Search Results (285)

Dry ports are becoming increasingly important elements of port–hinterland transport systems, particularly as maritime gateways face rising congestion, infrastructure pressure, and coordination challenges within global supply chains. As international trade expands and logistics networks grow more complex, inland terminals are progressively evolving into integrated intermodal platforms that support more efficient freight distribution between seaports and their hinterlands. This study presents a PRISMA-based systematic review of research on dry port–seaport systems covering the period 1980–2025. Following a structured screening and selection procedure, peer-reviewed publications were identified and analyzed to examine conceptual developments, thematic orientations, geographical scope, and decision-making perspectives within the field. Particular attention is given to the growing relevance of digital transformation, including artificial intelligence and machine learning, in shaping future dry port operations and network design. By synthesizing existing contributions and identifying research gaps, this review provides a consolidated understanding of the evolution of dry port research and outlines key directions for advancing sustainable, resilient, and data-driven port–hinterland systems. Full article

The increasing deployment of autonomous vehicles in port logistics requires safety assessment methods that remain valid in mixed traffic environments. This study evaluates the safety of mixed automated guided vehicle (AGV) and human-driven vehicle (HDV) traffic in a seaport terminal connected to an external urban road network. A microscopic traffic model was developed in AIMSUN Next to represent gate areas, internal roads, storage-yard access, berth interfaces, and external container-truck traffic. HDVs were modeled using a Gipps-based car-following model, whereas AGVs were represented through an Adaptive Cruise Control framework. Vehicle trajectories were exported to the Surrogate Safety Assessment Model (SSAM), where Time-to-Collision (TTC) and Post-Encroachment Time (PET) were used to detect and classify conflicts. Six staged fleet-composition scenarios were evaluated in 36 simulation runs, ranging from fully human-driven operation to full automation. Total conflicts decreased from 89 in the fully human-driven scenario to 43 in the fully automated scenario (−51.7%), while rear-end conflicts decreased from 70 to 30 (−57.1%). Crossing conflicts remained relatively stable across scenarios. At the same time, mean TTC decreased from 0.80 to 0.24 s and mean PET from 1.57 to 0.38 s, indicating tighter but more coordinated interactions under automated control. These results show that automation improves longitudinal safety performance in port traffic, but also that conventional TTC and PET thresholds calibrated for human-driven traffic may not be directly applicable to automated port operations. Automation-sensitive surrogate safety criteria are therefore needed for seaport mixed-traffic evaluation. Full article

Heavy vehicles leave a significant impact on passenger vehicles, which results in traffic instability. The size, acceleration, and behaviour of heavy vehicles notably influence the traffic flow. Considering this, traffic engineers have developed Passenger Car Equivalency (PCE) to examine the capacity, Level of Service (LOS), and flow of the urban roads. The aim of this study is to analyze the King Abdulaziz (KA) freeway in Dammam, Saudi Arabia, where heavy vehicles represent 35% of the peak hour traffic, which exceeds the PCE value given in the Highway Capacity Manual (HCM). This study addresses the given gap by employing the saturation headway approach. The study findings reveal PCE values of 1.78 for moving towards the port and 1.81 for coming from the port, respectively. These values are in line with the patterns of HCM, as the indication of low PCE denotes the appearance of increased heavy vehicles. Furthermore, the LOS was known to be of level E, reflecting frequent delays and slowdowns. The capacity in operations was reduced by 44–45%, thus emphasizing the requirement for strategic traffic approaches with functional interventions for heavy vehicle routes. Full article

This paper presents an analysis of the risk of failure of port structures in a modern seaport due to vessel impacts. The analysis addresses potential damage related to port maneuvers of self-maneuvering vessels and possible risk reduction options that can be applied to enhance port resilience. The proposed system model—including ship, port infrastructure, and environment—enabled the observation of both implemented and anticipated future risk reduction measures. The analysis was carried out using the ferry terminal in the large Polish Port of Gdynia as a case study. A Bayesian influence diagram—including decisions related to the implementation of risk reduction options—was used to determine the total risk associated with Ro-Pax ferry port calls. Sustainable risk management led to the implementation of a cloud-based monitoring system and, subsequently, to the design of a new terminal in line with the green port concept. The main result of the study was a quantitative assessment of the risk of damage to port infrastructure caused by ferries, related to ship maneuvering operations. A comparative assessment of the two locations demonstrated improved safety and reduced environmental pollution in the new Public Ferry Terminal. This improvement was made possible mainly by reduced spatial risk and the implementation of cold-ironing technology. Full article

Container congestion remains a persistent operational challenge in seaports because berth, yard, and gate processes are tightly coupled, demand is volatile, and control actions often operate under delayed feedback. Reinforcement learning (RL) is increasingly proposed for adaptive terminal decision support, yet the literature still says little about the mechanism through which RL may reduce congestion in practice. This study therefore develops a simulation-based mechanism framework in which RL improves congestion outcomes primarily by increasing Operational Learning Stability (OLStab), defined here as the consistency and governability of learning-enabled operational decisions under variability and disruption. A queueing-based, gate-focused terminal simulator is used as the data-generating process, with gate congestion treated as a reduced-form proxy for broader terminal congestion pressure. The statistical layer is interpreted cautiously as an internal mechanism consistency check within synthetic data rather than as empirical causal identification. Results show that RL is strongly associated with higher OLStab and that OLStab is the dominant pathway linking RL to lower congestion pressure in the simulated environment. Logistics Efficiency (LE) is directionally consistent with congestion reduction in bivariate analysis but adds limited incremental mediation once OLStab is jointly modeled. The theorized moderation by Decision Latency Sensitivity (DLS) is not robustly recovered within the examined latency range. Overall, the study contributes a more bounded explanation of how RL may reduce congestion in a designed gate-focused terminal control environment and highlights learning stability as a practical screening criterion for future digital twin and pilot deployment studies. Full article

The main objective of the present study is to reveal the palette of pigments and the other specific constituent materials as well as the techniques used by the Roman artists to create the mural paintings discovered in the ancient city of Tomis, the modern-day Constanţa, Romania’s largest seaport and a major tourist hub on the Black Sea. This paper is an archeometric study based on the physical, chemical and biological analyses of the archeological Roman mural painting fragments from the ancient city of Tomis dating from the 5th to 6th century A.D. and to our knowledge is among the very few research studies carried out so far on the ancient Roman wall painting discovered in Romania. The methods of scientific investigation employed directly on the archeological fragments, on samples taken from the fragments and on the cross-sections prepared from the samples were: optical microscopy (OM), digital microscopy, X-ray fluorescence spectrometry (XRF) and attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR). Examination and analysis of the archeological mural fragments revealed that the painted fragments consist of ground support and successive layers of color displaying specific characteristics of the artistic technique, such as imitation of marble cladding or meticulous smoothing of the surface to achieve a glossy and compact finish. It was also found that fragments exhibit subtle variations in different colors, identified in general terms, showing seven color tones: cinnabar red, red-violet, red ochre, yellow ochre, white, gray-blue, gray-black and black. The physical–chemical and biological analyses carried out provide the diagnosis and theoretical basis for choosing an appropriate conservation methodology and the correct restoration treatment of the discovered mural painting, with a view to its museum display through exhibition and virtual reconstruction and scientific use by the setting up of a useful database for researchers or specialists in museums on Roman archeology and art. Full article

Countries are increasingly adopting hydrogen, leading to growing interest in developing sustainable hydrogen supply chains. Ports, being essential nodes in supply chains, must be prepared to facilitate hydrogen exports. However, there is a shortage of thorough port readiness studies for hydrogen exports. Existing research remains fragmented or confined to individual case studies, offering no transferable framework. This study fills this gap by creating a framework that covers four essential aspects of port readiness for exporting hydrogen: infrastructure, safety, legal, and management. The ports of the United Arab Emirates served as a case study, and the Delphi method was used to validate and contextualize the proposed framework. This study demonstrates the framework’s capacity to identify deficiencies in port readiness across multiple dimensions, helping stakeholders to plan and make decisions more easily. Full article

In the context of rapid digital transformation, smart seaports have emerged as crucial entities for enhancing operational efficiency and promoting sustainable port governance. To achieve sustainable development, the integration of advanced technologies into seaport operations has become essential. However, the existing literature primarily highlights the construction achievements of smart seaports, with limited investigation into the configuration mechanisms that account for variations in efficiency. This study analyzes eight representative smart seaports in China from 2019 to 2024. Based on the Technology–Organizational–Environment (TOE) framework, six condition variables are identified. Comprehensive technical efficiency is measured using the three-stage super-efficiency Slack-Based Measure (SBM) model. Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) are then employed to identify the configuration pathways leading to either high or non-high smart seaport operational efficiency. The findings indicate that no single factor is a necessary condition for high efficiency; instead, operational efficiency results from the synergistic interplay of multiple factors. Four distinct configuration pathways that lead to high efficiency are identified. Furthermore, a significant causal asymmetry exists between efficient and inefficient configurations, highlighting the contextual complexity inherent in smart seaport operational efficiency. This study provides a configurational perspective on the operational efficiency of smart seaports in order to offer policy and management insights for sustainable seaport operations. Full article

This paper examines the current state of Romanian seaports from the perspective of their transformation into smart ports, using the SWOT model to identify the most suitable strategies. It highlights the strengths and weaknesses of existing infrastructure, as well as the opportunities and risks, to outline coherent and sustainable courses of action for future development. A SWOT analysis was conducted based on information collected from a questionnaire sent to members of the maritime port authority, directors, and staff from various departments of the analyzed ports, as well as direct interviews with experts from the three ports. This analysis served as the foundation for developing strategies aimed at accelerating digitization, improving operational efficiency, and reducing environmental impact. The identified strategies were subsequently ranked using the AHP method. The weights assigned to the ten strategies emphasize the relative importance and systemic influence of each one on the process of ports transforming into smart entities. This study makes a significant contribution to the emerging literature on the transformation of Romanian seaports into “smart ports” by approaching this process through the lens of sustainable port development. Full article

This study investigates the selection and scaling of recorded strong ground motions in the time-domain spectral matching framework to realistically represent the seismic demands on the superstructure and secondary systems in the seismic design of complex facilities such as marine ports. The time-domain spectral matching method iteratively adjusts the original record in the time domain by adding wavelets with limited durations and specific period ranges to achieve compatibility with the specified target acceleration response spectrum. A site-specific probabilistic seismic hazard analysis (PSHA) was performed for a port facility in İskenderun Bay, an area affected by the 6 February 2023 earthquakes. Horizontal Ground-Motion Response Spectra (GMRS) were derived for different return periods. Based on the hazard deaggregation, recorded ground motions compatible with the seismotectonic context of the region and the site conditions were selected. These records were then processed using time-domain spectral matching (TDSM) to match their elastic response spectra with the target GMRS over specific period ranges. The method utilizes spectral matching in the time domain to improve the match with the target spectrum while preserving the phase information and non-stationary nature of the records. The results show that the mean spectral acceleration curves of the scaled records are highly consistent with the target GMRS over a wide range of periods and that near-fault pulse-like characteristics, when present, are reasonably preserved. These results confirm that time-domain spectral matching provides a reliable framework for the performance-based assessments of complex port infrastructures by achieving high compatibility with the target spectra while preserving the physical characteristics of the waveforms Full article

The global shipping fleet uses vast quantities of fossil fuels and releases significant levels of pollution. Supplying ships moored at quays in ports with onshore power allows them to shut down onboard engines, cutting fossil fuel use and reducing emissions. This is particularly significant when ports utilize green electricity. Equipping ports to connect serviced ships to onshore power grids involves substantial investments, which must be carefully optimized. The aim of this article is to develop a methodology, grounded in probability theory, for determining the electrical power required to connect ships to onshore power grids in ports. The proposed methodology was developed and validated through a case study of container terminal operations. By applying this methodology and considering the conditions of ship service in ports, it is possible to estimate both the number of ships and their berthing durations at quays, as well as the electrical power required from onshore networks to connect the vessels. The results of this research may be of interest to port managers, terminal operators, shipowners, and other stakeholders involved in the development of onshore power grids for ship connections in ports. Full article

Efficient resource allocation is critical for transportation hub operations, yet current scheduling systems require substantial domain-specific customization when deployed across different facilities. This paper presents a domain-adaptive deep reinforcement learning (DADRL) framework that learns transferable optimization policies for dynamic resource allocation across structurally similar transportation scheduling problems. The framework integrates dual-level heterogeneous graph attention networks for separating constraint topology from domain-specific features, hypergraph-based constraint modeling for capturing high-order dependencies, and hierarchical policy decomposition that reduces computational complexity from $O\left(mnT\right)$ to $O(m+n+T)$. Evaluated on realistic simulators modeling airport gate assignment (Singapore Changi: 50 gates, 300–400 daily flights) and seaport berth allocation (Singapore Port: 40 berths, 80–120 daily vessels), DADRL achieves 87.3% resource utilization in airport operations and 86.3% in port operations, outperforming commercial solvers under strict real-time constraints (Gurobi-MIP with 300 s time limit: 85.1%) while operating 270 times faster (1.1 s versus 298 s per instance). Given unlimited time, Gurobi achieves provably optimal solutions, but DADRL reaches 98.7% of this optimum in 1.1 s, making it suitable for time-critical operational scenarios where exact solvers are computationally infeasible. Critically, policies trained exclusively on airport scenarios retain 92.4% performance when applied to ports without retraining, requiring only 800 adaptation steps compared to 13,200 for domain-specific training. The framework maintains 86.2% performance under operational disruptions and scales to problems three times larger than training instances with only 7% degradation. These results demonstrate that learned optimization principles can generalize across transportation scheduling problems sharing common constraint structures, enabling rapid deployment of AI-based scheduling systems across multi-modal transportation networks with minimal customization and reduced implementation costs. Full article

In the context of the digital twin engineering of large smart hub seaports, port path planning faces more complex challenges, such as efficient logistics scheduling, unmanned transportation, coordination of port automation facilities, and rapid response to complex dynamic environments. Particularly in applications like robotic inspection, how to effectively plan paths, improve inspection efficiency, and ensure that robots complete tasks within their limited energy capacity has become a key issue in the design and realization of digital and intelligent seaport systems. To address these challenges, a path planning algorithm based on an improved Rapidly-exploring Random Tree (RRT) is proposed, considering the complexity and dynamics of the port’s digital twin environment. First, by optimizing the search strategy of the algorithm, the flexibility and adaptability of path planning can be enhanced, allowing it to better accommodate changes in the environment within the digital twin model. Secondly, an appropriate heuristic function is constructed for the digital twin seaport environment, which can effectively accelerate the convergence speed of the algorithm and improve path planning efficiency. Finally, trajectory smoothing techniques are applied to generate executable paths that comply with the robot’s motion constraints, enabling more efficient path planning in practical operations. To validate the feasibility of the proposed method, a combination of virtual and real digital twin environments is used, comparing the path planning results of the improved RRT algorithm with those of the traditional RRT algorithm. Experimental results show that the proposed improved algorithm outperforms the traditional RRT algorithm in terms of sampling frequency, planning time, path length, and smoothness, further validating the feasibility and advantages of this algorithm in the application of intelligent seaport digital twin engineering. Full article

The Port of Gdynia is the largest Baltic Sea port handling agricultural products and has adopted green port policies focused on sustainable development. Despite these measures, minor, unavoidable losses occur at transhipment points. With monthly grain transhipments ranging from 62,000 to 96,000 tonnes, accidental losses provide a significant supplementary food source for birds. Four species benefit most: the mallard, herring gull, common gull, and black-headed gull. These birds congregate primarily at transhipment sites, forming one of the largest winter concentrations in Poland. Together, they account for 93–96% of all waterbirds present in the port during winter, with maximum counts of 6232 mallards, 5815 herring gulls, 4482 common gulls, and 1624 black-headed gulls. The abundance of the first three species even exceeds the average winter counts of the nearby Natura 2000 site “Puck Bay,” established for its significance for wintering waterbirds. The energy content of spilled grain is sufficient to meet the daily energy requirements of these species, supporting their high numbers. These findings suggest that, despite intensive shipping and human activity along the port’s quays, unintentional food availability at port transhipment sites can support high waterbird abundances during winter, highlighting the potential conservation value of managing incidental food resources in industrial port environments. Full article