Smart Urban Mobility: Transport Planning in the Age of Big Data and Digital Twins
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
San Diego
Elsevier Science & Technology
2023
|
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (268 Seiten) |
| ISBN: | 9780128208915 |
Internformat
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV048831728 | ||
| 003 | DE-604 | ||
| 005 | 20230403 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 230224s2023 |||| o||u| ||||||eng d | ||
| 020 | |a 9780128208915 |9 978-0-12-820891-5 | ||
| 035 | |a (ZDB-30-PQE)EBC7192830 | ||
| 035 | |a (ZDB-30-PAD)EBC7192830 | ||
| 035 | |a (ZDB-89-EBL)EBL7192830 | ||
| 035 | |a (OCoLC)1371314600 | ||
| 035 | |a (DE-599)BVBBV048831728 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-2070s | ||
| 082 | 0 | |a 388.4 | |
| 084 | |a QY 300 |0 (DE-625)142216: |2 rvk | ||
| 100 | 1 | |a Semanjski, Ivana |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Smart Urban Mobility |b Transport Planning in the Age of Big Data and Digital Twins |
| 264 | 1 | |a San Diego |b Elsevier Science & Technology |c 2023 | |
| 264 | 4 | |c ©2023 | |
| 300 | |a 1 Online-Ressource (268 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |a 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? | |
| 505 | 8 | |a 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) | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 650 | 0 | 7 | |a Smart City |0 (DE-588)1061057097 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Verkehrsplanung |0 (DE-588)4062954-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Mobilität |0 (DE-588)4039785-3 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Mobilität |0 (DE-588)4039785-3 |D s |
| 689 | 0 | 1 | |a Smart City |0 (DE-588)1061057097 |D s |
| 689 | 0 | 2 | |a Verkehrsplanung |0 (DE-588)4062954-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Semanjski, Ivana |t Smart Urban Mobility |d San Diego : Elsevier Science & Technology,c2023 |z 9780128207178 |
| 912 | |a ZDB-30-PQE | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-034097306 | |
| 966 | e | |u https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=7192830 |l DE-2070s |p ZDB-30-PQE |q HWR_PDA_PQE |x Aggregator |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1815423683578036224 |
|---|---|
| adam_text | |
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Semanjski, Ivana |
| author_facet | Semanjski, Ivana |
| author_role | aut |
| author_sort | Semanjski, Ivana |
| author_variant | i s is |
| building | Verbundindex |
| bvnumber | BV048831728 |
| classification_rvk | QY 300 |
| collection | ZDB-30-PQE |
| 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 |
| ctrlnum | (ZDB-30-PQE)EBC7192830 (ZDB-30-PAD)EBC7192830 (ZDB-89-EBL)EBL7192830 (OCoLC)1371314600 (DE-599)BVBBV048831728 |
| dewey-full | 388.4 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 388 - Transportation |
| dewey-raw | 388.4 |
| dewey-search | 388.4 |
| dewey-sort | 3388.4 |
| dewey-tens | 380 - Commerce, communications, transportation |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nmm a2200000zc 4500</leader><controlfield tag="001">BV048831728</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20230403</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">230224s2023 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780128208915</subfield><subfield code="9">978-0-12-820891-5</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PQE)EBC7192830</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PAD)EBC7192830</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-89-EBL)EBL7192830</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1371314600</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV048831728</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-2070s</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">388.4</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QY 300</subfield><subfield code="0">(DE-625)142216:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Semanjski, Ivana</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Smart Urban Mobility</subfield><subfield code="b">Transport Planning in the Age of Big Data and Digital Twins</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">San Diego</subfield><subfield code="b">Elsevier Science & Technology</subfield><subfield code="c">2023</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2023</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (268 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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?</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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)</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Smart City</subfield><subfield code="0">(DE-588)1061057097</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Verkehrsplanung</subfield><subfield code="0">(DE-588)4062954-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Mobilität</subfield><subfield code="0">(DE-588)4039785-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Mobilität</subfield><subfield code="0">(DE-588)4039785-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Smart City</subfield><subfield code="0">(DE-588)1061057097</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Verkehrsplanung</subfield><subfield code="0">(DE-588)4062954-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Semanjski, Ivana</subfield><subfield code="t">Smart Urban Mobility</subfield><subfield code="d">San Diego : Elsevier Science & Technology,c2023</subfield><subfield code="z">9780128207178</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034097306</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=7192830</subfield><subfield code="l">DE-2070s</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">HWR_PDA_PQE</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV048831728 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:35:29Z |
| indexdate | 2024-11-11T11:02:20Z |
| institution | BVB |
| isbn | 9780128208915 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034097306 |
| oclc_num | 1371314600 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (268 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Elsevier Science & Technology |
| record_format | marc |
| spelling | Semanjski, Ivana Verfasser aut Smart Urban Mobility Transport Planning in the Age of Big Data and Digital Twins San Diego Elsevier Science & Technology 2023 ©2023 1 Online-Ressource (268 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources 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 Smart City (DE-588)1061057097 gnd rswk-swf Verkehrsplanung (DE-588)4062954-5 gnd rswk-swf Mobilität (DE-588)4039785-3 gnd rswk-swf Mobilität (DE-588)4039785-3 s Smart City (DE-588)1061057097 s Verkehrsplanung (DE-588)4062954-5 s DE-604 Erscheint auch als Druck-Ausgabe Semanjski, Ivana Smart Urban Mobility San Diego : Elsevier Science & Technology,c2023 9780128207178 |
| spellingShingle | Semanjski, Ivana Smart Urban Mobility Transport Planning in the Age of Big Data and Digital Twins 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 Smart City (DE-588)1061057097 gnd Verkehrsplanung (DE-588)4062954-5 gnd Mobilität (DE-588)4039785-3 gnd |
| subject_GND | (DE-588)1061057097 (DE-588)4062954-5 (DE-588)4039785-3 |
| title | Smart Urban Mobility Transport Planning in the Age of Big Data and Digital Twins |
| title_auth | Smart Urban Mobility Transport Planning in the Age of Big Data and Digital Twins |
| title_exact_search | Smart Urban Mobility Transport Planning in the Age of Big Data and Digital Twins |
| title_exact_search_txtP | Smart Urban Mobility Transport Planning in the Age of Big Data and Digital Twins |
| title_full | Smart Urban Mobility Transport Planning in the Age of Big Data and Digital Twins |
| title_fullStr | Smart Urban Mobility Transport Planning in the Age of Big Data and Digital Twins |
| title_full_unstemmed | Smart Urban Mobility Transport Planning in the Age of Big Data and Digital Twins |
| title_short | Smart Urban Mobility |
| title_sort | smart urban mobility transport planning in the age of big data and digital twins |
| title_sub | Transport Planning in the Age of Big Data and Digital Twins |
| topic | Smart City (DE-588)1061057097 gnd Verkehrsplanung (DE-588)4062954-5 gnd Mobilität (DE-588)4039785-3 gnd |
| topic_facet | Smart City Verkehrsplanung Mobilität |
| work_keys_str_mv | AT semanjskiivana smarturbanmobilitytransportplanningintheageofbigdataanddigitaltwins |