Urban expansion has ushered in a landscape of opportunities and challenges across housing, transportation, education, health, and the economy. In response to these evolving dynamics, the application of artificial intelligence (AI) and computer vision (CV) technologies has emerged as a transformative solution. While smart traffic monitoring and advanced parking distribution systems have eased urban pressures, optimizing mobility remains pivotal in the context of burgeoning smart cities. However, the seaport industry grapples with formidable issues in the efficient transport of containers. In light of this, the fusion of AI and CV technology holds promise as a solution. This study pioneers a systematic review, representing a novel exploration, delving into a comprehensive evaluation of the existing literature. It scrutinizes the profound advantages AI and CV offer in constructing sustainable, efficient parking ecosystems within seaports. Our methodological approach encompasses data collection, rigorous quality assessment, and meticulous exploration of the application of CV and AI in the realm of smart parking management. The findings underscore the pivotal role of AI and CV technologies in the development of efficient, sustainable transportation systems, particularly for optimizing container movement within seaports. This research presents a comprehensive analysis of the literature in the area of the application of AI and CV technologies in optimizing parking management at seaports, shedding light on the potential for sustainable transportation solutions in this critical domain. As these technologies usher in enhancements in traffic management, parking space allocation, and container logistics within seaports, this study represents a vital and timely contribution to the field, serving as a cornerstone for future innovations in seaport management and the broader context of smart cities.

The rapid urbanization phenomenon has introduced multifaceted challenges across various domains, including housing, transportation, education, health, and the economy. This necessitates a significant transformation of seaport operations in order to optimize smart mobility and facilitate the evolution of intelligent cities. This conceptual paper presents a novel mathematical framework rooted in deep learning techniques. Our innovative model accurately identifies parking spaces and lanes in seaport environments based on crane positions, utilizing live Closed-Circuit Television (CCTV) camera data for real-time monitoring and efficient parking space allocation. Through a comprehensive literature review, we explore the advantages of merging artificial intelligence (AI) and computer vision (CV) technologies in parking facility management. Our framework focuses on enhancing container drayage efficiency within seaports, emphasizing improved traffic management, optimizing parking space allocation, and streamlining container movement. The insights from our study provide a foundation that could have potential implications for real-world applications. By integrating cutting-edge technologies, our proposed framework not only enhances the efficiency of seaport operations, but also lays the foundation for sustainable and intelligent seaport systems. It signifies a significant leap toward the realization of intelligent seaport operations, contributing profoundly to the advancement of urban logistics and transportation networks. Future research endeavors will concentrate on the practical implementation and validation of this pioneering mathematical framework in real-world seaport environments. Additionally, our work emphasizes the crucial need to explore further applications of AI and CV technologies in seaport logistics, adapting the framework to address the evolving urbanization and transportation challenges. These efforts will foster continuous advancements in the field, shaping the future of intelligent seaport operations.

The purpose of this study is to test the model of Self-determined Motivation with perceived restorative environment in nature-based outdoor recreation context. This Model is based on Deci & Ryan(1985)’s Self-determination Theory, and has a new need, nature-relatedness need, adding to the Hierarchical Model of Intrinsic and Extrinsic Motivation of Vallerand(1997). Perceived restorative environment of Attentive Restoration Theory(Kaplan, 1995) was used as a preceding factor influencing basic psychological needs. To achieve the goal of study, a total of 366 cases of hikers were surveyed at four Mountains. However, competence needs fulfillment had no effect on self-determined motivation. As a result, the hypothesis 1 was all supported and the hypothesis 2 was partially supported. Perceived restorative environment and basic psychological needs fulfillment had a significant relationship. The factor that had the greatest influence on intrinsic motivation was social need fulfillment, and the nature-relatedness need fulfillment factor was revealed to be the most influential on the identified motivation. The theoretical and practical implications based on the results analysis were suggested.

Real-time spatial coordination in high-density seaport container yards is a bottleneck in global supply chains. In these environments, GPS signal denial and severe visual occlusion render traditional theoretical scheduling models ineffective. To resolve this operational gap, this paper introduces HY-EnYO, a novel dual-stream ensemble architecture designed to automate the identification of vacant parking bays using monocular CCTV footage without requiring physical sensor infrastructure. Addressing a fundamental limitation of standard single-stream deep learning detectors, which suffer from severe cross-class feature confusion when mapping massive infrastructure alongside dynamic assets within a unified latent space, the proposed framework decouples visual extraction into parallel sub-networks specialized for yard cranes ( M c ) and terminal trucks ( M t ). While this feature extraction remains probabilistic, final occupancy state reasoning is resolved by a deterministic logic engine. This engine projects perspective-aware parking polygons and evaluates Intersection over Union (IoU) overlap against a spatial threshold ( τ = 0.08 ), mathematically counteracting camera perspective distortion and sensor jitter. Compared to standard single-stream baselines, HY-EnYO reduces the computational parameter footprint by over 67% (from 36.49 M to 12.02 M), requires a peak VRAM allocation of only 0.10 GB, and achieves an inference latency of 21.82 ms, ensuring edge-device feasibility. Extensive evaluation on real-world CCTV datasets from the Busan seaport demonstrates peak detection accuracies (maP@0.5) of 0.899 for trucks and 0.999 for cranes. Evaluated across a 316-instance test sequence, the deterministic spatial engine achieves a perfect occupancy precision of 1.000 (preventing false-positive routing deadlocks) and an overall system accuracy of 98.0%, maintaining high operational accuracies of 97.0% and 98.0% under simulated adverse weather and low-illumination conditions, respectively. The framework provides a mathematically rigorous, highly efficient spatial matrix, establishing a data-driven foundation for automated seaport traffic coordination.