Statistical Analysis for Study of the Effect of Dark Clothing Color of Female Pedestrians on the Severity of Accident Using Machine Learning Methods

The color and brightness of pedestrian clothing are among the factors that could increase the severity of their accidents due to the lack of visibility, especially at night. Today, as most Iranian females tend to wear hijab or dark clothing, the necessity of investigating female pedestrian accidents...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021, Vol.2021, p.1-21
Main Authors: Hosseinian, Seyed Mohsen, Najafi Moghaddam Gilani, Vahid, Mirbaha, Babak, Abdi Kordani, Ali
Format: Article
Language:English
Subjects:
Accidents
Age
Artificial neural networks
Basis functions
Color
Engineering
Factor analysis
Fatalities
Females
Frequency analysis
Independent variables
Injuries
Machine learning
Multilayer perceptrons
Night
Pedestrian crossings
Pedestrians
Roads & highways
Statistical analysis
Traffic accidents & safety
Traffic safety
Transportation planning
Visibility
Weather
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Summary:The color and brightness of pedestrian clothing are among the factors that could increase the severity of their accidents due to the lack of visibility, especially at night. Today, as most Iranian females tend to wear hijab or dark clothing, the necessity of investigating female pedestrian accidents influenced by clothing color is important. Many studies have been performed to analyze the severity of pedestrian accidents, but a study has not yet been conducted to determine the effect of the dark clothing color of female pedestrians on the severity of accidents. Therefore, in this study, 12 independent variables affecting the severity of female pedestrian accidents such as clothing color, age, accident time, day, weather condition, education, pedestrian action, crossing facilities, crossing permit, job, road classification, and fault status were studied. Frequency analysis, Friedman test (FT), and Factor Analysis (FA) methods, as well as modeling methods of Multiple Logistic Regression (MLR) and Artificial Neural Networks (ANNs) using Multilayer Perceptron (MLP) and Radius Basis Function (RBF), were used. Results indicated that clothing color had a significant influence on pedestrian accidents, and chador and dark clothing color increased the probability of accidents, especially at night. The MLP model had a better prediction percentage than the rest, the prediction accuracy of which was 94.6%. Finally, safety solutions were presented according to the results to reduce pedestrian accidents and increase road safety.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/5567638