Journal of Rail Transport Planning & Management最新文献

Robust travel time predictions are of prime importance in managing any transportation infrastructure, and particularly in rail networks where they have major impacts both on traffic regulation and passenger satisfaction. We aim at predicting the travel time of trains on rail sections at the scale of an entire rail network in real-time, by estimating trains’ delays relative to a theoretical circulation plan.

Predicting the evolution of a given train’s delay is a uniquely hard problem, distinct from mainstream road traffic forecasting problems, since it involves several hard-to-model phenomena: train spacing, station congestion and heterogeneous rolling stock among others. We first offer empirical evidence of the previously unexplored phenomenon of delay propagation at the scale of a railway network, leading to delays being amplified by interactions between trains and the network’s physical limitations.

We then contribute a novel technique using the transformer architecture and pre-trained embeddings to make real-time massively parallel predictions for train delays at the scale of the whole rail network (over 3000 trains at peak hours, making predictions at an average horizon of 70 min). Our approach yields very positive results on real-world data when compared to currently-used and experimental prediction techniques.

Increasing demand on heavily-used rail corridors in line with the modernization of the signaling architecture are key drivers for migrating to modern, moving-block based train control in the European railway network. In order to maximally profit from the increase of reliability and reduction of costs associated with shifting towards full ETCS Level 3 from a network management perspective, additional requirements on the fleet management level arise. Amongst other things, if track vacancy detection equipment is to be eliminated, all trains operating on these lines need to be equipped with on-board train integrity (OTI) monitoring solutions. In order to facilitate the planning of the OTI network migration processes, a MINLP-model is proposed which allows economic optimization of OTI migration in view of fleet allocation and the removal of trackside equipment for train integrity verification within the network. The model is tested in a case-study based on a generic network abstracted from the Austrian mainline network and found to significantly enhance planning compared to heuristic migration strategies.

We propose a mixed-integer linear programming model to generate and optimize periodic timetables with integrated track choice in the context of railway construction sites. When a section of a railway network becomes unavailable, the nearby areas are typically operated close to their capacity limits, and hence carefully modeling headways and allowing flexible routings becomes vital. We therefore discuss first how to integrate headway constraints into the Periodic Event Scheduling Problem (PESP) that do not only prevent overtaking, but also guarantee conflict-free timetables in general and particularly inside stations. Secondly, we introduce a turn-sensitive event-activity network, which is able to integrate routing alternatives for turnarounds at stations, e.g., turning at a platform vs. at a pocket track for metro-like systems. We propose several model formulations to include track choice, and finally evaluate them on six real construction site scenarios on the S-Bahn Berlin network.

Rail wagon planning is an important type of planning in the rail transport network since its costs are high. Therefore, enhancing the accuracy of fleet planning could result in cost efficiency. In this research, we aim to model and optimize the fleet size and allocation problem in a rail transportation network. We proposed a novel two-step algorithm composed of two meta-heuristic steps to optimize the allocation of the fleet between origins and destinations and to determine the optimal number of wagons. Besides, we provided a case study of Iran’s minerals rail transport network. The results demonstrate that 4925 wagons are required to transport approximately 17.7 million tons of minerals per year, roughly 10% less than the current proportion. The validity of the results was also analyzed by using a comparison between our algorithm and CEPLEX for a test dataset. It is shown that our approach outperforms CEPLEX in terms of speed while retaining the same performance.

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.