## 1 Introduction  
In our rapidly urbanizing world, urban centers face an array of opportunities and challenges encompassing sectors such as housing, transportation, education, healthcare, and the economy [1]. As we navigate these intricate issues, the integration of artificial intelligence (AI) and computer vision (CV) emerges as a potent tool for crafting smarter urban ecosystems. Among various sectors, one area that has gained prominence is the role of intelligent mobility in the evolution of smart cities [2; 3; 4]. Within this context, container transportation at seaports has become a linchpin, especially concerning the efficient management of parking facilities for drayage trucks, which are crucial for transporting containers between ships and their designated areas. The pressures of urbanization have intensified the challenges at port facilities. Ports in major urban hubs now face increased cargo volumes, leading to significant congestion and logical intricacies. These scenarios underscore the urgent need for innovative strategies within seaport operations. This investigation delves into the domain of container drayage operations at seaports, pivotal to global trade. Our research zeroes in on addressing the efficiency challenges of managing the parking facilities for drayage trucks. Furthermore, while recent efforts have aimed at collecting performance data for ports, the absence of standardized comparative data complicates performance evaluation. Various metrics have been explored, as indicated by the World Bank's Container Port Performance Index (CPPI) [5], but consistent quality data remain elusive. Figure 1 depicts the operational layout of a seaport's container terminal. The diagram portrays a seaport section with two clearly delineated lanes, marked by dashed lines, designated for the movement of container trucks. On the left, cranes hover above stacks of containers, showcasing the storage and handling mechanisms. In the central portion, container trucks of different colors can be seen, possibly indicating different categories or operational statuses. Additionally, strategically placed Closed-Circuit Television (CCTV) cameras suggest a focus on security and operational monitoring within the terminal. Our study's distinctive contribution is two-fold. First, it underscores the potential of AI and CV, especially deep learning and transfer learning, in enhancing seaport parking systems. By leveraging these advanced techniques, we aim to process the video data  
Figure 1: A detailed layout of a container terminal within a seaport, showcasing distinct lanes designated for the directional flow of container trucks. The visualization further highlights key infrastructure elements, such as container stacks, cranes for container handling, and strategically positioned CCTV cameras for surveillance and operational monitoring. from strategically positioned CCTV cameras at seaports, introducing a transformative framework for sustainable parking systems, especially in the context of container drayage operations [3; 6]. Second, the research brings to light an innovative model enabled by deep learning, which, when applied to CCTV footage, can provide real-time data on available parking slots, amplifying the efficiency of drayage operations. Our objectives are multifaceted. We aim to provide a thorough review of the existing literature on AI and CV and their implications in transport systems and parking management. In identifying the research gaps specifically tied to seaport container drayage parking, we seek to offer unique insights into the discourse. Furthermore, we aspire to unveil a state-of-the-art model rooted in deep learning principles to revolutionize CCTV video data interpretation. The core hypotheses guiding our research are that AI and CV, when aptly harnessed, can vastly improve the identification of optimal lanes and parking slots for drayage trucks and that our proposed model will excel in accuracy and efficiency compared to the existing systems.