## 4 Discussion: Comprehensive Analysis of the Deep-Learning-Oriented Framework  
The discussion section provides an in-depth examination and elucidation of our proposed deep-learning-oriented framework, delving into its strengths, limitations, and implications for the management of container drayage parking facilities within seaports. Our deep learning model exhibits a multitude of strengths that render it highly suited to addressing the complexities of lane identification and parking space detection in seaport environments. Using sophisticated deep learning methodologies, notably CNN, our model can demonstrate exceptional precision and resilience in analyzing the video data captured by CCTV cameras. Its ability to handle intricate and dynamic scenarios, diverse lighting conditions, and the substantial presence of containers attests to its adaptability and effectiveness within authentic seaport settings. Furthermore, the integration of transfer learning techniques enhances the model's performance by capitalizing on the knowledge and features acquired from extensive datasets. This approach empowers the model to draw upon pre-trained networks, facilitating generalization and accurate predictions, even with limited labeled training data. The adoption of transfer learning not only conserves computational resources, but also amplifies the efficiency and effectiveness of the training process [46]. However, it is crucial to acknowledge the inherent limitations of our proposed model. Seaport settings are characterized by significant heterogeneity and are subject to various environmental factors, including adverse weather conditions and obstructions caused by container movements. These factors can challenge the model's accuracy and dependability. Despite being trained on labeled data, there may be instances in which the model encounters scenarios or anomalies inadequately represented in the training dataset. Therefore, the ongoing refinement and adaptation of the model to address novel and evolving challenges become indispensable. Additionally, the successful implementation of the proposed model relies heavily on the availability and quality of the video data captured by the CCTV cameras. Considerations related to the camera placement, coverage, and resolution assume paramount importance, as they can significantly impact the model's performance. Thus, meticulous attention must be given to optimizing the CCTV camera setup and developing robust maintenance protocols to ensure the availability of high-quality video data for analysis. The implications of our deep-learning-based model are important for the management of container drayage parking facilities within seaports. By accurately identifying lanes and parking spaces, the model facilitates efficient traffic management, the optimal allocation of parking spaces, and the streamlined movement of containers. This, in turn, contributes to heightened operational efficiency, reduced congestion, and overall improvements in productivity across seaport operations. Integrating the model into the existing parking facility management system enables real-time decision making and proactive measures to address parking challenges, fostering the development of more sustainable and efficient transportation networks within seaports. Our proposed deep-learning-based model demonstrates immense potential in addressing the challenges associated with lane identification and parking spot detection in container drayage at seaports. While the model exhibits commendable accuracy and adaptability, careful consideration must be given to its limitations and associated considerations. Several limitations and challenges were encountered during the development and conceptualization of our mathematical framework for seaport parking management. One of the primary challenges was the diverse and dynamic nature of the seaport operations, including varying lighting conditions, container types, and traffic patterns.