Insights into a spatially embedded social network from a large-scale snowball sample

Illenberger, Johannes; Kowald, Matthias; Axhausen, Kay W.; Nagel, Kai

doi:10.1140/epjb/e2011-10872-0

Cited by 16 publications

(10 citation statements)

References 41 publications

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“…The value of R 2 close to 1 indicates that the power-law fit has a high goodness. Similar finding is reported in a real survey conducted in the metropolitan area of Zurich, where tie distance observed within 10 km follows a power-low distribution with exponent −0.5 [48].…”

Section: Spatial Characteristics Analysissupporting

confidence: 90%

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Growing Spatially Embedded Social Networks for Activity-Travel Analysis Based on Artificial Transportation Systems

Chen

Zhu

Cao

2014

IEEE Trans. Intell. Transport. Syst.

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Social activity-travel has gained more and more attention as it is a growing percentage of the whole travel. To study its generation mechanism and behavioral characteristics, social network data are usually essential. However, due to individual privacy, it is rather difficult for traditional methods such as questionnaires to collect abundant reliable data. Therefore, we propose a novel method to grow realistic social networks based on artificial transportation systems (ATS). By incorporating the activity-travel simulation provided by ATS and a new agent-based model for social interaction, the method takes into account human mobility to generate spatially embedded social networks. Human mobility shapes and impacts social networks dynamically but is usually ignored by related studies. A case study based on computational experiments is carried out to verify the method. The results indicate that the method can generate social networks with similar topological and spatial characteristics to real social networks.Index Terms-Activity-based traffic simulation, agent, artificial transportation systems (ATS), reinforcement learning, spatially embedded social networks.

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Section: Spatial Characteristics Analysissupporting

confidence: 90%

“…From a large-scale snowball sampling, Illenberger et al found that the attribute that induces the strongest degree of homophily is age, and gender is in the second place [48]. Thus, we take age and gender as agents' social properties and set 2/3 and 1/3 as their respective weights in the social distance calculation.…”

Section: A Parameter Settingsmentioning

confidence: 99%

Growing Spatially Embedded Social Networks for Activity-Travel Analysis Based on Artificial Transportation Systems

Chen

Zhu

Cao

2014

IEEE Trans. Intell. Transport. Syst.

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“…Several transformations of the distance variable were tried. A logarithmic transformation maximizes the goodness-of-fit and was used in the final specification (shown in the table) (see also Illenberger et al, 2011). Based on gender and age-class, similarity measures were defined in a straight forward way.…”

Section: Estimation Resultsmentioning

confidence: 99%

“…The difference is caused by the fact that the model underpredicts very large distances (more than 250 km) which are also rare in the observed set but nevertheless have quite a strong influence on the average and standard deviation. This is due to the log transformation, which penalizes long distance ties (see Illenberger et al, 2011). Furthermore, contributing to this difference is the restriction inherent in the assumed synthetic population that predicted ties are confined to fall within the borders of Switzerland, whereas reported ties are not.…”

Section: Generating a Population-wide Social Networkmentioning

confidence: 99%

An agent-based random-utility-maximization model to generate social networks with transitivity in geographic space

2013

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“…An early social networks study, in context, but not based on MATSim, is by Marchal and Nagel (2005). Further work based on or, again, in context of MATSim was undertaken by Hackney (2009);Illenberger (2012); Illenberger et al (2011);Kowald et al (2009). The most recent work on joint trips is reported in Chapter 28.…”

Section: Considering Social Contactsmentioning

confidence: 99%

The Multi-Agent Transport Simulation MATSim

Axhausen¹

2016

482

123

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PrefaceDeveloping complex so ware for over a decade with a heterogeneous group of engineers and scientists, each with widely di erent skill levels and expertise across multiple locations around the world, requires dedication and mechanisms unusual for a university environment. This book is one of these mechanisms. It allows us, collectively, to take stock and present a coherent state-of-the-system: for us and anyone interested in this approach. It highlights basics for the student who wants to undertake a small rst research project as part of his or her degree, provides a description of the main functionalities, in detail, for the engineer setting up MATSim (MultiAgent Transport Simulation) to conduct a policy analysis and, nally, ts the approach into the theoretical background of complex systems in computer science and physics.The choice of the additional e-book format is an advantage, as it allows us to keep the book upto-date with future chapters, revisions and, if necessary, errata. Equally importantly it allows you, the readers, to select those sections relevant to your needs.The book comes at an important time for the system; for most of the rst decade, its use was limited to the original developers and users in Berlin and Zürich. It is now much more widely consulted around the world, as we document in the chapter summarizing contributions on scenarios so far.Scenario: This term will occur again and again. In MATSim context, it is de ned as the combination of speci c agent populations, their initial plans and activity locations (home, work, education), the network and facilities where, and on which, they compete in time-space for their slots and modules, i.e., behavioral dimensions, which they can adjust during their search for equilibrium. Within these scenarios, the user can experiment and explore with behavioral utility function parameters, with the sampling rate of the population between 1 % and 100 %, with algorithm parameters, e.g., the share of the sample engaged in replanning in any iteration, or behavioral dimensions or exact settings necessary to avoid gridlock due to the tra c ow dynamics. The creation of a scenario is a substantial e ort, and the framework makes a number of tools available to accelerate it: population synthesizers, network editors, network converters between popular formats and the MATSim representation, e.g., OSM (OpenStreetMap) or GTFS (General Transit Feed Speci cation), semi-automatic network matching to join information, among others.A large group of colleagues has been involved and many of them are contributors to this book; this is a list of those involved, other than ourselves, in Berlin, Singapore and Zürich. Additional contributors are mentioned as authors of their respective chapters in this book. We hope to acknowledge the contributions of more colleagues from other groups in future versions of this book and in the so ware.Special thanks go to a number of people who greatly helped improving this book beyond their own chapters. Benjamin Kickhöfer's deep knowledge of ...

show abstract

Insights into a spatially embedded social network from a large-scale snowball sample

Cited by 16 publications

References 41 publications

Growing Spatially Embedded Social Networks for Activity-Travel Analysis Based on Artificial Transportation Systems

Growing Spatially Embedded Social Networks for Activity-Travel Analysis Based on Artificial Transportation Systems

An agent-based random-utility-maximization model to generate social networks with transitivity in geographic space

The Multi-Agent Transport Simulation MATSim

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