Search Results (156)

Various factors influence the formation and adjustment of network freight prices, including transportation costs, cargo characteristics, and policies and regulations. The interaction of these factors increases the difficulty of accurately predicting network freight prices through regressions or other machine learning models, especially when the amount and quality of training data are limited. This paper introduces large language models (LLMs) to predict network freight prices using their inherent prior knowledge. Different data sorting methods and serialization strategies are employed to construct the corpora of LLMs, which are then tested on multiple base models. A few-shot sample dataset is constructed to test the performance of models under insufficient information. The Chain of Thought (CoT) is employed to construct a corpus that demonstrates the reasoning process in freight price prediction. Cross entropy loss with LoRA fine-tuning and cosine annealing learning rate adjustment, and Mean Absolute Error (MAE) loss with full fine-tuning and OneCycle learning rate adjustment to train the models, respectively, are used. The experimental results demonstrate that LLMs are better than or competitive with the best comparison model. Tests on a few-shot dataset demonstrate that LLMs outperform most comparison models in performance. This method provides a new reference for predicting network freight prices. Full article

Efficient maintenance is paramount for rail transport systems to avoid catastrophic accidents. Therefore, a method that enables the early detection of defects in critical components is crucial for increasing the availability of rolling stock and reducing maintenance costs. This work’s main contribution is the proposal of a methodology for analyzing vibration signals. The vibration signals, obtained from a bogie axle on a test bench, are decomposed into intrinsic functions, to which classical signal processing techniques are then applied. Finally, decision trees are employed to characterize the axle’s state, yielding excellent results. Full article

Compared to road trucks, the use of trains to move goods along railway lines is a more sustainable freight transport system. In Norway, where several main lines are single tracks, the insufficient length of many of the existing passing loops considerably restricts the operational and economic benefits of long trains. This brief technical note revolves around the possible upgrade of the Røros line connecting Oslo and Trondheim to accommodate 650 m-long freight trains as an alternative to the heavily trafficked Dovre line. Pivoting on regulatory standards, this exploratory work identifies the minimum set of infrastructure modifications required to achieve the necessary increase in capacity by extending the existing passing loops and creating a branch line. The results indicate that 8 freight train routes can be efficiently implemented, in addition to the 12 existing passenger train routes. This brief technical note employs building information modeling software Trimble Novapoint edition 2024 to position the existing railway infrastructure on topographic data and visualize the suggested upgrade. Notwithstanding the limitations of this exploratory work, dwelling on capacity calculation and the design of infrastructure upgrades, the results demonstrate that modest and well-placed interventions can significantly enhance the strategic value of a single-track rail corridor. This brief technical note sheds light on the main areas to be addressed by future studies to achieve a comprehensive evaluation of the infrastructure upgrade, also covering technical construction and economic aspects. Full article

The transport industry in the European Union plays a key role in the economy. However, due to persistent political, social, and technological changes, examining optimization strategies in transportation has become a crucial task to minimize expenditure, promote sustainable solutions, and address environmental degradation concerns. This study analyzes the effectiveness of a new truck trailer design, adapted from existing European models, which improves load capacity through an extended trailer length. The increased length (and, by extension, volume) is expected to reduce the number of vehicles for freight transportation, thereby improving road congestion and reducing environmental impacts, which include GHG emissions and overall carbon footprint. To achieve this objective, a comprehensive analysis of current European regulations on articulated vehicles and road trains was carried out, alongside a review of related case studies implemented or under development across the European Union member states. Additionally, a pilot study was conducted using the proposed 18 m semi-trailer across 14 real-life freight routes involving loads from several suppliers and manufacturers. This study therefore demonstrates the economic benefits and reduction in pollutant emissions related to the extended design and evaluates its impact on road infrastructure conditions, given the total length of 20.55 m. Full article

The “carbon neutral power generation” policy of the European Union requires the phasing out of fossil fuel power plants. These plants still play a crucial role in the energy mix in many countries; therefore, efforts are put forward to lower their CO₂ emissions. The available solution for an existing coal plant is the implementation of biomass co-firing, which allows it to reduce twice its carbon footprint in order to achieve the level of natural gas plants, which are preferable on the way to zero-emission power generation. However the side effect is a significant increase in the bulk fuel volumes that are acquired, handled, and finally supplied to the power plant units. A necessary extension of the complex logistic system for unloading, quality tagging, storing, and transporting biomass may increase the plant’s noise emissions beyond the allowed thresholds. For a comprehensive assessment of the concept of expanding the power plant’s biofuel supply system (BSS), a discrete simulation model was built to dimension system elements and verify the overall correctness of the proposed solutions. Then, a dedicated noise emission model was built for the purposes of mandatory environmental impact assessment procedures for the planned expansion of the BSS. The noise model showed the possibility of exceeding the permissible noise levels at night in selected locations. The new simulations of the BSS model were used to analyze various scenarios of biomass supply with regard to alternative switching off the selected branches of the whole BSS. The length of the queue of unloaded freight trains delivering an average quality biomass after a period of 2 weeks is used as a key performance parameter of the BSS. A queue shorter than 1 freight train is accepted. Assuming the rising share of RESS in the Polish energy mix, the thermal plant’s 2-week average power output shall not exceed 70% of its maximum capacity. The results of the simulations indicate that under these constraints, the biofuel supplies can be sufficient regardless of the nighttime stops, if 50% of the supplied biomass volumes are delivered by trucks. If the trucks’ share drops to 25%, the plant’s 2-week average power output is limited to 45% of its maximum power. The use of digital spatial simulation models for a complex, cyclical-continuous transport system to control its operation is an effective method of addressing environmental conflicts at the design stage of the extension of industrial installations in urbanized areas. Full article

Although the transition to electric vehicles (EVs) is well underway and expected to continue in global car markets, most vehicles on the world’s roads will be powered by internal combustion engine vehicles (ICEVs) and fossil fuels for the foreseeable future, possibly well past 2050. Thus, good environmental performance and effective emission control of ICE vehicles will continue to be of paramount importance if the world is to achieve the stated air and climate pollution reduction goals. In this study, we review 228 publications and identify four main issues confronting these objectives: (1) cheating by vehicle manufacturers, (2) tampering by vehicle owners, (3) malfunctioning emission control systems, and (4) inadequate in-service emission programs. With progressively more stringent vehicle emission and fuel quality standards being implemented in all major markets, engine designs and emission control systems have become increasingly complex and sophisticated, creating opportunities for cheating and tampering. This is not a new phenomenon, with the first cases reported in the 1970s and continuing to happen today. Cheating appears not to be restricted to specific manufacturers or vehicle types. Suspicious real-world emissions behavior suggests that the use of defeat devices may be widespread. Defeat devices are primarily a concern with diesel vehicles, where emission control deactivation in real-world driving can lower manufacturing costs, improve fuel economy, reduce engine noise, improve vehicle performance, and extend refill intervals for diesel exhaust fluid, if present. Despite the financial penalties, undesired global attention, damage to brand reputation, a temporary drop in sales and stock value, and forced recalls, cheating may continue. Private vehicle owners resort to tampering to (1) improve performance and fuel efficiency; (2) avoid operating costs, including repairs; (3) increase the resale value of the vehicle (i.e., odometer tampering); or (4) simply to rebel against established norms. Tampering and cheating in the commercial freight sector also mean undercutting law-abiding operators, gaining unfair economic advantage, and posing excess harm to the environment and public health. At the individual vehicle level, the impacts of cheating, tampering, or malfunctioning emission control systems can be substantial. The removal or deactivation of emission control systems increases emissions—for instance, typically 70% (NO_x and EGR), a factor of 3 or more (NO_x and SCR), and a factor of 25–100 (PM and DPF). Our analysis shows significant uncertainty and (geographic) variability regarding the occurrence of cheating and tampering by vehicle owners. The available evidence suggests that fleet-wide impacts of cheating and tampering on emissions are undeniable, substantial, and cannot be ignored. The presence of a relatively small fraction of high-emitters, due to either cheating, tampering, or malfunctioning, causes excess pollution that must be tackled by environmental authorities around the world, in particular in emerging economies, where millions of used ICE vehicles from the US and EU end up. Modernized in-service emission programs designed to efficiently identify and fix large faults are needed to ensure that the benefits of modern vehicle technologies are not lost. Effective programs should address malfunctions, engine problems, incorrect repairs, a lack of servicing and maintenance, poorly retrofitted fuel and emission control systems, the use of improper or low-quality fuels and tampering. Periodic Test and Repair (PTR) is a common in-service program. We estimate that PTR generally reduces emissions by 11% (8–14%), 11% (7–15%), and 4% (−1–10%) for carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen (NO_x), respectively. This is based on the grand mean effect and the associated 95% confidence interval. PTR effectiveness could be significantly higher, but we find that it critically depends on various design factors, including (1) comprehensive fleet coverage, (2) a suitable test procedure, (3) compliance and enforcement, (4) proper technician training, (5) quality control and quality assurance, (6) periodic program evaluation, and (7) minimization of waivers and exemptions. Now that both particulate matter (PM, i.e., DPF) and NO_x (i.e., SCR) emission controls are common in all modern new diesel vehicles, and commonly the focus of cheating and tampering, robust measurement approaches for assessing in-use emissions performance are urgently needed to modernize PTR programs. To increase (cost) effectiveness, a modern approach could include screening methods, such as remote sensing and plume chasing. We conclude this study with recommendations and suggestions for future improvements and research, listing a range of potential solutions for the issues identified in new and in-service vehicles. Full article

Multimodal freight transport systems, integrating maritime, rail, and road modes, play a vital role in modern logistics but face elevated operational, human, and environmental risks due to their complexity and interdependencies. To address the limitations of conventional risk assessment methods, this study proposes a hybrid risk modeling framework that integrates fault tree analysis (FTA), dynamic fault trees (DFTs), and fuzzy logic reasoning. This approach supports the modeling of sequential failures and captures qualitative uncertainties such as human fatigue and inadequate training. The framework incorporates reliability metrics, including Mean Time to Failure (MTTF) and Mean Time Between Failures (MTBF), enabling the quantification of system resilience and identification of critical failure pathways. Application of the model revealed human error, particularly procedural violations, insufficient training, and fatigue, as the dominant risk factor across transport modes. Road transport exhibited the highest probability of risk occurrence (p = 0.9960), followed by rail (p = 0.9937) and maritime (p = 0.9900). By integrating probabilistic reasoning with qualitative insights, the proposed model offers a flexible decision support tool for logistics operators and policymakers, enabling scenario-based risk planning and enhancing system robustness under uncertainty. 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

Accurately simulating the thermal behavior of wheel–brake shoe friction on long downhill ramps is challenging due to the complexity of modeling appropriate heat source models. This study investigates heat generation during the frictional braking process of freight train wheels and brake shoes under long-slope conditions. Four heat source models—constant, modified Gaussian, sinusoidal, and parabolic distributions—were developed based on energy conservation principles and validated through experimental data. A thermomechanical coupled finite element model was established, incorporating a moving heat source to analyze the effects of different models on wheel tread temperature distribution and its evolution over time. The results show that all four models effectively simulate frictional heat generation, with computed temperatures, deviating by only 6.0–8.2% from experimental measurements, confirming their accuracy and reliability. Among the models, the modified Gaussian distribution heat source, with its significantly higher peak local heat flux (2.82 times that of the constant model) and rapid attenuation, offers the most precise simulation of the non-uniform temperature distribution in the contact region. This leads to a 40% increase in the temperature gradient variation rate and effectively reproduces the “hot spot” effect. The new non-uniform heat source model accurately captures local temperature dynamics and predicts frictional heat transfer and thermal damage trends. The modified Gaussian distribution model outperforms others in simulating local temperature peaks, offering support for optimizing braking system models and improving thermal damage prediction. Future research will refine this model by incorporating factors like material wear, environmental conditions, and dynamic contact characteristics. 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

Buffer stops have always been installed on blind tracks to mitigate the hazards associated with overruns due to insufficient or wrong braking. Conventional buffer stops fixed to the rails may absorb only limited energy while Energy-Absorbing Buffers Stops (EABS) dissipate higher energy hydraulically and/or by friction from sliding blocks clamped to the rail head. The assessment of EABS performances in terms of maximum stopping distance and maximum allowed deceleration is usually performed by using the common kinematic rules of motion and considering the overrunning train as a single mass hitting the buffer stop. This paper studies the dynamic characteristics of the collision of entire trains with a friction EABS applying a Longitudinal Train Dynamics (LTD) approach. Several realistic scenarios using the UIC approved TrainDy software were simulated considering various train compositions, with different types of vehicles (locomotives, freight wagons and passenger coaches) and different kinds of buffers. The results show that high dynamic loads are exerted on the vehicles within the train, while the average deceleration and the stopping distance are not greatly influenced when compared with a simpler Finite Element Method (FEM) approach that does not consider the train composition. The progressive application of the EABS braking force increases the stopping distance but can reduce the peak deceleration of about 50%. The results may be used to tune the design parameters of friction EABS according to the currently available specifications and standards for rolling stock structural assessment considering that no international standards for EABS exist currently. Full article

For reliable autonomous train operation, detecting and classifying obstacles on or near rail tracks, and accurately estimating the distance to these obstacles, is essential. This task is more challenging in low-light conditions, common for freight trains that operate primarily at night. This paper proposes a novel method, FuzzyH, for estimating the distance between a thermal camera and detected obstacles using image-plane homography. By leveraging the homography between the image and rail track planes, and incorporating a fuzzy logic system, the method improves distance estimation accuracy and eliminates the need for complex calibration. This paper also explores the symmetry and asymmetry of fuzzy membership functions and rules. The system was validated on Serbian railways under simulated real-world conditions, demonstrating reliable performance. A key contribution of this method is the use of fuzzy membership functions tailored to specific distance ranges, based on experimental data and domain knowledge, such as regulatory braking distances. This approach improves over traditional methods by offering reliable distance estimates in low-light environments and simplifying the calibration process, ultimately enhancing system accuracy and robustness. Full article

This study investigates the multifractal characteristics of the tanker freight market from 1998 to 2024. Using multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrending moving average (MF-DMA), we analyze temporal correlations and volatility, revealing subtle differences in multifractal features before and after 2010. We further examine the influence of key external factors—including economic disturbances (the 2008 financial crisis), technological innovations (the 2014 Shale Oil Revolution), supply chain disruptions (the COVID-19 pandemic), and geopolitical uncertainties (the Russia–Ukraine conflict)—on market complexity. Building on this, a predictive framework is introduced, leveraging the Baltic Dirty Tanker Index (BDTI) to forecast Brent oil prices. By integrating multifractal analysis with machine learning models (e.g., XGBoost, LightGBM, and CatBoost), our framework fully exploits the predictability from the freight index to oil prices across the above four major global events. The results demonstrate the potential of combining multifractal analysis with advanced machine learning models to improve forecasting accuracy and provide actionable insights during periods of heightened market volatility. On average, the coefficient of determination (R²) increases by approximately 62.65% to 182.54% for training and 55.20% to 167.62% for testing, while the mean squared error (MSE) reduces by 60.83% to 92.71%. This highlights the effectiveness of multifractal analysis in enhancing model performance, especially in more complex market conditions post-2010. Full article

The majority of freight trains are characterized by a braking system that does not guarantee synchronous braking between different wagons. This results in the generation of considerable in-train forces during emergency braking operations, which are sometimes imposed by the railway infrastructure due to certain running speeds being exceeded. The magnitude of in-train forces is contingent upon a number of factors, the most significant ones being the length, mass and load composition of the trainset, in addition to the specific braking imposed. The application of excessive compressive in-train forces has the potential to cause the wagon to derail, particularly if the wagon is lightweight and traversing a small radius curve. Similarly, excessive tensile in-train forces applied to the screw couplers can cause them to fail, typically through fatigue, resulting in train disruption and necessitating the recovery of both portions of the trainset. The objective of this study is to perform a preliminary analysis of the UIC (International Union of Railways) unified screw couplers fatigue phenomenon, employing load spectra computed by the UIC 1.4.6 software TrainDy. A possible future development is developing a maintenance model functional to predict the extent of damage in freight wagon screw couplers during their service life. Full article

The safe operation of freight train equipment is crucial to the stability of the transportation system. With the advancement of intelligent monitoring technology, vision-based anomaly detection methods have gradually become an essential approach to train equipment condition monitoring. However, due to the complexity of train equipment inspection scenarios, existing methods still face significant challenges in terms of accuracy and generalization capability. Freight trains defect detection models are deployed on edge computing devices, onboard terminals, and fixed monitoring stations. Therefore, to ensure the efficiency and lightweight nature of detection models in industrial applications, we have improved the YOLOv8 model structure and proposed a network architecture better suited for train equipment anomaly detection. We adopted the lightweight MobileNetV4 as the backbone to enhance computational efficiency and adaptability. By comparing it with other state-of-the-art lightweight networks, we verified the superiority of our approach in train equipment defect detection tasks. To enhance the model’s ability to detect objects of different sizes, we introduced the Content-Guided Attention Fusion (CGAFusion) module, which effectively strengthens the perception of both global context and local details by integrating multi-scale features. Furthermore, to improve model performance while meeting the lightweight requirements of industrial applications, we incorporated a staged knowledge distillation strategy on large-scale datasets. This approach significantly reduces model parameters and computational costs while maintaining high detection accuracy. Extensive experiments demonstrate the effectiveness and efficiency of our method, proving its competitiveness compared with other state-of-the-art approaches. Full article