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

• Author: Coors, Volker.
• TeilnehmendeR: Pietruschka, Dirk.; Zeitler, Berndt.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2022.; ©2022.
• Year of Publication: 2022
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (392 pages)

Table of 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 `Österreichischer 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.