Skip to main content
Intended for healthcare professionals
Restricted access
Research article
First published online April 28, 2019

Brain Activity Involved in Vehicle Velocity Changes in a Sag Vertical Curve on an Expressway: Vector-Based Functional Near-Infrared Spectroscopy Study

Abstract

A sag vertical curve on an expressway, where a downgrade changes to an upgrade, often causes reduced vehicle velocity that results in traffic congestion and occasional accidents. This study conducted an experiment on an actual expressway. The experiment used functional near-infrared spectroscopy (fNIRS) to measure the impact of a sag on driver brain activity while driving. fNIRS provides real-time monitoring of localized hemoglobin concentration changes in the cerebral cortex and can detect brain activity by calculating cerebral oxygen exchange. Cluster analysis of vehicle velocity over 965 m from the sag point revealed a constant velocity group (69.7%) and a reduced velocity group (30.3%) with significant velocity reduction [–9.8 ±6.5 km/h (p = .01)] in the first 425 m. Brain activity in the constant velocity group increased significantly in the parietal association cortex (PAC) and the supplementary motor area (SMA) (p < .05). In the subsequent 450 m, vehicle slowdowns gradually disappeared, and PAC activity in the reduced velocity group began to increase followed by increased prefrontal cortical activity. These findings suggest the possibility that the presence or absence of activity in the PAC, which is responsible for visuomotor coordination, spatial perception, and attention, influences differences in vehicle velocity reduction. The simultaneous activation of the PAC and SMA may indicate that the motor-related functions were activated on the basis of the driver's perception of the road environment and vehicle speed. This possibility suggests that traffic safety measures that stimulate the PAC to better awareness may be effective in reducing slowdowns after a sag.

Get full access to this article

View all access and purchase options for this article.

References

1. Murano T., Shirota T., Kuwahara M., and Oguchi T. Method for Forecasting Traffic Accident Occurrence on Expressways. Toshiba Review, Vol. 67, No. 12, 2012, pp. 23–26. http://www.toshiba.co.jp/tech/review/2012/12/67_12pdf/a07.pdf. Accessed July 28, 2014.
2. Hatakenaka H., Hirasawa T., Yamada K., Yamada H., Katayama Y., and Maeda M. Development of AHS for Traffic Congestion in Sag Sections. Presented at ITS World Congress, London, 2006. http://www.nilim.go.jp/lab/qcg/japanese/3paper/pdf/2006_10_itswc_1.pdf. Accessed July 28, 2014.
3. Makino H., Ouchi H., Hirasawa T., and Yamada K. Development of AHS for Traffic Congestion in Sag Sections. Expressways and Automobiles, Vol. 49, No. 7, 2006, pp. 54–58. http://www.nilim.go.jp/lab/qcg/japanese/3paper/pdf/2006_07_kousoku.pdf. Accessed July 28, 2014.
4. Oguchi T. Relationship Between Traffic Congestion Phenomena and Road Alignments at Sag Sections on Motorways. Proceedings of the Japan Society of Civil Engineers, Vol. 524, 1995, pp. 69–78. http://library.jsce.or.jp/jsce/open/00037/524/524-123007.pdf. Accessed July 28, 2014.
5. Iida K., Miki T., Mori Y., Oguchi T., and Matsumoto K. Analysis of Drivers' Behavior During Sag Sections Based on the Results of Actual Driving Experiment and Indoor Driving Experiment. Proceedings of the Japan Society of Civil Engineers, Vol. 22, No. 2, 1999, p. 967.
6. Mehler B., Reimer B., Coughlin J. F., and Dusek J. A. Impact of Incremental Increases in Cognitive Workload on Physiological Arousal and Performance in Young Adult Drivers. In Transportation Research Record: Journal of the Transportation Research Board, No. 2138, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 6–12.
7. Yoshino K., Oka N., Yamamoto K., Takahashi H., and Kato T. Functional Brain Imaging Using Near-Infrared Spectroscopy During Actual Driving on an Expressway. Frontiers in Human Neuroscience, Vol. 7, No. 882, 2013.
8. Yoshino K., Oka N., Yamamoto K., Takahashi H., and Kato T. Correlation of Prefrontal Cortical Activation with Changing Vehicle Speeds in Actual Driving: A Vector-Based Functional Near-Infrared Spectroscopy Study. Frontiers of Human Neuroscience, Vol. 7, No. 895, 2013.
9. Matcher S. J., Elwell C. E., Cooper C. E., Cope M., and Delpy D. T. Performance Comparison of Several Published Tissue Near-Infrared Spectroscopy Algorithms. Analytical Biochemistry, Vol. 227, 1995, pp. 54–68.
10. Kato T., Kamei A., Takashima S., and Ozaki T. Human Visual Cortical Function During Photic Stimulation Monitoring by Means of Near-Infrared Spectroscopy. Journal of Cerebral Blood Flow and Metabolism, Vol. 13, No. 3, 1993, pp. 516–520.
11. Kato T. Apparatus for Evaluating Biological Function. United States Patent: US7065392. 2006. http://www.wipo.int/patentscope/search/en/WO2003068070. Accessed July 28, 2014.
12. Yoshino K., Oka N., Yamamoto K., Takahashi H., and Kato T. Distance-Based Conversion of Time-Series Functional Brain Monitoring Data in an Outdoor Study. Presented at 20th Annual Meeting of the Organization for Human Brain Mapping, Hamburg, Germany, 2014.
13. Sack A. T. Parietal Cortex and Spatial Cognition. Behavioural Brain Research, Vol. 202, No. 2, 2009, pp. 153–161.
14. Krause V., Bashir S., Pollok B., Caipa A., Schnitzler A., and Pascual-Leone A. 1 Hz rTMS of the Left Posterior Parietal Cortex (PPC) Modifies Sensorimotor Timing. Neuropsychologia, Vol. 50, No. 14, 2012, pp. 3729–3735.
15. Long X., Liao W., Jiang C., Liang D., Qiu B., and Zhang L. Health Aging: An Automatic Analysis of Global and Regional Morphological Alterations of Human Brain. Academic Radiology, Vol. 19, No. 7, 2012, pp. 785–793.
16. Tankus A., Yeshurun Y., Flash T., and Fried I. Encoding of Speed and Direction of Movement in the Human Supplementary Motor Area. Journal of Neurosurgery, Vol. 110, No. 6, 2009, pp. 1304–1316.
17. Tanji J. New Concepts of the Supplementary Motor Area. Current Opinion in Neurobiology, Vol. 6, 1996, pp. 782–787.
18. Hikosaka O., Nakamura K., Sakai K., and Nakahara H. Central Mechanisms of Motor Skill Learning. Current Opinion in Neurobiology, Vol. 12, 2002, pp. 217–222.
19. Baird B., Smallwood J., Gorgolewski K. J., and Margulies D. S. Medial and Lateral Networks in Anterior Prefrontal Cortex Support Metacognitive Ability for Memory and Perception. Journal of Neurosurgery, Vol. 33, No. 42, 2013, pp. 16657–16665.
20. Cho S. S., Pellecchia G., Ko J. H., Ray N., Obeso I., Houle S., and Strafella A. P. Effect of Continuous Theta Burst Stimulation of the Right Dorsolateral Prefrontal Cortex on Cerebral Blood Flow Changes During Decision Making. Brain Stimulation, Vol. 5, No. 2, 2012, pp. 116–23.
21. Oguchi T., and Iida K. Applicability of Driving Simulator Technique for Analysis of Car-Following Behavior at Sag Sections on Expressways. Traffic Engineering, Vol. 38, No. 4, 2003, pp. 41–50.
22. Nicolas-Alonso J. F., and Gomez-Gil J. Brain Computer Interfaces, A Review. Sensors, No. 12, 2012, pp. 1211–1279.

Cite article

Cite article

Cite article

OR

Download to reference manager

If you have citation software installed, you can download article citation data to the citation manager of your choice

Share options

Share

Share this article

Share with email
EMAIL ARTICLE LINK
Share on social media

Share access to this article

Sharing links are not relevant where the article is open access and not available if you do not have a subscription.

For more information view the Sage Journals article sharing page.

Information, rights and permissions

Information

Published In

Article first published online: April 28, 2019
Issue published: January 2015

Rights and permissions

© 2015 National Academy of Sciences.
Request permissions for this article.

Authors

Affiliations

Yoshitomo Orino
Traffic Control Center, Hachioji Branch, Central Nippon Expressway Co. Ltd., Utsugi-Cho 231, Hachiouji-Shi, Tokyo 192-8648, Japan.
Kayoko Yoshino
Department of Brain Environmental Research, KatoBrain Co. Ltd., 13-15-104, Shirokanedai 3, Minato-Ku, Tokyo, 108-0071, Japan.
Noriyuki Oka
Department of Brain Environmental Research, KatoBrain Co. Ltd., 13-15-104, Shirokanedai 3, Minato-Ku, Tokyo, 108-0071, Japan.
Kouji Yamamoto
Department of Environment–Engineering, Tokyo Branch, Central Nippon Expressway Co. Ltd., 3-1, Toranomon 4, Minato-Ku, Tokyo 105-6011, Japan.
Hideki Takahashi
Department of Environment–Engineering, Central Nippon Expressway Co. Ltd., Mitsui-Sumitomo Bank Nagoya Building, 2-18-19, Nishiki, Naka-Ku, Nagoya 460-0003, Japan.
Toshinori Kato
Department of Brain Environmental Research, KatoBrain Co. Ltd., 13-15-104, Shirokanedai 3, Minato-Ku, Tokyo, 108-0071, Japan.

Notes

Metrics and citations

Metrics

Journals metrics

This article was published in Transportation Research Record: Journal of the Transportation Research Board.

VIEW ALL JOURNAL METRICS

Article usage*

Total views and downloads: 50

*Article usage tracking started in December 2016


Altmetric

See the impact this article is making through the number of times it’s been read, and the Altmetric Score.
Learn more about the Altmetric Scores



Articles citing this one

Receive email alerts when this article is cited

Web of Science: 0

Crossref: 8

  1. A Methodological Review of fNIRS in Driving Research: Relevance to the...
    Go to citation Crossref Google Scholar
  2. Impact of Auditory Alert on Driving Behavior and Prefrontal Cortex Res...
    Go to citation Crossref Google Scholar
  3. Re-assessing hazard recognition ability in occupational environment wi...
    Go to citation Crossref Google Scholar
  4. Vector-Based Approach for the Detection of Initial Dips Using Function...
    Go to citation Crossref Google Scholar
  5. The study of driver’s brain activity and behaviour on DS test using fN...
    Go to citation Crossref Google Scholar
  6. A Novel Method for Classifying Driver Mental Workload Under Naturalist...
    Go to citation Crossref Google Scholar
  7. The Study of Driver's Brain Activity and Behavior Using fNIRS During A...
    Go to citation Crossref Google Scholar
  8. Effective Connectivity Analysis of the Brain Network in Drivers during...
    Go to citation Crossref Google Scholar

Figures and tables

Figures & Media

Tables

View Options

Get access

Access options

If you have access to journal content via a personal subscription, university, library, employer or society, select from the options below:


Alternatively, view purchase options below:

Purchase 24 hour online access to view and download content.

Access journal content via a DeepDyve subscription or find out more about this option.

View options

PDF/ePub

View PDF/ePub