Journal of Rail Transport Planning & Management最新文献

Accurately scheduling dwell times is vital to ensure punctual and reliable railway services, but the stochastic nature of dwell times makes this a non-trivial task. An important step towards scheduling accurate dwell times is to gain an in-depth understanding of the mechanics that influence dwell times, which is commonly done by modelling the mean dwell time. It is, however, of more interest to understand the conditional distribution of dwell times. The study presented here proposes the use of quantile regression to study the conditional distribution of dwell times at different percentile. To do so, a year's worth of highly detailed train operation and passenger count data is used. The results indicate that the use of quantile regression over ordinary least squares regression is justifiable and beneficial. Numerical examples show the importance of arrival punctuality on dwell times, whereas the effect of the volume of boarding passengers at the critical door is limited. The results of the model presented here can help steer the discourse towards scheduling dwell times that more accurately reflect the actual situation by taking station-specific parameters into account. Doing so will help to increase the punctuality of railways and with it the attractiveness and effectiveness of railways.

Railway traffic management requires a timely and accurate redefinition of routes and schedules in response to detected perturbations of the original timetable. To date, most of the (automated) solutions to this problem require a central authority to make decisions for all the trains in a given control area. An appealing alternative is to consider trains as intelligent agents able to self-organize and determine the best traffic management strategy. This could lead to more scalable and resilient approaches, that can also take into account the real-time mobility demand. In this paper, we formalize the concept of railway traffic self-organization and we present an original design that enables its real-world deployment. We detail the principles at the basis of the sub-processes brought forth by the trains in a decentralized way, explaining their sequence and interaction. Moreover, we propose a preliminary proof of concept based on a realistic setting representing traffic in a French control area. The results allow conjecturing that self-organizing railway traffic management may be a viable option, and foster further research in this direction.

The optimization of shunting operations has been recognized as a cost-effective measure for rail companies, but its impact on rolling stock management has not been fully explored. Previous research on shunting operations has often focused on operational decisions, neglecting the effect of maintenance scheduling activities in an integrated approach. Our study aims to examine the long-term effects of mileage-based maintenance on freight train management and its impact on shunting operations and fleet management. We propose 4 Shunt-In policies based on practitioners' criteria that describe the wagon allocation to outbound trains based on both mileage and trajectories-based shunting criteria. To evaluate the methodology, we conduct simulations using a projected timetable from 2020 to 2040 of the freight services at the Bettembourg Multimodal Terminal in Luxembourg. The results are compared against a theoretical No-Maintenance scenario to assess the underestimation's magnitude. Our findings show that mileage-based maintenance has a significant impact on various aspects of freight management and that the choice of wagons in an outbound train can result in up to 11% increase in the number of shunting operations, as well as high underestimation in terms of expected fleet size and average mileage to be performed by the rolling stocks.

Regulation, governance and organisational issues are sensitive aspects in the market of public utilities especially when these markets require and aim at initiating homogenisation processes.

The paper aims at understanding how European countries responded to supranational legislation, in terms of governance, management and organisational aspects, on the establishment of Railway Regulatory Bodies and if the steps taken by the European Union were sufficient to fulfil the aim of creating an integrated railway area.

We use a neo-institutional lens to interpret documentary sources, such as regulatory sources (Regulations, circulars and directives of the EU) and the responses given to a semi-structured questionnaire submitted to the 28 European Railway Regulatory Authorities.

The paper shows that this regulatory field faced differentiated isomorphism and that there is a growing trend towards an organisational model we characterised as ‘Generalist Agency’.

We contribute to the literature on the regulation of public utilities, showing that differentiated isomorphism may occur when coercive isomorphism by supranational legislation is mitigated by national differences, and provide policy implications on governance and regulation of the transport industry.

The evolving complexities of railroad systems also increase their vulnerability to failure from human error. This study compared the outcomes of two workflows that incorporated 11 different machine learning techniques to identify characteristics of railroad operations that are generally associated with human-caused accidents. The first workflow engineered features from the fixed attribute fields of a large railroad accident database and the second applied natural language processing to extract features from the unstructured accident narratives. Both workflows applied a Shapely game-theoretic model to rank the importance of features based on their marginal contribution towards predicting accident cause. Among several interesting findings, some of the most unexpected were that human-caused accidents are generally not associated with high train speeds nor derailment type accidents, and that shoving cars is riskier than pulling cars. Those, and other findings, from this study can inform management decisions, planning, and policies to minimize the risk of human-caused accidents.

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.