Railway Freight Research Articles

Possibilities of Implementing Augmented Reality in Railway Freight Transport

Abstract The analysis of technological procedures in transport and logistics with the aim of innovating the usual procedures is an ongoing process, not only in the present. The development of information technology in recent decades has created new opportunities for innovative procedures, but the innovations themselves do not take place as quickly as the development allows. There are many reasons for this, from the point of view of operational safety to the relevance of technological progress to rail transport. Based on the analysis of technological processes in rail freight transport at the Žilina - Teplička marshalling yard and the characteristics of Augmented Reality, the aim is to assess the possibilities of implementing Augmented Reality technology in the conditions of the processing procedures of sets of vehicles of the destination train in freight transport. The implementation options will be evaluated based on their feasibility in three variants. The implementation of augmented reality must always represent a simplification, acceleration, or cost reduction of the usual technological procedures.

The impact of new western land and sea corridor development on port deep hinterland transport service and route selection

New transportation corridor development aims to provide more convenient transportation conditions for port deep hinterland areas and to enhance the two-way opening of land and sea through the utilization of various transport modes, such as railroad, highway, and water transportation. In this paper, the microcontainer transport service and route selection problem is introduced within a new transportation corridor context, and a decision-making tool is provided for shippers to develop door-to-door service plans. A multiobjective 0–1 planning model is constructed with the objective of minimizing the total transportation cost, transportation time, and carbon emissions. The loading and unloading time windows of railroad and waterway transportation, as well as the time window of consignee receipt, are considered in model construction to obtain the nondominated Pareto optimal front. The validity of both the model and algorithm is demonstrated in this paper by using the Chongqing-Hamburg and Chengdu-Hamburg routes, following the development of the New Western Land and Sea Corridor (NWLSC), as examples. The example results show that the NWLSC is appealing for time-sensitive cargoes, and its economic benefits can be enhanced if relevant policies such as railroad freight reductions are supported. The findings in this paper can provide decision support for western provinces in the implementation of targeted policies to regulate and optimize the regional transportation structure, which can further promote the opening up of western Chinese provinces as well as the integrated and coordinated development of coastal and inland regions, providing practical value for reducing carbon emissions.

Proposal of operational concept for bulk substrates transport by freight trains and its application within case study of aggregate transport for construction of Vlachovice dam

Railway freight transport represents an advantageous way for regular transport of bulk substrates. As a result of changes within the industry and energy sectors as well as in the field of consumer behaviour, such business cases decline significantly. Nevertheless, transport such of the characteristic have being (and will be) still realized however, in addition, quantities of transported goods will be known in advance – as a part of major construction or environmental remediation projects.In the period of 2020–2021, the Department of Transporting Systems in the CTU in Prague, Faculty of Transportation Sciences prepared the study “Vlára, Vlachovice Hydraulic Structure, Logistic Study of Material Transport for the Dam Construction” for the state enterprise Povodí Moravy (the Morava River Basin). Among other things, the study dealt with transport of more than 2.5 million tonnes from two quarries to the construction site of the Vlachovice dam by goods trains (the Vlachovice dam will be built in Moravia – the eastern part of the Czech Republic). It was necessary to determine an optimal route of the goods trains, their parameters and general operating concept so that the transport costs would be minimised and, concurrently, the transport performance would be as high as possible.This paper sets itself a goal to present a general procedure and principles for proposal of freight trains for a business case characterized by long-term and regular transport of large quantities of goods. For the stated case study, transport technology in the railway station Bohuslavice nad Vláří is discussed in detail.

Deep Learning Based Method for Early Warning of Price Risk in Railroad Bulk Freight Transport with Consideration of Time Series of Comprehensive Impact Index

The aim in this study is to enhance the price competitiveness of medium- and long-haul railroad bulk cargoes in the transportation market. Efficiently triggering an early warning for railroad bulk freight price risk is crucial. This paper discusses relevant factors affecting the pricing of railway freight from four aspects, namely the macroeconomy, the transportation market, the freight owner, and the railway enterprise. We construct a comprehensive index system for generating an early warning of railroad bulk freight prices. Utilizing a collected data set of 20 factor indicators that affected prices from 2015 to 2017, we calculate a comprehensive price risk warning index using the integrated entropy weight and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. We propose a risk warning level classification method based on the k-means clustering method to determine five levels of price risk warning. Subsequently, we establish comprehensive index prediction methods for issuing price risk warnings based on the back propagation (BP) neural network and the deep learning long short-term memory (LSTM) network. We verify that the prediction performance of the deep learning LSTM network is significantly better than that of the BP neural network by combining all coal freight data from the Shanghai Railway Bureau for the period 2015 to 2017 as a case study. The proposed method can predict the level of freight price risk more accurately in the next phase, based on the status of the factor indicators, and be used to assist railway departments in developing a more reasonable freight price adjustment strategy to scientifically mitigate freight price risk.

Ecological solutions for freight trains

The article presents the potential of freight railway transport in terms of saving of emissions and maximizing the efficiency of operation. Attention is paid to the transport of freight trains on electrified lines with higher gradients (up to 25‰), alternatively with the possibility of a trip to non-electrified lines and sidings, which generate significant extra costs in the operation of trains and are often a reason for the carrier to deploy a locomotive of independent traction (diesel-electric) even in electrified line sections, even at the cost of increased operating costs and increased emissions. There are many such lines and possibilities of use within the Central European region and the Czech Republic.In the first part of the article, the basic categorisation of dual-mode freight locomotives and their suitability for individual operating programmes is presented. This part also describes current situation on the rolling stock market in general and directions of development taken by various locomotive manufacturers.Case studies of deployment of real freight locomotives on chosen Czech railway lines are used in the next part to present the possibilities for energy and cost savings when replacing a vehicle with independent traction by an electric or dual-mode locomotive, at least in sections equipped with overhead catenary. The pros and cons of using several possible solutions are described in detail. Further considerable savings can be achieved by, among other things, using regenerative braking and returning the generated electricity back to the catenary system when driving downhill. In this respect, however, it is also necessary to cooperate with the infrastructure manager, who must also adapt the railway lines to the operation of these vehicles.Finally, general rules for operational deployments on which the replacement of a conventional vehicle by a dual or pure electric vehicle can be considered are formulated.

Research on design methodology for railway freight service combination plans to meet diverse demands

PurposeFacing the diverse needs of large-scale customers, based on available railway service resources and service capabilities, this paper aims to research the design method of railway freight service portfolio, select optimal service solutions and provide customers with comprehensive and customized freight services.Design/methodology/approachBased on the characteristics of railway freight services throughout the entire process, the service system is decomposed into independent units of service functions, and a railway freight service combination model is constructed with the goal of minimizing response time, service cost and service time. A model solving algorithm based on adaptive genetic algorithm is proposed.FindingsUsing the computational model, an empirical analysis was conducted on the entire process freight service plan for starch sold from Xi'an to Chengdu as an example. The results showed that the proposed optimization model and algorithm can effectively guide the design of freight plans and provide technical support for real-time response to customers' diversified entire process freight service needs.Originality/valueWith the continuous optimization and upgrading of railway freight source structure, customer demands are becoming increasingly diverse and personalized. Studying and designing a reasonable railway freight service plan throughout the entire process is of great significance for timely response to customer needs, improving service efficiency and reducing design costs.

Modeling and Energy-Optimal Control for Freight Trains based on Data-Driven Approaches

This paper focuses on the energy optimization problem of traction substations. The paper addresses the difficulty of considering time-varying parameters and environmental characteristics of freight train in mechanism modeling. In response, a new optimization method called “Modeling and Energy-Optimal Control for Freight Trains based on Data-Driven Approaches” is proposed. Firstly, a data-driven model considering the “Train-Track-Power grid” (TTP) is constructed based on recorded data. The energy optimization problem is regarded as a finite Markov decision process. A dynamic programming (DP) algorithm is utilized to optimize the train’s control force. The data-driven model is then used to solve for the train’s speed curve, traction substation power, and contact network voltage. Secondly, a multi-input multi-output self-organizing fuzzy neural network (MIMO-SOFNN) model is designed during the modeling process, and an Levenberg–Marquardt algorithm with self-adaption damping factor (SA-LM) is proposed for model parameter learning. Finally, experimental analysis is conducted to validate the high accuracy of the MIMO-SOFNN model compared to five other models. The effectiveness of the SA-LM algorithm is also verified through a comparison with five other algorithms. In the energy optimization experiments, when compared with the actual operation data of a freight railway company in China, the proposed energy optimization method in this paper reduces the energy consumption of the traction substation by 34.8%.

Low-temperature characteristics of rubbers and performance tests of type 120 emergency valve diaphragms

PurposeThe type 120 emergency valve is an essential braking component of railway freight trains, but corresponding diaphragms consisting of natural rubber (NR) and chloroprene rubber (CR) exhibit insufficient aging resistance and low-temperature resistance, respectively. In order to develop type 120 emergency valve rubber diaphragms with long-life and high-performance, low-temperatureresistant CR and NR were processed.Design/methodology/approachThe physical properties of the low-temperature-resistant CR and NR were tested by low-temperature stretching, dynamic mechanical analysis, differential scanning calorimetry and thermogravimetric analysis. Single-valve and single-vehicle tests of type 120 emergency valves were carried out for emergency diaphragms consisting of NR and CR.FindingsThe low-temperature-resistant CR and NR exhibited excellent physical properties. The elasticity and low-temperature resistance of NR were superior to those of CR, whereas the mechanical properties of the two rubbers were similar in the temperature range of 0 °C–150 °C. The NR and CR emergency diaphragms met the requirements of the single-valve test. In the low-temperature single-vehicle test, only the low-temperature sensitivity test of the NR emergency diaphragm met the requirements.Originality/valueThe innovation of this study is that it provides valuable data and experience for future development of type 120 valve rubber diaphragms.

Causative analysis of freight railway accident in specific scenes using a data-driven Bayesian network

As the freight railway system is a typical complex system, freight railway accidents have various and complex accident scenes. A Data-Driven Bayesian Network (DDBN) with random variables representing scene elements, accident causes, and accident consequences as nodes, and conditional dependencies between nodes as edges is proposed to identify the most significant accident causes in various and complex specific accident scenes. First, an unsupervised-supervised method is designed to define the states of nodes in the DDBN, considering the characteristics of the data involving both continuous and discrete states. Second, a greedy algorithm is proposed to mine the causal sequence between nodes, and the direction of edges in DDBN is established accordingly. Then, an NB-K2-MLE approach is proposed to generate the structure and parameters of the DDBN from data. Finally, a risk calculation function based on DDBN is proposed to calculate the risk of various accident causes in specific scenes. In empirical analysis based on real accident data, the inference accuracy of DDBN reached 87.92 %, with precision and recall exceeding 70 %. More importantly, the research results indicate that the distribution of accident causes will concentrate with the refinement and specific of the scene, and the main accident causes at the macro level cannot be fully applicable to accident prevention in specific scenes. The DDBN constructed in this study can provide data support for the determination of the significant accident causes and the development of targeted accident prevention strategies in various and complex specific scenes.

Energy harvesting potential assessment and systematic design for energy-regenerative shock absorbers on railway freight wagons

A large amount of vibration energy is dissipated in the secondary suspension systems of railway freight wagons, which can be harvested as renewable power supplies to serve more smart devices for onboard applications. This paper explores the vibration energy harvesting potential of freight wagons and deals with the systematic design issues of energy-regenerative shock absorbers (ERSAs). By considering the ERSA force interaction and realistic track irregularity, a vehicle-track coupled model is established to predict a more accurate vibration response. The parameter sensibility analysis reveals that the operation speed, vehicle load, and track irregularity are the most critical factors that can significantly affect the power generation performance. In addition, vibration energy harvesting potential assessment is conducted on American, German, and Chinese track spectrums and several field-measured freight lines, indicating an average power potential ranging from 33 to 960 W per absorber with a full-loaded freight wagon running at 90 km/h. Finally, a systematic design approach for ERSAs is proposed based on the prior feasibility assessment, a hybrid Grey Wolf Optimization and Particle Swarm Optimization (GWO-PSO) algorithm, and the vehicle-ERSA coupled model. The digital twin of an ERSA has been established and validated by a series of experimental tests. Taking the average power as the objective and setting the suspension vibration velocity, maximum generator rotation velocity, and maximum ERSA force as constraints, the optimized ERSA exhibits an output power of 63 W and 20.22% shock absorption on the secondary suspension. Meanwhile, the GWO-PSO has demonstrated an enhanced exploration ability than the conventional GWO in dealing with the constrained optimization problem of the ERSA design.

The optimal solution to the energy-efficient train control in a multi-trains system–Part 2: the optimality and the uniqueness

When a train travels in a multi-trains system, the power flow of other trains and the track grades make up the spatial–temporal area (STA) for the train. Finding the optimal solution for the energy-efficient train control (EETC) problem in STA can help reduce the net energy consumption. This paper studies the analytic method to obtain the optimal solution. In Part 1, the algorithm for the problem was designed. The underlying structure of the algorithm is the connection between three optimal states through optimal feasible strategy. In Part 2, the optimality of the optimal feasible strategy is verified through a generalised local energy functional, and its uniqueness is proved based on the variational method. Additionally, we discuss the influence of external power on the optimal solution of the classical EETC problem. Case studies using data for a real freight railway line are given to illustrate our results.

The optimal solution to the energy-efficient train control in a multi-trains system-part 1: the algorithm design

When a train travels in a multi-trains system, the power flow of other trains and the track grades make up the spatial–temporal area (STA) for the train. Finding the optimal solution for the energy-efficient train control problem in STA can help reduce the net energy consumption. This paper studies the analytic method to obtain the optimal solution. In Part 1, we propose an algorithm specifically designed for this problem. The underlying structure of the algorithm is the connection between three optimal states through the optimal feasible strategy. We propose an algebraic method to calculate the optimal feasible strategy and discuss how it intersects with the speed limit. In Part 2, we will discuss the optimality and uniqueness of the optimal feasible strategy. Case studies using data from a real freight railway line are given to demonstrate the effectiveness of the proposed algorithm.

An electromagnetic vibration energy harvesting system based on series coupling input mechanism for freight railroads

Vibration energy harvesting technology is characterized by wide distribution, is pollution-free and independent of weather and climate, and is suitable for powering low-power sensors to ensure efficient and safe operation in freight railroads. This paper proposed an electromagnetic vibration energy harvester based on a series coupling input mechanism for the self-powered sensors in freight railroads. The design utilizes only one rack for vibration energy input to minimize the moment acting on the vibration source during the working process. Two pinions meshed with the rack convert the up and down vibrations into a two-way rotation. The one-way bearings and another pair of gears convert the opposite rotations of two parallel shafts into one-way rotation of the generator shaft, generating electricity. Supercapacitors and rectifier voltage regulator modules are utilized to store electrical energy efficiently. A peak power of 10.219 W and maximum mechanical efficiency of 64.31% is obtained in the experiment equipped with a flywheel under the 8 mm-4 Hz sinusoidal vibration excitation. The experimental results showed that the flywheel can enable the proposed harvester to achieve better power generation performance when the amplitude and frequency are relatively high.

Forecasting stock closing prices with an application to airline company data

Forecasting stock market movements is a challenging task from the practitioners’ point of view. We explore how model selection via the least absolute shrinkage and selection operator (LASSO) approach can be better used to forecast stock closing prices using real-world datasets of daily stock closing prices of three major international airlines. Combining the LASSO method with multiple external data sources in our model leads to a robust and efficient method to predict stock behavior. We also compare our approach with ridge, tree, and support vector machine regressions, as well as neural network approaches to model the data. We include lags of each external variable and response variable in the model, resulting in a total of 870 predictor variables. The empirical results indicate that the LASSO-fitted model is the most effective when compared to other approaches we consider. The results show that the closing price of an airline stock is affected by its closing price for the previous days and those of other types of airlines and is significantly correlated with the Shanghai Composite Index for the previous day and 3 days prior. Other influencing factors include the positive impact of the Shanghai Composite Index daily share volume, the negative impact of loan interest rates, the amount of highway passenger and railway freight turnover, etc.

Discussion on Fatigue Reliability Evaluation Method of Welded Structure of Carbody Based on Digital Twin

Abstract Taking the key welds of the heavy-duty gondola carbody as the research object, first, the fatigue assessment of the carbody is carried out based on the quasi-static the main S–N curve method, and the quasi-static evaluation method is discussed; second, the typical welds are selected. Based on the full-size bench physical test and virtual test of railway freight carbody, the quasi-static method is discussed; finally, the line test is used to verify the results. The results show that: (1) among all the load conditions in the quasi-static analysis of railway freight cars, the damage of the core plate needs to be considered according to different parts of the carbody, and this condition is effective for the evaluation of the bearing area of the linear load transfer of the carbody for the welds with nonlinear load transfer such as floor, the stress response caused by core plate load has the problem of over-estimation of damage. (2) The lap weld of floor and beam is less damaged under heavy load, but the stress response is larger in empty state, which indicates that the vibration effect should be considered in the design. (3) The virtual test can make up for the problem of traditional quasi-static analysis. The combination of the two can better realize the fatigue reliability evaluation of carbody.

Prediction of railroad track geometry change using a hybrid CNN-LSTM spatial-temporal model

Track geometry is highly important for ensuring railroad safety. Predicting track geometry degradation can support preventive maintenance by identifying and prioritizing track segments that are more prone to potential track geometry defects. This paper develops a novel machine learning approach that can simultaneously geospatially align track geometry data from multiple inspections accounting for positional errors, and also predict foot-by-foot track geometry change over time. The proposed position correction method considers multiple geometry parameters to provide highly accurate positional information for geometry measurements, which is a prerequisite for foot-by-foot track geometry prediction. A hybrid CNN (convolutional neural network)-LSTM (long short-term memory neural network) machine learning model is developed to account for spatial–temporal dependence with respect to foot-by-foot track geometry change. Specifically, CNN is used to incorporate spatial dependence of track geometry on adjacent segments. LSTM is applied to learn dynamic changes in track geometry data over time to consider the temporal dependence. The hybrid CNN-LSTM model is validated using track inspection data provided by one major freight railroad in the United States. The results show that our positional error correction method can reduce the relative positional error to less than 1 foot with a 99% confidence interval. Our proposed CNN-LSTM model outperforms four other models, including a naïve model (using the last observation), multi-layer perceptron (MLP), plain CNN, and plain LSTM, for both short-term and long-term prediction periods. The proposed hybrid machine learning methodology can be adapted to various other freight and passenger rail lines. The predictive track geometry change information can be used by the industry to plan and prioritize resources for preventative maintenance, yielding benefits in safety and operational efficiency.

Experimental Evaluation of Monocular Visual–Inertial SLAM Methods for Freight Railways

Experimental Evaluation of Monocular Visual–Inertial SLAM Methods for Freight Railways

ESTIMATING THE DEMAND FOR RAILWAY FREIGHT TRANSPORTATION: A CASE STUDY IN KAZAKHSTAN

This article focuses on the critical importance of demand estimates for effective planning and decision-making in the railway freight transportation industry. Various departments within transportation companies, including marketing, production, distribution, and finance departments, heavily rely on accurate demand forecasts to make informed decisions. Forecasting demand is a crucial aspect of managing business processes, and the methods for doing this can vary across different industries. The ultimate goal remains consistent—to obtain precise predictions of future demand by analyzing historical data and current environmental factors. In the context of transportation services, accurate demand forecasting is essential for successful operational planning and management of functional areas such as transportation operations, marketing, and finance. The current case study specifically examines the National Company Kazakhstan Temir Zholy (KTZ), a transport and logistics holding engaged in rail transportation in Kazakhstan. KTZ’s main sources of income are related to freight transportation. The volume of cargo transportation (in tons) and the freight turnover play a significant role in assessing demand and forecasting future revenues from freight traffic. Different techniques for demand forecasting are explored, including qualitative and quantitative methods. Qualitative methods rely on judgments and opinions, while quantitative methods utilize historical data or identify causal relationships between variables. Overall, the present study highlights the critical role of demand forecasting in the railway freight transportation industry and its impact on efficient planning and decision-making processes.

Использование коэффициентов эластичности спроса по цене для оценки платежеспособности грузов на железнодорожном транспорте с тарифными целями (на примере РФ в 2006–2023 гг.)

Purpose: To study the possibility of using price elasticity coefficients of demand to assess the solvency of freight in railway transport for tariff purposes (on the example of the Russian Federation in 2006-2023). Methods: Statistical method, econometric modeling, analysis, synthesis, systematic approach, comparative method have been used. Results: The coefficients of price elasticity of demand have been estimated for freight transported through the infrastructure of Russian Railways, separately for the infrastructure-locomotive and wagon components of the railway tariff. Based on the analysis of the results of this assessment, the reasons for deviations from the research hypothesis have been identified and the prospects and conditions for the use of price elasticity coefficients for assessing the solvency of freight in railway transport with tariff goals in the Russian Federation have been formulated. Practical significance: The results obtained can be used in the development of tariff policy measures for the freight railway transport of the Russian Federation, as well as for further developments in the field of methodology for constructing freight railway tariffs.

Analysis and Forecast of Railway Freight Volume based on Prophet-Deep AR Model

Analysis and Forecast of Railway Freight Volume based on Prophet-Deep AR Model