2022
Ahmady-Moghaddam, Nima; Osterholz, Daniel; Clemen, Thomas
Implementation and Application of a Base Model for Agent-Based Modelling Situated in Hamburg, Germany Konferenzbeitrag
In: Simulation in den Umwelt- und Geowissenschaften (Workshop Müncheberg 2021), S. 99–110, GI (Gesellschaft für Informatik) Shaker Verlag, 2022, ISBN: 978-3-8440-8551-8.
Abstract | BibTeX | Tags: agent-based model, decision support systems, MARS, model development, multimodal travel, sohh, urban planning
@inproceedings{Ahm2022a,
title = {Implementation and Application of a Base Model for Agent-Based Modelling Situated in Hamburg, Germany},
author = {Ahmady-Moghaddam, Nima and Osterholz, Daniel and Clemen, Thomas},
isbn = {978-3-8440-8551-8},
year = {2022},
date = {2022-04-29},
booktitle = {Simulation in den Umwelt- und Geowissenschaften (Workshop Müncheberg 2021)},
volume = {1},
issue = {1},
pages = {99--110},
publisher = {Shaker Verlag},
organization = {GI (Gesellschaft für Informatik)},
abstract = {Agent-based models (ABMs) that are set in a specific geographic region tend to be rooted in similar geodata to define the setting and, by extension, tend to have a similar representation in their
environments. At the core, their agent types also tend to be similar. For example, ABMs that model a city’s traffic flow are likely populated by representations of vehicles and people as agents, just as ABMs that model the spread of infectious diseases might be populated by people and pathogens as agents. If these data and components that are common to ABMs with similar settings are gathered and implemented in a generalized fashion, the resulting model can potentially be used to develop a wide range of more domain-specific scenarios in the given setting. We refer to such a model as a base model. In this article, we describe the conception and implementation of a base model for the city of Hamburg, Germany. The process of data collection and preparation is outlined, and the portability of the approach to other geographic settings is highlighted. The base model's applicability is demonstrated by using it to create two simulation scenarios, each focused on a different domain and research question. Simulation data are analysed to address the research questions and showcase the base model’s potential. While data availability is one of the main limiting factors of a base model's efficacy, we find that a well-maintained and up-to-date base model can be a valuable tool for modellers and stakeholders, especially when required to make informed decisions under time constraints.},
keywords = {agent-based model, decision support systems, MARS, model development, multimodal travel, sohh, urban planning},
pubstate = {published},
tppubtype = {inproceedings}
}
Agent-based models (ABMs) that are set in a specific geographic region tend to be rooted in similar geodata to define the setting and, by extension, tend to have a similar representation in their
environments. At the core, their agent types also tend to be similar. For example, ABMs that model a city’s traffic flow are likely populated by representations of vehicles and people as agents, just as ABMs that model the spread of infectious diseases might be populated by people and pathogens as agents. If these data and components that are common to ABMs with similar settings are gathered and implemented in a generalized fashion, the resulting model can potentially be used to develop a wide range of more domain-specific scenarios in the given setting. We refer to such a model as a base model. In this article, we describe the conception and implementation of a base model for the city of Hamburg, Germany. The process of data collection and preparation is outlined, and the portability of the approach to other geographic settings is highlighted. The base model's applicability is demonstrated by using it to create two simulation scenarios, each focused on a different domain and research question. Simulation data are analysed to address the research questions and showcase the base model’s potential. While data availability is one of the main limiting factors of a base model's efficacy, we find that a well-maintained and up-to-date base model can be a valuable tool for modellers and stakeholders, especially when required to make informed decisions under time constraints.
environments. At the core, their agent types also tend to be similar. For example, ABMs that model a city’s traffic flow are likely populated by representations of vehicles and people as agents, just as ABMs that model the spread of infectious diseases might be populated by people and pathogens as agents. If these data and components that are common to ABMs with similar settings are gathered and implemented in a generalized fashion, the resulting model can potentially be used to develop a wide range of more domain-specific scenarios in the given setting. We refer to such a model as a base model. In this article, we describe the conception and implementation of a base model for the city of Hamburg, Germany. The process of data collection and preparation is outlined, and the portability of the approach to other geographic settings is highlighted. The base model's applicability is demonstrated by using it to create two simulation scenarios, each focused on a different domain and research question. Simulation data are analysed to address the research questions and showcase the base model’s potential. While data availability is one of the main limiting factors of a base model's efficacy, we find that a well-maintained and up-to-date base model can be a valuable tool for modellers and stakeholders, especially when required to make informed decisions under time constraints.
2021
Lenfers, Ulfia Annette; Ahmady-Moghaddam, Nima; Glake, Daniel; Ocker, Florian; Ströbele, Jonathan; Clemen, Thomas
In: Land 2021, Bd. 11, Nr. 10, 2021, ISSN: 2073-445X.
Abstract | Links | BibTeX | Tags: adaptive behavior, agent-based model, decision support systems, multimodal travel, sohh, urban planning
@article{Lenfers2021b,
title = {Incorporating Multi-Modal Travel Planning into an Agent-Based Model: A Case Study at the Train Station Kellinghusenstraße in Hamburg},
author = {Ulfia Annette Lenfers and Nima Ahmady-Moghaddam and Daniel Glake and Florian Ocker and Jonathan Ströbele and Thomas Clemen},
editor = {Simon Elias Bibri},
url = {https://www.mdpi.com/2073-445X/10/11/1179/htm},
doi = {10.3390/land10111179},
issn = {2073-445X},
year = {2021},
date = {2021-11-03},
journal = {Land 2021},
volume = {11},
number = {10},
abstract = {Models can provide valuable decision support in the ongoing effort to create a sustainable and effective modality mix in urban settings. Modern transportation infrastructures must meaningfully combine public transport with other mobility initiatives such as shared and on-demand systems. The increase of options and possibilities in multi-modal travel implies an increase in complexity when planning and implementing such an infrastructure. Multi-agent systems are well-suited for addressing questions that require an understanding of movement patterns and decision processes at the individual level. Such models should feature intelligent software agents with flexible internal logic and accurately represent the core functionalities of new modalities. We present a model in which agents can choose between owned modalities, station-based bike sharing modalities, and free-floating car sharing modalities as they exit the public transportation system and seek to finish their personal multi-modal trip. Agents move on a multi-modal road network where dynamic constraints in route planning are evaluated based on an agent’s query. Modality switch points (MSPs) along the route indicate the locations at which an agent can switch from one modality to the next (e.g., a bike rental station to return a used rental bike and continue on foot). The technical implementation of MSPs within the road network was a central focus in this work. To test their efficacy in a controlled experimental setting, agents optimized only the travel time of their multi-modal routes. However, the functionalities of the model enable the implementation of different optimization criteria (e.g., financial considerations or climate neutrality) and unique agent preferences as well. Our findings show that the implemented MSPs enable agents to switch between modalities at any time, allowing for the kind of versatile, individual, and spontaneous travel that is common in modern multi-modal settings. },
keywords = {adaptive behavior, agent-based model, decision support systems, multimodal travel, sohh, urban planning},
pubstate = {published},
tppubtype = {article}
}
Models can provide valuable decision support in the ongoing effort to create a sustainable and effective modality mix in urban settings. Modern transportation infrastructures must meaningfully combine public transport with other mobility initiatives such as shared and on-demand systems. The increase of options and possibilities in multi-modal travel implies an increase in complexity when planning and implementing such an infrastructure. Multi-agent systems are well-suited for addressing questions that require an understanding of movement patterns and decision processes at the individual level. Such models should feature intelligent software agents with flexible internal logic and accurately represent the core functionalities of new modalities. We present a model in which agents can choose between owned modalities, station-based bike sharing modalities, and free-floating car sharing modalities as they exit the public transportation system and seek to finish their personal multi-modal trip. Agents move on a multi-modal road network where dynamic constraints in route planning are evaluated based on an agent’s query. Modality switch points (MSPs) along the route indicate the locations at which an agent can switch from one modality to the next (e.g., a bike rental station to return a used rental bike and continue on foot). The technical implementation of MSPs within the road network was a central focus in this work. To test their efficacy in a controlled experimental setting, agents optimized only the travel time of their multi-modal routes. However, the functionalities of the model enable the implementation of different optimization criteria (e.g., financial considerations or climate neutrality) and unique agent preferences as well. Our findings show that the implemented MSPs enable agents to switch between modalities at any time, allowing for the kind of versatile, individual, and spontaneous travel that is common in modern multi-modal settings.
Glake, Daniel; Ritter, Norbert; Ocker, Florian; Ahmady-Moghaddam, Nima; Osterholz, Daniel; Lenfers, Ulfia; Clemen, Thomas
Hierarchical Semantics Matching For Heterogeneous Spatio-Temporal Sources Buchkapitel
In: S. 565–575, Association for Computing Machinery, New York, NY, USA, 2021, ISBN: 978-1-4503-8446-9.
Abstract | Links | BibTeX | Tags: agent-based model. sohh, MARS, Multi-Agent Model, sohh
@inbook{Glake2021a,
title = {Hierarchical Semantics Matching For Heterogeneous Spatio-Temporal Sources},
author = {Glake, Daniel and Ritter, Norbert and Ocker, Florian and Ahmady-Moghaddam, Nima and Osterholz, Daniel and Lenfers, Ulfia and Clemen, Thomas},
url = {https://doi.org/10.1145/3459637.3482350},
doi = {10.1145/3459637.3482350},
isbn = {978-1-4503-8446-9},
year = {2021},
date = {2021-10-26},
pages = {565–575},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Spatio-temporal data are semantically valuable information used for various analytical tasks to identify spatially relevant and temporally limited correlations within a domain. The increasing availability and data acquisition from multiple sources with their typically high heterogeneity are getting more and more attention. However, these sources often lack interconnecting shared keys, making their integration a challenging problem. For example, publicly available parking data that consist of point data on parking facilities with fluctuating occupancy and static location data on parking spaces cannot be directly correlated. Both data sets describe two different aspects from distinct sources in which parking spaces and fluctuating occupancy are part of the same semantic model object. Especially for ad hoc analytical tasks on integrated models, these missing relationships cannot be handled using join operations as usual in relational databases. The reason lies in the lack of equijoin relationships, comparing for equality of strings and additional overhead in loading data up before processing. This paper addresses the optimization problem of finding suitable partners in the absence of equijoin relations for heterogeneous spatio-temporal data, applicable to ad hoc analytics. We propose a graph-based approach that achieves good recall and performance scaling via hierarchically separating the semantics along spatial, temporal, and domain-specific dimensions. We evaluate our approach using public data, showing that it is suitable for many standard join scenarios and highlighting its limitations.},
keywords = {agent-based model. sohh, MARS, Multi-Agent Model, sohh},
pubstate = {published},
tppubtype = {inbook}
}
Spatio-temporal data are semantically valuable information used for various analytical tasks to identify spatially relevant and temporally limited correlations within a domain. The increasing availability and data acquisition from multiple sources with their typically high heterogeneity are getting more and more attention. However, these sources often lack interconnecting shared keys, making their integration a challenging problem. For example, publicly available parking data that consist of point data on parking facilities with fluctuating occupancy and static location data on parking spaces cannot be directly correlated. Both data sets describe two different aspects from distinct sources in which parking spaces and fluctuating occupancy are part of the same semantic model object. Especially for ad hoc analytical tasks on integrated models, these missing relationships cannot be handled using join operations as usual in relational databases. The reason lies in the lack of equijoin relationships, comparing for equality of strings and additional overhead in loading data up before processing. This paper addresses the optimization problem of finding suitable partners in the absence of equijoin relations for heterogeneous spatio-temporal data, applicable to ad hoc analytics. We propose a graph-based approach that achieves good recall and performance scaling via hierarchically separating the semantics along spatial, temporal, and domain-specific dimensions. We evaluate our approach using public data, showing that it is suitable for many standard join scenarios and highlighting its limitations.
Nima Ahmady-Moghaddam Ulfia A. Lenfers, Daniel Glake; Clemen, Thomas
In: Sustainability, Bd. 13, Nr. 13, 2021.
Abstract | Links | BibTeX | Tags: agent-based model, decision support systems, IoT sensors, MARS, model development, multimodal travel, real-time data, simulation correction, smart cities, sohh, urban planning
@article{Lenfers2021,
title = {Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model},
author = {Ulfia A. Lenfers, Nima Ahmady-Moghaddam, Daniel Glake, Florian Ocker, Daniel Osterholz, Jonathan Ströbele and Thomas Clemen},
editor = {Philippe J. Giabbanelli and Arika Ligmann-Zielinska},
url = {https://doi.org/10.3390/su13137000},
doi = {10.3390/su13137000},
year = {2021},
date = {2021-06-22},
journal = {Sustainability},
volume = {13},
number = {13},
abstract = {The current trend towards living in big cities contributes to an increased demand for efficient and sustainable space and resource allocation in urban environments. This leads to enormous pressure for resource minimization in city planning. One pillar of efficient city management is a smart intermodal traffic system. Planning and organizing the various kinds of modes of transport in a complex and dynamically adaptive system such as a city is inherently challenging. By deliberately simplifying reality, models can help decision-makers shape the traffic systems of tomorrow. Meanwhile, Smart City initiatives are investing in sensors to observe and manage many kinds of urban resources, making up a part of the Internet of Things (IoT) that produces massive amounts of data relevant for urban planning and monitoring. We use these new data sources of smart cities by integrating real-time data of IoT sensors in an ongoing simulation. In this sense, the model is a digital twin of its real-world counterpart, being augmented with real-world data. To our knowledge, this is a novel instance of real-time correction during simulation of an agent-based model. The process of creating a valid mapping between model components and real-world objects posed several challenges and offered valuable insights, particularly when studying the interaction between humans and their environment. As a proof-of-concept for our implementation, we designed a showcase with bike rental stations in Hamburg-Harburg, a southern district of Hamburg, Germany. Our objective was to investigate the concept of real-time data correction in agent-based modeling, which we consider to hold great potential for improving the predictive capabilities of models. In particular, we hope that the chosen proof-of-concept informs the ongoing politically supported trends in mobility—away from individual and private transport and towards—in Hamburg.},
keywords = {agent-based model, decision support systems, IoT sensors, MARS, model development, multimodal travel, real-time data, simulation correction, smart cities, sohh, urban planning},
pubstate = {published},
tppubtype = {article}
}
The current trend towards living in big cities contributes to an increased demand for efficient and sustainable space and resource allocation in urban environments. This leads to enormous pressure for resource minimization in city planning. One pillar of efficient city management is a smart intermodal traffic system. Planning and organizing the various kinds of modes of transport in a complex and dynamically adaptive system such as a city is inherently challenging. By deliberately simplifying reality, models can help decision-makers shape the traffic systems of tomorrow. Meanwhile, Smart City initiatives are investing in sensors to observe and manage many kinds of urban resources, making up a part of the Internet of Things (IoT) that produces massive amounts of data relevant for urban planning and monitoring. We use these new data sources of smart cities by integrating real-time data of IoT sensors in an ongoing simulation. In this sense, the model is a digital twin of its real-world counterpart, being augmented with real-world data. To our knowledge, this is a novel instance of real-time correction during simulation of an agent-based model. The process of creating a valid mapping between model components and real-world objects posed several challenges and offered valuable insights, particularly when studying the interaction between humans and their environment. As a proof-of-concept for our implementation, we designed a showcase with bike rental stations in Hamburg-Harburg, a southern district of Hamburg, Germany. Our objective was to investigate the concept of real-time data correction in agent-based modeling, which we consider to hold great potential for improving the predictive capabilities of models. In particular, we hope that the chosen proof-of-concept informs the ongoing politically supported trends in mobility—away from individual and private transport and towards—in Hamburg.
Clemen, Thomas; Ahmady-Moghaddam, Nima; Lenfers, Ulfia A; Ocker, Florian; Osterholz, Daniel; Ströbele, Jonathan; Glake, Daniel
Multi-Agent Systems and Digital Twins for Smarter Cities Konferenzbeitrag
In: Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, S. 45–55, ACM, 2021, ISBN: 9781450382960.
Abstract | Links | BibTeX | Tags: digital twin, IoT, MARS, sohh
@inproceedings{Clemen2021,
title = {Multi-Agent Systems and Digital Twins for Smarter Cities},
author = {Thomas Clemen and Nima Ahmady-Moghaddam and Ulfia A Lenfers and Florian Ocker and Daniel Osterholz and Jonathan Ströbele and Daniel Glake},
url = {https://dl.acm.org/doi/10.1145/3437959.3459254},
doi = {10.1145/3437959.3459254},
isbn = {9781450382960},
year = {2021},
date = {2021-05-01},
booktitle = {Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation},
volume = {1},
number = {1},
pages = {45–55},
publisher = {ACM},
abstract = {An intelligent combination of the Internet of Things (IoT) and approaches to modeling and simulation is one of the most challenging endeavors for future cities, manufacturing industries, and predictive maintenance. Digital Twins take on a unique role here. However, the question of what a Digital Twin is and what differentiates it from a regular model is still open. We present an experimental setup for integrating an existing simulation model of Hamburg's traffic system with the city's real-time sensor network. The Digital Twin is implemented using the large-scale multi-agent framework MARS. The entire process from the model description to retrieving real-time data from the IoT sensors and incorporating it in the simulation is presented. As a first prototypical example, a multi-modal mobility model was connected to real-world bike-sharing locations in Hamburg. We find that the combination of multi-agent systems and IoT sensors as a Digital Twin shows enormous potential for city planners, policy stakeholders, and other decision-makers. By correcting the course of a simulation via real-time data, the corridor-of-uncertainty that is intrinsic to some simulation models' use can be reduced significantly. Furthermore, any divergence of simulated and sampled data can lead to a deeper understanding of complex adaptive systems like big cities.},
keywords = {digital twin, IoT, MARS, sohh},
pubstate = {published},
tppubtype = {inproceedings}
}
An intelligent combination of the Internet of Things (IoT) and approaches to modeling and simulation is one of the most challenging endeavors for future cities, manufacturing industries, and predictive maintenance. Digital Twins take on a unique role here. However, the question of what a Digital Twin is and what differentiates it from a regular model is still open. We present an experimental setup for integrating an existing simulation model of Hamburg's traffic system with the city's real-time sensor network. The Digital Twin is implemented using the large-scale multi-agent framework MARS. The entire process from the model description to retrieving real-time data from the IoT sensors and incorporating it in the simulation is presented. As a first prototypical example, a multi-modal mobility model was connected to real-world bike-sharing locations in Hamburg. We find that the combination of multi-agent systems and IoT sensors as a Digital Twin shows enormous potential for city planners, policy stakeholders, and other decision-makers. By correcting the course of a simulation via real-time data, the corridor-of-uncertainty that is intrinsic to some simulation models' use can be reduced significantly. Furthermore, any divergence of simulated and sampled data can lead to a deeper understanding of complex adaptive systems like big cities.
2020
Weyl, Julius; Lenfers, Ulfia; Clemen, Thomas; Glake, Daniel; Panse, Fabian; Ritter, Norbert
LARGE-SCALE TRAFFIC SIMULATION FOR SMART CITY PLANNING WITH MARS Konferenz
2020.
Abstract | Links | BibTeX | Tags: sohh
@conference{Weyl2020,
title = {LARGE-SCALE TRAFFIC SIMULATION FOR SMART CITY PLANNING WITH MARS},
author = {Julius Weyl and Ulfia Lenfers and Thomas Clemen and Daniel Glake and Fabian Panse and Norbert Ritter},
url = {https://www.researchgate.net/publication/338886618_LARGE-SCALE_TRAFFIC_SIMULATION_FOR_SMART_CITY_PLANNING_WITH_MARS},
year = {2020},
date = {2020-01-01},
abstract = {Understanding individual mobility in larger cities is an important success factor for future smart cities. Related simulation scenarios incorporate enormous numbers of agents, with the disadvantage of long run times. In order to provide large-scale and multimodal traffic simulations, we developed MARS V3. Adapting the Modeling and Simulation as a Service (MSaaS) paradigm, a seamless workflow can be provided to the modeling community. An integrated domain-specific language allows model descriptions without a technical overhead. For this study, selected parts of an individual-based traffic model of the City of Hamburg, Germany, were taken as an example. The entire workflow from model development, open data integration, simulation, and result analysis will be described and evaluated. Performance was measured for local and cloud-based simulation execution for up to one million agents. First results show that this concept can be utilized for building decision support systems for smart cities in the near future.},
keywords = {sohh},
pubstate = {published},
tppubtype = {conference}
}
Understanding individual mobility in larger cities is an important success factor for future smart cities. Related simulation scenarios incorporate enormous numbers of agents, with the disadvantage of long run times. In order to provide large-scale and multimodal traffic simulations, we developed MARS V3. Adapting the Modeling and Simulation as a Service (MSaaS) paradigm, a seamless workflow can be provided to the modeling community. An integrated domain-specific language allows model descriptions without a technical overhead. For this study, selected parts of an individual-based traffic model of the City of Hamburg, Germany, were taken as an example. The entire workflow from model development, open data integration, simulation, and result analysis will be described and evaluated. Performance was measured for local and cloud-based simulation execution for up to one million agents. First results show that this concept can be utilized for building decision support systems for smart cities in the near future.
2019
Glake, Daniel; Weyl, Julius; Lenfers, Ulfia; Clemen, Thomas
SmartOpenHamburg Verkehrssimulation: Automatisierte OpenData Integration für Multi-Agenten Simulationen mit MARS Konferenz
2019.
Abstract | BibTeX | Tags: sohh
@conference{Glake2019,
title = {SmartOpenHamburg Verkehrssimulation: Automatisierte OpenData Integration für Multi-Agenten Simulationen mit MARS},
author = {Daniel Glake and Julius Weyl and Ulfia Lenfers and Thomas Clemen},
year = {2019},
date = {2019-08-01},
abstract = {Die Analyse von geographischen Daten, insbesondere von Straßennetzwerken ist für Stadtplaner und Entscheider von großer Bedeutung. Um Veränderungen in der Stadtentwicklung zu planen, werden häufig Verkehrssimulationen eingesetzt, die zunehmend durch Multi-Agenten Modelle realisiert werden. Die daraus resultierenden Analysemöglichkeiten auf Graphen, Vektordaten und Simulationsergebnisse, sowohl mit zeitlichem Verlauf oder bezogen auf einen festgelegten Zeitpunkt, sind vielfältig. Diese Möglichkeiten werden jedoch häufig durch begrenzte Datenverfügbarkeit- und konsistenz als Simulationsgrundlage eingeschränkt. Um diesen Herausforderungen zu begegnen, wird in dieser Arbeit die MARS OpenData Import Pipeline (https://mars-group.org/smartopenhamburg5/) und ein Ausschnitt des OpenData Werkzeugs vorgestellt, mit dem sich verschiedene OpenData Quellen für spatiale und temporale Daten sammeln und automatisiert in ein gemeinsames Datenmodell integrieren lassen. Der Import ermöglicht die unmittelbare Nutzung der Daten innerhalb einer MARS Simulation zur Bereit-stellung von Agentenumgebungen. Darüber hinaus sind die Daten durch die Verwendung standardisierter GIS Formate auch abseits dessen nutzbar. Wir demonstrieren eine mikroskopische Verkehrssimulation der Stadt Hamburg und erläutern wie diese von der Pipeline aktiv Gebrauch macht.},
keywords = {sohh},
pubstate = {published},
tppubtype = {conference}
}
Die Analyse von geographischen Daten, insbesondere von Straßennetzwerken ist für Stadtplaner und Entscheider von großer Bedeutung. Um Veränderungen in der Stadtentwicklung zu planen, werden häufig Verkehrssimulationen eingesetzt, die zunehmend durch Multi-Agenten Modelle realisiert werden. Die daraus resultierenden Analysemöglichkeiten auf Graphen, Vektordaten und Simulationsergebnisse, sowohl mit zeitlichem Verlauf oder bezogen auf einen festgelegten Zeitpunkt, sind vielfältig. Diese Möglichkeiten werden jedoch häufig durch begrenzte Datenverfügbarkeit- und konsistenz als Simulationsgrundlage eingeschränkt. Um diesen Herausforderungen zu begegnen, wird in dieser Arbeit die MARS OpenData Import Pipeline (https://mars-group.org/smartopenhamburg5/) und ein Ausschnitt des OpenData Werkzeugs vorgestellt, mit dem sich verschiedene OpenData Quellen für spatiale und temporale Daten sammeln und automatisiert in ein gemeinsames Datenmodell integrieren lassen. Der Import ermöglicht die unmittelbare Nutzung der Daten innerhalb einer MARS Simulation zur Bereit-stellung von Agentenumgebungen. Darüber hinaus sind die Daten durch die Verwendung standardisierter GIS Formate auch abseits dessen nutzbar. Wir demonstrieren eine mikroskopische Verkehrssimulation der Stadt Hamburg und erläutern wie diese von der Pipeline aktiv Gebrauch macht.