Forced lane changing behavior significantly impacts traffic flow and safety in urban expressway merging areas, necessitating detailed analysis of its underlying patterns and decision-making factors. This study utilizes UAV aerial photography and image processing technology to collect vehicle trajectory data from a merging area on an expressway in Wuhan. Through this data, mandatory lane changing events are identified, and associated micro- and macro-parameters are calculated. The study explores the statistical properties and distribution characteristics of these events, focusing on lane changing length, position, and driving distance. Spearman’s correlation analysis further elucidates the impact of various parameters on forced lane changing behavior, revealing significant correlations between lane-changing positions and micro-factors such as time, distance, spacing, and speed difference, as well as macro traffic parameters like flow rate, density, and spatial occupancy. Interestingly, lane changing behavior tends to increase and occur at lower speeds in environments with high traffic density and occupancy, potentially leading to traffic bottlenecks. The analysis also highlights that lane changing vehicles often adjust their speeds to lower than the preceding vehicle but higher than the following one, indicating a preference for changing lanes under less traffic pressure. Additionally, in situations requiring emergency lane changes, drivers appear to choose moments with higher Time to Collision (TTC) values to minimize collision risks. This comprehensive study offers valuable insights for enhancing traffic control and safety monitoring on urban expressways.