Traffic Safety Evaluation Based on Vision and Signal Timing Data

Authors

  • Won-Ho Suh Department of Transportation and Logistics Engineering, Hanyang University, Korea
  • Jin-Woo Park Department of Transportation and Logistics Engineering, Hanyang University, Korea
  • Jung-Ryul Kim Department of Transportation and Logistics Engineering, Hanyang University, Korea

Keywords:

image processing, vehicle detecting, traffic safety, pedestrian safety

Abstract

With the advancement of recent image processing technologies numerous imaging applications have been developed to collect traffic information. In this study, image processing technology is applied to roadway safety to detect and measure the conflict between pedestrians and vehicular traffic and investigate the correlations between the conflict and signal timing data. By using an image processing technique, pedestrian and vehicular movements are detected and the distance between these two are calculated to detect conflicts between them. In this study, Machine Learning techniques are utilized to facilitate more accurate and efficient detection. It is assumed that the intersection is accident-prone if conflicts between pedestrian and vehicular traffic appear more frequently than other intersections and correlations exist depending on the status of signal timing, for example at the end of green signal for a particular movement. This approach is expected to identify accident-prone intersections without actually experiencing crashes; therefore, this would potentially reduce social costs associated with traffic accidents.

Author Biography

Won-Ho Suh, Department of Transportation and Logistics Engineering, Hanyang University, Korea

Assistant Professor
Department of Transportation and Logistics Engineering

References

K. Ismail, T. Sayed, and N. Saunier, “Automated analysis of pedestrian-vehicle: conflicts context for before-and-after studies,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2198, pp. 52-64, 2010.

A. Laureshyn, “Superpixel based road user tracker,” Workshop on the Comparison of Surrogate Measures of Safety Extracted from Video Data, Transportation Research Board 93rd Annual Meeting, January 2014.

T. Madsen, “Automatic detection of conflicts at signalized intersections,” Workshop on the Comparison of Surrogate Measures of Safety Extracted from Video Data, Transportation Research Board 93rd Annual Meeting, January 2014.

W. Suh, “Mitigating initialization bias in transportation modeling applications,” Proceedings of Engineering and Technology Innovation, vol. 3 pp. 34-36, 2016.

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Published

2017-12-20

How to Cite

[1]
W.-H. Suh, J.-W. Park, and J.-R. Kim, “Traffic Safety Evaluation Based on Vision and Signal Timing Data”, Proc. eng. technol. innov., vol. 7, pp. 37–40, Dec. 2017.

Issue

Vol. 7 (2017)

Section

Articles

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