In an effort to understand and decrease alcohol-impaired driving as a primary collision factor In California, the research team designed an evaluation plan for California Senate Bill 1046 and its focus on ignition interlock devices as a sentence for Driving Under Influence offense. This plan will evaluate whether Senate Bill 1046 affected the Driving Under the Influence crash frequency and severity, and whether sociodemographic and geographic factors influence its effectiveness. This report lays the foundation for the evaluation that will be conducted in 2024. The research team conducted a meta-analysis of the last 12 years of literature and research on ignition interlock programs inside and outside the United States. Based on the findings of this analysis, the recommended evaluation plan of the law revolves around three research questions that focus on the changes in the frequency/severity of DUI-related crashes in California, the impact of the law on recidivism and on interlock installation rates. To respond to these questions, the research team recommends a list of data that should be collected, such as the number of injuries and deaths resulting from alcohol-related motor vehicle accidents, installation rates of ignition interlocks compared to the prior five-year period, the number of individuals who were required to have an ignition interlock device installed who were convicted of an alcohol-related violation, as well as number of lockouts while an interlock is installed. The research team proposed several statistical approaches for the analysis of this data, such as descriptive statistics, time series analysis, analysis of variance, and logistic regression.
MARIA CHIERICHETTI, PHD
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 lie in the broad field of safety of transportation, with a focus on policy definition and evaluation as well as structural integrity. She has authored several journal publications and conference proceedings.
ARMIN MOGHADAM, PHD
Dr. Armin Moghadam is an Assistant Professor in the Department of Aviation and Technology in the College of Engineering at San Jose State University. Prior to joining SJSU, he worked as a research fellow in Software Analytics and Pervasive Parallelism, and Advanced Machinery Engineering and Manufacturing Systems labs at Iowa State University. Dr. Moghadam holds a PhD in Advanced Machinery Engineering and Manufacturing Systems. His research interests focus on the application of computer vision and deep learning in agriculture and manufacturing. Dr. Moghadam teaches courses such as Machine Learning Technology and Applications, Internet of Things (sensor integration), and Automation and Control Systems in the Department of Technology.
FATEMEH DAVOUDI, PHD
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’s degree in Engineering and Technology Management, and a PhD in Industrial Technology with a minor in Statistics. She teaches, conducts research 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 systems. Dr. Davoudi is the graduate advisor for the Master of Science in Quality Assurance and leads the Machine Learning & Safety Analytics Lab in the Department of Technology.