Causality Effects of Interventions and Stressors on Driving Behaviors under Typical Conditions

Juan Pablo Gomez, Derya Akleman, Ergun Akleman and Ioannis Pavlidis
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Juan Pablo Gomez: Department of Statistics, Texas A&M University, College Station, TX 77843, USA
Derya Akleman: Department of Statistics, Texas A&M University, College Station, TX 77843, USA
Ergun Akleman: Departments of Visualization & Computer Science and Engineering, Texas A&M University, College Station, TX 77843, USA
Ioannis Pavlidis: Computational Physiology Lab, University of Houston, Houston, TX 77004, USA

Mathematics, 2018, vol. 6, issue 8, 1-15

Abstract: In this paper, we demonstrate that interventions and stressors do not necessarily cause the same distractions in all people; therefore, it is impossible to evaluate the impacts of interventions and stressors on traffic accidents. We analyzed publicly available multimodal data that was collected through one of the largest controlled experiments on distracted driving. A crossover design was used to examine the effects of actual and perceived interventions and stressors in driving behaviors and parallel designs on reactivity to a startling event. To analyze this data and make recommendations, we developed and compared a wide variety of mixed effects statistical models and machine learning methods to evaluate the effects of interventions and stressors on driving behaviors.

Keywords: machine learning; data mining and statistical analysis; human behavior understanding; driving with distraction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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