Leverage Data Streams for Better Operational Decision-Making

The Case of Smart Sustainable Mobility Environments

Prinz, Christoph

• Publisher: Cuvillier Verlag
• Year: 2023
• eBook ISBN:
  9783736968028
• Print ISBN:
  9783736978027
• Edition: 1

Smart sustainable mobility ecosystems promise to address society's expectation of environmentally friendly on-demand mobility. While the technology stack to build such ecosystems is just around the corner in the form of connected, automated, and electric vehicles, strategies to deploy and operate such fleets in a coordinated manner must still be advanced. Most of such optimization challenges highly depend on the nature of customer demand, vehicle supply, and environmental influences. Hence, this dissertation investigates how available data streams from mobility ecosystems can be leveraged in Information Systems to solve related decision problems. The overarching goal of this work is to generate design knowledge to improve vehicle availability, provider profitability, and environmental sustainability for such ecosystems. Applying quantitative methods to real-world data from shared vehicle systems generates insights into the nature of demand and supply. Combining it with an analysis of empirical research on vehicle relocation algorithms builds the foundation for two artifact designs. The first artifact enables the development and simulation-based evaluation of operation modes for vehicle fleets. The second artifact enables artificial intelligence-based decision support for the vehicle rebalancing problem. The insights are finally incorporated and generalized to a nascent design theory on data-enabled operational decision-making in the context of smart sustainable mobility environments. The findings have multifaceted implications for researchers concerned with data-enabled value creation in Green IS, shared economy and smart mobility, and business analytics and data science. Furthermore, guidance for fleet providers to improve system attractiveness and for society to experience the potential amount of vehicle access without personal ownership is provided.

Table of contents

• Preface
• Abstract
• Table of Contents
• List of Figures
• List of Tables
• List of Abbreviations
• A. Foundations
• I. Introduction
• I.1 Motivation
• I.2 Research Gap and Research Questions
• I.3 Structure of the Dissertation
• I.4 Research Positioning
• I.5 Research Design
• I.6 Anticipated Contributions and Implications
• II. Research Background
• II.1 Smart and Sustainable Mobility
• II.2 Decision Support Systems
• II.3 Big Data, Machine Learning, and Artificial Intelligence
• III. Related Work
• B. Studies on Data-Enabled Operational Decision Making
• I. Assessing the Status Quo
• 1. Study 1: Archetypes of Carsharing RelocationAlgorithms: A Perspective on Problem Space, SolutionSpace, and Evaluation
• 1.1 Introduction
• 1.2 Research Background
• 1.3 Research Approach
• 1.4 Results
• 1.5 Discussion
• 1.6 Conclusion
• II. Designing a Development Environment for OperationalModes
• 1. Study 2: CASSI: Designing a Simulation Environment forVehicle Relocation in Carsharing
• 1.1 Introduction
• 1.2 Related Work
• 1.3 Research Approach
• 1.4 Results
• 1.5 Discussion
• 1.6 Conclusion
• 1.7 Appendix: CASSI’s System Definition Interfaces
• III. Modeling Spatiotemporal Demand
• 1. Study 3: Increasing the Business Value of Free-FloatingCarsharing Fleets By Applying Machine Learning-basedRelocations
• 1.1 Introduction
• 1.2 Literature Review
• 1.3 Research Approach
• 1.4 Results
• 1.5 Evaluation
• 1.6 Discussion
• 1.7 Conclusion
• 2. Study 4: “Now You Can See Me!” – UnconstrainingDemand for Effective Service Operation: The Case ofVehicle Relocation in Free-Floating Carsharing
• 2.1 Introduction
• 2.2 Literature Review
• 2.3 Research Approach and Results
• 2.4 Discussion
• 2.5 Conclusion
• 3. Study 5: How Far are You Gonna Go? UnderstandingPedestrian Catchment Areas in Shared Mobility Systems
• 3.1 Introduction
• 3.2 Utilitarian Theory in Discrete Choice Modeling
• 3.3 Hypothesis Development
• 3.4 Methodology
• 3.5 Results
• 3.6 Discussion of Results and Future Research
• 3.7 Conclusion
• 3.8 Acknowledgements
• C. Contributions
• I. Findings and Results
• I.1 Findings for the Status Quo of Vehicle RelocationAlgorithms
• I.2 Findings for Decision Support of Operational Modes
• I.3 Findings for the Spatiotemporal Demand Model
• II. Design Theory
• III. Implications for Research, Practice, and Society
• IV. Concluding Remarks
• IV.1 Limitations and Future Research Opportunities
• IV.2 Conclusion
• References
• Appendix A. Overview of the Author’s Individual StudyContribution
• Appendix B. Overview of Further Published Studies
• Appendix C. Curriculum Vitae