This study presents an artificial intelligence-based approach for the pose detection of passengers' skeletons when boarding and alighting from an urban bus in Valpara & iacute;so, Chile. Using the AlphaPose pose estimator and an activity recognition model based on Random Forest, video data were processed to analyze the poses and activities of passengers. The results obtained allow for an evaluation of safety and ergonomics in public transportation, providing valuable information for improving design and accessibility in buses. This approach not only enhances understanding of passenger behavior but also contributes to the optimization of bus systems to accommodate diverse needs, ensuring a safer and more comfortable environment for all users. AlphaPose accurately estimates the posture of passengers, offering insights into their movements when interacting with the bus. In addition, the Random Forest model recognizes a variety of activities, from walking to sitting, helping to analyze how passengers interact with the space. The analysis helps identify areas where improvements can be made in terms of accessibility, comfort, and safety, contributing to the overall design of public transport systems. This study opens up new possibilities for AI-driven urban transportation analysis and can serve as a foundation for future improvements in transportation planning.