Deutsche Bahn
  • Berlin, Germany
Recent publications
This prospective longitudinal epidemiological study was aimed at investigating the occupational SARS-CoV-2 infection risk of long distance train services in Germany. Three different employee groups (train attendants, train drivers and maintenance workers) within the workforce of the German railway carrier Deutsche Bahn Fernverkehr AG were studied based on their contact frequency with passengers and colleagues. Approximately 1100 employees were tested by PCR for acute infections and by antibody detection for past infections in June 2020, October 2020 and February 2021. Cumulative incidence (acute and past infections) after the third (final) test series in February 2021 was 8.5% (95% interval CI 6.8-10.4): 8.5% (95% CI 6.2-11.2) for train attendants, 5.5% (95% CI 2.9-9.5) for train drivers and 11.8% (95% CI 7.6-17.2) for maintenance workers. Between June 2020 and October 2020, the incidence was 1.2% (95% CI 0.6-2.3): 1.2% (95% CI 0.4-2.7) for train attendants, 1.1% (95% CI 0.1-3.9) for train drivers and 1.4% (95% CI 0.17-5.10) for maintenance workers. Between October 2020 and February 2021, it was 5.1% (95% CI 3.6-6.8): 5.2% (95% CI 3.3-7.8) for train attendants, 1.6% (95% CI 0.3-4.5) for train drivers and 8.8% (95% CI 4.9-14.3) for maintenance workers. Thus, contrary to expectation our exploratory data did not show train attendants to be at the highest risk of SARS-CoV-2 infections among the employee groups. In line with expectations, train drivers, representing the low contact group, seemed at lowest occupational risk.
Über die Notwendigkeit umfangreicher Öffentlichkeitsarbeit (ÖA) bei Bauprojekten besteht gesellschaftlicher Konsens. Dennoch muss diese Frage immer wieder neu beantwortet werden, um nicht hinter den aktuellen Erkenntnisstand zu Bedeutung und Instrumenten der ÖA im Kontext der Gesellschaft zurückzufallen. Bauvorhaben, zumal wenn sie Verbindungen durch Straßen oder Schienen beinhalten, sind seit jeher Streitpunkte. Sie sind konfliktträchtig, weil sie meist als Linienbaustellen Veränderungen und Eingriffe für viele Interessengruppen bringen. In diesem Kap. 7 wird alles beschrieben, was – insbesondere durch die Auftraggeberseite – an ÖffentlichkeitsarbeitÖffentlichkeitsarbeit in der Planungsphase, besonders jedoch in der Realisierungsphase eines Infrastrukturprojektes zu tun ist. Dies schließt insbesondere die Zusammenarbeit und den Umgang mit den Beteiligten und Betroffenen in der Gesellschaft, in Behörden und in der Politik („Stakeholdern“) ein, aber auch die Zusammenarbeit mit den Medien. Elemente der Öffentlichkeitsarbeit wie Bürgerversammlungen, Infobroschüren und Infoveranstaltungen, aber auch die Einrichtung von Infozentren – sowie deren elektronische Entsprechung – werden erläutert.
Jegliche Marketingaktivitäten fordern heutzutage ein starkes Datenmanagement, das durch verschiedenste Spielteilnehmer aufgebaut wird. Es ist zu raten, Regeln und Prozesse dieser Zusammenarbeit aufzusetzen, um von Beginn an eine größtmögliche Transparenz über kollaborative Aufgabenteilung und -verantwortung zu besitzen. Die Interaktion der Marketingexperten gegenüber verschiedensten Fachbereichen und die partnerschaftliche Arbeit im Verbund (mit Daten-Office, Datenstrategen, Datennutzern, Datenanalysten) nimmt zu, verändert den Alltag nachhaltig und fordert neue Fähigkeiten. Damit ist das Berufsbild der Marketingexperten einem ständigen Wandel ausgesetzt. Das Datenmanagement lebt durch die operative Tätigkeit innerhalb der Fachabteilung; den Datenrollen Data-Steward und -Owner kommt dabei eine wichtige Bedeutung zu. Diese umfangreiche operative Leistung muss jedoch durch strategischen Willen ergänzt werden, neue Prozesse, Strukturen und Kulturdimensionen innerhalb der Gesamtunternehmung einzuführen und zu finanzieren.
The impact of microgrid applications expands with the increasing share of renewable energy resources in the energy supply and the progressing electrification of the transport sector. The additional energy demand of electric vehicles (EVs) may cause increased peak loads, leading to growing burdens on the power system. In this regard, microgrids allow to cover the energy demand of EVs utilizing local renewable energy resources and a battery storage system, thereby lowering the utilization of the power system. This paper discusses a self-consumption operational strategy for a real-world microgrid at the EUREF-Campus in Berlin, Germany. A rule-based control algorithm for the sustainable energy supply of EV charging stations is implemented and validated. Measurements are recorded with high resolution over three years from 2017 to 2019. The performance of the microgrid operation is assessed with regards to three distinct key indicators: self-consumption, autarky, and emission rate. The influence of selected operating parameters under real-world and ideal operating conditions are investigated. The results indicate that self-consumption and autarky rates are sensitive to the rated power capacity of a battery storage system rather than to its rated energy capacity. Moreover, the reduction of emissions through battery deployment for EV charging amounts up to 30% under ideal operating conditions. This underlines the potential of the microgrid concept for the sustainable application of EVs in urban areas.
Global malware campaigns and large-scale data breaches show how everyday life can be impacted when the defensive measures fail to protect computer systems from cyber threats. Understanding the threat landscape and the adversaries’ attack tactics to perform it represent key factors for enabling an efficient defense against threats over the time. Of particular importance is the acquisition of timely and accurate information from threats intelligence sources available on the web which can provide additional intelligence on emerging threats even before they can be observed as actual attacks. Currently, specific indicators of compromise (e.g. IP addresses, domains, hashsums of malicious files) are shared in a semi-automated and structured way via so-called threat feeds. Unfortunately, current systems have to deal with the trade-off between the timeliness of such an alert (i.e. warning at the first mention of a threat) and the need to wait for verification by other sources (i.e. warning after multiple sources have verified the threat). In addition, due to the increasing number of open sources, it is challenging to find the right balance between feasibility and costs in order to identify a relatively small subset of valuable sources. In this paper, a method to automate the assessment of cyber threat intelligence sources and predict a relevance score for each source is proposed. Specifically, a model based on meta-data and word embedding is defined and experimented by training regression models to predict the relevance score of sources on Twitter. The results evaluation show that the assigned score allows to reduce the waiting time for intelligence verification, on the basis of its relevance, thus improving the time advantage of early threat detection.
Managers of manufacturing systems are constantly looking for ways to predict future production conditions. Due to the system’s complexity, modelling is effortful and never completed. So-called random forests of decision trees seem to be a promising machine-learning tool to forecast key figures of manufacturing systems. The selection of data to teach such classifiers significantly influences the quality of the prediction. However, quality of data and prediction decreases in case of a dynamic system. This paper deals with one possible way of data handling for decision forests in changing manufacturing system.
Mobilität und Transport in Europa unterliegen vielfältigen rechtlichen Anforderungen. Dies gilt nicht nur für sämtliche Verkehrsträger, sondern es bezieht sich auch auf zahlreiche Materien des nationalen und des europäischen Rechts. Dieser Befund ist weder überraschend noch beklagenswert. Die historische Entwicklung des Verkehrswesens, seine Sicherheitsrelevanz sowie die volkswirtschaftliche und politische Bedeutung funktionierender Verkehrsinfrastruktur haben nicht zuletzt deshalb in Rechtssetzung, Rechtsanwendung und Rechtsprechung ihre Spuren hinterlassen, weil allgemeine Regelungen in vielen Fällen nicht ohne Weiteres auf den Verkehrssektor übertragen werden konnten. Die Besonderheiten des Verkehrs und die weiter gehenden Besonderheiten der einzelnen Verkehrsträger haben immer wieder zu sektorspezifischen Regelungen Anlass gegeben, die zu allgemeinen Vorschriften in ein Spannungsverhältnis treten.
Der demografische Wandel bedeutet nicht nur Fachkräftemangel und damit verbunden die Herausforderung, Stellen zu besetzen, sondern stellt Unternehmen vor eine weitere Aufgabe: dem Erfüllen und Gerecht-Werden der Bedürfnisse und Werte verschiedener Mitarbeitendengenerationen. Spätestens seit dem Berufseinstieg der Millennials ist eine Veränderung bei den Arbeitnehmenden in Bezug auf Bedürfnisse, Werte und Anforderungen an Arbeit und Karriere zu spüren. Nur mit einer generationensensiblen Personal- und Karriereentwicklung wird es Unternehmen zukünftig gelingen, die Potenziale von Generationenvielfalt zu nutzen. Gleichzeitig liegt im generationensensiblen Umgang untereinander auch die Chance, Mitarbeitende langfristig zu binden. Der Beitrag skizziert Lernpräferenzen und Karrieremotive der einzelnen Beschäftigten-Generationen und gibt einen Überblick der verschiedenen, generationensensiblen Ansätze bei der Karriere- und Talententwicklung sowie der Förderung des Generationenmiteinanders.
In this work we develop methods to optimize an industrially-relevant logistics problem using quantum computing. We consider the scenario of partially filled trucks transporting shipments between a network of hubs. By selecting alternative routes for some shipment paths, we optimize the trade-off between merging partially filled trucks using fewer trucks in total and the increase in distance associated with shipment rerouting. The goal of the optimization is thus to minimize the total distance travelled for all trucks transporting shipments. The problem instances and techniques used to model the optimization are drawn from real-world data describing an existing shipment network in Europe. We show how to construct this optimization problem as a quadratic unconstrained binary optimization (QUBO) problem. We then solve these QUBOs using classical and hybrid quantum-classical algorithms, and explore the viability of these algorithms for this logistics problem.
In technical systems the analysis of similar load situations is a promising technique to gain information about the system’s state, its health or wearing. Very often, load situations are challenging to be defined by hand. Hence, these situations need to be discovered as recurrent patterns within multivariate time series data of the system under consideration. Unsupervised algorithms for finding such recurrent patterns in multivariate time series must be able to cope with very large data sets because the system might be observed over a very long time. In our previous work we identified discretization-based approaches to be very interesting for variable length pattern discovery because of their low computing time due to the simplification (symbolization) of the time series. In this paper we propose additional preprocessing steps for symbolic representation of time series aiming for enhanced multivariate pattern discovery. Beyond that we show the performance (quality and computing time) of our algorithms in a synthetic test data set as well as in a real life example with 100 millions of time points. We also test our approach with increasing dimensionality of the time series.
Zusammenfassung Die Corona-Pandemie stellt die Deutsche Bahn als größte Mobilitätsdienstleisterin 2020/21 vor besondere Herausforderungen. Es gilt Mitarbeitende und Fahrgäste bestmöglich zu schützen und gleichzeitig die Funktionsfähigkeit der Infrastruktur in Deutschland aufrechtzuerhalten. Das Betriebliche Gesundheitsmanagement stellt eine wichtige Säule einer funktionsfähigen und gesunden Organisation dar. Neben den bereits etablierten Formaten wurden explizite Unterstützungsformate während der Pandemie angeboten. Eine transparente und unternehmensinterne Kommunikation wird von den Mitarbeitenden als besonders unterstützend erlebt. Für die DB als lernende Organisation bot das letzte Jahr eine Vielzahl an Lernanreizen, die für die Weiterentwicklung des zukünftigen Arbeitens wertvoll sind.
For road construction, the morphological characteristics of coarse aggregates such as angularity and sphericity have a considerable influence on asphalt pavement performance. In traditional aggregate simulation processes, images of real coarse grains are captured, and their parameters are extracted manually for reproducing them in a numerical simulation such as Discrete Element Modeling (DEM). Generative Adversarial Networks can generate aggregate images, which can be stored in the Aggregate DEM Database directly. In this paper, it has been demonstrated that applying Auxiliary Classifier Wasserstein GANs with gradient penalty (ACWGAN-gp) is reliable and efficient for the establishment of an aggregate image database. In addition, the distribution of original images was compared with that of images generated based on ACGAN and ACWGAN-gp models. Generated images were validated through obtaining identifiable edge coordinates and represented as DEM input in the simulation process. The results prove that the ACWGAN-gp approach can be used for generating aggregate images for the DEM database. It successfully generates high-quality images of aggregates with a representative distribution of morphologies used for DEM simulation. This work shows convenience and efficiency for machine learning applications in the road construction field.
Am Beispiel des Güterverkehrs zeigt Dr. Sigrid Nikutta auf, dass und wie mehr Fairness in den Wettbewerb um die besten Lösungen zum Klimawandel eingeführt werden muss. Eine ganzheitliche Betrachtungsweise mit Einpreisung von ökologischen und sozialen Folgekosten auf der einen Seite sowie die Rückbesinnung auf die grundgesetzlich verankerte Eigenverantwortung eines jeden Einzelnen inklusive der Unternehmen selbst als Ansatzpunkt auf der anderen Seite. Neuentwickelte Geschäftsmodelle und Technologiesprünge als Beitrag der Wirtschaft, einheitliche europäische Rahmenbedingungen als Beitrag der Politik sowie ein verantwortungsbewusstes Handeln von Unternehmen und Bürgern werden das Rad zum Rollen bringen, anstatt es neu erfinden zu müssen.
Situation faced: The accounting function of the Deutsche Bahn Group, a public German railway company with 320,000 employees, went through a centralization process to transfer the entire company’s accounting activities from 350 locations to three Shared Service Centers. The decentralized accounting function prior to this transformation was qualitatively and economically unsustainable.
Service network design is an important optimization problem for intermodal freight transportation on a tactical level. It includes the decisions on choosing transportation modes and paths for commodities throughout the intermodal network. We present a stochastic service network design model with an integrated vehicle routing problem (SSND-VRP), which simultaneously covers transportation service choice and tour planning decisions for road transportation under consideration of uncertain transportation times. A sample average approximation approach is combined with an iterated local search in order to solve problem instances in a real-world case study for three intermodal road-rail networks in Central Europe. Results of the SSND-VRP are compared with its expected value model and a successive planning approach, demonstrating the possible cost reductions and the decrease in missed intermodal services that are achieved by the integrated stochastic model. In further parameter variation experiments we show that the attractiveness of rail transportation is highly sensitive to changes in intermodal costs, whereas the impact of delay reductions of the railway services is relatively low.
This paper describes an approach for optimizing media streaming services in mobile environments by utilizing the fifth-generation mobile network technology (5G), including millimeter Wave (mmWave) high speed data links, 5G Edge Computing and relevant state-of-the-art media streaming standards, such as Network Based Media Processing (NBMP) and Server and Network Assisted DASH (MPEG SAND). Although the solution described in this paper can be applied to media streaming in various mobility scenarios, the focus of this work is on rail environments and allowing video-on-demand (VoD) catalogues (Mediatheks) to be made available to train passengers via regular VoD apps – even when trains are not connected to the Internet. To achieve this objective, we introduce caching nodes inside trains and their respective stations. The work presented in this paper will be trialed in a railway station in Berlin, as part of the EU-funded research project, 5G-VICTORI.
This paper presents a model to anticipate the impact of Eddy Current Brakes (ECBs) installed in high-speed trains on the readouts of rail-side wheel sensors. The purpose is to anticipate false positive readouts of train wheels when traversing, one of the main obstacles for full ECB deployment. The ECB type EWB 154 from Knorr-Bremse and Wheel Sensor types RSR180 and RSR123 from Frauscher Sensor Technology are represented in a comprehensive model, integrating LTSpice and CST Microwave Studio. The wheel sensor predicted readout error is 4% compared to measurements when DC current is not applied to the ECB (passive case). It is demonstrated that the RSR180 is not compatible with ECBs, whereas the RSR123 is. The impact of active (DC current fed) brakes is analyzed when performing running tests with a high-speed ICE 3 train equipped with ECBs. The model is adjusted to study the saturation of the rail and ECB pole cores. The extra damping of the wheel sensor fingerprint is modeled by an extra 6% drop that may well be applicable to passive tests in a laboratory setting to shift to active tests without actually performing them. In this way, cost and time would be saved. Based on the model outcomes, a test bench is recommended for laboratory tests to emulate active behavior.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
195 members
Thomas Thiele
  • Digitization and Technology
Franziska Kühn
  • Department of Finance
Information
Address
Potsdamer Platz 2, 10785, Berlin, Germany
Website
https://www.deutschebahn.com