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Beside and Behind the Wheel: Factors that Influence Driving Stress and Driving Behavior

Author

Listed:
  • Víctor Corcoba Magaña

    (Department of Computer Science, University of Oviedo, 33003 Oviedo, Spain)

  • Xabiel García Pañeda

    (Department of Computer Science, University of Oviedo, 33003 Oviedo, Spain)

  • Roberto Garcia

    (Department of Computer Science, University of Oviedo, 33003 Oviedo, Spain)

  • Sara Paiva

    (Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Viana do Castelo, 4900-347 Viana do Castelo, Portugal)

  • Laura Pozueco

    (Department of Computer Science, University of Oviedo, 33003 Oviedo, Spain)

Abstract

A large percentage of traffic accidents are due to human errors. Driving behavior and driving stress influence the probability of making these mistakes. Both are influenced by multiple factors, among which might be elements such as age, gender, sleeping hours, or working hours. The objective of this paper is to study, in a real scenario and without forcing the driver’s state, the relationship between driving behavior, driving stress, and these elements. Furthermore, we aim to provide guidelines to improve driving assistants. In this study, we used 1050 driving samples obtained from 35 volunteers. The driving samples correspond to regular commutes from home to the workplace. ANOVA and ANCOVA tests were carried out to check if there are significant differences in the four factors analyzed. Although the results show that driving behavior and driving stress are affected by gender, age, and sleeping hours, the most critical variable is working hours. Drivers with long working days suffer significantly more driving stress compared to other drivers, with the corresponding effect on their driving style. These drivers were the worst at maintaining the safety distance.

Suggested Citation

  • Víctor Corcoba Magaña & Xabiel García Pañeda & Roberto Garcia & Sara Paiva & Laura Pozueco, 2021. "Beside and Behind the Wheel: Factors that Influence Driving Stress and Driving Behavior," Sustainability, MDPI, vol. 13(9), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4775-:d:542564
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    Citations

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    Cited by:

    1. Mohd Anjum & Sana Shahab, 2023. "Improving Autonomous Vehicle Controls and Quality Using Natural Language Processing-Based Input Recognition Model," Sustainability, MDPI, vol. 15(7), pages 1-21, March.
    2. Khondoker Billah & Hatim O. Sharif & Samer Dessouky, 2022. "How Gender Affects Motor Vehicle Crashes: A Case Study from San Antonio, Texas," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    3. Peng, Qiao & Bakkar, Yassine & Wu, Liangpeng & Liu, Weilong & Kou, Ruibing & Liu, Kailong, 2024. "Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).

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