Causality Effects of Interventions and Stressors on Driving Behaviors under Typical Conditions
1
Department of Statistics, Texas A&M University, College Station, TX 77843, USA
2
Departments of Visualization & Computer Science and Engineering, Texas A&M University, College Station, TX 77843, USA
3
Computational Physiology Lab, University of Houston, Houston, TX 77004, USA
*
Author to whom correspondence should be addressed.
Mathematics 2018, 6(8), 139; https://doi.org/10.3390/math6080139
Received: 13 July 2018 / Revised: 9 August 2018 / Accepted: 12 August 2018 / Published: 14 August 2018
(This article belongs to the Special Issue Human-Computer Interaction: New Horizons)
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.
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Keywords:
machine learning; data mining and statistical analysis; human behavior understanding; driving with distraction
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MDPI and ACS Style
Gomez, J.P.; Akleman, D.; Akleman, E.; Pavlidis, I. Causality Effects of Interventions and Stressors on Driving Behaviors under Typical Conditions. Mathematics 2018, 6, 139.
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