Cargo Volume Research Articles

China-EU Rail Freight: Analyzing TEU Traffic on the China Railway Express in 2019-2023

Since its launch in 2011, China Railway Express has annually increased the number of trains and volume of cargo transported along the China-Europe-China route, and was originally conceived as a land bridge between China and Europe. Trade relations between China and Europe play a crucial role in international economic development, and the development of China Railway Express is important for strengthening economic ties between China and Europe, offering diversified freight transport options, with delivery times shorter than sea freight and cheaper, than air freight. In recent years, the growth rate of China Railway Express’s main indicators has slowed down slightly, which may be due to the limited capacity of the main ports, as well as the global challenges that global logistics has faced in recent years due to the covid-19 epidemic, trade wars, sanctions and the unstable geopolitical situation in the world. In the literature, much attention is paid to the general indicators of China Railway Express and individual new routes, but there are practically no detailed statistics on specific directions of China Railway Express traffic to EU countries. Instead of the EU, general data is usually provided for the direction of Europe, which, in addition to the EU countries, also includes Russia, Kazakhstan and other countries along the Belt and Road that are not members of the EU. The purpose of this article is, based on a comparison of data from different statistical sources, to find out what volume of cargo transported along the China-EU-China route via China Railway Express and how this amount has changed since 2019. It turned out that, for example, the covid-19 epidemic did not negatively affect the volume of cargo transported along the China-EU-China route, however, starting in 2022 and after the imposition of sanctions against Russia, the main transit country for China Railway Express, the volume of cargo transported on the China–EU–China route decreased sharply, which, however, did not affect the overall freight traffic of China Railway Express, since the declining freight traffic to the EU was promptly redirected to Russia and other countries along the Belt and Road.

Applying geoinformation system technologies in transport logistics

The article discusses a geoinformation module developed for a transport enterprise, which calculates the minimum number of vehicles required to transport the declared volume of cargo, and also allows for systematization and structuring of work with data. Keywords GIS, vehicle routing problem, transport company

Assessing the Logistics Efficiency of Baltic Region Seaports Through DEA-BCC and Spatial Analysis

Efficient logistics is a key factor in the competitiveness of seaports, especially in regions such as the Baltic Sea, where ports play important roles as hubs in the European Union’s Trans-European transport network (TEN-T). However, there are a lack of comprehensive studies focusing on the logistics efficiency of Baltic Sea ports, especially those integrating technical and technological factors. This study aimed to assess changes in the logistics efficiency of 15 major ports in the Baltic Sea region between 2019 and 2023, taking into account the technological and infrastructure-related elements that influence port performance. The model developed by the authors integrates the nearest neighbour method for cluster identification, data envelopment analysis using the Banker, Charnes, and Cooper (DEA-BCC) model to assess the overall technical, pure technical, and scale logistics efficiency, and spatial autocorrelation analysis to explore spatial interactions. For the DEA-BCC model, constraints were defined for each port based on inputs (number and length of berths) and outputs (cargo and container volumes for 2019–2023). The spatial autocorrelation analysis examined the relationships among the Baltic Sea ports, container volumes, and logistic efficiency values derived from the DEA model. Recognizing the sensitivity of the weight matrix in previous studies, this paper introduced an enhanced two-factor weighting matrix that incorporated geographical distance and the port connectivity index, calculated by the United Nations Conference on Trade and Development (UNCTAD). The statistical reliability of the results was validated using z-scores and p-values. The results showed that the overall technical efficiency of the ports analysed during the period considered was 47.2%, the pure technical efficiency was 61.0%, and the average scale efficiency was around 76%, indicating that diminishing returns to scale dominated. The spatial analysis showed a strong correlation between port connectivity and efficiency, indicating that well-connected ports, such as Gdańsk and Gdynia, had a higher efficiency. The findings make a significant contribution to the understanding of the logistics efficiency of Baltic Sea ports and highlights the importance of regional cooperation, infrastructure improvements, and better connectivity strategies to improve the overall efficiency of seaports in the region.

Эффект синергии транспортной сопряженности цепей поставок по маршрутам международного транспортного коридора «Север — Юг»: сценарный анализ

International transport corridors (ITC), as the main element of the formation of a new geo-economics, are becoming the most important object of research and development. Their impact on the economic growth of the countries adjacent to the routes, on the efficiency of cargo owners, transport operators, carriers, and infrastructure owners has no clear theoretical or empirical estimates. The purpose of this study is to substantiate and calculate the synergy effect indicator of transport connectivity of the North – South ITC, namely, to assess the maximum volumes of cargo transported along all ITC routes, taking into account the factors limiting its throughput and carrying capacity. Research methods: the scenario forecast for the development of the ITC was obtained by sampling — scenario distribution. In calculating the indicators for assessing the economic effects of the ITC routes, the method of comparing digital data with risk factors limiting rail freight flows was used. The analysis of the distance matrix for three ITC routes was used. The forecast of the estimated indicators is made by the method of dynamic analysis of successive series of indicators of rail freight transportation, characterizing each route, their changes over time. Results: based on the following indicators: throughput and carrying capacity of the ITC railway infrastructure; sustainable development index of the countries of the region; logistics index (customs and border clearance; quality of trade and transport infrastructure); “transportation costs” — the contingency effects were calculated, reflecting the maximum volumes of transported goods, taking into account the connectivity of infrastructure and modes of transport along all routes of the North – South ITC. Practical significance: scenario calculation of contingency effects along the ITC routes can be used in projects for he development of the transport and logistics infrastructure of the ITC, since it allows assessing the effects of growth in freight throughput.

Two-Stage Genetic Algorithm for Optimization Logistics Network for Groupage Delivery

This study explored the optimization of groupage intercity delivery using a two-stage genetic algorithm (GA) framework, developed with the BaumEvA Python library. The primary objective was to minimize the transportation costs by strategically positioning regional branch warehouses within a logistics network. In the first stage, the GA selected optimal branch warehouse locations from a set of candidate cities. The second stage addressed the vehicle routing problem (VRP) by employing a combinatorial GA to optimize the delivery routes. The GA framework was designed to minimize the total costs associated with intercity and last-mile deliveries, factoring in warehouse locations, truck routes, and vehicle types for last-mile fulfillment while ensuring capacity constraints are adhered to. By solving both line haul and last-mile delivery subproblems, this solution adjusted variables related to warehouse placement, cargo volumes, truck routing, and vehicle selection. The integration of such optimization techniques into the logistics workflow allowed for streamlined operations and reduced costs.

Freight Volume Prediction for Logistics Sorting Centers Using an Integrated GCN-BiLSTM- Transformer Model

In this paper, a comprehensive learning model is proposed for predicting cargo volume at sorting centers, which integrates GCN (Graph Convolutional Network), BiLSTM (Bidirectional Long Short-Term Memory Network), ARIMA (Autoregressive Integrated Moving Average model), and Transformer models. To achieve this, a directed weighted graph is constructed considering the transport network and average cargo volume of each sorting center. The GCN model is employed to extract spatial features from the transport connection information of the sorting centers and these features are then fed into the BiLSTM network. The BiLSTM network leverages bidirectional information flow to learn the temporal characteristics of the data. Subsequently, the GCN-BiLSTM model, which combines spatial and temporal features, is used to predict the daily cargo volume for the next 30 days. The results demonstrate that the GCN-BiLSTM model and the ARIMA-BiLSTM integrated model significantly enhance prediction performance compared to single-model approaches.

Self-supervised anomaly detection and localization for X-ray cargo images: Generalization to novel anomalies

Robust detection of illicit items using X-ray inspection methods has gained increasing importance in recent years due to the large volume of cargo crossing international borders. In addition to detecting the presence of such items, determining their location, size, and shape is challenging due to the unpredictable nature of anomalies, but essential for expediting security inspections. Viewing the illicit items as anomalies relative to expected cargo, we propose a self-supervised learning framework consisting of an encoder–decoder–classifier–segmenter model, a multi-component loss function, coupled with a training strategy to extract discriminative features tailored for detection of the presence of anomalies, as well as localization of such items in X-ray cargo images. Our framework addresses the challenges posed by limited labeled data and offers a model capable of both detecting and localizing anomalies effectively. Moreover, we present a diverse dataset encompassing various cargo scenarios with and without anomalies, providing a robust evaluation environment for this class of problems. Unlike existing approaches, which are trained to detect specific types of objects with a fixed set of illicit items, our framework is adaptable to real-world scenarios where a wide range of illicit items may be present in the cargo. This versatility enhances the practical applicability of our model. We evaluate the performance of our framework on our dataset as well as two other publicly available datasets, demonstrating our method’s strong detection and localization performance even when faced with complex novel anomalies significantly different from those encountered during training.

Factors influencing the adoption of circular economy practices in polish seaports: an analysis of determinants and challenges

The purpose of the article is to fill the research gap in identifying and prioritizing the factors that determine the choice of a port for handling circular supply chains (CSC). To this end, Polish seaports handling CSC cargo with an average turnover of at least 100,000 tons in the last 10 years were analyzed. The authors analyzed CSC cargo occurring in seaports, in two stages, both in terms of quantity and quality. The first stage involved an analysis of the literature and the European Commission’s programs on the development of the Circular Economy (CE), followed by an analysis of the relationship between the size of the port, measured by the average volume of cargo handled at the studied port, and the average share of CSC cargo in total cargo handling. On the other hand, in the second stage, based on face-to-face interviews, the factors that determine the choice of a particular port for handling CSC cargo were extracted. The study revealed a significant relationship between port size and the share of CSC cargo in total cargo handling. Furthermore, the research identified and prioritized key factors influencing the choice of ports for CSC, providing valuable insights for port authorities and policymakers. These findings can serve as a foundation for further academic research aimed at optimizing port operations within circular supply chains and advancing the theoretical framework of circular economy logistics. Port authorities and businesses can leverage these insights to enhance strategic decision-making, improve operational efficiency, and strengthen their competitive advantage in the circular economy landscape.

Transforming coastal fisheries in Norway: Institutional implications of green technology implementation

In Norway, the coastal fishing fleet is essential for maintaining local value chains and securing employment systems in fisheries dependent areas. To uphold rural policy goals, this fleet segment is subject to a management regime with strict length restrictions. Concurrently, Norway is committed to significantly reducing greenhouse gas emissions in line with both national and international policies, including the Paris agreement. Achieving this reduction necessitates the replacement of traditional diesel engines with new green technologies. However, these green technologies, such as battery systems, electric engines, and gas tanks, require significantly more space onboard the vessels compared to traditional diesel engines. Hence, replacing compact diesel engines with green technologies will challenge the existing length restrictions. To accelerate the green transition for coastal vessels, a new regulatory framework has thus been placed high on the fisheries' political agenda. This paper outlines the coastal fleet's current management status, explores alternative regulatory parameters to ease the green transition, and considers the potential implications for the coastal fleet structure. Our study finds that replacing strict length regulations with more liberal cargo hold volume regulations may support the green transition, thereby enhancing environmental sustainability. However, the substitution of length restrictions with cargo hold volume regulations may shift the fleet structure towards larger vessels and alter their operating patterns, in turn impacting the maintenance of local value chains. The question that remains is how to meet the green transition while maintaining a diverse coastal fishing fleet.

Reiβscheiben ingots as markers of copper imports to the market of Medieval Novgorod

During the Middle Ages, massive ingots were the standard type of copper raw material obtained from European mines and exported across the Baltic and North Seas over vast distances. In Russian literature, they are known as “flat cakes” and “loafs”, while in the foreign one they are referred to as Reißscheiben (torn out disk). The article provides information about 24 cases of finding massive ingots. Most of the finds are concentrated on international trade routes and mark the main directions of movement of copper raw materials from mining operations located in the Western Carpathians, Erzgebirge and Eastern Alps, to the North and Baltic Seas. Recording and mapping of Reißscheiben that were not subject to remelting, and, therefore, retained the primary “isotopic marks”, provide valuable information for identifying sources of metal and establishing the routes of importing metal raw materials to the territory of medieval Rus. Huge volumes of copper shipwreck cargoes can be considered as reference samples when interpreting Pb-Pb data obtained for both different types of ingots and finished products.

A Study on Alternatives to Expand Gwangyang Port Cargo Volume Through Attracting E-commerce Related Companies

A Study on Alternatives to Expand Gwangyang Port Cargo Volume Through Attracting E-commerce Related Companies

Accident Prevention of BOXN Wagon Train

The safety and efficiency of railway freight transportation are critical to the functioning of many national economies, and India is no exception. BOXN wagons, widely used in the Indian railway system for transporting heavy goods such as coal, minerals, and other bulk materials, are essential for supporting industries across the country. However, due to their size, weight, and the volume of cargo they carry, these wagons are prone to accidents if not properly managed. Accidents involving BOXN trains can result in severe consequences, including loss of life, damage to property, and environmental degradation. One of the biggest and most significant industries in the world is the Indian goods railway sector. It is essential for the transportation of materials and goods across the nation. Indian Railways seeks to shorten commute times by boosting the average freight train speed efficiently from 22 mph to 50 mph. The adoption of new technologies is anticipated to have positive effects on the Indian goods railway sector as well. Smart Innovations, for instance, smart goods cars and digital signaling can contribute to reducing train accidents and help to increase railroad productivity using smart mobility.

Cargo Volume Forecasting for Logistics Sorting Centers Based on Random Forest Model

With the rapid development of logistics industry, logistics sorting center, as a key node in e-commerce logistics network, its cargo volume prediction and personnel scheduling strategy are of great significance to improve operational efficiency and reduce costs. The purpose of this study is to deeply analyze the cargo volume data of 57 sorting centers through machine learning method, establish an accurate cargo volume prediction model, and optimize the personnel scheduling strategy based on the prediction results. In this study, we preprocess the historical cargo data, including missing value filling, time series conversion and feature construction. Then, the random forest model was used to carry out fitting analysis on the cargo volume data of different sorting centers. By comparing the evaluation indexes such as mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE), the superiority of random forest model compared with neural network, support vector regression and linear regression models was verified. This study not only improves the accuracy of cargo volume forecast, but also realizes the reasonable allocation of human resources, and provides scientific decision support for the operation and management of logistics sorting center.

International Air Cargo Volume Forecasting based on the Stacking Fusion Model

Accurate forecasting of international air cargo volumes is crucial for air cargo carriers, which optimizes operational efficiency and profitability and strengthens market competitiveness and risk response capabilities. This study focuses on the open-source cargo data of over 100 international airlines in India from January 2015 to March 2017. Firstly, the missing data must be dealt with. Then, histograms and probability density plots are used to depict the distribution of the cargo volume and analyze the dynamics of the cargo volume of each company at annual and quarterly levels. Subsequently, key factors affecting cargo volumes are analyzed, with a special focus on the cargo performance of the Top 5 airlines, which is visualized in box plots. Given the time-series nature of cargo data, this study adopts the data sequential partitioning method. It innovatively utilizes the Stacking ensemble learning framework, integrating regression models such as Random Forest, Decision Tree, Extreme Gradient Boosting, and K-Nearest Neighbor as the base learners. The meta-learner is constructed to integrate the outputs of each base model, and the hyperparameters of the model are optimized by grid search and cross-validation. Comparison results show that the Stacking model has a higher R² value and lower MSE than any single base learners, indicating that it is more effective in international air cargo volume prediction. The prediction model based on stacking ensemble learning provides an effective strategy for improving air cargo prediction accuracy.

Research on cargo volume prediction and personnel scheduling optimization of sorting center based on BP neural network and improved genetic algorithm

With the development of Internet technology, e-commerce has become the primary choice for consumers to shop, and the huge order transaction volume has generated logistics problems. As an important part of the logistics network, the sorting platform is essential to improve its management efficiency. To enhance the efficiency of the sorting center, the topological relationship diagram is used to show the logistics network, and the cargo volume of the sorting center in the next 30 days and every hour is predicted based on the BP neural network model. Based on the mixed integer linear programming model and the improved genetic algorithm SCAGA model, the total man-days are minimized to ensure that the cargo volume is processed. At the same time, to ensure that the actual hourly efficiency is as balanced as possible, the optimization problem of personnel scheduling is studied. To maintain the efficient operation of the logistics industry, the accurate prediction of the number of goods and the reasonable scheduling of personnel, to provide a set of efficient and economical personnel scheduling programs for the logistics company, thus improving the overall operational efficiency and service quality.

Dynamic multi-objective time-temperature management for climacteric fruit cold storage considering ripeness windows and energy consumption

Cold chain logistics (CCL) can effectively maintain the quality and safety of perishable products through low-temperature circulation, but extra energy is densely used at the cost of economy and emissions. The multi-objective trade-off is critical for promoting sustainable time-temperature management (TTM) in CCL. For climacteric fruit in cold storage, the post-harvest ripening process should be controlled by TTM to meet various ripeness requirements of retailers with different distribution distances, market conditions. Meanwhile, randomly appeared order demands will dynamically influence complex sustainable decision-making of temperature-controlled path. Therefore, this paper aims to solve the dynamic multi-objective TTM optimization problem considering ripeness windows and energy consumption, for achieving online sustainable temperature control of climacteric fruit cold storage. Constructing 3D service window involving ripeness, outbound time, and cargo volume, then the multi-retailer satisfaction is designed as a significant optimization objective. Additionally, to improve operational efficiency and reduce environmental impact, energy consumption needs to be minimized under reasonable constraints. Following above objective strategy, a dynamic multi-objective TTM model is proposed with two-stage: 1) improved NSGA-II-based global optimization to find optimal temperature-controlled path for fixed retailers in real-time; 2) new stochastic retailer comes up after demand matching, if accepting order, an update optimization will be triggered. A numerical study was implemented to verify proposed method, with robustness value of 0.82–1. Compared with constant low-temperature storage, the optimized comprehensive loss is the smallest at 3.374. The results indicate that the hybrid-retailer demand-driven multi-criteria decision support is effective and robust for sustainable TTM of climacteric fruit cold chain.

Enhancing an AI Algorithm for Hydrocarbons Smart Management

The efficient and prompt delivery of commodities, which is one of a port's main functions, is of the utmost importance. An essential component for the success of both businesses and economies is the optimization of these processes. The power of machine learning algorithms in improving port operations has been demonstrated in recent years. They are skilled in foretelling and improving various supply chain components, which makes this apparent. In this paper, we present a case example demonstrating the use of machine learning techniques to improve hydrocarbon operations at the Tangier Med Port. A quartet of commonly used machine learning algorithms was chosen, and they were put through a battery of tests to see how well they predicted cargo volumes and optimized the storage of hydrocarbon liquids. Our research shows that the random forest method outperforms its competitors, predicting cargo volume with an accuracy of more than 90% while also significantly increasing the efficiency of storing hydrocarbon liquids. By implementing this method in a real-world setting, turnaround times were significantly shortened, productivity increased, and customer satisfaction increased. Our research intends to highlight the benefits that machine learning algorithms provide in improving port operations and, at the same time, provide essential insights into their smooth integration within real-time settings. As we look to the future, potential projects might explore other machine learning techniques. They also have the potential to be combined with auxiliary technologies, such as the Internet of Things (IoT), to advance the development of port operation optimization.

A novel method for evaluating ship concept performance in transport systems

Abstract Transport carries significant external costs such as climate change, accidents, pollution, and road congestion, driving national and international strategies for the development of new transport concepts. This includes shifting larger cargo volumes away from roads to more sustainable transport modes such as waterborne. The SEAMLESS project was launched in 2023 to answer to these needs by developing technology for autonomous waterborne zero-emission feeder-loop services. The realisation of such services depends on their modal competitiveness. Autonomous ships are expected to reduce transport costs and emissions, and ultimately improve logistical performance. There are, however, few published studies that quantify these impacts of autonomy. Furthermore, commercial waterborne autonomous transport services do not exist yet, limiting the possibilities for empirical analysis. Hence, research is needed to address exactly how and to what extent, autonomy improves competitiveness in different applications. This paper addresses the need for more empirical analyses of innovative waterborne transport performance, by presenting a novel method for evaluating ship concept performance in transport systems. The impacts of design choices are captured through hydrodynamic and logistical simulations. The method can be applied to transport systems consisting of both conventional and novel ship designs, operating on one or more routes including transhipments. It is implemented in the software SIMPACT and applied to a case study which establishes a shortsea feeder-loop service in the Bergen area in Norway. The Bergen municipality has decided that the container terminal is to be moved out of the city centre to reduce local traffic and emissions. However, in the absence of a competitive waterborne transport service in this region, the relocation will have the unfortunate consequence of an estimated annual net increase in regional truck traffic of 40,000 additional truck trips over 25km. By means of the proposed methodology, this paper investigates the feeder-loop concept and compare its quantified performance to truck transport and finds that competition is feasible.

Smart Management of Hydrocarbon Maritime Transport Using Ai Algorithms and Predictive Analytics: A Case Study of Tangier Med Port

Efficient and timely delivery of goods is considered the most critical operations for a port, and their optimization is crucial for the success of businesses and economies. In the last years, The machine learning algorithms have demonstrated that are advantageous in improving port operations, and this by the prediction and optimization of various aspects of the supply chain. In this paper, we present a case study of the implementation of machine learning algorithms for the optimization of hydrocarbon operations at Tangier Med Port. We selected four popular machine learning algorithms and tested their effectiveness in predicting cargo volumes and optimizing hydrocarbon liquid storage. Our results showed that the random forest algorithm outperformed the other algorithms with an accuracy of over 90% in predicting cargo volumes and a significant improvement in hydrocarbon liquid storage efficiency. The implementation of the algorithm in the production environment conduced to a considerable reduction in turnaround times, productivity improvement, then it has increased customer satisfaction. Our study is to demonstrate the advantages of the machine learning algorithms to improve the port operations and to provide the valuable insights into their implementation in real time scenarios. As a future work, we can study on exploring the use of other machine learning algorithms and their integration with other technologies such as the use of the Internet of Things (IoT) to promise further port operation optimization

광양항 항만배후단지 운영성과와 활성화방안 연구

Purpose: This study analyzes the supply plans and performance of the Gwangyang Port hinterland, and proposes activation strategies based on these findings. Research design, data, and methodology: A review of previous research was conducted to examine the definitions, functional changes, types, and designation criteria of port hinterlands. The current supply plans and development status of the Gwangyang Port hinterland were also analyzed. Results: The results show that while the volume of cargo in the hinterland is increasing, its contribution to the total volume of port cargo remains stagnant. Key issues identified include terminal operator productivity, lack of shipping routes, and excessive competition among logistics companies. Conclusions: The study proposes several activation strategies: improving terminal operator efficiency, enhancing support for high-value-added companies, promoting specialized hinterland areas for lifestyle logistics services, and strengthening the role of the Yeosu Gwangyang Port Authority.