The increasing train traffic over railway networks stretches the demand for capacity of railway yards and rolling stock maintenance locations, which increasingly limits performance and further growth. Therefore, the scheduling of rolling stock maintenance and the choice regarding optimal locations to perform maintenance is increasingly complicated. This research introduces a Maintenance Scheduling and Location Choice Problem (MSLCP). It simultaneously determines maintenance locations and maintenance schedules of rolling stock, while considering the available capacity of maintenance locations. Solving the MSLCP using one large Mixed Integer Programming appears not to perform well enough. Therefore, to solve the MSLCP, an optimization framework based on Logic-Based Benders’ Decomposition (LBBD) is proposed by combining two models, the Maintenance Location Choice Problem (MLCP) and the Activity Planning Problem (APP), to assess the capacity of an MLCP solution. Within the LBBD, four variants of cut generation procedures are introduced to improve the computational performance: a naive procedure, two heuristic procedures and the so-called min-cut procedure that aims to exploit the specific characteristics of the problem at hand. The framework is demonstrated on realistic scenarios from the Dutch railways. It is shown that the best choice for the cut generation procedure depends on the objective: when aiming to find a good but not necessarily optimal solution, the min-cut procedure performs best, whereas when aiming for the optimal solution, one of the heuristic procedures is the preferred option. The techniques used in the current research are new to the current field and offer interesting next research opportunities.

This paper provides an overview of the main results achieved within the Horizon 2020 Shift2Rail project named RAILS (Roadmaps for Artificial Intelligence Integration in the Rail Sector). The RAILS roadmapping process provided state-of-the-art, taxonomy, future research directions, and recommendations in three macro areas: Railway Safety and Automation, Predictive Maintenance and Defect Detection, and Traffic Planning and Management. RAILS findings shed light on the potential of intelligent technologies and provided essential guidelines for integrating machine learning into next-generation smart railways.

Disruptions occur frequently in railway networks, requiring timetable adjustments, while causing serious delays and cancellations. However, little is known about the performance dynamics during disruptions nor the extent to which the resilience curve applies in practice. This paper presents a data-driven quantification approach for an ex-post assessment of the resilience of railway networks. Using historical traffic realization data in the Netherlands, resilience curves are reconstructed using a new composite indicator, and quantified for a large set of single disruptions. The values of the resilience metrics are compared across disruptions of different causes using Welch's ANOVA and the Games-Howell test. Additionally, representative resilience curves for each disruption cause are determined. Results show a significant heterogeneity in the shape of the resilience curves, even within disruptions of the same cause. The proposed approach represents a useful decision support tool for practitioners to assess disruptions dynamics and propose best measures to improve resilience.

The dynamics of a moving train are usually described by means of a motion model based on Newton's second law. This model uses as input track geometry data and train characteristics like mass, the parameters that model the running resistance, the maximum tractive effort and power, and the brake rates to be applied. It can reproduce and predict train dynamics accurately if the mentioned train characteristics are carefully calibrated. The model constitutes the core element of a broad variety of railway applications, from timetabling tools to Driver Advisory Systems and Automatic Train Operation. Among the existing train motion model calibration techniques, those that use operational data are of particular interest, as they benefit from on- board recorded data, capturing the train dynamics during operation. In this literature review article we provide an overview of the train motion model calibration techniques that have been published in the scientific literature between January 2000 and December 2021 and either use operational data or can be minimally adapted to use it. To this end, we present a critical overview of the existing train motion model calibration approaches, distinguishing online calibration that analyzes data on- the-go and offline calibration that analyzes historical data batchwise. We propose a research agenda and highlight some potential goals to be tackled in the near future: from devising accurate online calibrators for eco-driving applications to quantitizing the physical sources of parameter variation. Last, we discuss practical recommendations for practitioners and scholars inferred from the current state of the art.

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.

Demand for railway transportation keeps on growing. Therefore, a thorough understanding of the capacity of railway networks is crucial. In this paper, the well-known compression method based on max-plus algebra is extended. A number of challenges are addressed to apply this compression method to large and complex networks, such as the one considered in this paper. Some trains have to be split artificially, while keeping the parts together during the compression. The trains should also be ordered explicitly, since there is no part of the infrastructure used by all trains. The results in this paper indicate that it is possible to thoroughly analyse the capacity by the adjusted compression method for large and complex networks, but the results should be interpreted with care. The results show, for instance, that the capacity occupation heavily depends on the size of the network that is considered and that it is not easy to give a clear, practical interpretation of the capacity occupation. Nevertheless, the method allowed to determine a number of critical paths and, even more importantly, a number of critical resources in the zones considered.

Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges.

During railways operations, unplanned events might occur which can result in rail traffic being heavily impacted. The paper proposes a passenger-centred resilience assessment for disruption scenarios which consist of multiple simultaneous disruptions. It combines train traffic operations, passenger flows and network restoration. To evaluate resilience, an optimization-based approach has been developed for solving the new infrastructure restoration and transport management (IRTM) problem. Additionally, this approach develops mitigation plans for the best infrastructure restoration and traffic recovery and it captures the time-dependent transport network performance during disruptions. The approach is general with respect to types of disruptions, and can be applied for evaluation against short disruptions (1–2 h) as well as more substantial ones (multiple days or weeks). The performance of the proposed approach has been demonstrated on a Dutch railway network. Furthermore, the resilience of the system is assessed against the critical infrastructure disruption scenarios in the network. This optimization-based approach shall enable decision makers to quantify accurately impacts of multiple disruptions by considering the created inconveniences to passengers in the railway operation due to these disruptions.

Employing regenerative braking in trains contributes to reducing the amount of energy used, especially when applied to commuter trains and to those used on very dense suburban networks. This paper presents a method to fine-tune the periodic timetable to improve the utilization of regenerative energy and to shave power peaks while maintaining the structure and robustness of the original timetable. First, a mixed-integer linear programming model based on the periodic event scheduling framework is proposed. A set of feasible timetables is determined and optimized with the aim of increasing synchronized acceleration and braking events at the same station, and maintaining the timetable robustness at the specified level. Next, a local search algorithm is developed to optimize the timetable such that the power peak value is minimized. The max-plus system model is adopted to estimate the delay propagation. Monte Carlo simulation is used to evaluate the utilization of regenerative energy and power peaks in random delayed circumstances. The proposed method was adopted to fine-tune the 2019 timetable for a sub-network of the Dutch railway. In the case of on- time scenarios, the optimized timetable increases the regenerative energy usage by almost 290% and decreases the 15-minute power peaks by 8.5%. In the case of delay scenarios, the optimized timetable outperforms the original timetable in terms of using regenerative energy and shaving power peaks.

Train movement dynamics are usually modelled by means of Newton's second law. The resulting dynamic equation can be very precise if the parameters that it depends on are determined accurately. However, these parameters may vary in time and show wide variations, making the calibration task nontrivial and jeopardizing the performance of a broad variety of applications in the railway industry: from timetable planning and railway traffic simulation to Driver Advisory Systems and Automatic Train Operation. In this article, the online train motion model calibration problem is addressed with a special focus on energy-efficient on-board applications. To this end, location and speed measurements are assumed to be available for a train running under normal operation conditions. A well-known real-time parameter estimation algorithm, the Unscented Kalman Filter, is combined with a driving regime calculator and a post-processing module in order to obtain bounds and statistics of parameters such as the maximum applied tractive effort and power, the applied brake rates, the cruise speed and the length of the final coasting and braking. The proposed framework is tested in a case study with real data from trains operating on the Eindhoven-’s-Hertogenbosch corridor in the Netherlands. Results obtained show that UKF is able to track the speed and location measurements and to estimate the parameters that model the running resistance in the dynamic equation. The proposed driving regime and the post-processing modules can determine the current regime accurately and give a deeper insight into the variations of the driving style, respectively.

Trains consist of one or more railway vehicles called rolling stock, which need interior and exterior cleaning and small technical checks on a daily basis. These services are executed at service locations (SLs). Scheduling rolling stock servicing tasks during an operational day is important to guarantee the fulfilment of servicing deadlines. Public transport companies face large scheduling problems, especially those with 24-hour-a-day operation. The expected increase in transport frequencies enhances the need for improving scheduling servicing tasks during an operational day. Therefore, the Rolling Stock Servicing Scheduling Problem (RS-SSP) is modelled comprising a MILP model. Complying with the planned timetable, the RS-SSP maximises the RS units being serviced during daytime. The RS-SSP allows RS exchanges between RS units having completed servicing and operating RS units requiring servicing. Due to this RS Exchange Concept, the number of RS units visiting the SL during daytime can be increased. The proposed RS-SSP model has been tested on a real-life case from the Dutch railways. For multiple scenarios, the model was able to exchange all running RS. Consequently, the capacity usage at SLs can be increased by the RS-SSP by shifting some of the excessive workload to daytime, and thus solving the capacity shortages.

Due to the covid19 crisis, public transport (PT) systems are facing new challenges. Regarding restrictive measures such as physical distancing and the successive returning of passengers after the “intelligent lockdown”, significant lack of transport capacity can be expected. In this paper, the transport capacity of a PT network is assessed, using a mathematical passenger route choice and train scheduling model. By analysing the overall number of transported passengers and the resulting link and train utilization; the networks capabilities of facilitating different demands under capacity restrictions (e.g. physical distancing) are addressed. The analysis is performed on the Dutch railway network. The results show that at most 50% of the pre-covid19 demand can be transported, while most of the trains will be highly utilized reaching their maximum occupation. Thus, significantly more parts of the network are becoming highly utilized, leading to a more congested and vulnerable system than in normal conditions before covid19.

Artificial Intelligence (AI) is becoming pervasive in most engineering domains, and railway transport is no exception. However, due to the plethora of different new terms and meanings associated with them, there is a risk that railway practitioners, as several other categories, will get lost in those ambiguities and fuzzy boundaries, and hence fail to catch the real opportunities and potential of machine learning, artificial vision, and big data analytics, just to name a few of the most promising approaches connected to AI. The scope of this paper is to introduce the basic concepts and possible applications of AI to railway academics and practitioners. To that aim, this paper presents a structured taxonomy to guide researchers and practitioners to understand AI techniques, research fields, disciplines, and applications, both in general terms and in close connection with railway applications such as autonomous driving, maintenance, and traffic management. The important aspects of ethics and explainability of AI in railways are also introduced. The connection between AI concepts and railway subdomains has been supported by relevant research addressing existing and planned applications in order to provide some pointers to promising directions.