## 5 Conclusions and Future Works
### Future Work  
Future research directions include exploring real-time implementation of the proposed models within ITS environments to evaluate their practical performance and computational efficiency. Additionally, incorporating a broader range of activities and more diverse user populations can enhance the generalizability of the models. Investigating sensor fusion techniques to optimally combine data from multiple sensors and exploring energy-efficient model architectures suitable for deployment on resource-constrained devices are also promising avenues. Finally, integrating explainable AI techniques can enhance the transparency of deep learning models, facilitating their acceptance in safety-critical ITS applications. The findings of this study open several promising avenues for future research aimed at enhancing the role of machine learning and deep learning in Intelligent Transportation Systems (ITS):  
1. Hybrid Model Development: Exploring hybrid models that integrate traditional machine learning techniques with advanced deep learning architectures such as LSTM, TCN, GRU, and Transformer. Such models could leverage the interpretability and efficiency of traditional methods alongside the sophisticated pattern recognition capabilities of deep learning, potentially yielding improved performance in dynamic ITS environments, particularly in real-time applications [42; 43]. 2. Customized Preprocessing Techniques: Investigating the customization of preprocessing strategies tailored to specific ITS applications. Traffic monitoring and driver behavior analysis may benefit from domain-specific data preprocessing that accounts for unique noise characteristics and data variability inherent in various sensor inputs. These refinements are likely to enhance model accuracy and adaptability in real-time conditions, enabling better detection of critical activities [44; 45]. 3. Personalized Models through Transfer Learning: Developing personalized models that adapt to individual driving patterns and environmental contexts using techniques like transfer learning. This approach could fine-tune models to cater to individual users, enhancing the precision of activity recognition and making systems more context-aware. Personalized models would significantly improve the relevance and accuracy of predictions in tailored transportation applications, addressing diverse user needs effectively [46]. 4. Integration of Multimodal Data Sources: Extending the research by combining smartphone sensor data with inputs from vehicle sensors, environmental monitoring systems, and other relevant technologies. Integrating multimodal data can lead to richer datasets and a deeper understanding of transportation behaviors and conditions, ultimately fostering more intelligent and responsive ITS solutions [28; 41]. 5. Real-Time Implementation and Optimization: Exploring the real-time implementation of the proposed models within ITS environments to evaluate their practical performance and computational efficiency. Future research should focus on addressing computational constraints and latency issues associated with real-time ITS applications. This includes optimizing models for scalability and ensuring efficient performance in live environments, where response times and computational resources are often limited [39]. 6. Energy-Efficient Architectures: Investigating energy-efficient model architectures suitable for deployment on resource-constrained devices like smartphones and embedded systems. This is crucial for practical ITS applications where power consumption can be a limiting factor. 7. Explainable AI Techniques: Integrating explainable AI methods to enhance the transparency and interpretability of deep learning models. This can facilitate their acceptance in safety-critical ITS applications by providing insights into model decision-making processes. 8. Expanded Activity Sets and Diverse Populations: Incorporating a broader range of activities and more diverse user populations to enhance the generalizability of the models.