Cover Image

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

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...

Full description

Bibliographic Details
Main Author: Semanjski, Ivana Cavar (Author)
Corporate Author: ScienceDirect (Online service)
Format: eBook
Language:English
Published: Amsterdam, Netherlands ; Kidlington, Oxford, United Kingdom ; Cambridge, MA, United States : Elsevier, [2023]
Subjects:
Urban transportation > Technological innovations.
Intelligent transportation systems.
Urban transportation policy.
Smart cities.
Transports urbains > Innovations.
Systèmes de transport intelligents.
Transports urbains > Politique gouvernementale.
Villes intelligentes.
Intelligent transportation systems
Smart cities
Urban transportation policy
Urban transportation > Technological innovations
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • 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.