Analysis of Traffic Accident Severity at Intersection Using Logistic Regression Model

Hsu, Yao Tzu and Chang, Shun Chi and Hsu, Tzu Hsin (2020) Analysis of Traffic Accident Severity at Intersection Using Logistic Regression Model. Journal of Engineering Research and Reports, 13 (4). pp. 1-9. ISSN 2582-2926

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Abstract

Accident severity analysis is an important issue in the field of traffic safety study, and intersections are also locations of relatively high accident rates in the roadway network. Therefore, the main purpose of this study is to establish a prediction model of intersection severity based on the binary logistic regression model of data mining technology. The data source of intersection accident is obtained from the Taichung City Police Department in Taiwan in 2018 and there are 27461 valid samples. The dependent variable is the severity of intersection accident. The independent variables include 9 variables such as month, time of accident, weather condition, light conditions, road type, road surface condition, traffic control type, accident type and vehicle type, and are analyzed by the forward selection (Wald). The research results show that time of accident, road surface condition, accident type and vehicle type have significant effects. The confusion matrix is used to verify the reliability of the model, and the results can be used as the references for reducing the degree of accident injury at the intersection in the future.

Item Type: Article
Subjects: Middle East Library > Engineering
Depositing User: Unnamed user with email support@middle-eastlibrary.com
Date Deposited: 05 Apr 2023 06:47
Last Modified: 29 Jul 2024 09:49
URI: http://editor.openaccessbook.com/id/eprint/334

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