Related Experiment Video
Updated: May 22, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
Published on: January 20, 2023
Analysis of risk factors affecting the severity of intersection crashes by logistic regression
Huiqin Chen1, Libo Cao, David B Logan
1State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, Hunan, China.
Objective:
The objective of this research was to study the risk factors that significantly influence the severity of intersection crashes for vehicle occupants, as well as for pedestrians and other vulnerable road users.
Methods:
Logistic regression was applied as the method in this study to analyze a data set of intersection crashes involving casualties in Victoria, Australia, for the period January 2000 to December 2009.
Results:
Seven risk factors obtained were found to be significantly associated with the severity of intersection crashes, including driver age and gender, speed zone, traffic control type, time of day, crash type, and seat belt usage.
Conclusions:
This study found that male drivers as well as older drivers (age 65 and above) had higher odds of being involved in fatal intersection crashes. Intersection crashes occurring between midnight and early morning (12:00 a.m. to 5:59 a.m.), in 100 km/h speed zones, or with no traffic control had a higher odds of a fatal outcome than their counterpart categories. Furthermore, intersection crashes involving pedestrians or a non-seat belt-wearing driver were more likely to lead to a fatal outcome. In general, identification of risk factors and the discussion of the odds ratio between levels on the impact of the intersection crash severity would be beneficial for road safety stakeholders to develop initiatives to reduce the severity of intersection crashes.
Related Concept Videos
Statistical Methods for Analyzing Epidemiological Data
Hypothesis Test for Test of Independence
H0: The two variables (factors)...
Hazard Rate
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Introduction to Test of Independence
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

