All publications from the Vadere group

@article{rahn-2024,

title = {Toward unraveling airborne pathogen transmission in crowds: Parameter study for an agent-based exposure model},

author = {Simon Rahn and Gerta Köster and Hans-Joachim Bungartz},

doi = {10.1016/j.ssci.2024.106524},

year  = {2024},

date = {2024-04-08},

urldate = {2024-04-08},

journal = {Safety Science},

volume = {175},

pages = {106524},

abstract = {The coronavirus disease 2019 (COVID-19) pandemic has put forth the integration of pathogen transmission into microscopic crowd models to simulate the exposure risk for individuals. However, it is crucial to account for uncertainties in the model input as long as we lack data, particularly regarding airborne transmission of the coronavirus. In this study, we quantify uncertainties in such simulations to increase their reliability and informative value. We consider this an integral aspect of model validation. This study relies on a model originally introduced in a previous contribution. We adapt it to simulate airborne virus transmission in several everyday situations. The locomotion layer of the model captures crowd management strategies, while its pathogen transmission layer returns the virtual persons? exposures to pathogens. We conduct a global sensitivity analysis to rank uncertain parameters according to their impact on the exposure risk and employ forward propagation techniques to quantify the output uncertainty. The sensitivity analysis reveals that two model parameters related to the extent and spread of aerosols are essential, whereas a parameter describing the decay of pathogens is barely influential. The forward propagation demonstrates how crowd management alleviates the exposure risk in the analyzed situations. Moreover, we identify aerosol spread as a dominant aspect on which research should focus.},

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@article{mayr-2023-cdyn,

title = {Designing mobile application messages to impact route choice: A survey and simulation study},

author = {Christina Maria Mayr and Anne Templeton and Gerta Köster},

editor = { Charitha Dias},

doi = {10.1371/journal.pone.0284540},

year  = {2023},

date = {2023-04-20},

urldate = {2023-04-20},

journal = {PLOS ONE},

volume = {18},

pages = {1-20},

abstract = {Crowd congestion is a common issue at train stations around major sports events, and puts passengers at risk and lowers service quality. Guiding arriving fans along less traveled routes may alleviate congestion. Smartphone apps provide a medium to deliver route suggestions but the messages they provide are pivotal to adherence. We explore how message design affects pedestrians’ willingness to follow route instructions. We present an online survey conducted with two groups: football fans, and students and faculty associates. We vary the presence of top down views of the route choices at train station Münchner Freiheit in Munich, real-time information on congestion, and appeals to team spirit. We compute a distribution of route choices that suggests that congestion may be reduced with the right combination of message components for each target group. We then use a computer simulation to investigate the congestion situation. Our results suggest that lowest congestion is achieved when people base their decisions on real-time information. The social identity approach is highlighted in our study as having a possible influence on message design. Moreover, it indicates that the implementation of such apps in real-life applications can improve safety. Our methodology can be applied to other scenarios to test the suitability of apps and message designs.},

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@article{mayr-2022-cdyn,

title = {Guiding crowds when facing limited compliance: Simulating strategies},

author = {Christina Maria Mayr and Gerta Köster},

doi = {10.1371/journal.pone.0276229},

year  = {2022},

date = {2022-11-11},

urldate = {2022-11-11},

journal = {PLOS ONE},

volume = {17},

number = {11},

pages = {1-24},

abstract = {At traffic hubs, it is important to avoid congestion of pedestrian streams to ensure safety and a good level of service. This presents a challenge, since distributing crowds on different routes is much more difficult than opening valves to, for example, regulate fluid flow. Humans may or may not comply with re-directions suggested to them typically with the help of signage, loudspeakers, apps, or by staff. This remains true, even if they perceive and understand the suggestions. Yet, simulation studies so far have neglected the influence of compliance. In view of this, we complement a state-of-the-art model of crowd motion and crowd behavior, so that we can vary the compliance rate. We consider an abstracted scenario that is inspired by a metro station in the city of Munich, where traffic regulators wish to make some passengers abandon the obviously shortest route so that the flow evens out. We investigate the effect of compliance for two very simple guiding strategies. In the first strategy, we alternate routes. In the second strategy, we recommend the path with the lowest crowd density. We observe that, in both cases, it suffices to reroute a small fraction of the crowd to reduce travel times. But we also find that taking densities into account is much more efficient when facing low compliance rates.},

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@article{rahn-2022,

title = {Modelling airborne transmission of SARS-CoV-2 at a local scale},

author = {Simon Rahn and Marion Gödel and Gerta Köster and Gesine Hofinger},

doi = {10.1371/journal.pone.0273820},

year  = {2022},

date = {2022-08-30},

urldate = {2022-08-30},

journal = {PLOS ONE},

volume = {17},

number = {8},

publisher = {Public Library of Science},

abstract = {The coronavirus disease (COVID-19) pandemic has changed our lives and still poses a challenge to science. Numerous studies have contributed to a better understanding of the pandemic. In particular, inhalation of aerosolised pathogens has been identified as essential for transmission. This information is crucial to slow the spread, but the individual likelihood of becoming infected in everyday situations remains uncertain. Mathematical models help estimate such risks. In this study, we propose how to model airborne transmission of SARS-CoV-2 at a local scale. In this regard, we combine microscopic crowd simulation with a new model for disease transmission. Inspired by compartmental models, we describe virtual persons as infectious or susceptible. Infectious persons exhale pathogens bound to persistent aerosols, whereas susceptible ones absorb pathogens when moving through an aerosol cloud left by the infectious person. The transmission depends on the pathogen load of the aerosol cloud, which changes over time. We propose a 'high risk' benchmark scenario to distinguish critical from non-critical situations. A parameter study of a queue shows that the new model is suitable to evaluate the risk of exposure qualitatively and, thus, enables scientists or decision-makers to better assess the spread of COVID-19 and similar diseases.},

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pubstate = {published},

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@article{goedel-2022b-cdyn,

title = {Towards learning dynamic origin-destination matrices from crowd density heatmaps},

author = {Marion Gödel and Daniel Lehmberg and Rebecca Brydon and Ernst Bosina and Gerta Köster},

doi = {10.1088/1742-5468/ac6255},

year  = {2022},

date = {2022-01-01},

journal = {JSTAT},

abstract = {Knowing the origins and destinations of pedestrians' paths is key to the initialization of crowd simulations. Unfortunately, they are difficult to measure in the real world. This is one major challenge for live predictions during events such as festivals, soccer games, protest marches, and many others. Sensor data can be used to feed real-world observations into simulations in real-time. As input data for this study, we use density heatmaps generated from real-world trajectory data obtained from stereo sensors. Density information is compact, of constant size, and in general easier to obtain than e.g., individual trajectories. Therefore, the information limitation improves the applicability to other scenarios. We include the absolute pedestrian trip counts from origins to destinations during a brief time interval in an OD matrix, including unknown destinations due to sensor errors. Our goal is to estimate these OD matrices from a series of density heatmaps for the same interval. For this, we compute the ground truth OD matrices and density heatmaps using real-world trajectory data from a train station. We employ linear regression as a statistical learning method for estimation. We observe that the linear share of the relationship between density and OD matrix is estimated successfully. Nevertheless, a portion of the data remains that cannot be explained. We attempt to overcome this difficulty with random forest as a nonlinear model. The results indicate that both a linear and a nonlinear model can estimate some features of the OD matrices. However, there is no clear winner in terms of the chosen metric, the $R2^$ score. Overall, our findings are a strong indicator that OD matrices can indeed be estimated from density heatmaps extracted automatically from sensors.},

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@article{goedel-2022-cdyn,

title = {Bayesian inference methods to calibrate crowd dynamics models for safety applications},

author = {Marion Gödel and Nikolai Bode and Gerta Köster and Hans-Joachim Bungartz},

doi = {10.1016/j.ssci.2021.105586},

year  = {2022},

date = {2022-01-01},

journal = {Safety Science},

volume = {147},

pages = {105586},

abstract = {Crowd simulation is a crucial tool to assess risks and engineer crowd safety at events and in built infrastructure. Simulations can be used for what-if studies, for real-time predictions, as well as to develop regulations for crowd safety. A reliable prediction requires a carefully calibrated model. Model parameters are often calibrated as point estimates, single parameter values for which the model evaluation fits given data best. In contrast, Bayesian inference provides a full posterior distribution for the fitted parameters that includes the residual uncertainty after calibration. In this work, we calibrate a microscopic model and an emulator derived from a microscopic model for crowd dynamics using point estimates and Approximate Bayesian Computation. We calibrate on data measuring the flow through a key scenario of crowd safety: a bottleneck. We vary the bottleneck width and demonstrate via three case studies the advantages and shortcomings of the two calibration techniques. In a case with a unimodal posterior, both methods yield similar results. However, one safety-relevant case study, that mimics the dynamics of evacuating people squeezing through an opening, exhibits a faster-is-slower dynamic where multiple free-flow speeds lead to the same flow. In this case, only Bayesian inference reveals the true bimodal shape of the posterior distribution. For multidimensional calibration, we illustrate that Bayesian inference allows accurate calibration by describing parameter relations. We conclude that, in practice, point estimation often seems sufficient, but Bayesian inference methods are necessary to capture important structural information about the uncertain parameters, and thus the physics of safety.},

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@article{lehmberg-2021-cdyn,

title = {Modeling Melburnians—Using the Koopman operator to gain insight into crowd dynamics},

author = { Daniel Lehmberg and Felix Dietrich and Gerta Köster},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0968090X21004265},

doi = {10.1016/j.trc.2021.103437},

year  = {2021},

date = {2021-12-01},

urldate = {2021-12-01},

journal = {Transportation Research Part C: Emerging Technologies},

volume = {133},

pages = {103437},

abstract = {Describing and forecasting city traffic is challenging, given the array of factors influencing the movement of pedestrians and vehicles. Faced with this complexity, research has focused on machine learning as a way to capture spatio-temporal traffic patterns, based on past sensor data. While the methods can accurately forecast high-dimensional observations, it is often hard to explain the models and their predictions. In many cases, only the predictive performance of an otherwise black box model is evaluated. An accurate but explainable alternative is provided by the Koopman operator, an emerging methodology for data-driven identification of high-dimensional and nonlinear dynamical systems. In this study, we showcase the method by analyzing pedestrian traffic data from Melbourne, Australia. We formulate our computations in the Extended Dynamic Mode Decomposition framework, where we approximate the Koopman operator in a function basis computed with time delay embedding and the Diffusion Map algorithm. The model captures the distinct temporal patterns of 11 traffic sensors simultaneously. Because the dynamics become linear in the operator-theoretic perspective, we can decompose the model into its spectral components. Importantly, these components facilitate interpretation and can increase scientific understanding of the underlying system and the model’s operations. For the Melbourne data, we show that the spectral components connect to the underlying state space geometry and indicate the model’s stability over a prediction horizon. Our study showcases how the Koopman operator framework offers explainable and accurate data-driven predictions in a real-world traffic system. The results can easily be transferred to other traffic systems.},

keywords = {},

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@article{rahn-2021-cdyn,

title = {Dynamics of a Simulated Demonstration March: An Efficient Sensitivity Analysis},

author = {Simon Rahn and Marion Gödel and Rainer Fischer and Gerta Köster},

doi = {10.3390/su13063455},

year  = {2021},

date = {2021-03-21},

urldate = {2021-03-21},

journal = {Sustainability},

volume = {13},

number = {6},

pages = {3455},

publisher = {MDPI},

abstract = {Protest demonstrations are a manifestation of fundamental rights. Authorities are responsible for guiding protesters safely along predefined routes, typically set in an urban built environment. Microscopic crowd simulations support decision-makers in finding sustainable crowd management strategies. Planning routes usually requires knowledge about the length of the demonstration march. This case study quantifies the impact of two uncertain parameters, the number of protesters and the standard deviation of their free-flow speeds, on the length of a protest march through Kaiserslautern, Germany. Over 1000 participants walking through more than 100,000 m2 lead to a computationally demanding model that cannot be analyzed with a standard Monte Carlo ansatz. We select and apply analysis methods that are efficient for large topographies. This combination constitutes the main novelty of this paper: We compute Sobol’ indices with two different methods, based on polynomial chaos expansions, for a down-scaled version of the original set-up and compare them to Monte Carlo computations. We employ the more accurate of the approaches for the full-scale scenario. The global sensitivity analysis reveals a shift in the governing parameter from the number of protesters to the standard deviation of their free-flow speeds over time, stressing the benefits of a time-dependent analysis. We discuss typical actions, for example floats that reduce the variation of the free-flow speed, and their effectiveness in view of the findings.},

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@article{mayr-2021,

title = {Analysis of information dissemination through direct communication in a moving crowd},

author = {Christina Maria Mayr and Stefan Schuhbäck and Lars Wischhof and Gerta Köster},

url = {https://www.sciencedirect.com/science/article/pii/S0925753521002307},

doi = {https://doi.org/10.1016/j.ssci.2021.105386},

issn = {0925-7535},

year  = {2021},

date = {2021-01-01},

journal = {Safety Science},

volume = {142},

pages = {105386},

abstract = {New generation mobile communication protocols, such as the 5G standards, allow direct communication between devices. This allows to disseminate information directly in a moving crowd. In a safety concept, this information could be used to redirect pedestrians away from danger. We couple state-of-the-art computer models of pedestrian motion and mobile device-to-device communication to build a model of this complex socio-technical system. The model captures the interplay between information dissemination and human behavior. We further harness methods of uncertainty quantification to pinpoint the parameters that most influence the systems functionality for a scenario where pedestrians are redirected. We bundle successful analysis methods to suggest a procedure for future studies. We find that, in our scenario, there are rare cases of information dissemination delayed by shadowing and additional network load from apps, where agents cannot be redirected in time. Our simulation tools and methodology can help to detect such problems and serve as a basis to investigate more complex scenarios and rerouting strategies.},

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pubstate = {published},

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@article{mayr-2021-cdyn,

title = {Social distancing with the Optimal Steps Model},

author = {Christina Maria Mayr and Gerta Köster},

doi = {10.17815/CD.2021.116},

year  = {2021},

date = {2021-01-01},

urldate = {2020-01-01},

journal = {Collective Dynamics},

volume = {6},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kleinmeier-2020b,

title = {Agent-based simulation of collective cooperation: from experiment to model},

author = {Benedikt Kleinmeier and Gerta Köster and John Drury},

doi = {10.1098/rsif.2020.0396},

year  = {2020},

date = {2020-10-07},

journal = {Journal of The Royal Society Interface},

volume = {17},

number = {171},

pages = {20200396},

abstract = {Simulation models of pedestrian dynamics have become an invaluable tool for evacuation planning. Typically, crowds are assumed to stream unidirectionally towards a safe area. Simulated agents avoid collisions through mechanisms that belong to each individual, such as being repelled from each other by imaginary forces. But classic locomotion models fail when collective cooperation is called for, notably when an agent, say a first-aid attendant, needs to forge a path through a densely packed group. We present a controlled experiment to observe what happens when humans pass through a dense static crowd. We formulate and test hypotheses on salient phenomena. We discuss our observations in a psychological framework. We derive a model that incorporates: agents’ perception and cognitive processing of a situation that needs cooperation; selection from a portfolio of behaviours, such as being cooperative; and a suitable action, such as swapping places. Agents’ ability to successfully get through a dense crowd emerges as an effect of the psychological model.},

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pubstate = {published},

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@article{lehmberg-2020-cdyn,

title = {datafold: data-driven models for point clouds and time series on manifolds},

author = {Daniel Lehmberg and Felix Dietrich and Gerta Köster and Hans-Joachim Bungartz},

doi = {10.21105/joss.02283},

year  = {2020},

date = {2020-01-01},

journal = {Journal of Open Source Software},

volume = {5},

number = {51},

pages = {2283},

publisher = {The Open Journal},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kleinmeier-2020-cdyn,

title = {Agent-Based Simulation of Collective Cooperation: From Experiment to Model},

author = {Benedikt Kleinmeier and Gerta Köster and John Drury},

doi = {10.1098/rsif.2020.0396},

issn = {1742-5662},

year  = {2020},

date = {2020-01-01},

journal = {Journal of the Royal Society Interface},

volume = {17},

pages = {20200396},

abstract = {Simulation models of pedestrian dynamics have become an invaluable tool for evacuation planning. Typically crowds are assumed to stream unidirectionally towards a safe area. Simulated agents avoid collisions through mechanisms that belong to each individual, such as being repelled from each other by imaginary forces. But classic locomotion models fail when collective cooperation is called for, notably when an agent, say a first-aid attendant, needs to forge a path through a densely packed group. We present a controlled experiment to observe what happens when humans pass through a dense static crowd. We formulate and test hypothesis on salient phenomena. We discuss our observations in a psychological framework. We derive a model that incorporates: agents' perception and cognitive processing of a situation that needs cooperation; selection from a portfolio of behaviours, such as being cooperative; and a suitable action, such as swapping places. Agents' ability to successfully get through a dense crowd emerges as an effect of the psychological model.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{goedel-2020-cdyn,

title = {Sensitivity Analysis for Microscopic Crowd Simulation},

author = {Marion Gödel and Rainer Fischer and Gerta Köster},

doi = {10.3390/a13070162},

year  = {2020},

date = {2020-01-01},

issuetitle = {Methods and Applications of Uncertainty Quantification in Engineering and Science},

journal = {Algorithms},

volume = {13},

abstract = {Microscopic crowd simulation can help to enhance the safety of pedestrians in situations that range from museum visits to music festivals. To obtain a useful prediction, the input parameters must be chosen carefully. In many cases, a lack of knowledge or limited measurement accuracy add uncertainty to the input. In addition, for meaningful parameter studies, we first need to identify the most influential parameters of our parametric computer models. The field of uncertainty quantification offers standardized and fully automatized methods that we believe to be beneficial for pedestrian dynamics. In addition, many methods come at a comparatively low cost, even for computationally expensive problems. This allows for their application to larger scenarios. We aim to identify and adapt fitting methods to microscopic crowd simulation in order to explore their potential in pedestrian dynamics. In this work, we first perform a variance-based sensitivity analysis using Sobol' indices and then crosscheck the results by a derivative-based measure, the activity scores. We apply both methods to a typical scenario in crowd simulation, a bottleneck. Because constrictions can lead to high crowd densities and delays in evacuations, several experiments and simulation studies have been conducted for this setting. We show qualitative agreement between the results of both methods. Additionally, we identify a one-dimensional subspace in the input parameter space and discuss its impact on the simulation. Moreover, we analyze and interpret the sensitivity indices with respect to the bottleneck scenario.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{goedel-2018-cdyn,

title = {Can we learn where people go?},

author = {Marion Gödel and Gerta Köster and Daniel Lehmberg and Manfred Gruber and Angelika Kneidl and Florian Sesser},

doi = {10.17815/CD.2020.43},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of Pedestrian and Evacuation Dynamics 2018},

journal = {Collective Dynamics},

abstract = {In most agent-based simulators, pedestrians navigate from origins to destinations. Consequently, destinations are essential input parameters to the simulation. While many other relevant parameters as positions, speeds and densities can be obtained from sensors, like cameras, destinations cannot be observed directly. Our research question is: Can we obtain this information from video data using machine learning methods? We usedensity heatmaps, which indicate the pedestrian density within a given camera cutout, as input to predict the destination distributions. For our proof of concept, we train a Random Forest predictor on an exemplary data set generated with the Vadere microscopic simulator. The scenario is a crossroad where pedestrians can head left, straight or right. In addition, we gain first insights on suitable placement of the camera. The results motivate an in-depth analysis of the methodology.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{koester-2019-cdyn,

title = {Walking on stairs: Experiment and model},

author = {Gerta Köster and Daniel Lehmberg and Angelika Kneidl},

doi = {10.1103/PhysRevE.100.022310},

year  = {2019},

date = {2019-01-01},

journal = {Phys. Rev. E},

volume = {100},

pages = {022310},

publisher = {American Physical Society},

abstract = {An increasing global population forces urban planners to construct buildings and infrastructure that is extremely deep and high. Elevators and escalators serve skyscrapers and tunnels, but in an emergency people still have to walk on stairs. Computer simulations can mitigate risks of escape situations. For these situations,pedestrian locomotion models need to match reality well. Motion on stairs, however, is not nearly as well understood as motion in the plane. Publications are scarce and some arecontradictory. As a result, movement on stairs is usually modeled by slowing down pedestrians by a fixed factor. But is this justified? And what happens at intermediate landings? This contribution aims to clarify inconclusive results of previous research and provide new information to directly incorporate empirical results into a parsimoniouscomputer model. The algorithms are freely available through an open-source framework. After outlining the shortcomings of existing approaches, we present three experiments, from which we derive requirements for the computer model. Reenacting computer experiments shows the extent to which our model meets our observations. We conclude with an applied example, simulating an evacuation of Germany's famous Neuschwanstein Castle.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{schoettl-2019-cdyn,

title = {Investigating the Randomness of Passengers' Seating Behavior in Suburban Trains},

author = {Jakob Schöttl and Michael J Seitz and Gerta Köster},

doi = {10.3390/e21060600},

issn = {1099-4300},

year  = {2019},

date = {2019-01-01},

journal = {Entropy},

volume = {21},

number = {6},

abstract = {In pedestrian dynamics, individual-based models serve to simulate the behavior of crowds so that evacuation times and crowd densities can be estimated or the efficiency of public transportation optimized. Often, train systems are investigated where seat choice may have a great impact on capacity utilization, especially when passengers get in each other's way. Therefore, it is useful to reproduce passengers' behavior inside trains. However, there is surprisingly little research on the subject. Do passengers distribute evenly as it is most often assumed in simulation models and as one would expect from a system that obeys the laws of thermodynamics? Conversely, is there a higher degree of order? To answer these questions, we collect data on seating behavior in Munich's suburban trains and analyze it. Clear preferences are revealed that contradict the former assumption of a uniform distribution. We subsequently introduce a model that matches the probability distributions we observed. We demonstrate the applicability of our model and present a qualitative validation with a simulation example. The model's implementation is part of the free and open-source Vadere simulation framework for pedestrian dynamics and thus available for further studies. The model can be used as one component in larger systems for the simulation of public transport.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{koester-2019b-cdyn,

title = {Walking on stairs: field experiment},

author = {Gerta Köster and Daniel Lehmberg},

editor = {Forschungszentrum Jülich. Institute for Advanced Simulation 7: Civil Safety Research},

url = {https://ped.fz-juelich.de/da/doku.php?id=walking_stairs},

year  = {2019},

date = {2019-01-01},

urldate = {2019-11-06},

abstract = {The basis of the data are two videos of pedestrians walking on stairs from field experiments at the University of Applied Sciences Munich (Hochschule München).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kleinmeier-2019-cdyn,

title = {Vadere: An Open-Source Simulation Framework to Promote Interdisciplinary Understanding},

author = {Benedikt Kleinmeier and Benedikt Zönnchen and Marion Gödel and Gerta Köster},

doi = {10.17815/CD.2019.21},

year  = {2019},

date = {2019-01-01},

journal = {Collective Dynamics},

volume = {4},

abstract = {Pedestrian dynamics is an interdisciplinary field of research. Psychologists, sociologists, traffic engineers, physicists, mathematicians and computer scientists all strive to understand the dynamics of a moving crowd. 

In principle, computer simulations offer means to further this understanding. Yet, unlike for many classic dynamical systems in physics, there is no universally accepted locomotion model for crowd dynamics. On the contrary, a multitude 

of approaches, with very different characteristics, compete. Often only the experts in one special model type are able to assess the consequences these characteristics have on a simulation study. Therefore, scientists from all disciplines who 

wish to use simulations to analyze pedestrian dynamics need a tool to compare competing approaches. Developers, too, would profit from an easy way to get insight into an alternative modeling ansatz. Vadere meets this interdisciplinary demand 

by offering an open-source simulation framework that is lightweight in its approach and in its user interface while offering pre-implemented versions of the most widely spread models.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kleinmeier-2019,

title = {Vadere: An Open-Source Simulation Framework to Promote Interdisciplinary Understanding},

author = {Benedikt Kleinmeier and Benedikt Zönnchen and Marion Gödel and Gerta Köster},

doi = {10.17815/CD.2019.21},

year  = {2019},

date = {2019-01-01},

journal = {Collective Dynamics},

volume = {4},

abstract = {Pedestrian dynamics is an interdisciplinary field of research. Psychologists, sociologists, traffic engineers, physicists, mathematicians and computer scientists all strive to understand the dynamics of a moving crowd. 

In principle, computer simulations offer means to further this understanding. Yet, unlike for many classic dynamical systems in physics, there is no universally accepted locomotion model for crowd dynamics. On the contrary, a multitude 

of approaches, with very different characteristics, compete. Often only the experts in one special model type are able to assess the consequences these characteristics have on a simulation study. Therefore, scientists from all disciplines who 

wish to use simulations to analyze pedestrian dynamics need a tool to compare competing approaches. Developers, too, would profit from an easy way to get insight into an alternative modeling ansatz. Vadere meets this interdisciplinary demand 

by offering an open-source simulation framework that is lightweight in its approach and in its user interface while offering pre-implemented versions of the most widely spread models.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zoennchen-2018-cdyn,

title = {A parallel generator for sparse unstructured meshes to solve the eikonal equation},

author = {Benedikt Zönnchen and Gerta Köster},

doi = {10.1016/j.jocs.2018.09.009},

issn = {1877-7503},

year  = {2018},

date = {2018-01-01},

journal = {Journal of Computational Science},

volume = {32},

pages = {141--147},

abstract = {Mesh generation is the first step in a wide range of applications including navigation for robots or virtual agents in pedestrian simulations. To find the shortest travel time to a target, a common technique is to solve the eikonal equation on a mesh. We propose EikMesh, an extension of the DistMesh algorithm. EikMesh is a fast parallel mesh generator that reduces the number of mesh points, and thus the computation time, while maintaining precision of numerical solvers on the mesh. It automatically refines where desired, in our case, where the eikonal equation undergoes changes, e.g. near obstacles. The first crucial step is the generation of a sophisticated initial mesh which reduces the number of smoothing steps. In addition, EikMesh avoids expensive Delaunay-re-triangulations. Space filling curves manage storage space in a cache-friendly manner. EikMesh scales better than the parallelized traditional DistMesh and significantly outperforms it for a number of test cases.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dietrich-2018-cdyn,

title = {Fast and flexible uncertainty quantification through a data-driven surrogate model},

author = {Felix Dietrich and Florian Künzner and Tobias Neckel and Gerta Köster and Hans-Joachim Bungartz},

doi = {10.1615/Int.J.UncertaintyQuantification.2018021975},

year  = {2018},

date = {2018-01-01},

journal = {International Journal for Uncertainty Quantification},

volume = {8},

pages = {175--192},

abstract = {To assess a computer model's descriptive and predictive power, the model's response to uncertainties in the input must be quantified. However, simulations of complex systems typically need a lot of computational resources, and thus prohibit exhaustive sweeps of high-dimensional spaces. Moreover, the time available to compute a result for decision systems is often very limited. In this paper, we construct a data-driven surrogate model from time delays of observations of a complex, microscopic model. We employ diffusion maps to reduce the dimensionality of the delay space. The surrogate model allows faster generation of the quantity of interest over time than the original, microscopic model. It is a non-intrusive method, and hence does not need access to the model formulation. In contrast to most other surrogate approaches, the construction allows quantities of interest that are not closed dynamically, because a closed state space is constructed through Takens delay embedding. Also, the surrogate can be stored to and loaded from storage with very little effort. The surrogate model is decoupled from the original model, and the fast execution speed allows to quickly evaluate many different parameter distributions. We demonstrate the capability of the approach in combination with forward UQ on a parametrized Burgers? equation, and the microscopic simulation of a train station. The surrogate model can accurately capture the dynamical features in both examples, with relative errors always smaller than ten percent. The simulation time in the real-world example can be reduced by an order of magnitude.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dietrich-2016b-cdyn,

title = {Numerical Model Construction with Closed Observables},

author = {Felix Dietrich and Gerta Köster and Hans-Joachim Bungartz},

doi = {10.1137/15M1043613},

year  = {2016},

date = {2016-01-01},

journal = {SIAM Journal on Applied Dynamical Systems},

volume = {15},

number = {4},

pages = {2078--2108},

keywords = {},

pubstate = {published},

tppubtype = {article}

}