Exploring the Relationship Between Mandatory Helmet Use Regulations and Adult Cyclists’ Behavior in California Using Hybrid Machine Learning Models

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Exploring the Relationship Between Mandatory Helmet Use Regulations and Adult Cyclists’ Behavior in California Using Hybrid Machine Learning Models

Abstract: 

In California, bike fatalities increased by 8.1% from 2015 to 2016. Even though the benefits of wearing helmets in protecting cyclists against trauma in cycling crash has been determined, the use of helmets is still limited, and there is opposition against mandatory helmet use, particularly for adults. Therefore, exploring perceptions of adult cyclists regarding mandatory helmet use is a key element in understanding cyclists’ behavior, and determining the impact of mandatory helmet use on their cycling rate. The goal of this research is to identify sociodemographic characteristics and cycling behaviors that are associated with the use and non-use of bicycle helmets among adults, and to assess if the enforcement of a bicycle helmet law will result in a change in cycling rates. This research develops hybrid machine learning models to pinpoint the driving factors that explain adult cyclists’ behavior regarding helmet use laws. 

 

Authors: 

FATEMEH DAVOUDI

Dr. Fatemeh Davoudi is an Assistant Professor in the Department of Aviation and Technology in the College of Engineering at San Jose State University. She has a bachelor's degree in Mathematics, a master degree in Engineering and Technology Management, and a PhD in Industrial Technology, with a minor in Statistics. Dr. Davoudi’ s diverse background has trained her to teach, conduct research and produce scholarly literature in areas of statistical survey development, design and analysis of experiments, statistical modeling, and applications of machine learning tools for decision-making to improve safety outcomes in industrial incidents. She has authored several publications that analyze occupational incidents in manufacturing operations to reduce injury severity and enhance occupational safety. She has been teaching undergraduate and graduate level course with focus on statistical analysis for engineering and technology students since 2015. Dr. Davoudi is currently leading the Machine Learning & Safety Analytics Lab at San Jose State University, Technology Department.

MARIA CHIERICHETTI

Dr. Maria Chierichetti is an Assistant Professor in the Department of Aerospace Engineering in the College of Engineering at San Jose State University. Prior to joining SJSU, she was a faculty member at the University of Cincinnati and at Worcester Polytechnic Institute. Dr. Chierichetti holds a PhD and an MS in Aerospace Engineering (with a minor in Mathematics) from the Georgia Institute of Technology, and an MS and a BS in Aeronautical Engineering from Politecnico di Milano, Italy. Her current research interests focus on integrating machine learning with current analysis tools to understand whether structural damage has occurred. She has authored several journal publications and conference proceedings.

Published: 
October 2021
Keywords: 
Statistical analysis
Cyclists’ behavior
machine learning classifiers

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MCTM
CSUTC
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NTFC

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