Skip to main content

Effect of Age on Driving Behavior and a Neurophysiological Interpretation

  • Conference paper
  • First Online:
Book cover HCI in Mobility, Transport, and Automotive Systems (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13335))

Included in the following conference series:

  • 1448 Accesses

Abstract

This study investigated the effect of age on driving behavior and provided a neurophysiological interpretation. Two age group of participants have driven on a 10-mile interstate highway on a driving simulator under different traffic density and driving mode. Driving performance, eye movement, brain activity, and subjective workload were measured. Results showed that age didn’t affect the driving performance or brain activity. But the subjective workload and eye movement were significantly different among the two age groups. Moreover, drivers’ subjective workload was not consistent with the eye movement. The study should provide insights to future studies about the effect of human factors in driving behavior.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. World Health Organization: Road traffic injuries, 21 June 2021. https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries

  2. Mueller, A.S., Trick, L.M.: Driving in fog: the effects of driving experience and visibility on speed compensation and hazard avoidance. Accid. Anal. Prev. 48, 472–479 (2012)

    Article  Google Scholar 

  3. Bao, S., Wu, L., Yu, B., Sayer, J.R.: An examination of teen drivers’ car-following behavior under naturalistic driving conditions: with and without an advanced driving assistance system. Accid. Anal. Prev. 147, 1–7 (2020)

    Article  Google Scholar 

  4. National Highway Traffic Safety Administration (NHTSA). Traffic Safety Facts 2019: Young Drivers (Report No. DOT HS 813 130). U.S. Department of Transportation, June 2021. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813130externalicon

  5. Simons-Morton, B., Lerner, N., Singer, J.: The observed effects of teenage passengers on the risky driving behavior of teenage drivers. Accid. Anal. Prev. 37(6), 973–982 (2005)

    Article  Google Scholar 

  6. Konstantopoulos, P., Chapman, P., Crundall, D.: Driver’s visual attention as a function of driving experience and visibility. Using a driving simulator to explore drivers’ eye movements in day, night and rain driving. Accid. Anal. Prev. 42(3), 827–834 (2010)

    Article  Google Scholar 

  7. Derrig, R.A., Sequi-Gomez, M., Abtahi, A., Liu, L.: The effect of population safety belt usage rates on motor vehicle-related fatalities. Accid. Anal. Prev. 34(1), 101–110 (2002)

    Article  Google Scholar 

  8. Hijar, M., Carrillo, C., Flores, M., Anaya, R., Lopez, V.: Risk factors in highway traffic accidents: a case control study. Accid. Anal. Prev. 32(5), 703–709 (2000)

    Article  Google Scholar 

  9. Norris, F.H., Matthews, B.A., Riad, J.K.: Characterological, situational, and behavioral risk factors for motor vehicle accidents: a prospective examination. Accid. Anal. Prev. 32(4), 505–515 (2000)

    Article  Google Scholar 

  10. Zhang, J., Lindsay, J., Clarke, K., Robbins, G., Mao, Y.: Factors affecting the severity of motor vehicle traffic crashes involving elderly drivers in Ontario. Accid. Anal. Prev. 32(1), 117–125 (2000)

    Article  Google Scholar 

  11. Hartwich, F., Beggiato, M., Krems, J.: Driving comfort, enjoyment and acceptance of automated driving – effects of drivers’ age and driving style familiarity. Ergonomics 61(8), 1017–1032 (2018)

    Article  Google Scholar 

  12. Zhang, Q., Yang, X., Robert, L.: Drivers’ age and automated vehicle explanations. Sustainability 13(4), 1948 (2021)

    Article  Google Scholar 

  13. Sportillo, D., Paljic, A., Ojeda, L.: On-road evaluation of autonomous driving training. In: 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 182–190. IEEE Press, Daegu (2019)

    Google Scholar 

  14. Wu, Y., et al.: Age-related differences in effects of non-driving related tasks on takeover performance in automated driving. J. Saf. Res. 72, 231–238 (2020)

    Article  Google Scholar 

  15. Li, S., Blythe, P., Guo, W., Namdeo, A.: Investigating the effects of age and disengagement in driving on driver’s takeover control performance in highly automated vehicles. Transp. Plan. Technol. 42(5), 470–497 (2019)

    Article  Google Scholar 

  16. Körber, M., Gold, C., Lechner, D., Bengler, K.: The influence of age on the take-over of vehicle control in highly automated driving. Transp. Res. F: Traffic Psychol. Behav. 39, 19–32 (2016)

    Article  Google Scholar 

  17. Li, T., Zhao, R., Liu, Y., Li, Y., Li, G.: Evaluate the effect of age and driving experience on driving performance with automated vehicles. In: Stanton, N. (ed.) Advances in Human Aspects of Transportation, vol. 270, pp. 155–161. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80012-3_19

    Chapter  Google Scholar 

  18. Yoshino, K., Oka, N., Yamamoto, K., Takahashi, H., Kato, T.: Functional brain imaging using near-infrared spectroscopy during actual driving on an expressway. Front. Hum. Neurosci. 7 (2013). https://doi.org/10.3389/fnhum.2013.00882

  19. Unni, A., Ihme, K., Jipp, M., Rieger, J.W.: Assessing the driver’s current level of working memory load with high density functional near-infrared spectroscopy: a realistic driving simulator study. Front. Hum. Neurosci. 11 (2017). https://doi.org/10.3389/fnhum.2017.00167

  20. Zhao, R., Liu, Y., Li, Y., Tokgoz, B.: An investigation of resilience in human driving and automatic driving in freight transportation system. In: IIE Annual Conference, pp. 974–979. Norcross (2021)

    Google Scholar 

  21. Liu, Y., Zhao, R., Li, T., Li, Y.: An investigation of the impact of autonomous driving on driving behavior in traffic jam. In: IIE Annual Conference, pp. 986–991. Norcross (2021)

    Google Scholar 

Download references

Acknowledgements

This research was partially supported by the Center for Advances in Port Management (CAPM) at Lamar University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the CAPM.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yueqing Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, T., Zhao, R., Liu, Y., Liu, X., Li, Y. (2022). Effect of Age on Driving Behavior and a Neurophysiological Interpretation. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2022. Lecture Notes in Computer Science, vol 13335. Springer, Cham. https://doi.org/10.1007/978-3-031-04987-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-04987-3_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04986-6

  • Online ISBN: 978-3-031-04987-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics