | Article |
| id | doaj-art-21f0506ce4f04b49a3fdd11580c29e40 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-21f0506ce4f04b49a3fdd11580c29e402025-02-03T01:32:14ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/85216498521649Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression ModelDaiquan Xiao0Xuecai Xu1Li Duan2School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, ChinaThis study is intended to investigate the influencing factors of injury severity by considering the heterogeneity issue of unobserved factors at different arterials and the spatial attributes in geographically weighted regression models. To achieve the objectives, geographically weighted panel logistic regression model was developed, in which the geographically weighted logistic regression model addressed the injury severity from the spatial perspective, while the panel data model accommodated the heterogeneity attributed to unobserved factors from the temporal perspective. The geo-crash data of Las Vegas metropolitan area from 2014 to 2016 was collected, involving 27 arterials with 25,029 injury samples. By comparing the conventional logistic regression model and geographically weighted logistic regression models, the geographically weighted panel logistic regression model showed preference to the other models. Results revealed that four main factors, human-beings (drivers/pedestrians/cyclists), vehicles, roadway, and environment, were potentially significant factors of increasing the injury severity. The findings provide useful insights for practitioners and policy makers to improve safety along arterials.http://dx.doi.org/10.1155/2019/8521649 |
| spellingShingle | Daiquan Xiao Xuecai Xu Li Duan Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model Journal of Advanced Transportation |
| title | Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model |
| title_full | Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model |
| title_fullStr | Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model |
| title_full_unstemmed | Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model |
| title_short | Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model |
| title_sort | spatial temporal analysis of injury severity with geographically weighted panel logistic regression model |
| url | http://dx.doi.org/10.1155/2019/8521649 |
| work_keys_str_mv | AT daiquanxiao spatialtemporalanalysisofinjuryseveritywithgeographicallyweightedpanellogisticregressionmodel AT xuecaixu spatialtemporalanalysisofinjuryseveritywithgeographicallyweightedpanellogisticregressionmodel AT liduan spatialtemporalanalysisofinjuryseveritywithgeographicallyweightedpanellogisticregressionmodel |