## 2 Related Work
Seaports present unique challenges, including dynamic environments, varying lighting conditions, and the presence of substantial container volumes. Unfortunately, limited research has been carried out to address these specific challenges by harnessing the potential of AI and CV technologies. Thus, this research endeavor seeks to bridge this critical gap by introducing an innovative deep-learning-based model meticulously customized to cater to the distinctive demands and intricacies of container drayage operations within seaport parking facilities. To enhance our understanding of the application of AI and CV technologies in seaport operations, recent research endeavors provide valuable insights and methodologies. [PERSON] (2010) [22] highlights the complexity of seaport container terminals and the need for multi-agent models to optimize operations, shedding light on reducing in-terminal drayage truck wait times as a critical concern [23]. [PERSON], [PERSON], and [PERSON] (2013) [24] delve into the efficient planning of drayage operations, focusing on vehicle routing problems and two-phase algorithms to enhance operational efficiency [2]. [PERSON], [PERSON], and [PERSON] (2016) [25] emphasize the significance of a methodological framework that considers various factors affecting the dwell time (DT) of containers in container terminals. Workload forecasting and the integration of container terminals into the supply chain are crucial, and the paper utilizes artificial neural networks (ANN) to identify the determinants of dwell time (DT) [26]. [PERSON], [PERSON], and [PERSON] (2016) [27] also address the optimization of dwell time (DT) in container terminals, highlighting the importance of workload forecasting and considering factors such as the container size, type, and discharge date, the vessel's port of origin, and the commodities transported [18]. [PERSON], [PERSON], and [PERSON] (2022) [9] provide a comprehensive review of container port drayage studies, discussing various methodological and technological angles and the shift toward e-commerce platforms. They emphasize the significance of container port drayage in the long-haul intermodal container transportation chain and its impact on various aspects, including economic advantages, management, environment, and social benefits [19]. Furthermore, in addressing the distribution challenges within seaports, the \"simple shuttle problem\" has been proposed as a potential solution [28]. Seaports encompass a range of activities beyond import and export, including loading, unloading, storage, and delivery. Delays in these processes can result in substantial economic losses. Recent research by [PERSON] (2021) [29], focusing on Industry 4.0 in Malaysian seaports, highlights the adaptations made by Hamburg Port authorities, including the implementation of \"SmartPort\" for efficient truck parking and data access (SmartPort Logistics and SmartPort Monitor). Seaport design inherently poses challenges for truck traffic and parking, often leading to extensive queues [30]. A proposed lane allocation framework optimizes truck flow by implementing appointments and designated lanes for different truck types. Efficient container storage management is paramount in seaports. Models such as mixed-integer linear programing [31] and heuristic algorithms [32; 33] have been instrumental in optimizing storage systems. Constraints such as size, weight, color, and destination influence space allocation. Positive outcomes have been observed in studies, including one conducted at the Port of Montreal [23], which proposed layout improvements and specialized handling equipment. Container storage optimization plays a pivotal role in the overall efficiency and productivity of seaport operations, exerting a substantial influence on the intricacies of port logistics. Additionally, the truck parking system within seaports assumes critical importance, as it can significantly impact the traffic congestion during peak hours.