Understanding crash potential associated with teen driving: Survey analysis using multivariate graphical method |
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| Institution: | 1. Texas A&M Transportation Institute, Texas A&M University System, 3135 TAMU, College Station, TX 77843, United States;2. Texas A&M Transportation Institute, Texas A&M University System, 701 N. Post Oak Rd. Suite 430, Houston, TX 77024, United States;3. Texas A&M Transportation Institute, Texas A&M University System, 1100 NW Loop 410, Suite 605, San Antonio, TX, United States;1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu，China;2. National Engineering Laboratory of Integrated Transportation Big Data Application Technology, West Park, High-Tech District, Chengdu, China;3. Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States;1. Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, IA, United States;2. Department of Occupational and Environmental Health, College of Public Health, University of Iowa, Iowa City, IA, United States;3. Injury Prevention and Research Center, College of Public Health, University of Iowa, Iowa City, IA, United States;4. University of Iowa Public Policy Centre, Iowa City, IA, United States;1. Economics, Karlstad Business School, Karlstad University, Karlstad, Sweden;2. Institute of Medicine, Health Metrics Unit, University of Gothenburg, Sweden;3. Centre for Public Safety, Karlstad University, Sweden;4. Swedish Civil Contingencies Agency, Sweden;1. Department of Civil & Architectural Engineering, University of Wyoming, Office: EN 3084, 1000 E University Ave, Dept. 3295, Laramie, WY 82071, United States of America;2. Department of Mathematics and Statistics, University of Wyoming, Office: Ross Hall 336, Laramie, WY 82071, United States of America;3. Wyoming Technology Transfer Center, 1000 E. University Avenue, Department 3295, Laramie, WY 82071, United States of America |
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| Abstract: | Introduction: Teen crash involvement is usually higher than other age groups, and they are typically overrepresented in car crashes. To infer teen drivers' understanding of crash potentials (factors that are associated with crash occurrence), two sources of data are generally used: retrospective data and prospective data. Retrospective data sources contain historical crash data, which have limitations in determining teen drivers' knowledge of crash potentials. Prospective data sources, like surveys, have more potential to minimize the research gap. Prior studies have shown that teen drivers are more likely to be involved in crashes during their early driving years. Thus, there is a benefit in examining how teen drivers' understanding of crash potentials change during their transition through licensing stages (i.e., no licensure to unrestricted licensure). Method: This study used a large set of teen driver survey data (a dataset from approximately 88,000 respondents) of Texas teens to answer the research question. Researchers provided rankings of the crash potentials by gender and licensure stages using a multivariate graphical method named taxicab correspondence analysis (TCA). Results: The findings show that driving behavior and understanding of crash potentials differ among teens based upon various licensing stages. Practical applications: Findings from this study can help government authorities to refine policies of teen driver licensing and implement potential countermeasures for safety improvement. |
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