Automated Analysis and Validation of Right-Turn Merging Behavior

This article describes an automated approach for the analysis of right-turn merging behavior of vehicles. Traditional methods for collecting merging behavior data are labor intensive, suffer from reliability issues, are time consuming, and costly. Automated video merging behavior analysis is advocated as alternative data collection procedure resolving many shortcomings in the manual data collection. The main elements of the behavior analysis include merging conflicts, gap acceptance, and lane discipline. Traffic conflicts provide invaluable information that can be used to assess safety factors and to understand potential collision mechanisms. Gap acceptance is important for developing merging vehicles modeling frameworks. Lane discipline of merging vehicles is significant in showing potential aggressive and dangerous merging maneuvers and driver compliance to traffic rules. The article advocates automated computer vision as the engine to capture and analyze various merging behavior elements. The analysis is demonstrated using a case study from Doha, Qatar. A validation of the results was performed that demonstrated the soundness of the methodology and potential benefits for automated behavior data collection. The microscopic behavior data captured using the proposed automated methodology can be useful for use in road design, traffic management and safety evaluation.