Vision Technology Based Traffic Safety Analysis Using Signal Data
Recent vision technology allows traffic engineers to analyze traffic safety based on image processing applications. Vehicle trajectory data including vehicle position and speed are extracted and converted into traffic conflicts related variables. These variables are analyzed to find correlations with crash data and geometric design variables and included in the statistical analysis process to evaluate the safety of intersections. In this paper, signal timing information data is included in the analysis process to investigate if there are any correlations with traffic conflict data. For example, traffic conflicts happen more frequently in a certain movement at certain signal time phase. The goal of this paper is to develop a method for vision technology-based traffic safety analysis process using traffic signal data to identify crash prone movement and signal to time at a given intersection. The proposed technique is demonstrated in real-world video data collected in an intersection. This paper is expected to provide more insight and technique in traffic safety evaluation.
K. Ismail, T. Sayed, and N. Saunier, “N. Automated analysis of pedestrian-vehicle conflicts: context for before-and-after studies,” Journal of the Transportation Research Board, vol. 2198, no. 1, pp. 52-64, January 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, November 2013, pp. 1-5.
T. Madsen, C. Bahnsen, H. Lahrmann, and T. B. Moeslund, “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, J. Park, and J. Kim, “Traffic safety evaluation based on vision and signal timing data,” Proceedings of Engineering and Technology Innovation, vol. 7, pp. 37-40, 2017.
N. Smaoui, K. Kim, O. Gnawali, Y. Lee, and W. Suh, “Respirable dust monitoring in construction sites and visualization in building information modeling using real-time sensor data,” Sensors and Materials, vol. 30, pp. 1775-1786, 2018.
W. Suh, J. Anderson, A. Guin, and M. Hunter, “Evaluation of video detection system as a traffic data collection method,” Scientia Iranica, vol. 22, pp. 2092-2102, May 2015.
M. Park, J. Kim, Y. Lee, J. Park, and W. Suh, “Vision-based surveillance system for monitoring traffic conditions,” Multimedia Tools and Applications, vol. 76, no. 23, pp. 25343-25367, December 2017.
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