Search Results (98)

This study evaluates the financial viability of different main engine–fuel configurations for a container vessel on a standardized Trans-Pacific route. Using Net Present Value (NPV) analysis over a 10 year evaluation period (2024–2033), it compares six propulsion scenarios, combining three Wärtsilä engine types and four fuel alternatives (HFO, LFO, LNG, Methanol). The framework integrates technical parameters, including engine power and fuel consumption, with financial instruments such as the Weighted Average Cost of Capital (WACC) and the Capital Asset Pricing Model (CAPM). Results show that the LNG-powered Wärtsilä 8V31DF achieves the highest NPV. Despite requiring the highest initial capital expenditure (CAPEX), this configuration delivers superior financial performance and remains robust under volatile market conditions. Sensitivity tests with ±20% freight–fuel shocks and alternative discount rates (5%, 7.18%, 10%) confirm that the relative ranking of propulsion options is stable. Methanol yields negative NPVs under current prices but could become competitive with bio-methanol cost reductions or strong carbon pricing. Limitations include constant non-fuel OPEX, fixed sea state, and the exclusion of explicit carbon price scenarios. From a policy perspective, LNG appears most viable in the short term, while long-term strategies should consider ammonia and hydrogen in line with IMO decarbonization pathways. Full article

Sea–rail intermodal transport offers high efficiency and environmental benefits, yet its development in China remains limited. Existing studies have mainly assessed the macro-level benefits of sea–rail intermodal transport policies, but rigorous evidence on whether incentive policies work and how their effects differ across policy types remains scarce, which limits evidence-based policy design and efficient allocation between subsidies and capacity expansion. To address this gap, a dual-policy identification framework was established that combines a multi-period difference-in-differences model with event study analysis and used station–month data from China to assess the independent effects, underlying mechanisms, and spatiotemporal heterogeneity of railway freight price subsidies and freight train expansion on container throughput. The results indicate that both policies significantly increased container throughput. Railway freight price subsidies exhibited stronger and more persistent effects with a certain lag, whereas freight train expansion produced rapid but short-lived responses. The impacts of both policies were more pronounced in short-distance transport, but weakened or even turned negative over longer distances. Moreover, the number of participating entities served as a key mediating pathway, while information sharing positively moderates policy impacts. This study makes theoretical contributions to the identification of heterogeneity, mechanism analysis, and spatiotemporal characterization of SRIT incentive policy effects, while offering refined and actionable guidance for SRIT policy optimization. Full article

This research presents an overview of transportation mode choice, emphasizing key influencing factors and a range of methodological approaches from traditional Random Utility Theory (RUT) models to modern Machine Learning (ML) techniques. A comprehensive review covered 875 papers, which were screened for relevance. The search was conducted on ScienceDirect and Google Scholar between October and November 2024 using the keywords transport and choice model. Search results were reviewed until several consecutive entries no longer contained content relevant to the topic. After the screening and exclusion process, 106 papers remained for analysis. The review reveals that the Multinomial Logit (MNL) model remains the most widely used approach for modeling transportation mode choice, despite a growing interest in ML methods. Cars and buses dominate in passenger transport studies, while trucks, trains, and ships are most common in freight research. Data is typically collected through surveys (for passenger transport) and interviews (for freight), though some studies use secondary sources. Geographically, Asia and Europe are most represented, with regions like South America underrepresented. Travel time and cost are key variables, with increasing attention to the built environment in passenger studies and service reliability in freight studies. Overall, most studies aim to address real-world transport challenges. The review highlights the persistent gap between theoretical advancements and real-world applicability. To support this analysis, it examines the specific research objectives and findings of each study. Full article

Container liner shipping companies operate within a complex environment where they must balance profitability and service reliability. Meanwhile, evolving regulatory policies, such as surcharges imposed on ships of a particular origin or type on specific trade lanes, introduce new operational challenges. This study addresses the heterogeneous ship routing and demand acceptance problem, aiming to maximize two conflicting objectives: weekly profit and total transport volume. We formulate the problem as a bi-objective mixed-integer programming model and prove that the ship chartering constraint matrix is totally unimodular, enabling the reformulation of the model into a partially relaxed MIP that preserves optimality while improving computational efficiency. We further analyze key mathematical properties showing that the Pareto frontier consists of a finite union of continuous, piecewise linear segments but is generally non-convex with discontinuities. A case study based on a realistic liner shipping network confirms the model’s effectiveness in capturing the trade-off between profit and transport volume. Sensitivity analyses show that increasing freight rates enables higher profits without large losses in volume. Notably, this paper provides a practical risk management framework for shipping companies to enhance their adaptability under shifting regulatory landscapes. Full article

To enhance the operational efficiency of container multimodal transportation and mitigate carbon emissions during freight transit, this study investigates carbon emission-conscious multimodal transportation route optimization models and solution methodologies. Addressing the path optimization challenges under uncertain conditions, triangular fuzzy numbers are employed to characterize transportation time uncertainty, while a scenario-based robust regret model is formulated to address freight price volatility. Concurrently, the temporal value attributes of cargo are incorporated by transforming transportation duration into temporal costs within the model framework. Through the implementation of four distinct low-carbon policies, carbon emissions are either converted into cost metrics or established as constraint parameters, thereby constructing an optimization model with total cost minimization as the objective function. For model resolution, fuzzy chance-constrained programming is adopted for defuzzification processing. Subsequently, a multi-strategy improved whale optimization algorithm (WOA) is developed to solve the formulated model. Numerical case studies are conducted to validate the proposed methodology through comparative analysis with conventional WOA implementations, demonstrating the algorithm’s enhanced computational efficiency. The experimental results confirm the model’s capability to adapt multimodal transportation schedules for cargo with varying temporal value attributes and effectively reduce CO₂ emissions under different carbon reduction policies. This research establishes a comprehensive decision-making framework that provides logistics enterprises with a valuable reference for optimizing low-carbon multimodal transportation operations. Full article

As global trade and container transportation continue to grow, port collection and distribution systems face increasing challenges, including congestion, inefficiency, and environmental impact. Traditional ground-based transportation methods often exacerbate these issues, especially under uncertain demand conditions. This study aims to optimize freight flow allocation in port collection and distribution networks by integrating traditional and innovative transportation modes, including underground container logistics systems, under demand uncertainty. A stochastic optimization model is developed, incorporating transportation, environmental, carbon tax and subsidy, and congestion costs while satisfying various constraints, such as capacity limits, time constraints, and low-carbon transport requirements. The model is solved using a hybrid algorithm combining an improved Genetic Algorithm and Simulated Annealing (GA-SA) with Deep Q-Learning (DQN). Numerical experiments and case studies, particularly focusing on A Port, demonstrate that the proposed approach significantly reduces total operational costs, congestion, and environmental impacts while enhancing system robustness under uncertain demand conditions. The findings highlight the potential of underground logistics systems to improve port logistics efficiency, providing valuable insights for future port management strategies and the integration of sustainable transportation modes. Full article

Maritime trade in Bangladesh is growing significantly, as observed by UNCTAD, which reported 3.20 mTEUs throughput in 2022. Additionally, the principal seaport, Chattogram Port, reported a port throughput of 3.27 mTEUs in 2024, the historical record for any port in Bangladesh. More than 50% of imports and exports, including empty containers, were handled in 2024 through 19 nos close dry ports in Chattogram City by applying small-scale intermodal systems, where the performance of pure intermodal from/to mid-range dry ports (3 Nos) to Chattogram Port is 2.53%. By 2030, the government wants all import and export operations to be conducted through dry ports. Furthermore, the current volume of international goods freight cannot be handled by the dry ports that are currently in place. This research applied mixed methods to explore the opportunities to set more dry ports and the application of intermodal systems for increasing the seaport’s efficiency, productivity, and competitiveness. The Focus Group Discussion (FGD) method was used to know the dry port location, investment, and policy in creating the opportunity to set up more dry ports in Bangladesh. In the findings, 82.50% of participants agreed that existing facilities are not enough and need to establish more dry ports to handle current and future volumes of containers. Moreover, the responses reveal a division of opinion on establishing a dry port outside of Chattogram, with a notable inclination towards opposition. According to 62% of respondents, dry ports outside Chattogram are necessary. To enhance intermodal connectivity and facilitate easier cargo transfers between ports and hinterland regions, integrated infrastructure development would be in line with national economic objectives. The research aims to investigate the possibilities for establishing additional dry ports across the country to boost seaport productivity, efficiency, and competitiveness by utilizing intermodal freight transportation systems to cut costs and time while also considering environmental factors like CO₂ emissions. Full article

This study addresses the critical environmental challenge of increasing carbon emissions from Thailand’s freight transport sector, focusing on container movement in the strategic Lat Krabang ICD–Laem Chabang Port corridor. The research quantifies and compares CO₂ emissions between rail and road container transport modes to identify potential carbon reduction strategies. A comprehensive activity-based methodology was employed, incorporating fuel consumption testing across multiple load conditions, detailed transport activity mapping, and the application of locally relevant emission factors. The results demonstrate that rail transport produces 32.82 kgCO₂eq/TEU compared to 53.13 kgCO₂eq/TEU for road transport, representing a 38.23% emission advantage. Fuel consumption testing revealed a power relationship between train weight and fuel consumption (y = 0.1121x^0.5147, R² = 0.97), indicating improving efficiency with increased loading. Terminal operations contribute significantly to rail transport’s emission profile, accounting for 36% of total emissions. The current modal split presents substantial opportunities for emission reduction through increased rail utilization. This study identifies and evaluates practical carbon reduction strategies across operational, technological, and policy dimensions, with priority interventions including load factor optimization, terminal efficiency improvements, locomotive modernization, and differential road pricing. This research contributes empirical evidence to support sustainable freight transport development in Thailand while establishing a methodological framework applicable to emission assessments in similar corridors throughout developing economies. Full article

In order to integrate the use of transportation resources, develop a reasonable sea–rail intermodal container transportation plan, and achieve cost reduction and efficiency improvement of the multimodal transportation system, a method for predicting the daily freight volume of sea–rail intermodal transportation based on a convolutional neural network (CNN) algorithm is proposed and a new feature processing method is used: weight assignment (WA). Firstly, we use qualitative methods to preliminarily select the indicators, and then use multiple interpolation to fill in the missing raw data. Next, Pearson and Spearman quantitative analysis methods are used, and the analysis results are grouped using the k-means, with the high correlation groups assigned high weights. Next, we use quadratic interpolation to obtain the daily data. Finally, a weight assignment-enhanced convolutional neural network (WACNN) model and seven other mainstream models are constructed, using the Yingkou port container throughput prediction as a case study. The research results indicate that the WACNN prediction model has the best performance and strong robustness. The research results can provide a reference basis for the planning of sea–rail intermodal container transportation and the allocation of transportation resources, and achieve the overall efficiency improvement of logistics systems. Full article

The Shanghai Containerized Freight Index (SCFI) and Ningbo Containerized Freight Index (NCFI) serve as crucial indicators for management and decision-making in China’s shipping industry. This study proposes a novel real-time rolling decomposition forecasting system integrating multiple influencing factors. The framework consists of two core modules: data preprocessing and prediction. In the data preprocessing stage, the Hampel filter is utilized to filter and revise each raw containerized freight index dataset, eliminating the adverse effects of outliers. Additionally, variational mode decomposition (VMD) technique is employed to decompose the time series in a rolling manner, effectively avoiding data leakage while extracting significant features. In the forecasting stage, the cheetah optimization algorithm (COA) optimizes the key parameters of the extreme gradient boosting (XGBoost) model, enhancing forecasting accuracy. The empirical analysis based on SCFI and NCFI data reveals that historical pricing serves as a critical determinant, with our integrated model demonstrating superior performance compared to existing methodologies. These findings substantiate the model’s robust generalization capability and operational efficiency across diverse shipping markets, highlighting its potential value for managerial decision-making in maritime industry practices. Full article

With the expansion of global trade, China’s commodity futures market has become increasingly intertwined with regional maritime logistics. The Ningbo Containerized Freight Index (NCFI), as a key regional indicator, reflects freight rate fluctuations and logistics efficiency in real time. However, limited research has explored how commodity futures data can enhance NCFI forecasting accuracy. This study aims to bridge that gap by proposing a hybrid deep learning model that combines recurrent neural networks (RNNs) and gated recurrent units (GRUs) to predict NCFI trends. A comprehensive dataset comprising 28,830 daily observations from March 2017 to August 2022 is constructed, incorporating the futures prices of key commodities (e.g., rebar, copper, gold, and soybeans) and market indices, alongside Clarksons containership earnings. The data undergo standardized preprocessing, feature selection via Pearson correlation analysis, and temporal partitioning into training (80%) and testing (20%) sets. The model is evaluated using multiple metrics—mean absolute Error (MAE), mean squared error (MSE), root mean square error (RMSE), and R²—on both sets. The results show that the RNN–GRU model outperforms standalone RNN and GRU architectures, achieving an R² of 0.9518 on the test set with low MAE and RMSE values. These findings confirm that integrating cross-market financial indicators with deep sequential modeling enhances the interpretability and accuracy of regional freight forecasting. This study contributes to sustainable shipping strategies and provides decision-making tools for logistics firms, port operators, and policymakers seeking to improve resilience and data-driven planning in maritime transport. Full article

Dynamic weighing systems, an advanced technology for traffic management, are designed to measure the weight of moving vehicles without obstructing traffic flow. These systems play a critical role in monitoring freight vehicle overloading, collecting weight-based tolls, and assessing the structural health of roads and bridges. However, due to the complex road traffic environment in real-world applications of dynamic weighing systems, some vehicles cannot be accurately weighed, even though precise parameter calibration was conducted prior to the system’s official use. The variation in driving behaviors among different drivers contributes to this issue. When different types and sizes of vehicles pass through the dynamic weighing area simultaneously, changes in the vehicles’ motion states are the main factors affecting weighing accuracy. This study proposes an improved SSD vehicle detection model to address the high sensitivity to vehicle occlusion and frequent vehicle ID changes in current multi-target tracking methods. The goal is to reduce detection omissions caused by vehicle occlusion. Additionally, to obtain more stable trajectory and speed data, a Gaussian Smoothing Interpolation (GSI) method is introduced into the DeepSORT algorithm. The fusion of dynamic weighing data is used to analyze the impact of changes in vehicle size and motion states on weighing accuracy, followed by compensation and experimental validation. A compensation strategy is implemented to address the impact of speed fluctuations on the weighing accuracy of vehicles approximately 12.5 m in length. This is completed to verify the feasibility of the compensation method proposed in this paper, which is based on vehicle information. A dataset containing vehicle length, width, height, and speed fluctuation information in the dynamic weighing area is constructed, followed by an analysis of the key factors influencing dynamic weighing accuracy. Finally, the improved dynamic weighing model for extracting vehicle motion state information is validated using a real dataset. The results demonstrate that the model can accurately detect vehicle targets in video footage and shows strong robustness under varying road illumination conditions. Full article

The determination of freight rates in liner shipping is influenced by the market dynamics and the strategic decisions of shipping alliances. This study investigates the effect of non-alliance tonnage on freight rates along the Asia–North America West Coast route, employing a quantile regression method. A dataset covering July 2021 to June 2023 was used, with bunker prices and the Dow Jones Index serving as control variables. The results reveal that the non-alliance share has a significant and negative impact on lower quantiles, suggesting that enhanced competition reduces freight rates when the prices are low. In contrast, this effect disappears at higher freight levels. Bunker prices and the stock market index also exhibit varying effects, depending on the quantile, with demand-side variables being more influential during low-freight conditions. These findings suggest that market concentration affects price-setting power, and quantile-based approaches offer deeper insights into these complex relationships than linear models. These insights contribute to the sustainable development of maritime transport by promoting fair competition, improving pricing transparency, and supporting efficient policy interventions in global liner shipping. Full article

This paper utilizes AIS (Automatic Identification System) data to study the micro-level features of the international container capacity correlation network and their impact on shipping freight rates. It proposes, for the first time, constructing a capacity correlation network based on the correlation of operational capacity between different shipping routes. This approach captures micro changes in the shipping market by observing the “synchronized increase and decrease” in operational capacity across all routes, whereby “one decreases while the other increases” between routes. Secondly, a continuous synchronization method is introduced to construct a capacity correlation network feature index, reflecting trends in the structural changes in the capacity correlation network. This method establishes the capacity correlation network’s features without causing information loss, while capturing all detailed characteristics of the network and assigning “weights” based on the continuity of all features. Finally, the impact of the capacity correlation network feature index on shipping freight rates is examined. Experimental results indicate that the capacity correlation network feature index has a significant impact on shipping freight rates, which cannot be explained by factors such as supply, demand, or costs. This study is beneficial for revealing the price formation mechanism in the shipping market from a micro perspective. Full article

The purpose of this paper is to examine the optimization of the HIV drug supply chain, with a dual focus on minimizing freight costs and delivery times. With the help of a dataset containing 10,325 instances of supply chain transactions, key variables, including “Country”, “Vendor INCO Term”, and “Shipment Mode”, were examined in order to develop a predictive model using Artificial Neural Networks (ANN) employing a Multi-Layer Perceptron (MLP) architecture. A set of ANN models were trained to forecast “freight cost” and “delivery time” based on four principal design variables: “Line Item Quantity”, “Pack Price”, “Unit of Measure (Per Pack)”, and “Weight (Kilograms)”. According to performance metrics analysis, these models demonstrated predictive accuracy following training. An optimization algorithm, configured with an “active-set” algorithm, was then used to minimize the combined objective function of freight cost and delivery time. Both freight costs and delivery times were significantly reduced as a result of the optimization. This study illustrates the potent application of machine learning and optimization algorithms to the enhancement of supply chain efficiency. This study provides a blueprint for cost reduction and improved service delivery in critical medication supply chains based on the methodology and outcomes. Full article