Provides the reader with application-orientated tools for studying complex socio-technical systems
Authored by leading researchers in the field
Includes data-driven case studies
As the interconnectivity between humans through technical devices is becoming ubiquitous, the next step is already in the making: ambient intelligence, i.e. smart (technical) environments, which will eventually play the same active role in communication as the human players, leading to a co-evolution in all domains where real-time communication is essential.
This topical volume, based on the findings of the Socionical European research project, gives equal attention to two highly relevant domains of applications: transport, specifically traffic, dynamics from the viewpoint of a socio-technical interaction and evacuation scenarios for large-scale emergency situations.
Care was taken to investigate as much as possible the limits of scalability and to combine the modeling using complex systems science approaches with relevant data analysis.
Part I : Introduction and literature reviews.- The Socionical FP7 Project and an outline of the volume.- Enhancing Crowd Evacuation & Traffic Management, through AmI technologies - A Review of the Literature.- A The Concept of `Co-evolution´ and its Application in the Social Sciences – A Review of the literature.- Part II: EMERGENCY.- Using mobile technology and a participatory sensing approach for crowd monitoring during large-scale mass gatherings.- Agent-based Modelling of Social Emotional Decision Making in Emergency Situations,- Designing Complex Socio-technical Systems: Empirically Grounded Simulations as Tools for Experience-based Design Space Explorations.- Part III: TRANSPORT.- Enhancing Future Mass ICT with Social Capabilities.- Emerging Phenomena During Driving Interactions.- Effective Assessment of AmI Intervention in Traffic Through Quantitative Measures.- Part IV: CITY SCALE.- City Scale Evacuation Simulation: A High-Performance Multi-Agent Simulation Framework.
Based on findings of the Socionical research network, this book examines two domains of applications: transport, specifically traffic, dynamics from the viewpoint of a socio-technical interaction and evacuation scenarios for large-scale emergency situations.
As the interconnectivity between humans through technical devices is becoming ubiquitous, the next step is already in the making: ambient intelligence, i.e. smart (technical) environments, which will eventually play the same active role in communication as the human players, leading to a co-evolution in all domains where real-time communication is essential.
This topical volume, based on the findings of the Socionical European research project, gives equal attention to two highly relevant domains of applications: transport, specifically traffic, dynamics from the viewpoint of a socio-technical interaction and evacuation scenarios for large-scale emergency situations.
Care was taken to investigate as much as possible the limits of scalability and to combine the modeling using complex systems science approaches with relevant data analysis.
Part I : Introduction and literature reviews.- The Socionical FP7 Project and an outline of the volume.- Enhancing Crowd Evacuation & Traffic Management, through AmI technologies - A Review of the Literature.- A The Concept of 'Co-evolution' and its Application in the Social Sciences - A Review of the literature.- Part II: EMERGENCY.- Using mobile technology and a participatory sensing approach for crowd monitoring during large-scale mass gatherings.- Agent-based Modelling of Social Emotional Decision Making in Emergency Situations,- Designing Complex Socio-technical Systems: Empirically Grounded Simulations as Tools for Experience-based Design Space Explorations.- Part III: TRANSPORT.- Enhancing Future Mass ICT with Social Capabilities.- Emerging Phenomena During Driving Interactions.- Effective Assessment of AmI Intervention in Traffic Through Quantitative Measures.- Part IV: CITY SCALE.- City Scale Evacuation Simulation: A High-Performance Multi-Agent Simulation Framework.
Inhaltsverzeichnis
Part I : Introduction and literature reviews.- The Socionical FP7 Project and an outline of the volume.- Enhancing Crowd Evacuation & Traffic Management, through AmI technologies - A Review of the Literature.- A The Concept of 'Co-evolution' and its Application in the Social Sciences - A Review of the literature.- Part II: EMERGENCY.- Using mobile technology and a participatory sensing approach for crowd monitoring during large-scale mass gatherings.- Agent-based Modelling of Social Emotional Decision Making in Emergency Situations,- Designing Complex Socio-technical Systems: Empirically Grounded Simulations as Tools for Experience-based Design Space Explorations.- Part III: TRANSPORT.- Enhancing Future Mass ICT with Social Capabilities.- Emerging Phenomena During Driving Interactions.- Effective Assessment of AmI Intervention in Traffic Through Quantitative Measures.- Part IV: CITY SCALE.- City Scale Evacuation Simulation: A High-Performance Multi-Agent Simulation Framework.
Klappentext
As the interconnectivity between humans through technical devices is becoming ubiquitous, the next step is already in the making: ambient intelligence, i.e. smart (technical) environments, which will eventually play the same active role in communication as the human players, leading to a co-evolution in all domains where real-time communication is essential.
This topical volume, based on the findings of the Socionical European research project, gives equal attention to two highly relevant domains of applications: transport, specifically traffic, dynamics from the viewpoint of a socio-technical interaction and evacuation scenarios for large-scale emergency situations.
Care was taken to investigate as much as possible the limits of scalability and to combine the modeling using complex systems science approaches with relevant data analysis.
Provides the reader with application-orientated tools for studying complex socio-technical systems
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Authored by leading researchers in the field
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Includes data-driven case studies
