May 2024

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33 Reads

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1 Citation

The Journal of Open Source Software

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May 2024

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33 Reads

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1 Citation

The Journal of Open Source Software

June 2023

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53 Reads

Ridepooling combines trips of multiple passengers in the same vehicle and may thereby provide a more sustainable option than transport by private cars. The efficiency and sustainability of ridepooling is typically quantified by key performance indicators such as the average vehicle occupancy or the total distance driven by all ridepooling vehicles relative to individual transport. However, even if the average occupancy is high and rides are shared, ridepooling services may increase the total distance driven due to additional detours and deadheading. Moreover, these key performance indicators are difficult to predict without large-scale simulations or actual ridepooling operation. Here, we propose a dimensionless parameter to estimate the sustainability of ridepooling by quantifying the load on a ridepooling service, relating characteristic timescales of demand and supply. The load bounds the relative distance driven and uniquely marks the break-even point above which the total distance driven by all vehicles of a ridepooling service falls below that of motorized individual transport. Detailed event-based simulations and a comparison with empirical observations from a ridepooling pilot project in a rural area of Germany validate the theoretical prediction. Importantly, the load follows directly from a small set of aggregate parameters of the service setting and is thus predictable a priori. The load may thus complement standard key performance indicators and simplify planning, operation and evaluation of ridepooling services.

September 2022

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125 Reads

The ongoing energy transition requires power grid extensions to connect renewable generators to consumers and to transfer power among distant areas. The process of grid extension requires a large investment of resources and is supposed to make grid operation more robust. Yet, counter-intuitively, increasing the capacity of existing lines or adding new lines may also reduce the overall system performance and even promote blackouts due to Braess' paradox. Braess' paradox was theoretically modeled but not yet proven in realistically scaled power grids. Here, we present an experimental setup demonstrating Braess' paradox in an AC power grid and show how it constrains ongoing large-scale grid extension projects. We present a topological theory that reveals the key mechanism and predicts Braessian grid extensions from the network structure. These results offer a theoretical method to understand and practical guidelines in support of preventing unsuitable infrastructures and the systemic planning of grid extensions.

September 2022

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252 Reads

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23 Citations

The ongoing energy transition requires power grid extensions to connect renewable generators to consumers and to transfer power among distant areas. The process of grid extension requires a large investment of resources and is supposed to make grid operation more robust. Yet, counter-intuitively, increasing the capacity of existing lines or adding new lines may also reduce the overall system performance and even promote blackouts due to Braess’ paradox. Braess’ paradox was theoretically modeled but not yet proven in realistically scaled power grids. Here, we present an experimental setup demonstrating Braess’ paradox in an AC power grid and show how it constrains ongoing large-scale grid extension projects. We present a topological theory that reveals the key mechanism and predicts Braessian grid extensions from the network structure. These results offer a theoretical method to understand and practical guidelines in support of preventing unsuitable infrastructures and the systemic planning of grid extensions.

May 2022

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62 Reads

Reliable functioning of supply and transport networks fundamentally support many non-equilibrium dynamical systems, from biological organisms and ecosystems to human-made water, gas, heat, electricity and traffic networks. Strengthening an edge of such a network lowers its resistance opposing a flow and intuitively improves the robustness of the system's function. If, in contrast, it deteriorate operation by overloading other edges, the counterintuitive phenomenon of \emph{Braess' paradox} emerges. How to predict which edges enhancements may trigger Braess' paradox remains unknown to date. To approximately locate and intuitively understand such Braessian edges, we here present a differential perspective on how enhancing any edge impacts network-wide flow patterns. First, we exactly map the prediction problem to a dual problem of electrostatic dipole currents on networks such that simultaneously finding \textit{all} Braessian edges is equivalent to finding the currents in the resistor network resulting from a constant current across one edge. Second, we propose a simple approximate criterion -- rerouting alignment -- to efficiently predict Braessian edges, thereby providing an intuitive topological understanding of the phenomenon. Finally, we show how to intentionally weaken Braessian edges to mitigate network overload, with beneficial consequences for network functionality.

October 2020

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183 Reads

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16 Citations

Teaching Statistics

Data and its applications are increasingly ubiquitous in the rapidly digitizing world and consequently, students across different disciplines face increasing demand to develop skills to answer both academia's and businesses' increasing need to collect, manage, evaluate, apply and extract knowledge from data and critically reflect upon the derived insights. On the basis of recent experiences at the University of Ttingen, Germany, we present a new approach to teach the relevant data science skills as an introductory service course at the university or advanced college level. We describe the outline of a complete course that relies on case studies and project work built around contemporary data sets, including openly available online teaching resources.

August 2020

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129 Reads

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14 Citations

Applied Network Science

Abstract Traffic is a challenge in rural and urban areas alike with negative effects ranging from congestion to air pollution. Ride-sharing poses an appealing alternative to personal cars, combining the traffic-reducing ride bundling of public transport with much of the flexibility and comfort of personal cars. Here we study the effects of the underlying street network topology on the viability of ride bundling analytically and in simulations. Using numerical and analytical approaches we find that system performance can be measured in the number of scheduled stops per vehicle. Its scaling with the request rate is approximately linear and the slope, that depends on the network topology, is a measure of the ease of ridesharing in that topology. This dependence is caused by the different growth of the route volume, which we compute analytically for the simplest networks served by a single vehicle.

May 2020

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393 Reads

Data science and its applications are increasingly ubiquitous in the rapidly digitizing world and consequently students across different disciplines face increasing demand to develop skills and awareness \cite{RISDALE2015, national2018data} to answer needs across all sectors to collect, manage, evaluate, apply and extract knowledge from data and critically reflect upon the derived insights. Against this backdrop, competencies to implement essential data analysis independently and to develop a basic understanding of more advanced processes and procedures used by data scientists in order to collaborate with them in a specific field of work are deemed desirable if not outright necessary in the future. As part of a joint initiative of several German universities and German businesses, the authors of this paper have developed a ``service-course'' that aims to teach fundamental data competencies to students from all disciplines at the University of Göttingen. See https://www.stifterverband.org/data-literacy-education for further information on this collaboration. The course especially addresses those students from outside STEM-subjects (science, technology, engineering, and mathematics) who generally have no prior experience with statistics or programming from their school education and highlights the importance of data competencies in prospective occupational fields for those students at the outset of their studies. We aim to provide all participating students with a fundamental understanding of the concepts and procedure of data science and motivate a fair share of them to pursue further courses geared to evolve their competencies in that regard. Moreover, the course aims to convey not only competencies from the domains of statistics and computer science but equally aims to develop the soft skills associated with data analysis, such as communicating the results in the context of tasks both inside and outside of university. The course builds on some role models, most prominently among them data8 (see http://data8.org/), the data science course developed at the University of Berkeley. Other similar courses are presented in \cite{Baumer.2015} and \cite{Hardin.2015}. However a number of factors and components of this course bring together both well-established and current pedagogies and practices of interest and value for current and future learning in introductory data science across disciplines. Despite a general agreement regarding the importance of statistics and programming competencies in most ministries, the complex federal structure of school education in Germany and the general problems of overhauling established school curricula yield the given status quo with no systematic programming, data science or statistics training prior to university for most students. Hence this course cannot assume even the basic statistics background commonly found in at least middle school in so many countries today. In addition, students choosing to do the course are not only in areas which are traditional foci of non-STEM statistics courses (such as Business), but also in Humanities programs, including Linguistics, Archaeology and History. Such diversity of interests is catered for in split tutorials which also use contemporary, complex and non-traditional data sets. What computer programming and how much are significant considerations in designing introductory data science courses. Assuming no prior programming experience left us free to choose a programming language. We chose to use Python despite the increasing use of R and R-based products in statistics courses and new data science courses. In the final phase of the course, a mandatory project is carried out in authentic workplace-linked data investigations involving all aspects of data science at an introductory level. The importance of authentic experiential learning of the full statistical data investigation process has long been recognised, but facilitating this at the introductory level has proved challenging. Data science adds more challenges to this through requirements in data wrangling, cleaning and visualisation. Therefore the course also builds on and extends to contemporary needs in learning from data, the objectives of courses with a particular focus on first hand real investigative experiences, such as \cite{Jersky02statisticalconsulting, RootThorme, chadjipadelis2006use, Halvorsen, westbrooke2014statistical, attygalle}. In this paper, we describe some details of the course, including the learning progression phases, tutorial work, projects, and openly available online teaching resources, consisting of slides, videos, exercises and solutions. Because the course is so new, only some initial evaluations are available; these are included in the discussion.

January 2020

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106 Reads

Traffic is a challenge in rural and urban areas alike with negative effects ranging from congestion to air pollution. Ride-sharing poses an appealing alternative to personal cars, combining the traffic-reducing ride bundling of public transport with much of the flexibility and comfort of personal cars. Here we study the effects of the underlying street network topology on the viability of ride bundling analytically and in simulations. Using numerical and analytical approaches we find that system performance can be measured in the number of scheduled stops per vehicle. Its scaling with the request rate is approximately linear and the slope, that depends on the network topology, is a measure of the ease of ridesharing in that topology. This dependence is caused by the different growth of the route volume, which we compute analytically for the simplest networks served by a single vehicle.

December 2019

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29 Reads

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18 Citations

Chaos (Woodbury, N.Y.)

Networks of phase oscillators are studied in various contexts, in particular, in the modeling of the electric power grid. A functional grid corresponds to a stable steady state such that any bifurcation can have catastrophic consequences up to a blackout. Also, the existence of multiple steady states is undesirable as it can lead to transitions or circulatory flows. Despite the high practical importance there is still no general theory of the existence and uniqueness of steady states in such systems. Analytic results are mostly limited to grids without Ohmic losses. In this article, we introduce a method to systematically construct the solutions of the real power load-flow equations in the presence of Ohmic losses and explicitly compute them for tree and ring networks. We investigate different mechanisms leading to multistability and discuss the impact of Ohmic losses on the existence of solutions.

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... Therefore, optimizing decentralized hybrid power grids has emerged as a critical consideration. Power grids transmit two types of alternating current (AC): inertial AC, derived from fossil fuel combustion or nuclear fission, and inertia-free AC, generated by renewable sources and connected via power electronic inertia-free inverters [14,15,16,17]. External disturbances such as transmission line disconnections or regional overloads can disrupt the grid, with inertia-free AC unable to recover spontaneously, potentially leading to widespread frequency desynchronization and grid failure [18,19,20,21,22]. ...

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September 2022

... It is very useful for malware detection and monitoring, and its strong syntax makes it perfect for building field applications. Python, with its extensive library base, is also appropriate for applications such as digital risk assessments and access testing [10]. ...

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October 2020

Teaching Statistics

... Another advantage of simulations is that the degree to which they represent reality may be freely adjusted. This makes it possible to both answer concrete operational questions (de Ruijter et al., 2023;Henao & Marshall, 2019;Lotze et al., 2022;Ruch et al., 2020;Wilkes et al., 2021;Zwick et al., 2021Zwick et al., , 2022) and investigate idealized system behavior, gaining deeper insights into the general properties of on-demand mobility systems (Herminghaus, 2019;Manik & Molkenthin, 2020;Molkenthin et al., 2020;Tachet et al., 2017;Zech et al., 2022). ...

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- Full-text available
August 2020

Applied Network Science

... Theoretical results on multistability were obtained in Refs. [26][27][28]. ...

- Citing Article
December 2019

Chaos (Woodbury, N.Y.)

... Moreover, it has been realized that sparse networks can be multistable, i.e., they support more than one stable synchronized state [14]. Again, we are left with the question of when these states exist [15,16]. Another important question is how vulnerable a given synchronous state is to perturbations [17][18][19]. ...

- Citing Article
- Full-text available
November 2016

Chaos (Woodbury, N.Y.)

... Another, more recent, related work (Manik et al 2017) investigates the computation of susceptibilities with respect to edges and nodes. In that paper, the authors consider a different model, namely Kuramoto's oscillator network (Kuramoto 1984, Rodrigues et al 2016. ...

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September 2016

PHYSICAL REVIEW E

... ) is a vector of cycle or loop flow amplitudes. We note that this decomposition proves to be useful in various linear power flow problems [39,40]. ...

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June 2016

Power Systems, IEEE Transactions on

... has only positive eigenvalues except for a trivial zero eigenvalue corresponding to the eigenvector (1, 1, . . . , 1) [2,35]. A sufficient condition for stability is that ...

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October 2014

The European Physical Journal Special Topics