UID:
edoccha_9961017511602883
Umfang:
1 online resource (268 pages)
ISBN:
9780128208915
Inhalt:
Smart Urban Mobility: Transport Planning in the Age of Big Data and Digital Twins explores the data-driven paradigm shift in urban mobility planning and examines how well-established practices and strong data analytics efforts can be better aligned to fit transport planning practices and "smart" mobility management needs. The book provides a comprehensive survey of the major big data and technology resources derived from smart cities research which are collectively poised to transform urban mobility. Chapters highlight the important aspects of each data source affecting applicability, along with the outcomes of smart mobility measures and campaigns. Transport planners, urban policymakers, public administrators, city managers, data scientists, and consulting companies managing smart city interventions and data-driven urban transformation projects will gain a better understanding of this up-and-coming research from this book’s detailed overview and numerous practical examples and best practices for operational deployment.
Anmerkung:
Front Cover -- SMART URBAN MOBILITY -- SMART URBAN MOBILITY: TRANSPORT PLANNING IN THE AGE OF BIG DATA AND DIGITAL TWINS -- Copyright -- Contents -- Preface -- 1 - Introduction -- 1.1 Objectives of the chapter -- 1.2 Word cloud -- 1.3 Introduction -- 1.4 Background -- 1.5 Why smart mobility and why now? -- 1.6 Audiences -- 1.6.1 Transport planners and practitioners -- 1.6.2 City officials and policy makers -- 1.6.3 University professors and students -- 1.6.4 Business analysts, data scientist, data engineers, and developers -- 1.6.5 Multidisciplinary urban planning and mobility projects managers -- 1.6.6 Citizen scientists and members of citizens' participation initiatives -- 1.6.7 Smart city and smart mobility advocates, consultants, and implementers -- 1.7 Chapter structure -- 1.7.1 Topics/chapters -- 1.7.1.1 Chapter 2: Introduction to smart mobility -- 1.7.1.2 Chapter 3: The new challenge of smart urban mobility -- 1.7.1.3 Chapter 4: Small and big data for mobility studies -- 1.7.1.4 Chapter 5: Data analytics -- 1.7.1.5 Chapter 6: Four step transport planning model and big data -- 1.7.1.6 Chapter 7: Data driven mobility management -- 1.7.1.7 Chapter 8: Digital twin -- 1.7.1.8 Chapter 9: Summary -- References -- 2 - Introduction to smart mobility -- 2.1 Objectives of the chapter -- 2.2 Word cloud -- 2.3 Mobility -- 2.3.1 Terminology/definitions -- 2.3.2 Urban mobility -- 2.4 Smart city -- 2.4.1 Sustainable city -- 2.4.2 Quality of life -- 2.4.3 Role of the new technologies in smart city -- 2.4.4 Responsive city -- 2.4.5 Smart city domains -- 2.5 Smart mobility -- References -- 3 - The new challenge of smart urban mobility -- 3.1 Objectives of the chapter -- 3.2 Word cloud -- 3.3 Urban population trends -- 3.3.1 Key urban population-related challenges -- 3.4 Multimodality -- 3.4.1 What transport modes exist in the city?.
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3.4.1.1 What is the difference between multimodal and intermodal transport? -- 3.4.1.2 What are sustainable transport modes? -- 3.4.2 Key multimodal mobility-related challenges -- 3.4.3 Example: transport mode competitiveness in an urban area -- 3.5 Connected mobility -- 3.5.1 Key connected mobility-related challenges -- 3.5.1.1 Data versus information -- 3.5.1.2 Some of the key mobility data-related challenges -- 3.5.1.2.1 Data standardization -- 3.5.1.2.2 Data availability -- 3.5.1.2.3 Data privacy -- 3.5.1.2.4 Measurability and quantification -- 3.5.1.2.5 Data openness -- 3.6 ConnectedX -- 3.6.1 Connected vehicles -- 3.6.2 Connected infrastructure -- 3.6.3 Connected traveler -- 3.6.4 Connected freight -- 3.6.5 Service-oriented perspective of ConnectedX -- 3.6.6 Autonomous vehicles -- 3.6.6.1 Example: autonomous vehicles (I) -- 3.6.6.2 Example: autonomous vehicles (II) -- 3.6.7 ConnectedX-related challenges -- 3.7 Electric vehicles -- 3.7.1 Electric vehicles related challenges -- 3.8 Shared mobility -- 3.8.1 Shared mobility-related challenges -- 3.8.2 Example: impact of shared mobility practices on electric vehicles -- 3.9 Mobility as a service -- 3.9.1 MaaS-related challenges -- 3.10 Governance -- 3.10.1 Governance-related challenges -- 3.11 Smart mobility innovations -- 3.11.1 Smart mobility innovation-related challenges -- 3.12 Change management -- 3.12.1 Change management-related challenges -- 3.13 State of the affairs -- References -- 4 - Small and big data for mobility studies -- 4.1 Objectives of the chapter -- 4.2 Word cloud -- 4.3 Introduction -- 4.4 Traditional data collection approaches -- 4.5 Big data for mobility studies -- 4.5.1 Global navigation satellite systems data -- 4.5.1.1 Example: GNSS data (I) -- 4.5.1.2 Example: GNSS data (II) -- 4.5.2 Mobile network data -- 4.5.2.1 Example: mobile network data (I).
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4.5.3 Mobile sensed data -- 4.5.3.1 Example: mobile sensed data (i) -- 4.5.3.2 Example mobile sensed data (ii) -- 4.5.4 Comparison of the three main big data sources for mobility studies -- 4.5.5 Other big data sources for mobility studies -- 4.5.5.1 Location-oriented sensing -- 4.5.5.1.1 Computer vision techniques -- 4.5.5.1.1.1 Example: computer vision -- 4.5.5.1.2 Bluetooth data -- 4.5.5.1.2.1 Example: bluetooth data -- 4.5.5.1.3 Ticketing data -- 4.5.5.1.3.1 Example ticketing data -- References -- 5 - Data analytics -- 5.1 Objectives of the chapter -- 5.2 Word cloud -- 5.3 Data analytics introduction -- 5.4 Data analytics workflow -- 5.4.1 Descriptive analytics -- 5.4.1.1 Descriptive statistics -- 5.4.1.1.1 Measures of dispersion and central tendencies -- 5.4.1.1.1.1 Arithmetic mean -- 5.4.1.1.1.2 Median and mode -- 5.4.1.1.1.3 Minimum and maximum -- 5.4.1.1.1.4 Range -- 5.4.1.1.1.5 Quartile -- 5.4.1.1.1.6 Variance -- 5.4.1.1.1.7 Standard deviation -- 5.4.1.1.1.8 Skewness and kurtosis -- 5.4.1.2 Exploratory data analysis -- 5.4.2 Diagnostic analytics -- 5.4.2.1 Example: diagnostic analytics -- 5.4.3 Predictive analytics -- 5.4.4 Prescriptive analytics -- 5.4.4.1 Example: predictive analytics -- 5.5 Machine learning -- 5.5.1 Supervised learning -- 5.5.2 Unsupervised learning -- 5.5.3 Reinforcement learning -- 5.5.4 Building and evaluating a machine learning algorithm -- 5.5.5 Common machine learning methods used for mobility analytics -- 5.5.5.1 Regression methods -- 5.5.5.2 Support vector machines -- 5.5.5.3 Decision tree -- 5.5.5.4 Artificial neural networks -- 5.6.5.5 kNN -- 5.5.5.6 Clustering -- 5.5.5.7 K-mean clustering -- 5.5.5.8 Cross-validation -- 5.5.6 Example classification: transport mode recognition -- 5.5.7 Example regression: travel time estimation -- 5.6 Data anonymization -- 5.6.1 Randomization -- 5.6.2 Generalization.
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5.6.3 Pseudonymization -- References -- 6 - Transport planning and big data -- 6.1 Objectives of the chapter -- 6.2 Word cloud -- 6.3 Four-step transportation planning model -- 6.3.1 Trip generation step -- 6.3.2 Trip distribution step -- 6.3.3 Mode choice step -- 6.3.4 Trip assignment step -- 6.4 Literature review of big data advances for four-step transport planning model -- 6.4.1 Literature review of big data advances for trip generation step -- 6.4.2 Example: detection of trip generation zones for tourism population -- 6.4.3 Literature review of big data advances for trip distribution step -- 6.4.4 Example: construction of OD matrix from big data -- 6.4.5 Literature review of big data advances for mode choice step -- 6.4.6 Example: rule-based transport mode detection from GNSS and GIS data -- 6.4.7 Literature review of big data advances route assignment step -- 6.4.8 Example: map matching -- References -- 7 - Data-driven mobility management -- 7.1 Objectives of the chapter -- 7.2 Word cloud -- 7.3 Introduction -- 7.4 Big data-driven mobility system monitoring -- 7.5 Analytics-based mobility management decision making support -- 7.6 Example: incentivization of mobility behavior -- 7.6.1 Theory of planned behavior as a conceptual framework -- 7.6.2 Applied market segmentation -- 7.6.3 Obtained insights -- 7.7 Example: mobility management as a service -- 7.7.1 The MMaaS architecture -- References -- 8 - Digital twin -- 8.1 Objectives of the chapter -- 8.2 Word cloud -- 8.3 Digital twin -- 8.3.1 Digital twin applications and complexities -- 8.3.2 Digital twin architecture -- 8.3.3 Digital twins' due time -- 8.3.4 Digital twin-related initiatives -- 8.4 Example: electric vehicle's digital shadow -- 8.5 Example: urban air mobility -- References -- 9 - Summary -- 9.1 Objectives of the chapter -- 9.2 Word cloud -- 9.3 About the book -- 9.4 Features.
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9.5 Summary of chapters -- 9.5.1 Chapter 1: introduction -- 9.5.2 Chapter 2: introduction to smart mobility -- 9.5.3 Chapter 3: the new challenge of smart urban mobility -- 9.5.3.1 Examples in Chapter 3 -- 9.5.4 Chapter 4: small and big data for mobility studies -- 9.5.4.1 Examples in Chapter 4 -- 9.5.5 Chapter 5: data analytics -- 9.5.5.1 Examples in Chapter 5 -- 9.5.6 Chapter 6: four step transport planning model and big data -- 9.5.6.1 Examples in Chapter 6 -- 9.5.7 Chapter 7: data-driven mobility management -- 9.5.7.1 Examples in Chapter 7 -- 9.5.8 Chapter 8: digital twin -- 9.5.8.1 Examples in Chapter 8 -- 9.5.9 Chapter 9: summary -- 9.6 Some smart mobility lessons learned -- 9.6.1 User needs -- 9.6.2 Strategy -- 9.6.3 Data and technology -- List of acronyms -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- Back Cover.
Weitere Ausg.:
Print version: Semanjski, Ivana Smart Urban Mobility San Diego : Elsevier Science & Technology,c2023 ISBN 9780128207178
Sprache:
Englisch
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