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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Juan Pablo Gomez & Derya Akleman & Ergun Akleman & Ioannis Pavlidis, 2018. "Causality Effects of Interventions and Stressors on Driving Behaviors under Typical Conditions," Mathematics, MDPI, vol. 6(8), pages 1-15, August.
    • Handle: RePEc:gam:jmathe:v:6:y:2018:i:8:p:139-:d:163716
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    References listed on IDEAS

    as
    1. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    2. David A. Bessler & Derya G. Akleman, 1998. "Farm Prices, Retail Prices, and Directed Graphs: Results for Pork and Beef," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(5), pages 1144-1149.
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    Cited by:

    1. Yanli Ma & Shouming Qi & Yaping Zhang & Guan Lian & Weixin Lu & Ching-Yao Chan, 2020. "Drivers’ Visual Attention Characteristics under Different Cognitive Workloads: An On-Road Driving Behavior Study," IJERPH, MDPI, vol. 17(15), pages 1-19, July.
    2. Yuhang Zhang & Yuan Wan & Jiahui Hao & Zaili Yang & Huanhuan Li, 2025. "Learning High-Order Features for Fine-Grained Visual Categorization with Causal Inference," Mathematics, MDPI, vol. 13(8), pages 1-23, April.

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