Skip to main content
Intended for healthcare professionals
Restricted access
Research article
First published online January 1, 2013

Hazardous Driving Event Detection and Analysis System in Vehicular Networks: Methodology and Field Implementation

Abstract

This study proposes a novel highway traffic surveillance system that is capable of detecting hazardous driving maneuvers through the use of an in-vehicle sensor and transmission of the event data detected to a traffic management center through vehicle-to-vehicle and vehicle-to-infrastructure wireless communication; this system is referred to as the “hazardous driving event detection and analysis system in vehicular networks.” Compared with existing surveillance systems, the main novel feature of the proposed system lies in its ability to detect hazardous driving maneuvers that have the potential to lead to crashes. Three major components of the system are introduced in this study: an algorithm for detecting hazardous driving events, a method for deriving a new index incorporating expert judgment for evaluation of the risk level of the traffic stream on the basis of analyses of hazardous events that are detected, and field implementation of the proposed system in a test bed with real-time and historical data. Extensive field tests were conducted in the test bed to fine-tune the prototypical system. The methodology and field implementation presented in this study have potential value to help highway traffic agencies monitor and evaluate traffic streams with a focus on traffic safety. The proposed system is expected to be effective as support for the development of various traffic information control strategies to enhance traffic safety on highways.

Get full access to this article

View all access and purchase options for this article.

References

1. Taylor M. C., Lynam D. A., and Baruya A. The Effects of Driver's Speed on the Frequency of Road Accidents. Report 421. Transport Research Laboratory, Crowthorne, United Kingdom, 2000.
2. Oh C., Oh J.-S., Ritchie S. G., and Chang M. Real-Time Estimation of Freeway Accident Likelihood. Presented at 80th Annual Meeting of the Transportation Research Board, Washington, D.C., 2001.
3. Zheng Z., Ahn S., and Monsere C. M. Impact of Traffic Oscillations on Freeway Crash Occurrences. Accident Analysis and Prevention, Vol. 42, 2010, pp. 626–636.
4. Han I., and Yang K. Recognition of Dangerous Driving Using Automobile Black Boxes. Journal of Korean Society of Transportation, Vol. 25, No. 5, 2007, pp. 149–160.
5. Beck K. H., Yan F., and Wang M. Q. Cell Phone Users, Reported Crash Risk, Unsafe Driving Behaviors and Dispositions: A Survey of Motorists in Maryland. Journal of Safety Research, Vol. 38, 2007, pp. 683–688.
6. Oh J.-T., Cho J.-H., Lee S.-Y., and Kim Y.-S. Development of a Data-Logger Classifying Dangerous Drive Behaviors. Journal of the Korea Institute of Intelligent Transport Systems, Vol. 7, No. 3, 2008, pp. 15–28.
7. Valaar W., Simpson H., and Robertson R. A Perceptual Map for Understanding Concern About Unsafe Driving Behaviours. Accident Analysis and Prevention, Vol. 40, 2008, pp. 1667–1673.
8. Oh J.-T., Cho J.-H., Lee S.-Y., and Kim Y.-S. Development of a Critical Value According to Dangerous Drive Behaviors. Journal of Korean Society of Road Engineers, Vol. 11, No. 1, 2009, pp. 69–83.
9. Shladover S. E., Polatkan G., Sengupta R., VanderWerf J., Ergen M., and Bougler B. Dependence of Cooperative Vehicle System Performance on Market Penetration. In Transportation Research Record: Journal of the Transportation Research Board, No. 2000, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 121–127.
10. Wischorf L., Ebner A., and Rohling H. Information Dissemination in Self-Organizing Intervehicle Networks. IEEE Transactions on Intelligent Transportation Systems, Vol. 6, No. 1, 2005, pp. 90–101.
11. Xu H., and Barth M. J. Travel Time Estimation Techniques for Traf-fic Information Systems Based on Intervehicle Communications. In Transportation Research Record: Journal of the Transportation Research Board, No. 1944, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 72–81.
12. Shladover S. E., and Kuhn T. M. Traffic Probe Data Processing for Full-Scale Deployment of Vehicle–Infrastructure Integration. In Transportation Research Record: Journal of the Transportation Research Board, No. 2086, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 115–123.
13. Vapnik V. The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995.
14. Oh C., and Jung E. Monitoring of Hazardous Driving Events Using In-Vehicle Gyro Sensor Data. Presented at 91st Annual Meeting of the Transportation Research Board, Washington, D.C., 2012.
15. Saaty T. L. The Analytic Hierarchy Process. McGraw-Hill, New York, 1980.
16. Saaty T. L. Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process. RWS Publications, Pittsburgh, Pa., 1994.
17. Miller G. A. The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. Psychological Review, Vol. 63, March 1956, pp. 61–97.
18. Saaty T. L. Absolute and Relative Measurement with the AHP: The Most Livable Cities in the United States. Socio-Economic Planning Sciences, Vol. 20, No. 6, 1986, pp. 327–331.
19. Saaty T. L. Multicriteria Decision Making: The Analytic Hierarchy Process. RWS Publications, Pittsburgh, Pa., 1990.
20. Oh C., Jung E., Rim H., Kang K., and Kang Y. Intervehicle Safety Warning Information System for Unsafe Driving Events: Methodology and Prototypical Implementation. In Transportation Research Record: Journal of the Transportation Research Board, No. 2324, Transportation Research Board of the National Academies, Washington, D.C., 2012, pp. 1–10.
21. Kang H., and Kim D. Vector Routing Protocols for Delay Tolerant Networks. International Journal of Ad Hoc and Ubiquitous Computing, Vol. 6, No. 1, 2010, pp. 40–52.
22. Woo R. N. R., and Han D. S. Performance of Vehicular-to-Infrastructure Communication Systems Based on IEEE 802.11a. Proc., Consumer Electronics (ICCE), 2010 Digest of Technical Papers International Conference, 2010, pp. 453–454.
23. Bae J. K., Song J. H., Ahn T. S., Park J. H., and Han D. S. A Vehicular Communication System Based on IEEE 802.11a/g for Ubiquitous-Transportation Sensor Network. Proc., Consumer Electronics (ICCE), 2010 Digest of Technical Papers International Conference, 2010, pp. 425–426.
24. Bae J. K., Woo R. N. R., Song J. H., Ahn T. S., and Han D. S. Development of Communication Module Based on IEEE 802.11a/g for u-TSN Service. Journal of the Institute of Electronics Engineers of Korea, Vol. 16, No. 12, 2009, pp. 117–124.
25. Development of Core Technologies for u-Transportation. Final report. Transportation System Innovation Program, The Korea Transport Institute, Kyunggi-do, South Korea, 2012.
26. Holland S. M. Cluster Analysis. Department of Geology, University of Georgia, Athens, 2006.
27. Kaufman L., and Rousseeuw P. J. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York, 1990.

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: January 1, 2013
Issue published: January 2013

Rights and permissions

© 2013 National Academy of Sciences.
Request permissions for this article.

Authors

Affiliations

Cheol Oh
Department of Transportation and Logistics Engineering, Hanyang University at Ansan, 1271, Sa-3 Dong, Sangnokgu, Ansan-Si, Kyunggi-Do 426-791, South Korea.
Eunbi Jeong
Department of Transportation and Logistics Engineering, Hanyang University at Ansan, 1271, Sa-3 Dong, Sangnokgu, Ansan-Si, Kyunggi-Do 426-791, South Korea.
Kyungpyo Kang
Center for Intelligent Transport Systems, Korea Transport Institute, 1160 Simindae-Ro, Ilsanseogu, Koyang City, Kyunggi-Do 411-701, South Korea.
Younsoo Kang
Center for Intelligent Transport Systems, Korea Transport Institute, 1160 Simindae-Ro, Ilsanseogu, Koyang City, Kyunggi-Do 411-701, South Korea.

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: 43

*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: 6

  1. Exploring the associations between driving volatility and autonomous v...
    Go to citation Crossref Google Scholar
  2. Abnormal driving behavior detection based on kernelization-sparse repr...
    Go to citation Crossref Google Scholar
  3. Automatic Unusual Driving Event Identification for Dependable Self-Dri...
    Go to citation Crossref Google Scholar
  4. Vehicle Behavior Learning via Sparse Reconstruction with $\ell_{2}-\el...
    Go to citation Crossref Google Scholar
  5. Cluster-based correlation of severe driving events with time and locat...
    Go to citation Crossref Google Scholar
  6. Cluster-based correlation of severe braking events with time and locat...
    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