Details

Autor(en) / Beteiligte

• Semanjski, Ivana,

Titel

Smart urban mobility : transport planning in the age of big data and digital twins

Ort / Verlag

Amsterdam, Netherlands : Elsevier,

Erscheinungsjahr

[2023]

Link zum Volltext

• Elsevier Books AutoHoldings

Beschreibungen/Notizen

• 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?.
• 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).
• 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.
• 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.
• 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.
• Description based on print version record.