Search Results (95)

Dynamic freight transportation systems are critical for improving logistics performance in developing economies where multimodal integration remains limited. Despite Lao People’s Democratic Republic (Lao PDR)’s substantial investments to transition from a landlocked to a land-linked hub, quantitative assessments capturing heterogeneous cargo behavior across its multimodal networks are scarce. This study develops a cargo-specific, multi-source, multi-sink minimum-cost flow (MCF) framework to evaluate freight transport performance in Lao PDR. The framework integrates road, railway, inland waterway, and air transport into a unified directed network, capturing heterogeneous behaviors across six cargo categories. Testing under three demand scenarios (Q500, Q1000, and Q2000 tons/day) across both export and import systems, the results from 99 feasible solutions indicate that railway transport consistently dominates long-distance corridors within the assumptions of the proposed framework, reflecting its structural cost advantages. Conversely, road transport primarily serves first- and last-mile connectivity, while inland waterways and air transport serve as supplementary modes. Ultimately, these findings provide scenario-based insights to support policy decision-making for cross-border infrastructure investments, regional dry port integration, and synchronized rail-road connectivity across the Greater Mekong Subregion (GMS). Full article

Accurate forecasting of vessel traffic flow (VTF) is essential for modern maritime and port management, as it improves route-planning efficiency, reduces congestion and collision risks, and optimizes port operations. This study proposes a novel deep learning framework, namely, the Bidimensional Empirical Mode Decomposition–Nocal Convolutional Neural Network–Transformer (BEMD–NocalCNN–Transformer), for high-precision VTF prediction. The proposed framework first applies the BEMD algorithm to decompose the original time-series data into high- and low-frequency components. The NocalCNN module is then employed to extract spatial features from each component, while the Transformer module captures temporal dependencies and predicts future traffic-flow trends. The final predictions are obtained by aggregating the outputs of the high- and low-frequency components. Sensitivity analyses are conducted on key parameters, including input sequence length, learning rate, number of iterations, and convolution kernel size, to optimize the model configuration. To comprehensively evaluate the proposed framework, SVM, BPNN, RNN, LSTM, GRU, Transformer, WVMA-LSTM, and NocalCNN–Transformer were implemented and evaluated using the same CFD and Wuhan datasets, data preprocessing procedures, training–testing partitions, prediction settings, and evaluation metrics. The experimental results demonstrate that the proposed model outperforms the benchmark models and achieves substantially lower prediction errors for both the Caofeidian Promontory (CFD) and Wuhan waterways. These findings demonstrate consistent prediction performance of the proposed framework and provide a robust technical foundation for intelligent maritime traffic management and port operation optimization. Full article

Accurate and robust localization is essential for intelligent vessels operating in complex marine and port environments. However, single-sensor localization is often affected by limited observation range, environmental occlusion, local interference, and sensor degradation. Although multi-sensor fusion can improve localization reliability, unknown cross-correlated measurement noise arising from shared disturbances, time synchronization errors, communication delays, and inconsistent fusion rates may degrade traditional information-filter-based fusion methods. To address this problem, this paper proposes an Adaptive Correlation Information Filter Network (ACIFNet) for multi-sensor fusion localization of intelligent vessels. ACIFNet preserves the recursive structure of the extended information filter and uses a Transformer-based network to learn adaptive information-domain fusion weights, thereby compensating for unknown inter-sensor correlations without explicitly estimating the full correlation covariance matrix. Experiments on constant-velocity, coordinated-turn (CV), and three-degree-of-freedom vessel motion models, together with a real-world restricted-waterway dataset, demonstrate that ACIFNet achieves higher localization accuracy and stability than Edge Incorporative Fusion (EIF)-inexact fusion, measurement fusion, and KalmanNet. In the CV and three-degree-of-freedom experiments, ACIFNet reduces the mean RMSE by 48.7%, 23.2%, and 26.1%, respectively, compared with KalmanNet. On the real-world dataset, ACIFNet achieves a mean position error of 9.90 m, an RMSE of 11.24 m, and a cross-track error of 8.72 m. These results show that ACIFNet effectively combines the interpretability of information filtering with the adaptive representation capability of neural networks for robust multi-sensor fusion localization under unknown cross-correlated measurement noises. Full article

Even though the literature extensively examines aspects of the efficiency, waterway infrastructure, and competitiveness of inland waterway transport (IWT), a systemic composite index that integrates navigational, operational, and digital factors of IWT infrastructure into a single comparable evaluation system at the level of port hinterlands has not been identified. This study proposes a multi-criteria inland waterway transport infrastructure index (IWTI) designed to assess complex infrastructure conditions. The IWTI measures infrastructural readiness, physical navigational, operational, and digital prerequisites that enable the realisation of IWT potential. The index is calculated using the multi-criteria decision-making method TOPSIS, with criterion weights determined based on expert evaluation (n = 7) data. Rank stability was tested via Spearman’s rank correlation sensitivity analysis under three alternative weighting scenarios. The methodology was applied to assess seven ports in the Baltic Sea region using data from 2023. The IWTI ranges from 0.00 (Tallinn does not have an IWT connection) to 0.80 (Szczecin). The study revealed significant regional differences: the hinterland of the HaminaKotka port is characterised by a mature Saimaa canal and lake system; the hinterlands of Szczecin and Klaipėda demonstrate strong potential for the Oder/Odra and Nemunas corridors; the port of Gdańsk was identified as a medium-potential case with clearly defined priorities for infrastructure improvements. The ports of Gdynia and Riga lack functional inland waterway connections, but the infrastructural potential scenario indicates significant IWTI growth opportunities upon the implementation of planned investments. Sensitivity analysis confirmed high stability of the rank (Spearman ρ ≥ 0.964 in all scenarios). The proposed index provides a structured methodological basis for evaluating IWT potential and can serve as a decision-making tool for infrastructure planning and transport policy development, contributing to sustainable cargo carriage and the decarbonisation goals of the transport sector. Full article

To optimize the freight distribution structure of ports and reduce carbon emissions from freight transportation, this paper develops a bi-level programming model for freight traffic shifting between roadway and waterway networks that incorporates carbon emissions. First, a complex freight network based on the roadway–water transport system is constructed, comprising roadway networks, inland waterway networks, maritime networks, and transshipment nodes. A traffic impedance model is then formulated within this complex network framework, integrating the roadway BPR function, the M/M/1 queuing model for lock passage time on inland waterways, and the M/M/c queuing model for port cargo handling into the impedance function. This allows micro-level congestion effects to be combined with macro-level traffic assignment. Next, a bi-level programming model for freight traffic shifting in the roadway–water network system is established, with carbon emissions incorporated. The NSGA-II algorithm is employed to determine the optimal carbon subsidy level, based on which the traffic distribution in the complex freight network is analyzed. Finally, the proposed model is applied to the roadway–waterway bimodal network in the Hangzhou Bay port area of Cixi. The results indicate that without subsidies, the waterway transport share is only 1.74%. The optimal subsidy efficiency frontier is identified at CNY 350,000/day, where the waterway share increases to 22.7% and carbon emissions decrease by 33.27 tons/day. The subsidy strategy evolves through three stages: first, prioritizing maritime shipping; second, jointly promoting inland and maritime shipping; and finally, shifting focus to infrastructure investment once subsidies reach saturation. This study offers a quantitative analytical tool for designing differentiated carbon subsidy policies to facilitate the road-to-waterway modal shift under fiscal constraints. Full article

Automatic Identification System (AIS) data provide valuable insights into ship behavior, supporting maritime safety, situational awareness, and operational efficiency capabilities that are increasingly required for autonomous ship functions and harbor maneuvering assistance. This review synthesizes recent research on AIS-based ship behavior analysis and modeling published between 2022 and 2024 using a structured literature search and screening process informed by PRISMA principles. The review presents a five-stage workflow, spanning data processing, data analysis, knowledge extraction, modeling, and runtime applications with emphasis on how these stages contribute to perception, prediction, and decision support in automated navigation. Four dimensions are considered in data analysis, including statistical analysis, safety indicators, situational awareness, and anomaly detection. The modeling approaches are categorized into classification, regression, and optimization, highlighting current limitations such as data quality, algorithmic transparency, and real-time performance, while also assessing runtime feasibility for onboard or edge deployment. Three runtime application directions are identified: autonomous vessel functions, remote monitoring and control operations, and onboard decision-support tools, with numerous studies focusing on constrained waterways and port-approach scenarios. Future directions suggest integrating multi-source data and advancing machine learning models to improve robustness in complex traffic and harbor environments. By linking theoretical insights with practical onboard needs, this study provides guidance for developing intelligent, adaptive, and safety-enhancing maritime systems. Full article

Inland ports play a strategic role in enhancing multimodal connectivity and promoting sustainable freight transport within European corridors. However, the drivers of inland port development remain insufficiently understood, particularly with respect to nonlinear dynamics, interaction effects, and regional heterogeneity. This study investigates the socio-economic, infrastructural, and spatial determinants of inland port throughput using an interpretable machine learning framework. An XGBoost model is built up to estimate eighteen ports’ throughput along the Romanian Danube, over the period 2010–2024. SHAP (Shapley Additive Explanations) values are employed to quantify global importance, nonlinear marginal effects, and interaction structures. Results show that spatial accessibility and road infrastructure are the most influential drivers, while economic sectoral structure and road infrastructure exert nonlinear and scale-dependent effects. Interaction analysis reveals that inland port development is synergy-driven rather than additive, with the strongest complementarities observed between spatial accessibility, multimodal infrastructure, and sectoral structure. Additionally, Kruskal–Wallis tests on SHAP contributions indicate significant heterogeneity across port administrations, suggesting that governance and regional context modulate the realization of economic and infrastructural potential. The findings contribute to port–hinterland interaction analysis by demonstrating that inland port performance emerges from multi-scale, nonlinear, and regionally mediated dynamics. Methodologically, the study illustrates the value of interpretable machine learning for transport systems research. Policy implications emphasize coordinated multimodal investments, accessibility enhancement, and region-specific development strategies to strengthen inland waterway integration within the European transport sector. Full article

While battery-powered propulsion represents a promising pathway for inland waterway freight, its widespread adoption is hindered by range anxiety and high investment costs. Strategic energy replenishment has emerged as a critical and cost-effective solution to extend voyage endurance and mitigate these barriers. This paper introduces a novel approach to optimize energy replenishment strategies for inland electric ships that considers the possibility of adopting multiple technologies (charging and battery swapping) and partial replenishment. The proposed approach not only identifies optimal replenishment ports but also determines the technology to employ and the corresponding amount of energy to replenish for each operation, aimed at minimizing total replenishment costs. This problem is formulated as a mixed-integer linear programming model. A case study of a 700-TEU electric container ship operating on two routes along the Yangtze River validates the effectiveness of the proposed approach. The methodology demonstrates superior performance over existing approaches by significantly reducing replenishment costs and improving solution feasibility, particularly in scenarios with tight schedules and limited technology availability. Furthermore, a sensitivity analysis examines the impacts of key parameters, offering valuable strategic insights for industry stakeholders. Full article

We propose a complexity-aware extension of the Course–Speed (CS) model that integrates an AIS-derived Traffic Complexity Index (TCI) based on change in speed (ΔV) and course (Δθ) to quantify maneuvering complexity in nonlinear crossing waters. The framework consists of: (i) data preprocessing and gating to ensure navigationally valid AIS samples; (ii) CS index computation using distribution-aware statistics; (iii) TCI estimation from variability in speed and course along intersecting flows; and (iv) an integrated CS–TCI for interpretable mapping and ranking. Using one year of AIS data from a high-density crossing area near the Korean coast, we show that the integrated index reveals crossing hotspots and small-vessel maneuvering burdens that are not captured by spatial regularity metrics alone. The results remain robust across reasonable parameter ranges (e.g., speed filter and σ-based weighting), and they align with operational observations in vessel traffic services (VTS). The proposed CS–TCI offers actionable decision support for port and coastal operations by jointly reflecting traffic smoothness and complexity; it can complement collision-risk screening and efficiency-oriented planning (e.g., energy and emission considerations). The approach is readily transferable to other crossing waterways and can be integrated with real-time monitoring to prioritize control actions in complex marine traffic environments. Full article

This study introduces a novel deep learning architecture for vessel trajectory prediction based on Automatic Identification System (AIS) data. The motivation stems from the increasing importance of maritime transport and the need for intelligent solutions to enhance safety and efficiency in congested waterways—particularly with respect to collision avoidance and real-time traffic management. Special emphasis is placed on river navigation scenarios that limit maneuverability with the demand of higher forecasting precision than open-sea navigation. To address these challenges, we propose a Principal Component Analysis (PCA)-driven layered attention mechanism integrated within an encoder–decoder model to reduce redundancy and enhance the representation of spatiotemporal features, allowing the layered attention modules to focus more effectively on salient positional and movement patterns across multiple time steps. This dual-level integration offers a deeper contextual understanding of vessel dynamics. A carefully designed evaluation framework with statistical hypothesis testing demonstrates the superiority of the proposed approach. The model achieved a mean positional error of 0.0171 nautical miles (SD: 0.0035), with a minimum error of 0.0006 nautical miles, outperforming existing benchmarks. These results confirm that our PCA-enhanced attention mechanism significantly reduces prediction errors, offering a promising pathway toward safer and smarter maritime navigation, particularly in traffic-critical riverine systems. While the current evaluation focuses on short-term horizons in a single river section, the methodology can be extended to complex environments such as congested ports or multi-ship interactions and to medium-term or long-term forecasting to further enhance operational applicability and generalizability. Full article

Inland waterway transport (IWT) is a key function of river systems worldwide. It is vulnerable to climate change, specifically to discharge extremes, and competes for water with multiple other functions. A clear framework describing its interests to inform decision-making during regular conditions as well as during climate extremes is as yet unavailable in the literature. To address this gap we examine how inland shipping is taken into account in waterway policies in the Netherlands. We apply the frame of reference method to ‘objectify’ current inland waterway transport (IWT) policies, addressing the themes of waterway capacity, safety, service level, and sustainability. By ‘objectifying’ we mean turning the implicit into an explicit ‘object’ of study on the one hand and revealing underlying ‘objectives’ on the other. We show that policies for waterway capacity and service level are well developed, while waterway safety policies are more implicit, and waterway resilience lacks a quantitative decision framework. We furthermore show that current policies mainly focus on regular conditions, leaving it unclear what changes under extreme river discharge conditions. The results provide important insights into shipping-related decision challenges during climate extremes, highlighting aspects that should be developed further to improve the climate resilience of inland shipping. While some of these implications are specific to the Dutch case, the method applied here can also be used for other river systems that support multiple functions. Full article

Background: The transportation of petroleum products via multimodal logistics systems is a complex process subject to operational inefficiencies and elevated risk exposure. The efficient and resilient transportation of petroleum products increasingly depends on multimodal logistics systems, where operational risks and process inefficiencies can significantly impact safety and performance. This study addresses the research question of how an integrated risk-based and workflow-driven approach can enhance the management of oil products logistics in complex port environments. Methods: A dual methodological framework was applied at the Port of Midia, Romania, combining a probabilistic risk assessment model, quantifying incident probability, infrastructure vulnerability, and exposure, with dynamic business process modeling (BPM) using specialized software. The workflow simulation replicated real-world multimodal oil operations across maritime, rail, road, and inland waterway segments. Results: The analysis identified human error, technical malfunctions, and environmental hazards as key risk factors, with an aggregated major incident probability of 2.39%. BPM simulation highlighted critical bottlenecks in customs processing, inland waterway lock transit, and road tanker dispatch. Process optimizations based on simulation insights achieved a 25% reduction in operational delays. Conclusions: Integrating risk assessment with dynamic workflow modeling provides an effective methodology for improving the resilience, efficiency, and regulatory compliance of multimodal oil logistics operations. This approach offers practical guidance for port operators and contributes to advancing risk-informed logistics management in the petroleum supply chain. Full article

The Pinglu Canal, China’s first strategic river–sea transport canal, connects the inland waterway trunk line with the Beibu Gulf International Hub Port, while a diversion channel extension is needed for port development. This study proposed a comparative study of extension routes to Fangchenggang through qualitative analysis and quantitative modeling. Eventually, Route 4 is selected considering the engineering feasibility, ecological impact, and hydrodynamic modeling. It yields the lowest engineering cost, effectively bypasses the ecological protection red lines and cultural heritage areas, and only intersects the controllable ecological restoration zones in a limited manner. Hydrodynamic results show moderate tidal velocities, with maximum rising and falling speeds of 0.72–0.80 m/s and 0.72–0.86 m/s, respectively. The sediment deposition intensity (0.06 m/a) and total volume (58,600 m³/a) are generally lower than the other alternatives. The comparative study shares useful insights into canal route selection and would support the sustainable development of the logistics transportation system in Southwest China. Full article

Dredging is essential for the maintenance of ports, waterways, lakes, and lagoons to ensure their operability and economic value. Over the last few decades, scientists have focused on the significant environmental challenges associated with dredging, including habitat destruction, loss of biodiversity, sediment suspension, and contamination with heavy metals and organic pollutants. The huge loss of sediment in coastal areas and the associated erosion processes are now forcing stakeholders to look ahead and turn potential problems into an opportunity to develop new sediment management strategies, beyond environmental protection, toward ecosystem restoration and coastal resilience. Moreover, the European and Italian strategies, such as the European Green Deal (EGD) and the Italian Ecological Transition Plan (PTE), highlight the need to reuse dredge sediment in circular economy strategies, transforming them into valuable resources for construction, agriculture, and environmental restoration projects. European legislation on dredging is fundamental to the issue of management and priorities of dredged materials, but the implementation rules are deferred to individual member states. In Italy, the Ministerial Decree 173/2016 covers the main aspects of dredge activities and dredge sediment management. Moreover, it encourages the remediation and reuse of the dredge sediment. This study starts with a comprehensive analysis of the innovative remediation techniques that minimize impacts and promote sustainable, beneficial sediment management. Different remediation methods, such as electrochemical treatments, chemical stabilization, emerging nanotechnologies, bioremediation, and phytoremediation, will be evaluated for their effectiveness in reducing pollution. Finally, we highlight new perspectives, integrated strategies, and multidisciplinary approaches that combine various technological innovations, including artificial intelligence, to enhance sediment reuse with the aim of promoting economic growth and environmental protection. Full article

The demand for interoperable, lifecycle-oriented data exchange in the port and waterway sector is intensifying amid global digital transformation and infrastructure modernisation. Traditional Building Information Modelling (BIM) practices often fail to capture the domain-specific complexity and multidisciplinary collaboration required in maritime infrastructure. This paper critically evaluates the IFC 4.3 schema as a foundational standard for openBIM-based integration in this sector, offering a semantic alignment framework designed for the planning, design, and operational phases of port projects. Rather than proposing schema extensions, the framework interprets existing IFC constructs to model port-specific assets while supporting environmental and geospatial integration. Two case studies, a master planning project for a shipyard and a design coordination project for a ship lock complex, demonstrate the schema’s capability to facilitate federated modelling, reduce semantic discrepancies, and enable seamless data exchange across disciplines and software platforms. The research delivers actionable implementation strategies for practitioners, identifies technical limitations in current toolchains, and outlines pathways for advancing standardisation efforts. It further contributes to the evolving discourse on digital twins, GIS-BIM convergence, and semantic enrichment in infrastructure modelling. This work provides a scalable, standards-based roadmap to improve interoperability and enhance the digital maturity of port and waterway infrastructure. Full article