Automated Cyclist Data Collection under High Density Conditions

This paper demonstrates the effectiveness of video analysis for a cyclist’s data collection in high-density environments. It attempts to address the shortcomings of conventional data collection methods by conducting an automated study to obtain real-world bicycle data and providing a validation scheme to assess the accuracy of the automated observations. Basic traffic quantities such as average speed, volume count, flow rate, and density are automatically estimated and validated. Furthermore, traffic analysis applications are conducted on the collected data as a demonstration of the capabilities of the automated computer vision system. The analysis is applied to a data set collected through video cameras at a cycling event at the University of British Columbia. The analysis indicates the feasibility to automate the cyclist traffic data collection process in challenging, dense conditions. The reported results can provide a motivation for traffic engineers to rely on automated data collection as guidance during the decision-making process and to explore further the relationship between the bike facilities width, the expected flows, the facilities performance, and level of safety.