Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model
This study investigates the various factors contributing to the severity of traffic collisions, with specific attention given to elements such as the involvement of pedestrians and cyclists, the roles played by motor vehicles, prevailing weather conditions, road characteristics, and geographical con...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/838 |
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| author | Rushdi Alsaleh Kawal Walia Ghoncheh Moshiri Yasmeen T. Alsaleh |
| author_facet | Rushdi Alsaleh Kawal Walia Ghoncheh Moshiri Yasmeen T. Alsaleh |
| author_sort | Rushdi Alsaleh |
| collection | DOAJ |
| description | This study investigates the various factors contributing to the severity of traffic collisions, with specific attention given to elements such as the involvement of pedestrians and cyclists, the roles played by motor vehicles, prevailing weather conditions, road characteristics, and geographical contexts. Drawing from a comprehensive dataset from the Virginia Department of Transportation, encompassing over 500,000 data points, this study utilizes two statistical models. Specifically, it utilizes Multinomial Logistic Regression and Multi-Level (Mixed Effect) Multinomial Logistic Regression, which accounts for group-level heterogeneity, to explore the intricate interplay between various factors and collision severity outcomes. The findings underscore the superiority of the Multi-Level Multinomial Logistic Regression model over the standard Multinomial Logistic Regression model in capturing road user severity. Furthermore, this paper highlights the heightened odds of fatalities associated with the presence of vulnerable road users, such as pedestrians and cyclists. Collisions involving unbelted drivers exhibited odds ratios exceeding 10, indicating a substantially elevated likelihood of severe outcomes compared to their belted counterparts. |
| format | Article |
| id | doaj-art-df96a302907844ebb384a0ba66336223 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-df96a302907844ebb384a0ba663362232025-01-24T13:21:00ZengMDPI AGApplied Sciences2076-34172025-01-0115283810.3390/app15020838Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression ModelRushdi Alsaleh0Kawal Walia1Ghoncheh Moshiri2Yasmeen T. Alsaleh3College of Engineering, Northeastern University, Vancouver, BC V6B 1Z3, CanadaMSE Department, University Canada West, Vancouver, BC V6Z 0E5, CanadaACSS Department, University Canada West, Vancouver, BC V6Z 0E5, CanadaCollege of Engineering & Architecture, Gulf University for Science and Technology, Hawally 32093, KuwaitThis study investigates the various factors contributing to the severity of traffic collisions, with specific attention given to elements such as the involvement of pedestrians and cyclists, the roles played by motor vehicles, prevailing weather conditions, road characteristics, and geographical contexts. Drawing from a comprehensive dataset from the Virginia Department of Transportation, encompassing over 500,000 data points, this study utilizes two statistical models. Specifically, it utilizes Multinomial Logistic Regression and Multi-Level (Mixed Effect) Multinomial Logistic Regression, which accounts for group-level heterogeneity, to explore the intricate interplay between various factors and collision severity outcomes. The findings underscore the superiority of the Multi-Level Multinomial Logistic Regression model over the standard Multinomial Logistic Regression model in capturing road user severity. Furthermore, this paper highlights the heightened odds of fatalities associated with the presence of vulnerable road users, such as pedestrians and cyclists. Collisions involving unbelted drivers exhibited odds ratios exceeding 10, indicating a substantially elevated likelihood of severe outcomes compared to their belted counterparts.https://www.mdpi.com/2076-3417/15/2/838traffic collision severitymulti-level mixed effect modelpedestriansvulnerable road userroad characteristics |
| spellingShingle | Rushdi Alsaleh Kawal Walia Ghoncheh Moshiri Yasmeen T. Alsaleh Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model Applied Sciences traffic collision severity multi-level mixed effect model pedestrians vulnerable road user road characteristics |
| title | Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model |
| title_full | Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model |
| title_fullStr | Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model |
| title_full_unstemmed | Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model |
| title_short | Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model |
| title_sort | traffic collision severity modeling using multi level multinomial logistic regression model |
| topic | traffic collision severity multi-level mixed effect model pedestrians vulnerable road user road characteristics |
| url | https://www.mdpi.com/2076-3417/15/2/838 |
| work_keys_str_mv | AT rushdialsaleh trafficcollisionseveritymodelingusingmultilevelmultinomiallogisticregressionmodel AT kawalwalia trafficcollisionseveritymodelingusingmultilevelmultinomiallogisticregressionmodel AT ghonchehmoshiri trafficcollisionseveritymodelingusingmultilevelmultinomiallogisticregressionmodel AT yasmeentalsaleh trafficcollisionseveritymodelingusingmultilevelmultinomiallogisticregressionmodel |