Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic and Road Safety · Traffic Prediction and Management Techniques · Traffic control and management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Engineering, Northeastern University, Vancouver, BC V6B 1Z3, Canada
MSE Department, University Canada West, Vancouver, BC V6Z 0E5, Canada
ACSS Department, University Canada West, Vancouver, BC V6Z 0E5, Canada
College of Engineering & Architecture, Gulf University for Science and Technology, Hawally 32093, Kuwait
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