Automated Collection of Cyclist Data Using Computer Vision Techniques

1 2 One of the main challenges in conducting detailed analysis of cyclist behavior is the lack of reliable 3 data. Collecting data through manual methods is a labour-intensive and time consuming process. 4 Two of the important areas of cyclist data collection are volume counts and average speed 5 measurement. A volume count is important as it provides the basis for necessary exposure measures 6 and conveys essential information of traffic patterns. It can also serve as a performance measure of 7 the facility. Cyclist speed data is used for traffic control and safety studies. Video sensors, when 8 complemented with computer vision can offer a promising approach for the automated collection of 9 traffic data. The approach is characterized by the wealth of data they can capture, store and analyze. 10 Through the application of computer vision techniques, it is possible to obtain precise spatial and 11 temporal measurements of the road-users in a resource-efficient way. This paper demonstrates the use 12 of a set of computer vision techniques for the automated collection of cyclist data. The cyclist tracks 13 obtained from video analysis are used to perform screen line counting as well as cyclist speed 14 measurements. The applications are demonstrated using a real-world data set from a roundabout in 15 Vancouver, British Columbia. Further analysis was conducted on the mean speed of cyclists with 16 regards to several factors such as the travel path, helmet usage, and group size. The motivation of 17 this research is to improve the understanding of cyclists’ behavior and how it varies under different 18 conditions. Several conclusions can be drawn from the analysis of cyclist speed behaviour. Group 19 size, travel path, lane position and helmet usage were found affect the cyclist mean speed. Single 20 cyclists had a slightly, but significantly higher mean cycling speed compared to group cyclists. The 21 mean cycling speed was highest for the cyclists using the road rather than the sidewalk. The mean 22 cycling speed decreases for non-helmet users. 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 TRB 2013 Annual Meeting Paper revised from original submittal.