Big Data and Mobility as a Service
- Length: 306 pages
- Edition: 1
- Language: English
- Publisher: Elsevier
- Publication Date: 2021-10-19
- ISBN-10: 0323901697
- ISBN-13: 9780323901697
- Sales Rank: #0 (See Top 100 Books)
Big Data and Mobility as a Service explores MaaS platforms that can be adaptable to the ever-evolving mobility environment. It looks at multi-mode urban crowd data to assess urban mobility characteristics, their shared transportation potential, and their performance conditions and constraints. The book analyzes the roles of multimodality, travel behavior, urban mobility dynamics and participation. Combined with insights on using big data to analyze market and policy decisions, this book is an essential tool for urban transportation management researchers and practitioners.
Cover image Title page Table of Contents Copyright Contributors Introduction 1: Background 2: Big data: Definition, history, today 3: MaaS: Definition, history, today 4: Big data X MaaS 5: Summary Chapter 1: MaaS system development and APPs Abstract 1: The development history of MaaS 2: The category of MaaS system 3: Study case 4: Future development trend of MaaS system Chapter 2: Spatio-temporal data preprocessing technologies Abstract 1: Introduction 2: Raw GPS data and workflow of data preprocessing 3: Key technologies and corresponding application 4: Case study 5: Conclusion Chapter 3: Travel similarity estimation and clustering Abstract 1: Introduction 2: Trajectory similarity 3: Travel pattern similarity 4: Origin-destination matrix similarity 5: Case study 6: Conclusion and future directions Chapter 4: Data fusion technologies for MaaS Abstract Acknowledgments 1: Introduction 2: Data formula 3: Categories of data fusion methods in MaaS 4: Data fusion based on deep learning 5: Decomposition-based methods 6: Challenging problems of data fusion in MaaS 7: Conclusions Chapter 5: Data-driven optimization technologies for MaaS Abstract 1: Overview of data-driven optimization for the urban mobility system 2: Overview of the general concept in MaaS System 3: Mobility resource allocation in MaaS system 4: Data-driven optimization technologies for resource allocation in MaaS 5: Real-world application and case study 6: Conclusions Chapter 6: Data-driven estimation for urban travel shareability Abstract Acknowledgment 1: Introduction 2: Emerging sharing transportation mode 3: Background to traditional data and their limitations 4: New and emerging source of data 5: Emerging form of key technologies 6: Case study of ABM in urban shareability estimation 7: Opportunities and challenges 8: Conclusions Chapter 7: Data mining technologies for Mobility-as-a-Service (MaaS) Abstract 1: Introduction of data mining technologies in MaaS system 2: Data mining technologies in MaaS system 3: Methodologies of data mining technologies used in MaaS system 4: Case study of data mining for MaaS: Bike sharing in Beijing during Covid-19 pandemic 5: Summary of chapter Chapter 8: MaaS and IoT: Concepts, methodologies, and applications Abstract 1: Introduction 2: Overview of the concept 3: Key technologies and methodologies 4: Application and case study 5: Conclusion and future directions Chapter 9: MaaS system visualization Abstract 1: Overview of the general concept 2: The key visualization technologies in MaaS for different stakeholders 3: Real-world application and case study 4: Conclusion and future directions Chapter 10: MaaS for sustainable urban development Abstract 1: Introduction 2: MaaS interacted with urban traffic and space 3: Strategies for MaaS in urban sustainable development at multiple scales 4: Case study 5: Conclusion Index
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