## 5 Conclusions  
This study demonstrates the viability of using artificial intelligence techniques for pose detection in public transportation. The combination of AlphaPose with Random Forest enables an accurate analysis of passenger behavior, providing valuable insights for the design of safer and more accessible infrastructures. This study was conducted using video data from a single urban area (Valparaiso, Chile), which, while complex, may not capture the full diversity of public transport scenarios. Future work will include the development of a larger and more diverse dataset specifically designed for human activity recognition in public transportation contexts. Additionally, preliminary tests are being conducted to evaluate the real-time performance of the system using dedicated hardware platforms. In this paper, three scenarios are tested to be analyzed when applying AlphaPose. In the case of passengers getting off the bus, it can be concluded that the ML-RF model does identify some erroneous activities, which can be considered outliers since they do not have enough weight to be considered relevant. These activities are typically brief misclassifications that are filtered out due to their low relevance (weights close to 0). Additionally, false positives--such as the incorrect recognition of falls--were mainly caused by unreliable skeletal estimation under occlusions or when the subjects were very far from the camera. These errors are considered a limitation of the pose estimation stage rather than the classification model itself. On the other hand, the model is capable of correctly recognizing the activities that take place at the appropriate time intervals, demonstrating that it can accurately track human motion in certain contexts. This performance highlights the balance between accurately identifying relevant activities and filtering out noise. The outliers that are considered irrelevant can be ignored, while the model successfully classifies the significant events in the video. One limitation identified is the misclassification of unfamiliar activities, such as 'running', which were not included in the training dataset. This results in incorrect labeling as similar activities (e.g.,'standing'). When passengers are running to get the bus, AlphaPose fails to estimate the pose of the person for just 1 s of the video; however, the ML-RF model is still able to correctly identify key activities such as \"standing\" and \"walking\" at the appropriate times. In addition, when the model encounters an activity it has not been trained on (like \"running\"), it predicts it as a similar activity, in this case, \"standing\". This demonstrates that the model has the capability to handle situations where it encounters unfamiliar activities by categorizing them under the most similar ones it has been trained on. Future work should include a broader range of activity classes during training to overcome this limitation. In the case of passengers waiting for the bus, the results demonstrate that the ML-RF model successfully recognized all the activities in the video, such as standing and walking, at the correct time intervals. It shows that the model can accurately track the behavior of pedestrians as they wait for the bus or move towards it. Additionally, AlphaPose effectively detected all the people in the video, correctly estimating the skeleton and pose of everyone, even in crowded scenes or when they interacted with the bus. The model was able to maintain high accuracy despite potential challenges like occlusions or varying distances from the camera. This behavior shows the strength of the system in dealing with common activities but also highlights a limitation: when faced with unfamiliar actions (like running), it can only fall back on similar activities. To improve the model, it could be trained with more activity types, such as running, to better recognize these movements and provide more accurate predictions.