Journal of Rail Transport Planning & Management最新文献

Hydrogen fuel cell multiple unit vehicles are acquiring a central role in the transition process towards carbon neutral trains operation in non-electrified regional railway networks. In addition to their primary role as a transport mean, these vehicles offer significant potential for applications in innovative concepts such as smart grids. Compared to the pure electric propulsion systems, fuel cell technology allows for cogeneration processes by recovering generated heat in addition to the provision of the electrical power. This paper presents the analysis of fuel cell hybrid-electric multiple unit vehicle employed in regional railway transport during regular service, and in vehicle-to-grid application during the off-service hours, providing the electrical and thermal energy for stationary consumers in terminal stations. The system dynamics are modelled using a backward-looking quasi-static simulation approach, with implemented real-time optimization-based control strategy for managing the power flows between different components. In a case study of selected vehicle and railway services in the Netherlands, the fuel cell system showed average hydrogen consumption of 0.4 kg/km, with the overall electrical efficiency of 38.89%. In vehicle-to-grid scenario, the system satisfied complete stationary power demand, and provided about 327 kWh of thermal energy during 2-h operation, reaching the overall cogeneration efficiency of 66.81%.

Blocking plan and train design (BP&TD) is critical in freight rail planning, which integrates blocking and train routing to determine which pairs of yards are to provide blocks and train services, and the sequence of blocks and train services for each shipment. In this paper, a system-level and restricted BP&TD problem is addressed in a two-stage method. In the first stage, a system-level BP design problem that takes line capacity into account is investigated. A candidate block set-oriented approach is proposed to reduce the solution space of the problem by generating a set of promising candidate blocks based on the K-shortest paths algorithm. Two integer linear programming (ILP) models are developed to address this problem in a sequential way and an integrated way, respectively. Based on the optimized blocks and a systematic analysis of the cost of double-block trains, a post-optimization method is proposed in the second stage to solve a system-level TD problem by consolidating some single-block trains into double-block trains. Numerical experiments conducted on two benchmark networks indicate that our approach is capable of obtaining high quality solutions for the system-level BP&TD problem efficiently, and the application of double-block trains helps to reduce the total car-hour cost.

Supervision, data analysis and communication algorithms monitor trains, exploiting most of their available computational power. On-board eco-driving algorithms such as Driver Advisory Systems (DAS) are no exception, as the computational power available limits their complexity and features. This was the case of Roltijd, the in-house developed DAS based on coasting advice of NS, the main Dutch passenger railway undertaking. This platform calculated the coasting curves at every second by integrating the equations of motion numerically, assuming that the track is flat. However, generating more complex driving advice required replacing this coasting curve calculation by a more computationally-efficient algorithm. In this article we propose a new coasting advice algorithm based on the analytical solutions of the train motion model, assuming that gradients and speed limits are piecewise constant functions of the train location. We analyse the qualitative properties of these solutions using bifurcation theory, showing that bifurcations arise depending on the value of the gradient and the applied tractive effort. We validate the proposed algorithm, finding that our algorithm is accurate and can be 15 times faster than the previous method. This allowed NS to implement our algorithm on their trains, contributing daily to the sustainable mobility of 1.3 million passengers.

The COVID-19 pandemic has imposed a dramatic effect on the mobility habits of both passengers and freight in the rail sector. Since the relaxation of COVID-19 restrictions worldwide, rail transport has been revitalised gradually. However, the new normal emerges with unprecedented issues, such as changed travel behaviour, lost profits, and a lack of personnel. In this paper, we determine the arising challenges due to COVID-19 and pandemics in general and subsequently propose several solutions to tackle these challenges in rail transport. These solutions cover multidisciplinary aspects such as passenger demand management, freight demand management, service design, automation, decentralisation and advanced railway technologies. By reviewing the relevant literature on COVID-19, public transport and particularly rail transport, we synthesise and identify promising lines of research that should devote more attention to a more efficient, effective and sustainable rail transport service. This paper provides policymakers, researchers, railway infrastructure managers and undertakings with an overview and an outlook for the impacts of the pandemic crisis and similar situations. It supports decision-making with more evidence and facilitates rail transport to restore its performance and reach its societal goal.

The human losses caused by the COVID-19 pandemic are an overriding concern, nevertheless it is important to discuss other effects that will come in the wake of the pandemic. This article evaluates the pandemic impacts on the rail freight production in Brazil, as railways play a key role on the logistics chain of Brazilian commodities. To achieve this objective, rail production data for the period between January 2006 and December 2021 was analysed using intervention and prediction methods based on Box-Jenkins ARIMA models, assisted by Monte Carlo simulations. The study reveals that, although the 2008 economic crisis caused an inflection in the time series of the analysed macroeconomic variables, it had no statistically significant impact on rail freight production, considering the time series as a whole. The results also confirm that the historical trend of growth in production has been maintained until the moment with no apparent impact from the COVID-19 pandemic. Given the prevailing uncertainty, the study examines other scenarios, projecting different degrees of expected production instability with a prediction horizon of 2025. These scenarios could be an important aid to plan short- and medium-term actions for this transport mode that is crucial to Brazilian economy and exports.

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.