Periodic timetabling with flexibility based on a mesoscopic topology

In the project smartrail 4.0 Swiss Federal Railways (SBB) aims for a higher degree in automatization of the railway value chain (e.g. line planning, timetabling and vehicle scheduling, etc.). In the context of an applied research project together with SBB, we have developed an extension of the Periodic Event Scheduling Problem (PESP) model. On one hand the extension is based on using a finer resolution of the track infrastructure, the so-called mesoscopic topology. The mesoscopic topology allows creating timetables with train lines assigned to track paths. On the other hand, we use a known, flexible PESP formulation (FPESP), i.e. we calculate time intervals instead of time points for the arrival resp. departures times at operating points. Both extensions (mesoscopic topology and flexibility) should enhance feasibility of the timetables on the microscopic infrastructure. We will call our model therefore track-choice, flexible PESP model (TCFPESP)

A Literature Review on Train Motion Model Calibration

The dynamics of a moving train are usually described by means of a motion model based on Newton&amp;#x2019;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.</p

Real-time train motion parameter estimation using an Unscented Kalman Filter

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.Transport and Plannin

Train motion model calibration: Research agenda and practical recommendations

An accurate train motion model is a key component of a wide spectrum of railway applications, from timetabling algorithms to Automatic Train Operation systems. Therefore, model calibration has become crucial in the railway industry, although this topic has not received the attention and recognition in academia that its practical relevance deserves. Several data-driven techniques have been devised to calibrate train dynamics models, although an overview that describes the current state of the art in the field and highlights the following steps to be researched is still missing in the literature. Thus, this article has four main goals. First, giving a brief insight into the broad variety of techniques used for train motion model calibration, focusing on those techniques that use on-board measurements and are applicable in railway operation. Second, highlighting the main research steps to be tackled, considering the current main challenges in railway research. Third, outlining practical recommendations to practitioners who need to calibrate their algorithms and applications. And fourth, contributing to giving train motion model calibration its due recognition.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Plannin

Optimal network electrification plan for operation of battery-electric multiple unit regional trains

The Netherlands have one of the highest rail electrification rates in the EU with over 75% of the railway network electrified (European Comission, 2018), offering environment-friendly trains operation. However, in order to achieve carbon neutral railway sector by 2050, significant investments are required to further improve environmental performance from trains operation, especially in regional nonelectrified networks with passenger services typically provided by diesel multiple unit (DMU) vehicles. Due to their low utilization, full electrification of such networks is often not economically viable, thus solutions are mainly sought in alternative propulsion system technologies, such as hydrogen fuel-cell multiple unit (FCMU) and battery-electric multiple unit (BEMU) vehicles (Klebsch et al., 2019). One of the main challenges in introducing BEMU trains is determining the electrification plan for the railway network, while satisfying requirements related to quality of service, maintaining current timetable, and vehicle-specific constraints. Previous research on BEMUs operation is mainly focused on continuous partial lines electrification, or eventually limited scenario analysis on intermittent electrification (Abdurahman et al., 2021), with the optimization-based methods still lacking in the literature. This study aims to fill this gap by proposing a method for developing an optimal electrification plan, while minimizing total costs and considering several electrification alternatives for each track section.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and PlanningRailway Engineerin

A Literature Review on Train Motion Model Calibration

The dynamics of a moving train are usually described by means of a motion model based on Newton&amp;#x2019;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.Transport and Plannin

Artificial Intelligence in Railway Transport: Taxonomy, Regulations and Applications

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