## 5 Conclusions and Future Works  
### Conclusions  
This study makes significant contributions to the field of Human Activity Recognition (HAR) within Intelligent Transportation Systems (ITS) by effectively classifying passenger activities into six distinct categories: walking, running, and stationary (both while using and not using the smartphone). These insights hold substantial implications for optimizing smart transportation systems, as they can enhance decision-making processes for drivers and improve the overall commuter experience. By leveraging these findings, the study lays a foundation for the future development of smart transportation systems aimed at making public transit more efficient and user-friendly, thereby encouraging greater adoption of public transportation.  
In this investigation, a comprehensive comparison was conducted between traditional machine learning models and advanced deep learning techniques. The models evaluated included Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Classifier (SVC), XGBoost, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCN), and Transformer models. The evaluation encompassed various data segmentation strategies, including both non-overlapping and 50% overlapping configurations, utilizing smartphone sensor data. The notable performance enhancements observed with overlapping data segments,particularly for deep learning models, underscore the advantages of integrating temporal continuity into activity recognition tasks.  
The results reveal that employing four sensors significantly elevates model performance compared to two-sensor configurations. The implementation of 50% overlapping data segments further augmented the models' generalization capabilities. Among the models evaluated, deep learning models--specifically LSTM, TCN, GRU, and Transformer--demonstrated superior outcomes. The TCN model exhibited exceptional performance in the four-sensor non-overlapping configuration, achieving the highest accuracy of 97.70%. In the same configuration, the LSTM and GRU models also achieved high accuracies of 97.52% and 97.38%, respectively, reinforcing their strong capacity for capturing both short-term and long-term dependencies in time-series data. The Transformer model also showed robust performance, highlighting the effectiveness of attention mechanisms in capturing temporal patterns.  
Traditional machine learning models such as KNN, DT, RF, and SVC showed reasonable performance but consistently underperformed compared to deep learning models, especially in complex scenarios characterized by multi-sensor inputs and overlapping data segments. Ensemble models like RF and XGBoost improved upon simpler models by leveraging multiple decision trees and gradient boosting techniques, respectively. However, they still fell short in capturing the intricate temporal dependencies inherent in sequential sensor data, which deep learning models managed more effectively.  
Moreover, the TCN model displayed notable performance across various configurations, particularly in handling the intricacies of overlapping sequences. Its architecture, leveraging dilated convolutions, facilitated the capture of temporal patterns over extended periods, making it an effective alternative to recurrent models like LSTM and GRU in certain scenarios. These findings accentuate the increasing significance of deep learning models, particularly TCN and LSTM, in real-time, context-aware ITS applications, such as driver monitoring and traffic condition prediction. This research not only highlights the strengths of deep learning methodologies in HAR tasks but also advocates for their broader application in advancing smart transportation systems [28; 41].