Journal of Rail Transport Planning & Management最新文献

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.

Planned maintenance and unplanned incidents cause service disruptions in freight railway classification yards, creating congestion, delaying railcars, and even impacting mainline operations. Understanding the recovery time and lingering performance impacts of yard disruptions is vital for the industry to plan disruption responses, promote efficient resource utilization, and improve resiliency. This paper compares two major types of yard disruptions (temporary closures of hump process and pulldown process) and quantifies the recovery pattern, measured by multiple performance metrics. The authors propose an analytical approach for estimating classification yard recovery time as a function of disruption duration and baseline capacity utilization. To validate the hypothetical approach, a series of experiments are conducted across a wide range of disruption durations and throughput volumes in a representative hump classification yard simulation model constructed using AnyLogic. The results indicate that recovery time is proportional to shutdown duration with a near constant recovery rate, and recovery rate increases approximately exponentially with throughput volume. These results are consistent with the hypothesized analytical relationships, suggesting that yard capacity may be estimated from disruption recovery rate. The methodology developed also enables future studies on interactions between yards and mainlines and developing planning-level parametric models of classification yard capacity and performance.

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.

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.

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.

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%.

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.

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.