Characteristics of Cyclist Crashes Using Polytomous Latent Class Analysis and Bias-Reduced Logistic Regression

Yuta Sekiguchi, Masayoshi Tanishita and Daisuke Sunaga
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Yuta Sekiguchi: Graduate School of Science and Engineering, Chuo University, Tokyo 112-8551, Japan
Masayoshi Tanishita: Department of Civil and Environmental Engineering, Chuo University, Tokyo 112-8551, Japan
Daisuke Sunaga: Department of Civil and Environmental Engineering, Chuo University, Tokyo 112-8551, Japan

Sustainability, 2022, vol. 14, issue 9, 1-15

Abstract: Although the number of cyclist crashes is decreasing in Japan, the fatality rate is not. Thus, reducing their severity is a major challenge. We used a polytomous latent class analysis to understand their characteristics and bias-reduced logistic regression to analyze their severity. Specifically, 90,696 combinations and 139,955 cyclist accidents were divided into 17 classes. The variable contributing the most to the classification was the crash location. Common fatality risks included older age groups and rural areas, whereas other factors differed among crash locations. Median strips, stop signs, and boundaries between the sidewalk and roadway affected the severity of crashes at intersections. Moreover, the existence of a median strip, collision partner, and time period affected the severity of crashes between intersections. On the sidewalks, the fatality risk was higher when the front part of the bicycle was subjected to the collision.

Keywords: cyclist crash; polytomous latent class analysis; bias-reduced logistic regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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