Pedestrian Crash Exposure Analysis Using Alternative Geographically Weighted Regression Models
In order to develop a sustainable, safe, and dynamic transportation system, proper attention must be paid to the safety of pedestrians. The purpose of this study is to analyze the surrogate measures related to pedestrian crash exposure in urban roads, including the use of sociodemographic characteri...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/6667688 |
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| _version_ | 1832550490413465600 |
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| author | Seyed Ahmad Almasi Hamid Reza Behnood Ramin Arvin |
| author_facet | Seyed Ahmad Almasi Hamid Reza Behnood Ramin Arvin |
| author_sort | Seyed Ahmad Almasi |
| collection | DOAJ |
| description | In order to develop a sustainable, safe, and dynamic transportation system, proper attention must be paid to the safety of pedestrians. The purpose of this study is to analyze the surrogate measures related to pedestrian crash exposure in urban roads, including the use of sociodemographic characteristics, land use, and geometric characteristics of the network. This study develops pedestrian exposure models using geographical spatial models including geographically weighted regression (GWR), geographically weighted Poisson regression (GWPR), and geographically weighted Gaussian regression (GWGR). In general, the results of the GWPR model show that the presence of a bus station, population density, type of residential use, average number of lanes, number of traffic control cameras, and sidewalk width are negatively associated with increasing the number of crashes. In this study, in order to identify traffic analysis zones (TAZ) based on the observed and predicted crash data, spatial distance-based methods using GWPR outputs have been used. This study shows the dispersion and density of pedestrian crashes without possessing the volume of pedestrians. Comparison of the performance of GWPR and Poisson models shows a significant spatial heterogeneity in the analysis. |
| format | Article |
| id | doaj-art-66f234a97bff48849b39c87972cca156 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-66f234a97bff48849b39c87972cca1562025-02-03T06:06:42ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/66676886667688Pedestrian Crash Exposure Analysis Using Alternative Geographically Weighted Regression ModelsSeyed Ahmad Almasi0Hamid Reza Behnood1Ramin Arvin2Departmnt of Transportation Planning, Faculty of Engineering, Imam Khomeini International University, Qazvin, IranDepartmnt of Transportation Planning, Faculty of Engineering, Imam Khomeini International University, Qazvin, IranDepartment of Civil & Environmental Engineering, University of Tennessee, Knoxville, USAIn order to develop a sustainable, safe, and dynamic transportation system, proper attention must be paid to the safety of pedestrians. The purpose of this study is to analyze the surrogate measures related to pedestrian crash exposure in urban roads, including the use of sociodemographic characteristics, land use, and geometric characteristics of the network. This study develops pedestrian exposure models using geographical spatial models including geographically weighted regression (GWR), geographically weighted Poisson regression (GWPR), and geographically weighted Gaussian regression (GWGR). In general, the results of the GWPR model show that the presence of a bus station, population density, type of residential use, average number of lanes, number of traffic control cameras, and sidewalk width are negatively associated with increasing the number of crashes. In this study, in order to identify traffic analysis zones (TAZ) based on the observed and predicted crash data, spatial distance-based methods using GWPR outputs have been used. This study shows the dispersion and density of pedestrian crashes without possessing the volume of pedestrians. Comparison of the performance of GWPR and Poisson models shows a significant spatial heterogeneity in the analysis.http://dx.doi.org/10.1155/2021/6667688 |
| spellingShingle | Seyed Ahmad Almasi Hamid Reza Behnood Ramin Arvin Pedestrian Crash Exposure Analysis Using Alternative Geographically Weighted Regression Models Journal of Advanced Transportation |
| title | Pedestrian Crash Exposure Analysis Using Alternative Geographically Weighted Regression Models |
| title_full | Pedestrian Crash Exposure Analysis Using Alternative Geographically Weighted Regression Models |
| title_fullStr | Pedestrian Crash Exposure Analysis Using Alternative Geographically Weighted Regression Models |
| title_full_unstemmed | Pedestrian Crash Exposure Analysis Using Alternative Geographically Weighted Regression Models |
| title_short | Pedestrian Crash Exposure Analysis Using Alternative Geographically Weighted Regression Models |
| title_sort | pedestrian crash exposure analysis using alternative geographically weighted regression models |
| url | http://dx.doi.org/10.1155/2021/6667688 |
| work_keys_str_mv | AT seyedahmadalmasi pedestriancrashexposureanalysisusingalternativegeographicallyweightedregressionmodels AT hamidrezabehnood pedestriancrashexposureanalysisusingalternativegeographicallyweightedregressionmodels AT raminarvin pedestriancrashexposureanalysisusingalternativegeographicallyweightedregressionmodels |