Journal of Rail Transport Planning & Management最新文献

A foreseeable challenge with a substantial increase in railway mode share will be how to uphold punctuality. Higher volumes of train traffic will result in timetables that are more sensitive to disruptions; whose severity and frequency is also expected to increase in light of greater asset utilization and climate change. This calls for a definitive understanding of the relationship between incidents and train delays as a prerequisite to developing robust timetables and disruption management strategies. In this paper we propose a novel framework for quantifying the impact of railway incidents on train delays. Using a case of the Swedish Railway Network, we compare the impact of different incidents on train delays. The impact of delay is defined as a factor of the incident rate, exposure rate, delay rate and historical average delay minutes per incident. A logistic model that estimates the probability of delay for any train, in the event of a failure, is also developed. Snow on track was established as most critical, resulting in the highest normalized delay minutes per train and the largest increase in the odds of delay for individual trains. The proposed framework & approach can be applied to other networks.

Energy and environmental sustainability in transportation are becoming ever more important. In Brazil, the system electric traction represents the largest consumption of electric energy in the subway system. Electrified railway systems play an important role in contributing to the reduction of energy usage and CO2 emissions compared with other transport modes. For subway transit systems with frequently cycles of departures and braking of trains, the effective use of regenerative braking energy is a significant way to reduce the net energy consumption. The recovery of regenerative energy produced by braking trains of a subway system is essential to increase its energy efficiency, however difficult to apply in the São Paulo subway due to the short headway between train. This paper proposes evaluate through traction power network modeling and the computer simulation the application and feasibility of using inverter substation (ISS) to maximize the recovery of regenerative braking energy. The results of the study of Line 1 - Blue of the São Paulo’s metro city indicate that using inverter substation could reduce the energy consumption at the rectifier substation (RSS) by nearly 12,94% compared to the system without the inverter substation.

We study the solution of the rolling stock rotation problem with predictive maintenance (RSRP-PdM) by an iterative refinement approach that is based on a state-expanded event-graph. In this graph, the states are parameters of a failure distribution, and paths correspond to vehicle rotations with associated health state approximations. An optimal set of paths including maintenance can be computed by solving an integer linear program. Afterwards, the graph is refined and the procedure repeated. An associated linear program gives rise to a lower bound that can be used to determine the solution quality. Computational results for six instances derived from real-world timetables of a German railway company are presented. The results show the effectiveness of the approach and the quality of the solutions.

International rail transport plays an essential role in the supply chain. Eurasian rail transport has been a successful example since its launch in 2011 and has increased over the last decade. However, a crucial question arises: Does this intercontinental rail service fulfill the transport requirements of the supply chain, and can it be quantified? Cost, reliability, transport time, and flexibility are essential factors in selecting transport modes in the supply chain. Current performance parameters mainly focus on efficiency, which may conflict with supply chain performance requirements. Before this research, it was difficult to quantify the international rail services; the frequently used tonne-based, volume-based (TEU), number of block trains, loaded, or empty container measurement forms cannot adequately capture the requirements of the global supply chain. Our literature review highlighted the need for standardized service attributes in international rail transport. Additionally, we identified a misalignment between rail performance and the demands of the global supply chain. This misalignment could impede the integration of international rail transport into the global supply chain. This paper contributes by developing universally applicable SCOR-Rail Key Performance Indicators (KPIs) for international rail transport based on the Supply-chain Operations Reference (SCOR) model. It bridges the measurement gap between the supply chain and international rail transport, giving railway companies a tool to design and monitor their services. They could also be easily integrated into rail planning for future research. Our initial assessment based on the proposed measurement framework revealed that Eurasian rail transport has the potential for improvement.

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.

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.