ICity. Transformative Research for the Livable, Intelligent, and Sustainable City : Research Findings of University of Applied Sciences Stuttgart.

By: Coors, VolkerContributor(s): Pietruschka, Dirk | Zeitler, BerndtMaterial type: TextTextPublisher: Cham : Springer International Publishing AG, 2022Copyright date: �2022Edition: 1st edDescription: 1 online resource (392 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9783030920968Genre/Form: Electronic books.Additional physical formats: Print version:: ICity. Transformative Research for the Livable, Intelligent, and Sustainable CityLOC classification: JF1315.2-2112Online resources: Click to View
Contents:
Intro -- Foreword -- Foreword -- Editorial -- Introduction -- I Mobility -- II Energy -- III Simulation and Data -- IV Urban Planning and Buildings -- Contents -- About the Contributors -- Part I: Mobility -- 1: How Innovative Mobility Can Drive Sustainable Development: Conceptual Foundations and Use Cases Using the Example of the iC... -- 1.1 Introduction -- 1.2 Sustainable Innovation and Mobility -- 1.2.1 Need for Sustainable Mobility Against the Background of ``Grand Challenges�� -- 1.2.2 Transdisciplinary Living Labs as a Basis for Ecosystems for Sustainable Innovation -- 1.3 Sustainable Mobility and Digitalization -- 1.4 Creating a Safer Cycling Infrastructure -- 1.5 Conclusion -- References -- 2: Interests of (In)frequent Bike Users: Analysis of Differing Target Groups� Needs Concerning the RouteMeSafe Application -- 2.1 Introduction -- 2.2 Study 1: UX Study with Frequent and Infrequent Cyclists -- 2.2.1 Hedonic-Pragmatic Model -- 2.2.2 Kano Model -- 2.2.3 Methodology -- 2.2.4 Results -- 2.2.5 Discussion -- 2.3 Study 2: Technology Acceptance Study with Frequent Cyclists -- 2.3.1 Unified Theory of Technology Acceptance 2 (UTAUT2) -- 2.3.2 Methodology -- 2.3.3 Results -- 2.3.4 Discussion -- 2.4 General Discussion -- Bibliography -- 3: Artificial Intelligence Supporting Sustainable and Individual Mobility: Development of an Algorithm for Mobility Planning a... -- 3.1 Introduction -- 3.1.1 Routing Apps: What They Provide Today -- 3.1.2 A Vision for Routing Apps: Individually Tailored, Sustainable Mobility -- 3.2 Objective -- 3.3 Development of the Algorithm for Personalized-Quantified Routing Including Self-Learning Units -- 3.3.1 Concept and Structure of the Algorithm -- 3.3.2 Metric and Scaling of the Factors -- 3.3.3 Utilizing Machine Learning for Improving the Algorithm -- 3.3.4 Application of the Algorithm in EmiLa.
3.3.5 Data Integration into the Application -- 3.4 Testing of the Algorithm -- 3.4.1 EmiLa Testing Results -- 3.5 Conclusions -- Bibliography -- 4: Challenges to Turn Transport Behavior into Emission-Friendly Use of Means of Transport -- 4.1 Development of Modal Split for Germany -- 4.2 Benchmark View of Modal Split for the Netherlands -- 4.3 Sharing as Opportunity to Extend Bicycle and Pedelec Use in Germany -- 4.4 Necessity for Further Research -- References -- 5: Positioning of Pedelecs for a Pedelec Sharing System with Free-Floating Bikes -- 5.1 Introduction -- 5.2 Materials and Methods -- 5.3 Sensor Tests -- 5.3.1 The Influence of Temperature -- 5.3.2 Variation of the Total Acceleration in Relation to the Inclination of the Sensor -- 5.4 Sensor Calibration and Alignment -- 5.5 Kalman Filter -- 5.6 Findings and Results -- 5.7 Necessity for Further Research -- References -- 6: Behavioural Development of University Graduates in the Area of Work-Related Mobility: A Study Conducted for the University ... -- 6.1 Introduction -- 6.1.1 Problem Definition -- 6.1.2 Research Definition -- 6.2 Research Methodology -- 6.3 Data Evaluation -- 6.3.1 Evaluation of Education-Related Mobility Behaviour -- 6.3.2 Evaluation of Work-Related Mobility Behaviour After Graduation -- 6.3.3 Evaluation of Current Work-Related Mobility Behaviour -- 6.4 Conclusion -- References -- 7: Cargo-Hitching in Long-Distance Bus Transit: An Acceptance Analysis -- 7.1 Introduction -- 7.2 Cargo-Hitching as an Alternative Delivery Concept -- 7.2.1 Definition -- Overview of Existing Concepts -- FlixBus as Cargo-Hitching Carrier -- 7.3 Adapting an Underlying Acceptance Model -- 7.4 Methodology: Acceptance Analysis of a Cargo-Hitching Model -- 7.4.1 Method -- 7.5 Results -- 7.5.1 Sample -- 7.5.2 Answering the First Research Question: The UTAUT2 Model.
7.5.3 Answering the Second Research Question: Wishes and Requirements of Potential Users -- 7.6 Discussion and Outlook -- References -- 8: Promoting Zero-Emission Urban Logistics: Efficient Use of Electric Trucks Through Intelligent Range Estimation -- 8.1 Introduction -- 8.2 The Need for Precise Energy Consumption and Range Estimation -- 8.3 Towards an Intelligent Method for Range Prediction -- 8.4 Results and Discussion -- 8.5 Conclusion -- References -- Part II: Energy -- 9: Increased Efficiency Through Intelligent Networking of Producers and Consumers in Commercial Areas Using the Example of Rob... -- 9.1 Introduction -- 9.2 Case Study Description -- 9.3 Study to Increase the Run Time of a CHP by an Absorption Chiller -- 9.3.1 Initial Status Analysis -- 9.3.2 Simulation Study -- Methodology -- Model Description -- Control Strategies -- Simulation Results -- 9.3.3 Conclusions -- 9.4 Development of a Simulation Programme for Modelling and Calculation of a Thermal Local Heat Supply -- 9.4.1 Initial Status Analysis -- 9.4.2 Simulation Study of ``Trasse West�� -- Methodology -- Network Types and Their Representability in ``spHeat�� -- Image of the Topography of a Network in ``spHeat�� -- Programme Sequence for the Calculation of the Variable Sizes of a Mesh in ``spHeat�� -- Model Description -- Hydraulically Separated Systems (Normal Case) or Direct Flow District Heating Networks -- Circulation Pumps (Single Pump or Pump Phalanx) -- Creating the Simulation Model in the INSEL-GUI -- Input Data from Measured Values -- 9.4.3 Summary and Outlook -- References -- 10: Case Study of a Hydrogen-Based District Heating in a Rural Area: Modeling and Evaluation of Prediction and Optimization Me... -- 10.1 Introduction -- 10.2 Related Work/State of the Art: Research -- 10.3 Methodology -- 10.4 Hydrogen System: Modeling, Design, and Control.
10.5 Simulation Results and Discussion -- 10.6 Conclusions -- 10.7 Outlook -- References -- 11: Parking and Charging: New Concepts for the Use of Intelligent Charging Infrastructure in Car Parks -- 11.1 Introduction -- 11.2 State of the Art -- 11.3 Pilot Projects -- 11.4 Technologies in Intelligent Car Parks -- 11.5 Conclusion and Outlook -- References -- Part III: Simulation and Data -- 12: ARaaS: Context-Aware Optimal Charging Distribution Using Deep Reinforcement Learning -- 12.1 Introduction -- 12.2 Architecture -- 12.3 User Scenario -- 12.4 Simulation Environment -- 12.5 Conclusion and Future Work -- References -- 13: A Multi-camera Mobile System for Tunnel Inspection -- 13.1 Introduction -- 13.2 Related Work -- 13.3 Proposed Method -- 13.3.1 Camera Selection -- 13.3.2 Light Selection -- 13.3.3 System Design -- 13.3.4 Time Synchronization -- 13.3.5 Tunnel Conditions -- 13.3.6 Image Processing Challenges -- 13.4 Conclusion -- References -- 14: Evaluation of Crowd-Sourced PM2.5 Measurements from Low-Cost Sensors for Air Quality Mapping in Stuttgart City -- 14.1 Introduction -- 14.2 Methodology -- 14.2.1 Study Area -- 14.2.2 Datasets -- 14.2.3 Data Preparation -- 14.2.4 Low-Cost Sensors� Evaluation -- 14.3 Results and Discussion -- 14.4 Conclusions -- References -- 15: Augmented Reality for Windy Cities: 3D Visualization of Future Wind Nature Analysis in City Planning -- 15.1 Introduction -- 15.2 Methodology -- 15.3 Dataset -- 15.4 Results -- 15.4.1 Discussion -- 15.5 Conclusion -- References -- 16: Storing and Visualising Dynamic Data in the Context of Energy Analysis in the Smart Cities -- 16.1 Introduction -- 16.2 Background -- 16.2.1 Energy Data Simulation of the 3D Building Models -- 16.2.2 Energy Data Management -- CityGML Application Domain Extension -- SensorThings API (STA) -- 16.2.3 3D Data Visualisation (Digital Globe) -- 16.3 Concept.
16.3.1 Computing and Visualising the Simulated Energy Data of 3D Building Models on-the-Fly -- 16.3.2 Using the PostgreSQL Database as a Datastore for the Simulated Energy Data of 3D Building Models -- 16.3.3 Using SensorThings for Managing the Simulated Energy Data of 3D Building Models -- 16.4 Implementation -- 16.4.1 Energy Simulation of the 3D Building Models with SimStadt Software -- 16.4.2 Managing Simulated Energy Data of 3D Building Models -- Approach 1: Managing Simulated Energy Data of 3D Building Models on-the-Fly -- Approach 2: Managing Simulated Energy Data of 3D Building Models Using a Database -- Approach 3: Managing Simulated Energy Data of 3D Building Models Using OGC SensorThings API -- 16.5 Evaluation -- 16.6 Conclusion -- References -- 17: Deep Learning Methods for Extracting Object-Oriented Models of Building Interiors from Images -- 17.1 Introduction -- 17.2 Related Work -- 17.3 Methodology -- 17.3.1 Overview -- 17.3.2 Workflow for Reconstructing Interior Rooms Based on Image Data and Deep Learning -- 17.4 Semantic Segmentation of Interiors -- 17.5 Classified Point Cloud -- 17.6 Reclassifying the Point Cloud -- 17.7 Quality Analysis of the Point Clouds -- 17.8 Automated Post-processing -- 17.9 Conclusions -- References -- Part IV: Urban Planning and Buildings -- 18: Cooperative Planning Strategies in Urban Development Processes -- 18.1 Introduction -- 18.2 Participation in Urban Planning Processes -- 18.2.1 Fundamentals and Legal Framework -- 18.2.2 Cooperative Planning as a Theoretical Practice -- 18.3 Case Study `�Osterreichischer Platz� -- 18.3.1 General Context -- 18.3.2 Strategy for Spatial Activation -- 18.3.3 Cooperative Process Development -- 18.4 Assessment -- 18.5 Conclusion -- References -- 19: On the Prospects of the Building Envelope in the Context of Smart Sustainable Cities: A Brief Review.
19.1 Emergence of Smart Urban Structures over the Course of Time.
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Intro -- Foreword -- Foreword -- Editorial -- Introduction -- I Mobility -- II Energy -- III Simulation and Data -- IV Urban Planning and Buildings -- Contents -- About the Contributors -- Part I: Mobility -- 1: How Innovative Mobility Can Drive Sustainable Development: Conceptual Foundations and Use Cases Using the Example of the iC... -- 1.1 Introduction -- 1.2 Sustainable Innovation and Mobility -- 1.2.1 Need for Sustainable Mobility Against the Background of ``Grand Challenges�� -- 1.2.2 Transdisciplinary Living Labs as a Basis for Ecosystems for Sustainable Innovation -- 1.3 Sustainable Mobility and Digitalization -- 1.4 Creating a Safer Cycling Infrastructure -- 1.5 Conclusion -- References -- 2: Interests of (In)frequent Bike Users: Analysis of Differing Target Groups� Needs Concerning the RouteMeSafe Application -- 2.1 Introduction -- 2.2 Study 1: UX Study with Frequent and Infrequent Cyclists -- 2.2.1 Hedonic-Pragmatic Model -- 2.2.2 Kano Model -- 2.2.3 Methodology -- 2.2.4 Results -- 2.2.5 Discussion -- 2.3 Study 2: Technology Acceptance Study with Frequent Cyclists -- 2.3.1 Unified Theory of Technology Acceptance 2 (UTAUT2) -- 2.3.2 Methodology -- 2.3.3 Results -- 2.3.4 Discussion -- 2.4 General Discussion -- Bibliography -- 3: Artificial Intelligence Supporting Sustainable and Individual Mobility: Development of an Algorithm for Mobility Planning a... -- 3.1 Introduction -- 3.1.1 Routing Apps: What They Provide Today -- 3.1.2 A Vision for Routing Apps: Individually Tailored, Sustainable Mobility -- 3.2 Objective -- 3.3 Development of the Algorithm for Personalized-Quantified Routing Including Self-Learning Units -- 3.3.1 Concept and Structure of the Algorithm -- 3.3.2 Metric and Scaling of the Factors -- 3.3.3 Utilizing Machine Learning for Improving the Algorithm -- 3.3.4 Application of the Algorithm in EmiLa.

3.3.5 Data Integration into the Application -- 3.4 Testing of the Algorithm -- 3.4.1 EmiLa Testing Results -- 3.5 Conclusions -- Bibliography -- 4: Challenges to Turn Transport Behavior into Emission-Friendly Use of Means of Transport -- 4.1 Development of Modal Split for Germany -- 4.2 Benchmark View of Modal Split for the Netherlands -- 4.3 Sharing as Opportunity to Extend Bicycle and Pedelec Use in Germany -- 4.4 Necessity for Further Research -- References -- 5: Positioning of Pedelecs for a Pedelec Sharing System with Free-Floating Bikes -- 5.1 Introduction -- 5.2 Materials and Methods -- 5.3 Sensor Tests -- 5.3.1 The Influence of Temperature -- 5.3.2 Variation of the Total Acceleration in Relation to the Inclination of the Sensor -- 5.4 Sensor Calibration and Alignment -- 5.5 Kalman Filter -- 5.6 Findings and Results -- 5.7 Necessity for Further Research -- References -- 6: Behavioural Development of University Graduates in the Area of Work-Related Mobility: A Study Conducted for the University ... -- 6.1 Introduction -- 6.1.1 Problem Definition -- 6.1.2 Research Definition -- 6.2 Research Methodology -- 6.3 Data Evaluation -- 6.3.1 Evaluation of Education-Related Mobility Behaviour -- 6.3.2 Evaluation of Work-Related Mobility Behaviour After Graduation -- 6.3.3 Evaluation of Current Work-Related Mobility Behaviour -- 6.4 Conclusion -- References -- 7: Cargo-Hitching in Long-Distance Bus Transit: An Acceptance Analysis -- 7.1 Introduction -- 7.2 Cargo-Hitching as an Alternative Delivery Concept -- 7.2.1 Definition -- Overview of Existing Concepts -- FlixBus as Cargo-Hitching Carrier -- 7.3 Adapting an Underlying Acceptance Model -- 7.4 Methodology: Acceptance Analysis of a Cargo-Hitching Model -- 7.4.1 Method -- 7.5 Results -- 7.5.1 Sample -- 7.5.2 Answering the First Research Question: The UTAUT2 Model.

7.5.3 Answering the Second Research Question: Wishes and Requirements of Potential Users -- 7.6 Discussion and Outlook -- References -- 8: Promoting Zero-Emission Urban Logistics: Efficient Use of Electric Trucks Through Intelligent Range Estimation -- 8.1 Introduction -- 8.2 The Need for Precise Energy Consumption and Range Estimation -- 8.3 Towards an Intelligent Method for Range Prediction -- 8.4 Results and Discussion -- 8.5 Conclusion -- References -- Part II: Energy -- 9: Increased Efficiency Through Intelligent Networking of Producers and Consumers in Commercial Areas Using the Example of Rob... -- 9.1 Introduction -- 9.2 Case Study Description -- 9.3 Study to Increase the Run Time of a CHP by an Absorption Chiller -- 9.3.1 Initial Status Analysis -- 9.3.2 Simulation Study -- Methodology -- Model Description -- Control Strategies -- Simulation Results -- 9.3.3 Conclusions -- 9.4 Development of a Simulation Programme for Modelling and Calculation of a Thermal Local Heat Supply -- 9.4.1 Initial Status Analysis -- 9.4.2 Simulation Study of ``Trasse West�� -- Methodology -- Network Types and Their Representability in ``spHeat�� -- Image of the Topography of a Network in ``spHeat�� -- Programme Sequence for the Calculation of the Variable Sizes of a Mesh in ``spHeat�� -- Model Description -- Hydraulically Separated Systems (Normal Case) or Direct Flow District Heating Networks -- Circulation Pumps (Single Pump or Pump Phalanx) -- Creating the Simulation Model in the INSEL-GUI -- Input Data from Measured Values -- 9.4.3 Summary and Outlook -- References -- 10: Case Study of a Hydrogen-Based District Heating in a Rural Area: Modeling and Evaluation of Prediction and Optimization Me... -- 10.1 Introduction -- 10.2 Related Work/State of the Art: Research -- 10.3 Methodology -- 10.4 Hydrogen System: Modeling, Design, and Control.

10.5 Simulation Results and Discussion -- 10.6 Conclusions -- 10.7 Outlook -- References -- 11: Parking and Charging: New Concepts for the Use of Intelligent Charging Infrastructure in Car Parks -- 11.1 Introduction -- 11.2 State of the Art -- 11.3 Pilot Projects -- 11.4 Technologies in Intelligent Car Parks -- 11.5 Conclusion and Outlook -- References -- Part III: Simulation and Data -- 12: ARaaS: Context-Aware Optimal Charging Distribution Using Deep Reinforcement Learning -- 12.1 Introduction -- 12.2 Architecture -- 12.3 User Scenario -- 12.4 Simulation Environment -- 12.5 Conclusion and Future Work -- References -- 13: A Multi-camera Mobile System for Tunnel Inspection -- 13.1 Introduction -- 13.2 Related Work -- 13.3 Proposed Method -- 13.3.1 Camera Selection -- 13.3.2 Light Selection -- 13.3.3 System Design -- 13.3.4 Time Synchronization -- 13.3.5 Tunnel Conditions -- 13.3.6 Image Processing Challenges -- 13.4 Conclusion -- References -- 14: Evaluation of Crowd-Sourced PM2.5 Measurements from Low-Cost Sensors for Air Quality Mapping in Stuttgart City -- 14.1 Introduction -- 14.2 Methodology -- 14.2.1 Study Area -- 14.2.2 Datasets -- 14.2.3 Data Preparation -- 14.2.4 Low-Cost Sensors� Evaluation -- 14.3 Results and Discussion -- 14.4 Conclusions -- References -- 15: Augmented Reality for Windy Cities: 3D Visualization of Future Wind Nature Analysis in City Planning -- 15.1 Introduction -- 15.2 Methodology -- 15.3 Dataset -- 15.4 Results -- 15.4.1 Discussion -- 15.5 Conclusion -- References -- 16: Storing and Visualising Dynamic Data in the Context of Energy Analysis in the Smart Cities -- 16.1 Introduction -- 16.2 Background -- 16.2.1 Energy Data Simulation of the 3D Building Models -- 16.2.2 Energy Data Management -- CityGML Application Domain Extension -- SensorThings API (STA) -- 16.2.3 3D Data Visualisation (Digital Globe) -- 16.3 Concept.

16.3.1 Computing and Visualising the Simulated Energy Data of 3D Building Models on-the-Fly -- 16.3.2 Using the PostgreSQL Database as a Datastore for the Simulated Energy Data of 3D Building Models -- 16.3.3 Using SensorThings for Managing the Simulated Energy Data of 3D Building Models -- 16.4 Implementation -- 16.4.1 Energy Simulation of the 3D Building Models with SimStadt Software -- 16.4.2 Managing Simulated Energy Data of 3D Building Models -- Approach 1: Managing Simulated Energy Data of 3D Building Models on-the-Fly -- Approach 2: Managing Simulated Energy Data of 3D Building Models Using a Database -- Approach 3: Managing Simulated Energy Data of 3D Building Models Using OGC SensorThings API -- 16.5 Evaluation -- 16.6 Conclusion -- References -- 17: Deep Learning Methods for Extracting Object-Oriented Models of Building Interiors from Images -- 17.1 Introduction -- 17.2 Related Work -- 17.3 Methodology -- 17.3.1 Overview -- 17.3.2 Workflow for Reconstructing Interior Rooms Based on Image Data and Deep Learning -- 17.4 Semantic Segmentation of Interiors -- 17.5 Classified Point Cloud -- 17.6 Reclassifying the Point Cloud -- 17.7 Quality Analysis of the Point Clouds -- 17.8 Automated Post-processing -- 17.9 Conclusions -- References -- Part IV: Urban Planning and Buildings -- 18: Cooperative Planning Strategies in Urban Development Processes -- 18.1 Introduction -- 18.2 Participation in Urban Planning Processes -- 18.2.1 Fundamentals and Legal Framework -- 18.2.2 Cooperative Planning as a Theoretical Practice -- 18.3 Case Study `�Osterreichischer Platz� -- 18.3.1 General Context -- 18.3.2 Strategy for Spatial Activation -- 18.3.3 Cooperative Process Development -- 18.4 Assessment -- 18.5 Conclusion -- References -- 19: On the Prospects of the Building Envelope in the Context of Smart Sustainable Cities: A Brief Review.

19.1 Emergence of Smart Urban Structures over the Course of Time.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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