Journal of Rail Transport Planning & Management最新文献

No country uses optimisation methods to establish a value maximising allocation of operators’ demand for scarce railway infrastructure. Using the same input as was used in the year 2020 (T20), when the official timetable for one specific line in Sweden was developed by the infrastructure manager, this paper presents the outcome of the combined use of dual optimisation and rapid branching to produce a solution to an actual optimisation problem. These methods generate a timetable with the same number of trains as in T20 and some qualities that are better than those of the official timetable. The pilot study therefore confirms that it is worthwhile to further develop formal optimisation mechanisms towards larger-scale applications. Being able to handle conflicts-of-interest on a single-track line means that the problem on a double track line has fewer restrictions.

The paper is devoted to the study and long-term forecasting of railway network sections operating. We call a railway network section a set of several railway stations interacting with each other and lines between them. We propose a methodology for mathematical modeling of train traffic on the railway network based on the queuing theory. The resulting models are a set of mathematical descriptions of the incoming train traffic and the train running within the system. Several Markovian Arrival Process are used to describe train arrivals from different directions. Such a description makes it possible to take into account the parameters of separate train flows, which depend on the category of trains and their directions. A queuing network in which all nodes have a finite capacity simulates the movement of trains through the system. This mathematical apparatus allows us to consider the nonlinear structure of the railway network, the operation features of stations, the capacity of railway lines, and the influence of random factors. To apply the methodology, we have chosen two objects that differ in infrastructure and properties of train flows. The first is located in the east of Russia and focused on servicing freight trains. The second is located in Germany, close to Belgium and The Netherlands borders. It is characterized by the predominance of passenger traffic. We construct mathematical models and perform numerical simulations. Based on the results obtained, the maximum allowable load is estimated, and bottlenecks in the structure of transport systems are found. Besides, we give some recommendations on how to increase capacity in the long term.

In order to mitigate consumption of electrical energy during periods of peak demand it is common for electricity system operators to offer financial incentives that encourage large corporate consumers to reduce power usage at designated critical times. For train operators on large rail networks it may be more profitable to limit energy consumption during these times, for selected individual train journeys, rather than always implementing a true optimal strategy that minimizes overall energy consumption. In this paper we use classical methods of constrained optimization to find driving strategies for a single train that minimize energy consumption subject to constraints on energy usage within designated high-demand time intervals during the journey. We illustrate our results by considering a journey with realistic parameters and two different nominal sets of energy consumption constraints.

In the signal planning process, the ERTMS speed profiles based on track nature needs to be complemented with additional constraints. The base profile resolving the allowed track speed, often includes several speed changes which can be difficult for the train driver to follow. One approach to deal with this problem is to filter the base profile, reducing the number of changes and giving the driver more time for attention.

This paper presents the effects of a speed profile filtering principle, based on possible time usage during a speed increase, on train energy consumption, train driver braking behavior, running time, and driver workload. In an Electrical Multiple Unit train driver simulator, 40 drivers tested three different speed profiles of a 19 km railway line. It can be concluded that differences in running time are small, that these small time-gains implies a high energy consumption cost, and that drivers tend to drive close to the indication braking curve in the beginning of the braking phase. Further, the drivers rated the driver task workload low for all filter conditions. Accordingly, a certain filter level is required to get capacity and energy effects.

Rail safety is a universal goal that every railroad system pursues. Comparing rail safety performance allows the identification of relative system strengths and weaknesses and potential adaptation of risk mitigation strategies from one railroad system to another. Achieving this requires comparable data from the railroad systems to be analyzed that are available and with high resolution so that fair comparisons can be established. This paper presented an international benchmarking framework for railroad safety-related data system and safety performance. A novel and standardized methodology was developed to collect railroad safety-related data sources among different countries and compare their data completeness and resolution. Six countries with high data availability and transparency were selected to demonstrate the benchmarking framework. High-level rail safety performance measures were derived and compared among these countries. The results showed that there are inconsistencies in the resolution of different types of rail safety data among the six countries. The countries that had the lowest and highest overall accident rate, grade crossing incident rate, and other safety performance metrices were identified. This research provided valuable insights into how railroad operators can improve their railroad safety-related data system and safety performance by benchmarking with other railroad operators to make the most efficient use of risk mitigation resources. The study also highlighted the importance of providing publicly available railroad safety-related data for the mutual benefits of overall safety of railroad systems around the world.

Freight train is selected to pick up and hang refrigerated containers according to railway freight train diagram in China. A multiobjective optimization model of railway cold-chain transportation route is established with total, time, and reliability costs as objective functions on the basis of dynamic train information. Different weights of three objective functions are obtained and weighted sum method is used to transform the multiobjective problem into a single-objective problem according to different transport demands of railway cold-chain transportation participants. An example of k short-circuit optimization algorithm based on genetic algorithm (GA) is designed to prove the feasibility and effectiveness of the proposed model. The empirical analysis showed that different transport times can be obtained by adjusting weights of various optimization objectives in the model to meet diverse needs of railway cold-chain transport participants and selecting differentiated shifts and transfer stations on the same route to provide a variety of transportation time limit options. Results of this study can provide guidance to decision makers in choosing railway transportation schemes.

Crowd at metro stations is usually a mixture of individuals and small groups of families or friends. However, limited research has focused on small group behaviours for metro safe evacuation evaluation and planning. In this study, a field observation at metro stations and a questionnaire survey were conducted to reveal the small group behaviour characteristics with different decision patterns and compactness. A cellular automaton (CA) based simulation model was proposed to reproduce small group behaviours of independent or joint decision pattern, with loose or close contact, reflecting the real-time trade-off between individual efficiency and group coherence. Impacts of small group behaviours on crowd dynamics were investigated by simulation experiments under diverse scenarios. Simulation experiments revealed that joint decision pattern and close contact of small groups were more likely to lead to longer evacuation time, lower average speed and stronger interference on the individuals. Deviations of estimated evacuation time due to small group behaviours were investigated and found to be common and widespread with different group decision pattern and compactness, congestion levels, proportions of groups in the crowd and exit layouts.

Fault text classification is a prerequisite task for railway engineers based historical train operation data to diagnose vehicle on-board equipment (VOBE) faults and formulate maintenance strategies. Aiming at the low efficiency and accuracy of manual fault text classification, based on Bidirectional Gated Recurrent Unit (BiGRU) and improved attention mechanism (IAtt), an intelligent VOBE fault text classification method is proposed in this paper. Combining the characteristics of the VOBE faults text, also called application event log (AElog) files, the Labeled-Doc2vec is used to generate sentence embedding to realize the vectorized representation of the fault texts, then input sentence embedding into BiGRU to extract the fault text features as the improved attention mechanism layer. Finally, the high-dimensional fault text features outputted by hidden are input into Softmax to complete the fault text classification. The experimental results show that the proposed method can analyze the semantics of fault text according to the train running state before and after the fault time, that is, it can realize text classification by combining context. Compared with other methods, the method in this paper obtains the optimal accuracy, precision, recall and F1-score, which shows that the proposed method can be applied to fault text classification of VOBE, effectively reduces the labor cost of fault text classification in practice, and improves the efficiency of fault text classification of VOBE.

The classic optimal train control problem is to drive a train on a track with known gradient over a fixed distance and within a specified time in such a way as to minimize tractive energy consumption. On level track the optimal strategies take two basic forms—a truncated strategy of optimal type with phases of maximum acceleration, coast and maximum brake which is typical of shorter metropolitan journeys, and an extended strategy of optimal type with phases of maximum acceleration, speedhold at the optimal driving speed, coast to the optimal braking speed, and maximum brake which is typical of longer journeys by freight trains and intercity passenger trains. The cost of these optimal strategies is uniquely determined by the journey distance and journey time. In this paper we extend a previously known formula for the partial rate of change of cost with respect to journey time to a formula for the full rate of change of cost that also incorporates the partial rate of change of cost with respect to journey distance.

Every day, railways experience disturbances and disruptions, both on the network and the fleet side, that affect the stability of rail traffic. Induced delays propagate through the network, which leads to a mismatch in demand and offer for goods and passengers, and, in turn, to a loss in service quality. In these cases, it is the duty of human traffic controllers, the so-called dispatchers, to do their best to minimize the impact on traffic. However, dispatchers inevitably have a limited depth of perception of the knock-on effect of their decisions, particularly how they affect areas of the network that are outside their direct control. In recent years, much work in Decision Science has been devoted to developing methods to solve the problem automatically and support the dispatchers in this challenging task. This paper investigates Machine Learning-based methods for tackling this problem, proposing two different Deep Q-Learning methods(Decentralized and Centralized). Numerical results show the superiority of these techniques respect to the classical linear Q-Learning based on matrices. Moreover the Centralized approach is compared with a MILP formulation showing interesting results. The experiments are inspired on data provided by a U.S. class 1 railroad.