## 4 Results and Discussion
### Model Performance Overview  
Decision Trees (DT) were selected for their simplicity and interpretability, making them ideal for real-time ITS applications where model transparency is crucial. While DT maintains a balance between accuracy and efficiency, it is somewhat prone to overfitting, particularly with smaller datasets.  
K-Nearest Neighbors (KNN) was included for its flexibility in handling diverse activity patterns. However, its performance can decline in high-dimensional spaces, which leads to computational challenges. Various values for \(k\) and distance metrics were tested to optimize KNN's sensitivity to the sensor data.  
Random Forest (RF) was introduced as an ensemble model to assess its classification consistency and ability to mitigate overfitting through the aggregation of multiple decision trees. RF produced reliable results across sensor configurations, demonstrating particularly strong performance in non-overlapping datasets, where it maintained high accuracy across various activities.  
Support Vector Classifier (SVC) with the RBF kernel was chosen for its capacity to manage non-linear data effectively, enhancing accuracy in activity recognition, especially with smaller datasets. Its strong generalization capabilities contributed to high classification performance.  
XGBoost, a gradient-boosting algorithm, was selected for its computational efficiency and advanced feature-handling capabilities. It effectively captured complex activity patterns, excelling in overlapping data segmentation, and achieving accuracy comparable to deep learning models such as TCN. Its robustness and precision make it particularly suitable for ITS applications where both accuracy and efficiency are paramount.  
Gated Recurrent Unit (GRU) was incorporated to evaluate its sequential learning capabilities while offering reduced computational requirements compared to LSTM. The gating mechanism in GRU enables effective learning of activity sequences, which is beneficial for real-time ITS applications. GRU performed well across sensor configurations, particularly in non-overlapping data, where its lower complexity supported faster inference.  
Long Short-Term Memory (LSTM) networks are well-suited for capturing long-term dependencies, which are crucial for distinguishing activities such as walking and running. However, its performance was slightly diminished with non-overlapping data configurations, as LSTM relies on the continuity of sequential data.  
Temporal Convolutional Networks (TCN), utilizing dilated convolutions to capture extended temporal dependencies, consistently achieved high accuracy, particularly in overlapping segmentation. Its ability to retain contextual information across sequences makes it an excellent choice for real-time ITS tasks.  
Transformer models, which leverage self-attention mechanisms, were evaluated for their ability to manage complex dependencies in sequential data without the limitations of recurrent connections. They excelled in overlapping segmentation scenarios, effectively capturing long-term dependencies across activities, achieving competitive accuracy, and highlighting their scalability for ITS applications.