## 4 Results
### First Scenario: Video with a Passenger Getting off the Bus  
In this first scenario, there is a video in which a person gets off a bus and then walks until they exit the scene (Figure 5). Afterward, the video shows another person getting out of a taxi (Figure 6) and walking away until the video ends. The model is trained to distinguish between these types of movements, including the transition from getting off the bus to walking away, and it uses the pose estimation data to track the person's movements. The identification of specific poses, such as the act of stepping off the bus and walking, allows the system to correctly classify the activity as \"getting off the bus\" and differentiate it from other similar actions, such as getting out of a car. By analyzing these behaviors, the model can provide valuable insights into passenger mobility and possible challenges in the flow of people in areas like bus stops and parking lots. Figures 5 and 6 show the pose estimations for each person. It can be observed that AlphaPose is able to correctly detect everyone in the scene and accurately estimate their skeleton and pose. AlphaPose's ability to precisely identify key points on the human body, such as joints and limbs, allows it to create an accurate representation of each person's posture. This capability is essential for understanding and classifying activities, as it provides valuable data about a person's movements and body alignment during actions like getting off the bus or walking away from a car. The accurate pose estimation ensures that the model can make reliable predictions regarding the activity being performed, which is a crucial aspect for further improving transportation designs and policies. With the pose estimation and the feature vector, the activity of each person is predicted using the ML-RF model. Figure 7 shows the activity recognized by the ML model. It can be seen that the model identified 5 activities throughout the video: falling backward, sitting down on a chair, walking, walking forward (towards the camera), and standing. Figure 5: Passenger getting off a bus in Valparaíso, Chile. Figure 6: Passenger getting off a taxi in Valparaíso, Chile. The Random Forest (RF) model takes the feature vectors derived from the pose estimation data and classifies the actions based on pre-trained patterns. This approach enables the system to recognize a wide range of activities, not just limited to those related to transportation but also including general movements and behaviors such as sitting or walking in different directions. The variety of activities identified in the video suggests that the model is capable of distinguishing between different types of human motion, which is vital for understanding how passengers interact with their environment in real-world scenarios. By recognizing these specific activities, transportation planners can gain a better understanding of how people behave in transit settings and potentially identify points where improvements can be made to enhance accessibility and efficiency. To understand the activities performed throughout the entire video, the evolution of activities over time is plotted. Figure 8 shows the activities performed by each person for each frame of the video. It can be observed that at several points in the video, the model incorrectly recognizes activities, such as falling, that are not actually occurring. These misclassifications can occur for various reasons, such as rapid or subtle body movements that the model might interpret as falling, or limitations in the model's ability to distinguish between similar activities, especially when the pose estimation data are not perfectly clear. For example, a sudden shift in posture or an abrupt movement could be falsely identified as falling backward, even if the person is simply adjusting their position. To better understand which activities are being correctly and incorrectly recognized, Figure 9 presents a graph that highlights the activity classifications over time.