## 1 Introduction  
Public transportation plays a crucial role in facilitating urban mobility, offering an efficient and eco-friendly alternative to private vehicles. It connects people to their work-places, schools, healthcare facilities, and recreational spaces, contributing to the overall functioning of a city. However, while the system itself may be well established in many urban centers, significant challenges remain in ensuring that passengers can board and alight from buses safely and comfortably [1].  
One of the primary issues is the inadequate infrastructure surrounding bus stops and stations [2]. Poorly designed or maintained bus stops can present obstacles such as uneven pavement, lack of proper ramps, or insufficient lighting, making it difficult for passengers, particularly those with disabilities or mobility impairments, to access the bus. These conditions can create hazardous situations where individuals may trip, fall, or struggle to board in a timely manner, increasing the risk of accidents [3].  
The design of the buses themselves can also contribute to safety concerns [4; 5]. Many older buses or those lacking modern updates may have high steps or narrow doorways that make it challenging for people with limited mobility, such as the elderly or those using wheelchairs or crutches, to enter or exit. Additionally, the interior layout of buses might not offer enough space for people with mobility aids or seated passengers, further exacerbating the difficulty for individuals to navigate the vehicle.  
Inadequate communication about the bus's arrival or expected stops can also add to the problem, particularly in situations where passengers have limited time to board or disembark [6]. This creates a scenario where people may rush to get on or off, increasing the risk of injury.  
Addressing these issues requires a comprehensive analysis of passenger behavior to ensure that those who require assistance are provided with the support they need. To do so, artificial intelligence is required to make public transportation safer and more inclusive, helping to ensure that all passengers, regardless of age or physical ability, have equal access to the benefits of urban transit systems.  
To address the above-mentioned issues, various methodologies based on artificial intelligence have been implemented. One of these is posture detection, which, through artificial intelligence, has been widely used in different contexts, such as public transport environments. AlphaPose is one of the most accurate tools for human pose estimation, while classification algorithms like Random Forest have proven to be effective in recognizing human activities based on pose data [7].  
AlphaPose is a deep learning model capable of estimating the pose of multiple individuals in complex environments, such as trains, buses, and public spaces [8]. AlphaPose takes RGB images and/or videos as input and performs the pose estimation of each person present in the image using a pre-trained model (the COCO dataset). The output includes the X and Y coordinates in the image plane of 17 key points or joints, along with the confidence index of detection for each key point. Finally, these 17 points are connected to form a skeleton representing the posture of the person or people present in each RGB image. AlphaPose outputs a.json file containing the pose estimation for each person in the image. For each individual, there are 51 features per image (17 \(\times\) (2 + 1)).  
For this study, AlphaPose was used to extract the coordinates of the body joints of passengers in the captured videos, and thereby analyze the movement of each passenger, which is the aim of this research. The data obtained include the positions of the head, torso, and limbs, which are then used as feature vectors for the activity recognition model (see Figure 1).  
Figure 1: Skeletonization for pose estimation of a passenger getting off an urban bus in Valparaiso, Chile.