## 2 Related Works and Our Contributions
### Our Contributions  
This research addresses critical gaps in HAR by focusing on smartphone positioning variability, comprehensive sensor data utilization, and the comparative performance of traditional machine learning and advanced deep learning models. Unlike previous studies that often rely on controlled environments and fixed sensor placements, our work investigates realistic smartphone positions and offers a thorough analysis of sensor data for improved HAR in dynamic, real-world scenarios. The key contributions of this study are as follows:  
1. Realistic Smartphone Positions: Previous works, such as [PERSON] et al. [23] and [PERSON] and [PERSON] [13], achieved high recognition performance by using fixed sensor positions. However, these approaches fail to address the variability in smartphone placements that naturally occurs in real-world settings. Our research bridges this gap by analyzing HAR in realistic smartphone positions--chest (when in use) and leg pockets (when not in use)--offering a more robust solution to position orientation dependency. This exploration significantly enhances recognition performance in diverse, uncontrolled environments, making HAR systems more applicable to everyday use.
2. Comprehensive Sensor Data Utilization: While prior studies like [PERSON] et al. [15] and [PERSON] et al. [16] have leveraged data from accelerometers and gyroscopes, they tend to neglect other key sensors such as the linear accelerometer and gravity sensor. Our research incorporates data from these additional sensors and compares it to the more common accelerometer-gyroscope data. Although the inclusion of these sensors yields only a marginal improvement in classification accuracy, it underscores the value of a comprehensive sensor suite, especially in scenarios requiring high precision, such as emergency detection and critical passenger behavior monitoring.
3. Comparison of Machine Learning and Deep Learning Models: Although prior research [12; 13] has shown that deep learning models often outperform traditional machine learning methods in HAR tasks, comparisons in real-world, position-independent settings remain limited. Our study addresses this gap by conducting a comprehensive comparative analysis of these approaches using position-independent data. We assess a wide range of models, encompassing traditional machine learning algorithms such as DT, KNN, RF, SVC, and XGBoost, alongside advanced deep learning architectures including GRU, LSTM, TCN, and Transformers. This extensive evaluation allows us to balance considerations of computational efficiency and accuracy. The results indicate that deep learning models, with their ability to capture complex temporal dependencies, are particularly effective in dynamic, real-world ITS conditions.
4. Enhancement of Passenger Behavior Recognition: Existing research, including work by [PERSON] and [PERSON] [14], often focuses on fixed environments and sensor placements, limiting the practical applicability of these findings in dynamic real-world scenarios. Our study advances passenger behavior recognition for intelligent transportation systems by tackling the challenges of real-world variability in smartphone positions. This contributes to improved traffic congestion management, enhanced emergency response capabilities, and optimized resource utilization within public transportation networks, making HAR systems more adaptable to real-world applications.  
In summary, our research offers a novel approach to Human Activity Recognition by investigating the impact of realistic smartphone positions, integrating a more comprehensive set of sensors, and conducting an in-depth comparison of machine learning and deep learning techniques. These contributions significantly advance the state of the art in HAR, providing practical solutions for real-world implementation and addressing limitations observed in prior studies.