Big Data and Mobility As a Service:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
San Diego
Elsevier
2021
|
| Schlagwörter: | |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Umfang: | xvii, 288 Seiten |
| ISBN: | 9780323901697 |
Internformat
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| 505 | 8 | |a Intro -- Big Data and Mobility as a Service -- Copyright -- Contents -- 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 -- 1. The development history of MaaS -- 1.1. The conception -- 1.2. The early application -- 1.3. MaaS alliance -- 1.4. Development -- 1.5. Revolution and innovation -- 2. The category of MaaS system -- 2.1. Level 0: No integration -- 2.2. Level 1: Information integration -- 2.3. Level 2: Integration of booking and payment -- 2.4. Level 3: Integration of the service offering -- 2.5. Level 4: Integration of societal goals -- 3. Study case -- 3.1. UbiGo -- 3.1.1. Introduction -- 3.1.2. Services -- 3.1.3. Characteristics -- 3.2. Whim -- 3.2.1. Introduction -- 3.2.2. Services -- 3.2.3. Characteristics -- 3.3. Moovit -- 3.3.1. Introduction -- 3.3.2. Services -- 3.3.3. Characteristics -- 3.4. Uber -- 3.4.1. Introduction -- 3.4.2. Services -- 3.4.3. Characteristics -- 4. Future development trend of MaaS system -- 4.1. Data-integrated -- 4.2. Future-oriented -- 4.3. Sustainable -- References -- Chapter 2: Spatio-temporal data preprocessing technologies -- 1. Introduction -- 2. Raw GPS data and workflow of data preprocessing -- 3. Key technologies and corresponding application -- 3.1. Outlier removement -- 3.2. Stay location detection -- 3.3. Travel segmentation -- 3.4. Travel mode detection -- 3.5. Map matching -- 3.6. Summary -- 4. Case study -- 4.1. Stay location detection: Life pattern analysis -- 4.1.1. Introduction -- 4.1.2. Problem and methodology -- 4.1.3. Result illustration and analysis -- 4.1.4. Conclusion -- 4.2. Travel segmentation and mode detection: Ride-sharing potential analysis -- 4.2.1. Introduction -- 4.2.2. Problem and methodology | |
| 505 | 8 | |a 4.2.3. Result illustration and analysis -- 4.2.4. Conclusion -- 4.3. Map matching: Estimation of urban scale PM emission -- 4.3.1. Introduction -- 4.3.2. Problem and methodology -- 4.3.3. Result illustration and analysis -- 4.3.4. Conclusion -- 5. Conclusion -- References -- Chapter 3: Travel similarity estimation and clustering -- 1. Introduction -- 2. Trajectory similarity -- 2.1. Point-to-point distance metric -- 2.2. Similarity function of trajectory -- 2.3. Trajectory clustering -- 3. Travel pattern similarity -- 3.1. Travel pattern extraction -- 3.2. Travel pattern expression -- 3.3. Travel pattern clustering -- 4. Origin-destination matrix similarity -- 4.1. Volume difference focused OD similarity measure -- 4.2. Image-based OD similarity measure -- 4.3. Transforming distance-based OD similarity measure -- 4.4. OD tableau similarity measure: Mobsimilarity -- 5. Case study -- 5.1. CDR-based travel estimation accuracy analysis -- 5.2. Metro usage pattern clustering -- 6. Conclusion and future directions -- References -- Chapter 4: Data fusion technologies for MaaS -- 1. Introduction -- 2. Data formula -- 2.1. Attribute and event data -- 2.2. Trajectory data -- 2.3. Origin-destination (OD) trip data -- 2.4. Correlation network -- 2.5. Environmental data -- 3. Categories of data fusion methods in MaaS -- 4. Data fusion based on deep learning -- 4.1. Fundamental building units of deep learning network -- 4.1.1. CNN -- 4.1.2. RNN -- 4.1.3. ConvLSTM -- 4.1.4. Autoencoder (AE) -- 4.1.5. Convolution graph neural network (ConvGNN) -- 4.2. Fusion strategy -- 4.2.1. Concatenation -- 4.2.2. Sum & -- Hadamard product -- 4.2.3. Attention mechanism -- 4.2.4. Graph fusion -- 4.2.5. Output-input structure -- 5. Decomposition-based methods -- 6. Challenging problems of data fusion in MaaS -- 6.1. Data quality -- 6.2. Model complexity | |
| 505 | 8 | |a 6.3. Data fusion in comparative analysis -- 7. Conclusions -- Acknowledgments -- References -- Chapter 5: Data-driven optimization technologies for MaaS -- 1. Overview of data-driven optimization for the urban mobility system -- 1.1. Data-driven dispatching methods for on-demand ridesharing -- 1.2. Data-driven scheduling methods for public transit -- 1.3. Data-driven rebalancing methods for bicycle-sharing -- 2. Overview of the general concept in MaaS System -- 2.1. Overview of the MaaS systems -- 2.2. Overview of data in MaaS systems -- 3. Mobility resource allocation in MaaS system -- 3.1. Mobility resource allocation framework in MaaS -- 3.2. Data-driven online stochastic resource allocation problems -- 4. Data-driven optimization technologies for resource allocation in MaaS -- 4.1. Sample average approximation -- 4.2. Robust optimization -- 4.3. Predictive analysis and prescriptive analysis -- 4.4. Machine learning-based robust optimization -- 5. Real-world application and case study -- 5.1. Problem description -- 5.2. Methodology -- 5.3. Results and discussion -- 6. Conclusions -- References -- Chapter 6: Data-driven estimation for urban travel shareability -- 1. Introduction -- 1.1. The emergence of sharing transportation -- 1.2. The significance of shareability estimation -- 1.3. Chapter organization -- 2. Emerging sharing transportation mode -- 2.1. Bicycle sharing -- 2.2. Ride sharing and taxi sharing -- 2.3. Customized bus -- 2.4. Characteristics of sharing transportation modes -- 3. Background to traditional data and their limitations -- 4. New and emerging source of data -- 4.1. Track and trace data -- 4.1.1. Mobile phone data -- 4.1.2. Smart card data -- 4.1.3. Taxi GPS data -- 4.1.4. Bicycle-sharing data -- 4.2. Geographic information data -- 4.2.1. Transportation network -- 4.2.2. Vector data -- 4.2.3. Point of interest data | |
| 505 | 8 | |a 4.2.4. Navigation data -- 4.3. Advantages and disadvantages of new data sources -- 5. Emerging form of key technologies -- 5.1. Agent-based modeling -- 5.2. How ABM can be applied in shareability estimation -- 5.2.1. Level 1: ABM in macroscopic policy assessment -- 5.2.2. Level 2: ABM in microscopic strategy evaluation -- 5.2.3. Level 3: ABM in both macroscopic and microscopic strategy optimization -- 6. Case study of ABM in urban shareability estimation -- 6.1. Dynamic electric fence for bicycle sharing -- 6.2. ABM simulation -- 6.3. Data and study area -- 6.4. Result of simulation -- 6.5. Evaluation of the result -- 7. Opportunities and challenges -- 7.1. Data acquisition -- 7.2. Demand prediction -- 7.3. Design improvement of ABM -- 7.4. Acceleration of large-scale ABM -- 8. Conclusions -- Acknowledgment -- References -- Chapter 7: Data mining technologies for Mobility-as-a-Service (MaaS) -- 1. Introduction of data mining technologies in MaaS system -- 2. Data mining technologies in MaaS system -- 2.1. What is data mining? -- 2.2. Object of data mining -- 2.3. Classical steps of data mining -- 2.4. Types of transportation data -- 2.4.1. Static data -- 2.4.2. Fixed detector data -- 2.4.3. Mobile detector data -- 2.4.4. Operation data -- 3. Methodologies of data mining technologies used in MaaS system -- 3.1. Support vector machine -- 3.1.1. linear SVM in linearly separable case -- 3.1.2. linear SVM in linearly inseparable case -- 3.1.3. Nonlinear SVM -- 3.2. Linear regression -- 3.2.1. Least square method -- 3.2.2. Maximum likelihood estimation -- 3.3. Decision tree -- 3.3.1. The structure of decision tree -- 3.3.2. Attribute partition selection -- Information entropy -- Information gain -- Rate of information gain -- Gini index -- 3.4. Clustering analysis -- 3.4.1. Similarity measurement -- Numerical variable -- 3.4.2. Clustering algorithms | |
| 505 | 8 | |a K-means -- Objective function -- Hierarchical clustering -- Algorithm -- Density-based spatial clustering of applications with noise (DBSCAN) -- Algorithm -- Grid-based clustering -- Algorithm -- 4. Case study of data mining for MaaS: Bike sharing in Beijing during Covid-19 pandemic -- 5. Summary of chapter -- References -- Chapter 8: MaaS and IoT: Concepts, methodologies, and applications -- 1. Introduction -- 2. Overview of the concept -- 2.1. Overview of the general concept -- 2.2. Challenges of IoT application in MaaS -- 3. Key technologies and methodologies -- 3.1. Intelligent transportation equipment -- 3.2. Communication protocols for the Internet of Things -- 3.3. Microservices based on the Internet of Things -- 3.4. Cloud computing based on the Internet of Things -- 3.5. Edge computing -- 3.6. Security technologies for the Internet of Things -- 4. Application and case study -- 4.1. Background introduction -- 4.2. System framework -- 4.3. Core function -- 5. Conclusion and future directions -- References -- Chapter 9: MaaS system visualization -- 1. Overview of the general concept -- 2. The key visualization technologies in MaaS for different stakeholders -- 2.1. The perspective of demanders of mobility -- 2.2. The perspective of supplier of transportation service -- 2.2.1. Monitoring -- Object movement monitoring -- Operation status monitoring -- 2.2.2. Analysis and optimization -- 2.3. The perspective of city manager -- 3. Real-world application and case study -- 3.1. Case for demanders of mobility -- 3.2. Case for supplier of transportation service -- 3.3. Case for city manager -- 3.4. Open-source visualization tools and libraries -- 4. Conclusion and future directions -- References -- Chapter 10: MaaS for sustainable urban development -- 1. Introduction -- 2. MaaS interacted with urban traffic and space -- 2.1. Urban traffic structure | |
| 505 | 8 | |a 2.2. Urban spatial structure | |
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Datensatz im Suchindex
| _version_ | 1818990007984062464 |
|---|---|
| any_adam_object | |
| author | Zhang, Haoran |
| author_facet | Zhang, Haoran |
| author_role | aut |
| author_sort | Zhang, Haoran |
| author_variant | h z hz |
| building | Verbundindex |
| bvnumber | BV048526111 |
| classification_rvk | ZO 4660 |
| contents | Intro -- Big Data and Mobility as a Service -- Copyright -- Contents -- 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 -- 1. The development history of MaaS -- 1.1. The conception -- 1.2. The early application -- 1.3. MaaS alliance -- 1.4. Development -- 1.5. Revolution and innovation -- 2. The category of MaaS system -- 2.1. Level 0: No integration -- 2.2. Level 1: Information integration -- 2.3. Level 2: Integration of booking and payment -- 2.4. Level 3: Integration of the service offering -- 2.5. Level 4: Integration of societal goals -- 3. Study case -- 3.1. UbiGo -- 3.1.1. Introduction -- 3.1.2. Services -- 3.1.3. Characteristics -- 3.2. Whim -- 3.2.1. Introduction -- 3.2.2. Services -- 3.2.3. Characteristics -- 3.3. Moovit -- 3.3.1. Introduction -- 3.3.2. Services -- 3.3.3. Characteristics -- 3.4. Uber -- 3.4.1. Introduction -- 3.4.2. Services -- 3.4.3. Characteristics -- 4. Future development trend of MaaS system -- 4.1. Data-integrated -- 4.2. Future-oriented -- 4.3. Sustainable -- References -- Chapter 2: Spatio-temporal data preprocessing technologies -- 1. Introduction -- 2. Raw GPS data and workflow of data preprocessing -- 3. Key technologies and corresponding application -- 3.1. Outlier removement -- 3.2. Stay location detection -- 3.3. Travel segmentation -- 3.4. Travel mode detection -- 3.5. Map matching -- 3.6. Summary -- 4. Case study -- 4.1. Stay location detection: Life pattern analysis -- 4.1.1. Introduction -- 4.1.2. Problem and methodology -- 4.1.3. Result illustration and analysis -- 4.1.4. Conclusion -- 4.2. Travel segmentation and mode detection: Ride-sharing potential analysis -- 4.2.1. Introduction -- 4.2.2. Problem and methodology 4.2.3. Result illustration and analysis -- 4.2.4. Conclusion -- 4.3. Map matching: Estimation of urban scale PM emission -- 4.3.1. Introduction -- 4.3.2. Problem and methodology -- 4.3.3. Result illustration and analysis -- 4.3.4. Conclusion -- 5. Conclusion -- References -- Chapter 3: Travel similarity estimation and clustering -- 1. Introduction -- 2. Trajectory similarity -- 2.1. Point-to-point distance metric -- 2.2. Similarity function of trajectory -- 2.3. Trajectory clustering -- 3. Travel pattern similarity -- 3.1. Travel pattern extraction -- 3.2. Travel pattern expression -- 3.3. Travel pattern clustering -- 4. Origin-destination matrix similarity -- 4.1. Volume difference focused OD similarity measure -- 4.2. Image-based OD similarity measure -- 4.3. Transforming distance-based OD similarity measure -- 4.4. OD tableau similarity measure: Mobsimilarity -- 5. Case study -- 5.1. CDR-based travel estimation accuracy analysis -- 5.2. Metro usage pattern clustering -- 6. Conclusion and future directions -- References -- Chapter 4: Data fusion technologies for MaaS -- 1. Introduction -- 2. Data formula -- 2.1. Attribute and event data -- 2.2. Trajectory data -- 2.3. Origin-destination (OD) trip data -- 2.4. Correlation network -- 2.5. Environmental data -- 3. Categories of data fusion methods in MaaS -- 4. Data fusion based on deep learning -- 4.1. Fundamental building units of deep learning network -- 4.1.1. CNN -- 4.1.2. RNN -- 4.1.3. ConvLSTM -- 4.1.4. Autoencoder (AE) -- 4.1.5. Convolution graph neural network (ConvGNN) -- 4.2. Fusion strategy -- 4.2.1. Concatenation -- 4.2.2. Sum & -- Hadamard product -- 4.2.3. Attention mechanism -- 4.2.4. Graph fusion -- 4.2.5. Output-input structure -- 5. Decomposition-based methods -- 6. Challenging problems of data fusion in MaaS -- 6.1. Data quality -- 6.2. Model complexity 6.3. Data fusion in comparative analysis -- 7. Conclusions -- Acknowledgments -- References -- Chapter 5: Data-driven optimization technologies for MaaS -- 1. Overview of data-driven optimization for the urban mobility system -- 1.1. Data-driven dispatching methods for on-demand ridesharing -- 1.2. Data-driven scheduling methods for public transit -- 1.3. Data-driven rebalancing methods for bicycle-sharing -- 2. Overview of the general concept in MaaS System -- 2.1. Overview of the MaaS systems -- 2.2. Overview of data in MaaS systems -- 3. Mobility resource allocation in MaaS system -- 3.1. Mobility resource allocation framework in MaaS -- 3.2. Data-driven online stochastic resource allocation problems -- 4. Data-driven optimization technologies for resource allocation in MaaS -- 4.1. Sample average approximation -- 4.2. Robust optimization -- 4.3. Predictive analysis and prescriptive analysis -- 4.4. Machine learning-based robust optimization -- 5. Real-world application and case study -- 5.1. Problem description -- 5.2. Methodology -- 5.3. Results and discussion -- 6. Conclusions -- References -- Chapter 6: Data-driven estimation for urban travel shareability -- 1. Introduction -- 1.1. The emergence of sharing transportation -- 1.2. The significance of shareability estimation -- 1.3. Chapter organization -- 2. Emerging sharing transportation mode -- 2.1. Bicycle sharing -- 2.2. Ride sharing and taxi sharing -- 2.3. Customized bus -- 2.4. Characteristics of sharing transportation modes -- 3. Background to traditional data and their limitations -- 4. New and emerging source of data -- 4.1. Track and trace data -- 4.1.1. Mobile phone data -- 4.1.2. Smart card data -- 4.1.3. Taxi GPS data -- 4.1.4. Bicycle-sharing data -- 4.2. Geographic information data -- 4.2.1. Transportation network -- 4.2.2. Vector data -- 4.2.3. Point of interest data 4.2.4. Navigation data -- 4.3. Advantages and disadvantages of new data sources -- 5. Emerging form of key technologies -- 5.1. Agent-based modeling -- 5.2. How ABM can be applied in shareability estimation -- 5.2.1. Level 1: ABM in macroscopic policy assessment -- 5.2.2. Level 2: ABM in microscopic strategy evaluation -- 5.2.3. Level 3: ABM in both macroscopic and microscopic strategy optimization -- 6. Case study of ABM in urban shareability estimation -- 6.1. Dynamic electric fence for bicycle sharing -- 6.2. ABM simulation -- 6.3. Data and study area -- 6.4. Result of simulation -- 6.5. Evaluation of the result -- 7. Opportunities and challenges -- 7.1. Data acquisition -- 7.2. Demand prediction -- 7.3. Design improvement of ABM -- 7.4. Acceleration of large-scale ABM -- 8. Conclusions -- Acknowledgment -- References -- Chapter 7: Data mining technologies for Mobility-as-a-Service (MaaS) -- 1. Introduction of data mining technologies in MaaS system -- 2. Data mining technologies in MaaS system -- 2.1. What is data mining? -- 2.2. Object of data mining -- 2.3. Classical steps of data mining -- 2.4. Types of transportation data -- 2.4.1. Static data -- 2.4.2. Fixed detector data -- 2.4.3. Mobile detector data -- 2.4.4. Operation data -- 3. Methodologies of data mining technologies used in MaaS system -- 3.1. Support vector machine -- 3.1.1. linear SVM in linearly separable case -- 3.1.2. linear SVM in linearly inseparable case -- 3.1.3. Nonlinear SVM -- 3.2. Linear regression -- 3.2.1. Least square method -- 3.2.2. Maximum likelihood estimation -- 3.3. Decision tree -- 3.3.1. The structure of decision tree -- 3.3.2. Attribute partition selection -- Information entropy -- Information gain -- Rate of information gain -- Gini index -- 3.4. Clustering analysis -- 3.4.1. Similarity measurement -- Numerical variable -- 3.4.2. Clustering algorithms K-means -- Objective function -- Hierarchical clustering -- Algorithm -- Density-based spatial clustering of applications with noise (DBSCAN) -- Algorithm -- Grid-based clustering -- Algorithm -- 4. Case study of data mining for MaaS: Bike sharing in Beijing during Covid-19 pandemic -- 5. Summary of chapter -- References -- Chapter 8: MaaS and IoT: Concepts, methodologies, and applications -- 1. Introduction -- 2. Overview of the concept -- 2.1. Overview of the general concept -- 2.2. Challenges of IoT application in MaaS -- 3. Key technologies and methodologies -- 3.1. Intelligent transportation equipment -- 3.2. Communication protocols for the Internet of Things -- 3.3. Microservices based on the Internet of Things -- 3.4. Cloud computing based on the Internet of Things -- 3.5. Edge computing -- 3.6. Security technologies for the Internet of Things -- 4. Application and case study -- 4.1. Background introduction -- 4.2. System framework -- 4.3. Core function -- 5. Conclusion and future directions -- References -- Chapter 9: MaaS system visualization -- 1. Overview of the general concept -- 2. The key visualization technologies in MaaS for different stakeholders -- 2.1. The perspective of demanders of mobility -- 2.2. The perspective of supplier of transportation service -- 2.2.1. Monitoring -- Object movement monitoring -- Operation status monitoring -- 2.2.2. Analysis and optimization -- 2.3. The perspective of city manager -- 3. Real-world application and case study -- 3.1. Case for demanders of mobility -- 3.2. Case for supplier of transportation service -- 3.3. Case for city manager -- 3.4. Open-source visualization tools and libraries -- 4. Conclusion and future directions -- References -- Chapter 10: MaaS for sustainable urban development -- 1. Introduction -- 2. MaaS interacted with urban traffic and space -- 2.1. Urban traffic structure 2.2. Urban spatial structure |
| ctrlnum | (OCoLC)1349544986 (DE-599)BVBBV048526111 |
| dewey-full | 388.0285 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 388 - Transportation |
| dewey-raw | 388.0285 |
| dewey-search | 388.0285 |
| dewey-sort | 3388.0285 |
| dewey-tens | 380 - Commerce, communications, transportation |
| discipline | Wirtschaftswissenschaften Verkehr / Transport |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>10742nam a2200469zc 4500</leader><controlfield tag="001">BV048526111</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">t|</controlfield><controlfield tag="008">221021s2021 xx |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780323901697</subfield><subfield code="9">978-0-323-90169-7</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1349544986</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV048526111</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-573</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">388.0285</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ZO 4660</subfield><subfield code="0">(DE-625)160578:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Haoran</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Big Data and Mobility As a Service</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">San Diego</subfield><subfield code="b">Elsevier</subfield><subfield code="c">2021</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xvii, 288 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">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</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">Intro -- Big Data and Mobility as a Service -- Copyright -- Contents -- 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 -- 1. The development history of MaaS -- 1.1. The conception -- 1.2. The early application -- 1.3. MaaS alliance -- 1.4. Development -- 1.5. Revolution and innovation -- 2. The category of MaaS system -- 2.1. Level 0: No integration -- 2.2. Level 1: Information integration -- 2.3. Level 2: Integration of booking and payment -- 2.4. Level 3: Integration of the service offering -- 2.5. Level 4: Integration of societal goals -- 3. Study case -- 3.1. UbiGo -- 3.1.1. Introduction -- 3.1.2. Services -- 3.1.3. Characteristics -- 3.2. Whim -- 3.2.1. Introduction -- 3.2.2. Services -- 3.2.3. Characteristics -- 3.3. Moovit -- 3.3.1. Introduction -- 3.3.2. Services -- 3.3.3. Characteristics -- 3.4. Uber -- 3.4.1. Introduction -- 3.4.2. Services -- 3.4.3. Characteristics -- 4. Future development trend of MaaS system -- 4.1. Data-integrated -- 4.2. Future-oriented -- 4.3. Sustainable -- References -- Chapter 2: Spatio-temporal data preprocessing technologies -- 1. Introduction -- 2. Raw GPS data and workflow of data preprocessing -- 3. Key technologies and corresponding application -- 3.1. Outlier removement -- 3.2. Stay location detection -- 3.3. Travel segmentation -- 3.4. Travel mode detection -- 3.5. Map matching -- 3.6. Summary -- 4. Case study -- 4.1. Stay location detection: Life pattern analysis -- 4.1.1. Introduction -- 4.1.2. Problem and methodology -- 4.1.3. Result illustration and analysis -- 4.1.4. Conclusion -- 4.2. Travel segmentation and mode detection: Ride-sharing potential analysis -- 4.2.1. Introduction -- 4.2.2. Problem and methodology</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.2.3. Result illustration and analysis -- 4.2.4. Conclusion -- 4.3. Map matching: Estimation of urban scale PM emission -- 4.3.1. Introduction -- 4.3.2. Problem and methodology -- 4.3.3. Result illustration and analysis -- 4.3.4. Conclusion -- 5. Conclusion -- References -- Chapter 3: Travel similarity estimation and clustering -- 1. Introduction -- 2. Trajectory similarity -- 2.1. Point-to-point distance metric -- 2.2. Similarity function of trajectory -- 2.3. Trajectory clustering -- 3. Travel pattern similarity -- 3.1. Travel pattern extraction -- 3.2. Travel pattern expression -- 3.3. Travel pattern clustering -- 4. Origin-destination matrix similarity -- 4.1. Volume difference focused OD similarity measure -- 4.2. Image-based OD similarity measure -- 4.3. Transforming distance-based OD similarity measure -- 4.4. OD tableau similarity measure: Mobsimilarity -- 5. Case study -- 5.1. CDR-based travel estimation accuracy analysis -- 5.2. Metro usage pattern clustering -- 6. Conclusion and future directions -- References -- Chapter 4: Data fusion technologies for MaaS -- 1. Introduction -- 2. Data formula -- 2.1. Attribute and event data -- 2.2. Trajectory data -- 2.3. Origin-destination (OD) trip data -- 2.4. Correlation network -- 2.5. Environmental data -- 3. Categories of data fusion methods in MaaS -- 4. Data fusion based on deep learning -- 4.1. Fundamental building units of deep learning network -- 4.1.1. CNN -- 4.1.2. RNN -- 4.1.3. ConvLSTM -- 4.1.4. Autoencoder (AE) -- 4.1.5. Convolution graph neural network (ConvGNN) -- 4.2. Fusion strategy -- 4.2.1. Concatenation -- 4.2.2. Sum &amp -- Hadamard product -- 4.2.3. Attention mechanism -- 4.2.4. Graph fusion -- 4.2.5. Output-input structure -- 5. Decomposition-based methods -- 6. Challenging problems of data fusion in MaaS -- 6.1. Data quality -- 6.2. Model complexity</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">6.3. Data fusion in comparative analysis -- 7. Conclusions -- Acknowledgments -- References -- Chapter 5: Data-driven optimization technologies for MaaS -- 1. Overview of data-driven optimization for the urban mobility system -- 1.1. Data-driven dispatching methods for on-demand ridesharing -- 1.2. Data-driven scheduling methods for public transit -- 1.3. Data-driven rebalancing methods for bicycle-sharing -- 2. Overview of the general concept in MaaS System -- 2.1. Overview of the MaaS systems -- 2.2. Overview of data in MaaS systems -- 3. Mobility resource allocation in MaaS system -- 3.1. Mobility resource allocation framework in MaaS -- 3.2. Data-driven online stochastic resource allocation problems -- 4. Data-driven optimization technologies for resource allocation in MaaS -- 4.1. Sample average approximation -- 4.2. Robust optimization -- 4.3. Predictive analysis and prescriptive analysis -- 4.4. Machine learning-based robust optimization -- 5. Real-world application and case study -- 5.1. Problem description -- 5.2. Methodology -- 5.3. Results and discussion -- 6. Conclusions -- References -- Chapter 6: Data-driven estimation for urban travel shareability -- 1. Introduction -- 1.1. The emergence of sharing transportation -- 1.2. The significance of shareability estimation -- 1.3. Chapter organization -- 2. Emerging sharing transportation mode -- 2.1. Bicycle sharing -- 2.2. Ride sharing and taxi sharing -- 2.3. Customized bus -- 2.4. Characteristics of sharing transportation modes -- 3. Background to traditional data and their limitations -- 4. New and emerging source of data -- 4.1. Track and trace data -- 4.1.1. Mobile phone data -- 4.1.2. Smart card data -- 4.1.3. Taxi GPS data -- 4.1.4. Bicycle-sharing data -- 4.2. Geographic information data -- 4.2.1. Transportation network -- 4.2.2. Vector data -- 4.2.3. Point of interest data</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.2.4. Navigation data -- 4.3. Advantages and disadvantages of new data sources -- 5. Emerging form of key technologies -- 5.1. Agent-based modeling -- 5.2. How ABM can be applied in shareability estimation -- 5.2.1. Level 1: ABM in macroscopic policy assessment -- 5.2.2. Level 2: ABM in microscopic strategy evaluation -- 5.2.3. Level 3: ABM in both macroscopic and microscopic strategy optimization -- 6. Case study of ABM in urban shareability estimation -- 6.1. Dynamic electric fence for bicycle sharing -- 6.2. ABM simulation -- 6.3. Data and study area -- 6.4. Result of simulation -- 6.5. Evaluation of the result -- 7. Opportunities and challenges -- 7.1. Data acquisition -- 7.2. Demand prediction -- 7.3. Design improvement of ABM -- 7.4. Acceleration of large-scale ABM -- 8. Conclusions -- Acknowledgment -- References -- Chapter 7: Data mining technologies for Mobility-as-a-Service (MaaS) -- 1. Introduction of data mining technologies in MaaS system -- 2. Data mining technologies in MaaS system -- 2.1. What is data mining? -- 2.2. Object of data mining -- 2.3. Classical steps of data mining -- 2.4. Types of transportation data -- 2.4.1. Static data -- 2.4.2. Fixed detector data -- 2.4.3. Mobile detector data -- 2.4.4. Operation data -- 3. Methodologies of data mining technologies used in MaaS system -- 3.1. Support vector machine -- 3.1.1. linear SVM in linearly separable case -- 3.1.2. linear SVM in linearly inseparable case -- 3.1.3. Nonlinear SVM -- 3.2. Linear regression -- 3.2.1. Least square method -- 3.2.2. Maximum likelihood estimation -- 3.3. Decision tree -- 3.3.1. The structure of decision tree -- 3.3.2. Attribute partition selection -- Information entropy -- Information gain -- Rate of information gain -- Gini index -- 3.4. Clustering analysis -- 3.4.1. Similarity measurement -- Numerical variable -- 3.4.2. Clustering algorithms</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">K-means -- Objective function -- Hierarchical clustering -- Algorithm -- Density-based spatial clustering of applications with noise (DBSCAN) -- Algorithm -- Grid-based clustering -- Algorithm -- 4. Case study of data mining for MaaS: Bike sharing in Beijing during Covid-19 pandemic -- 5. Summary of chapter -- References -- Chapter 8: MaaS and IoT: Concepts, methodologies, and applications -- 1. Introduction -- 2. Overview of the concept -- 2.1. Overview of the general concept -- 2.2. Challenges of IoT application in MaaS -- 3. Key technologies and methodologies -- 3.1. Intelligent transportation equipment -- 3.2. Communication protocols for the Internet of Things -- 3.3. Microservices based on the Internet of Things -- 3.4. Cloud computing based on the Internet of Things -- 3.5. Edge computing -- 3.6. Security technologies for the Internet of Things -- 4. Application and case study -- 4.1. Background introduction -- 4.2. System framework -- 4.3. Core function -- 5. Conclusion and future directions -- References -- Chapter 9: MaaS system visualization -- 1. Overview of the general concept -- 2. The key visualization technologies in MaaS for different stakeholders -- 2.1. The perspective of demanders of mobility -- 2.2. The perspective of supplier of transportation service -- 2.2.1. Monitoring -- Object movement monitoring -- Operation status monitoring -- 2.2.2. Analysis and optimization -- 2.3. The perspective of city manager -- 3. Real-world application and case study -- 3.1. Case for demanders of mobility -- 3.2. Case for supplier of transportation service -- 3.3. Case for city manager -- 3.4. Open-source visualization tools and libraries -- 4. Conclusion and future directions -- References -- Chapter 10: MaaS for sustainable urban development -- 1. Introduction -- 2. MaaS interacted with urban traffic and space -- 2.1. 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| id | DE-604.BV048526111 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T19:47:32Z |
| institution | BVB |
| isbn | 9780323901697 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033902930 |
| oclc_num | 1349544986 |
| open_access_boolean | |
| owner | DE-573 |
| owner_facet | DE-573 |
| physical | xvii, 288 Seiten |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Elsevier |
| record_format | marc |
| spelling | Zhang, Haoran Verfasser aut Big Data and Mobility As a Service San Diego Elsevier 2021 ©2022 xvii, 288 Seiten txt rdacontent n rdamedia nc rdacarrier Description based on publisher supplied metadata and other sources Intro -- Big Data and Mobility as a Service -- Copyright -- Contents -- 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 -- 1. The development history of MaaS -- 1.1. The conception -- 1.2. The early application -- 1.3. MaaS alliance -- 1.4. Development -- 1.5. Revolution and innovation -- 2. The category of MaaS system -- 2.1. Level 0: No integration -- 2.2. Level 1: Information integration -- 2.3. Level 2: Integration of booking and payment -- 2.4. Level 3: Integration of the service offering -- 2.5. Level 4: Integration of societal goals -- 3. Study case -- 3.1. UbiGo -- 3.1.1. Introduction -- 3.1.2. Services -- 3.1.3. Characteristics -- 3.2. Whim -- 3.2.1. Introduction -- 3.2.2. Services -- 3.2.3. Characteristics -- 3.3. Moovit -- 3.3.1. Introduction -- 3.3.2. Services -- 3.3.3. Characteristics -- 3.4. Uber -- 3.4.1. Introduction -- 3.4.2. Services -- 3.4.3. Characteristics -- 4. Future development trend of MaaS system -- 4.1. Data-integrated -- 4.2. Future-oriented -- 4.3. Sustainable -- References -- Chapter 2: Spatio-temporal data preprocessing technologies -- 1. Introduction -- 2. Raw GPS data and workflow of data preprocessing -- 3. Key technologies and corresponding application -- 3.1. Outlier removement -- 3.2. Stay location detection -- 3.3. Travel segmentation -- 3.4. Travel mode detection -- 3.5. Map matching -- 3.6. Summary -- 4. Case study -- 4.1. Stay location detection: Life pattern analysis -- 4.1.1. Introduction -- 4.1.2. Problem and methodology -- 4.1.3. Result illustration and analysis -- 4.1.4. Conclusion -- 4.2. Travel segmentation and mode detection: Ride-sharing potential analysis -- 4.2.1. Introduction -- 4.2.2. Problem and methodology 4.2.3. Result illustration and analysis -- 4.2.4. Conclusion -- 4.3. Map matching: Estimation of urban scale PM emission -- 4.3.1. Introduction -- 4.3.2. Problem and methodology -- 4.3.3. Result illustration and analysis -- 4.3.4. Conclusion -- 5. Conclusion -- References -- Chapter 3: Travel similarity estimation and clustering -- 1. Introduction -- 2. Trajectory similarity -- 2.1. Point-to-point distance metric -- 2.2. Similarity function of trajectory -- 2.3. Trajectory clustering -- 3. Travel pattern similarity -- 3.1. Travel pattern extraction -- 3.2. Travel pattern expression -- 3.3. Travel pattern clustering -- 4. Origin-destination matrix similarity -- 4.1. Volume difference focused OD similarity measure -- 4.2. Image-based OD similarity measure -- 4.3. Transforming distance-based OD similarity measure -- 4.4. OD tableau similarity measure: Mobsimilarity -- 5. Case study -- 5.1. CDR-based travel estimation accuracy analysis -- 5.2. Metro usage pattern clustering -- 6. Conclusion and future directions -- References -- Chapter 4: Data fusion technologies for MaaS -- 1. Introduction -- 2. Data formula -- 2.1. Attribute and event data -- 2.2. Trajectory data -- 2.3. Origin-destination (OD) trip data -- 2.4. Correlation network -- 2.5. Environmental data -- 3. Categories of data fusion methods in MaaS -- 4. Data fusion based on deep learning -- 4.1. Fundamental building units of deep learning network -- 4.1.1. CNN -- 4.1.2. RNN -- 4.1.3. ConvLSTM -- 4.1.4. Autoencoder (AE) -- 4.1.5. Convolution graph neural network (ConvGNN) -- 4.2. Fusion strategy -- 4.2.1. Concatenation -- 4.2.2. Sum & -- Hadamard product -- 4.2.3. Attention mechanism -- 4.2.4. Graph fusion -- 4.2.5. Output-input structure -- 5. Decomposition-based methods -- 6. Challenging problems of data fusion in MaaS -- 6.1. Data quality -- 6.2. Model complexity 6.3. Data fusion in comparative analysis -- 7. Conclusions -- Acknowledgments -- References -- Chapter 5: Data-driven optimization technologies for MaaS -- 1. Overview of data-driven optimization for the urban mobility system -- 1.1. Data-driven dispatching methods for on-demand ridesharing -- 1.2. Data-driven scheduling methods for public transit -- 1.3. Data-driven rebalancing methods for bicycle-sharing -- 2. Overview of the general concept in MaaS System -- 2.1. Overview of the MaaS systems -- 2.2. Overview of data in MaaS systems -- 3. Mobility resource allocation in MaaS system -- 3.1. Mobility resource allocation framework in MaaS -- 3.2. Data-driven online stochastic resource allocation problems -- 4. Data-driven optimization technologies for resource allocation in MaaS -- 4.1. Sample average approximation -- 4.2. Robust optimization -- 4.3. Predictive analysis and prescriptive analysis -- 4.4. Machine learning-based robust optimization -- 5. Real-world application and case study -- 5.1. Problem description -- 5.2. Methodology -- 5.3. Results and discussion -- 6. Conclusions -- References -- Chapter 6: Data-driven estimation for urban travel shareability -- 1. Introduction -- 1.1. The emergence of sharing transportation -- 1.2. The significance of shareability estimation -- 1.3. Chapter organization -- 2. Emerging sharing transportation mode -- 2.1. Bicycle sharing -- 2.2. Ride sharing and taxi sharing -- 2.3. Customized bus -- 2.4. Characteristics of sharing transportation modes -- 3. Background to traditional data and their limitations -- 4. New and emerging source of data -- 4.1. Track and trace data -- 4.1.1. Mobile phone data -- 4.1.2. Smart card data -- 4.1.3. Taxi GPS data -- 4.1.4. Bicycle-sharing data -- 4.2. Geographic information data -- 4.2.1. Transportation network -- 4.2.2. Vector data -- 4.2.3. Point of interest data 4.2.4. Navigation data -- 4.3. Advantages and disadvantages of new data sources -- 5. Emerging form of key technologies -- 5.1. Agent-based modeling -- 5.2. How ABM can be applied in shareability estimation -- 5.2.1. Level 1: ABM in macroscopic policy assessment -- 5.2.2. Level 2: ABM in microscopic strategy evaluation -- 5.2.3. Level 3: ABM in both macroscopic and microscopic strategy optimization -- 6. Case study of ABM in urban shareability estimation -- 6.1. Dynamic electric fence for bicycle sharing -- 6.2. ABM simulation -- 6.3. Data and study area -- 6.4. Result of simulation -- 6.5. Evaluation of the result -- 7. Opportunities and challenges -- 7.1. Data acquisition -- 7.2. Demand prediction -- 7.3. Design improvement of ABM -- 7.4. Acceleration of large-scale ABM -- 8. Conclusions -- Acknowledgment -- References -- Chapter 7: Data mining technologies for Mobility-as-a-Service (MaaS) -- 1. Introduction of data mining technologies in MaaS system -- 2. Data mining technologies in MaaS system -- 2.1. What is data mining? -- 2.2. Object of data mining -- 2.3. Classical steps of data mining -- 2.4. Types of transportation data -- 2.4.1. Static data -- 2.4.2. Fixed detector data -- 2.4.3. Mobile detector data -- 2.4.4. Operation data -- 3. Methodologies of data mining technologies used in MaaS system -- 3.1. Support vector machine -- 3.1.1. linear SVM in linearly separable case -- 3.1.2. linear SVM in linearly inseparable case -- 3.1.3. Nonlinear SVM -- 3.2. Linear regression -- 3.2.1. Least square method -- 3.2.2. Maximum likelihood estimation -- 3.3. Decision tree -- 3.3.1. The structure of decision tree -- 3.3.2. Attribute partition selection -- Information entropy -- Information gain -- Rate of information gain -- Gini index -- 3.4. Clustering analysis -- 3.4.1. Similarity measurement -- Numerical variable -- 3.4.2. Clustering algorithms K-means -- Objective function -- Hierarchical clustering -- Algorithm -- Density-based spatial clustering of applications with noise (DBSCAN) -- Algorithm -- Grid-based clustering -- Algorithm -- 4. Case study of data mining for MaaS: Bike sharing in Beijing during Covid-19 pandemic -- 5. Summary of chapter -- References -- Chapter 8: MaaS and IoT: Concepts, methodologies, and applications -- 1. Introduction -- 2. Overview of the concept -- 2.1. Overview of the general concept -- 2.2. Challenges of IoT application in MaaS -- 3. Key technologies and methodologies -- 3.1. Intelligent transportation equipment -- 3.2. Communication protocols for the Internet of Things -- 3.3. Microservices based on the Internet of Things -- 3.4. Cloud computing based on the Internet of Things -- 3.5. Edge computing -- 3.6. Security technologies for the Internet of Things -- 4. Application and case study -- 4.1. Background introduction -- 4.2. System framework -- 4.3. Core function -- 5. Conclusion and future directions -- References -- Chapter 9: MaaS system visualization -- 1. Overview of the general concept -- 2. The key visualization technologies in MaaS for different stakeholders -- 2.1. The perspective of demanders of mobility -- 2.2. The perspective of supplier of transportation service -- 2.2.1. Monitoring -- Object movement monitoring -- Operation status monitoring -- 2.2.2. Analysis and optimization -- 2.3. The perspective of city manager -- 3. Real-world application and case study -- 3.1. Case for demanders of mobility -- 3.2. Case for supplier of transportation service -- 3.3. Case for city manager -- 3.4. Open-source visualization tools and libraries -- 4. Conclusion and future directions -- References -- Chapter 10: MaaS for sustainable urban development -- 1. Introduction -- 2. MaaS interacted with urban traffic and space -- 2.1. Urban traffic structure 2.2. Urban spatial structure Big Data (DE-588)4802620-7 gnd rswk-swf Mobilitätsplattform (DE-588)1263605907 gnd rswk-swf Big Data (DE-588)4802620-7 s Mobilitätsplattform (DE-588)1263605907 s DE-604 Song, Xuan Sonstige oth Shibasaki, Ryosuke Sonstige oth Erscheint auch als Online-Ausgabe 978-0-323-90170-3 |
| spellingShingle | Zhang, Haoran Big Data and Mobility As a Service Intro -- Big Data and Mobility as a Service -- Copyright -- Contents -- 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 -- 1. The development history of MaaS -- 1.1. The conception -- 1.2. The early application -- 1.3. MaaS alliance -- 1.4. Development -- 1.5. Revolution and innovation -- 2. The category of MaaS system -- 2.1. Level 0: No integration -- 2.2. Level 1: Information integration -- 2.3. Level 2: Integration of booking and payment -- 2.4. Level 3: Integration of the service offering -- 2.5. Level 4: Integration of societal goals -- 3. Study case -- 3.1. UbiGo -- 3.1.1. Introduction -- 3.1.2. Services -- 3.1.3. Characteristics -- 3.2. Whim -- 3.2.1. Introduction -- 3.2.2. Services -- 3.2.3. Characteristics -- 3.3. Moovit -- 3.3.1. Introduction -- 3.3.2. Services -- 3.3.3. Characteristics -- 3.4. Uber -- 3.4.1. Introduction -- 3.4.2. Services -- 3.4.3. Characteristics -- 4. Future development trend of MaaS system -- 4.1. Data-integrated -- 4.2. Future-oriented -- 4.3. Sustainable -- References -- Chapter 2: Spatio-temporal data preprocessing technologies -- 1. Introduction -- 2. Raw GPS data and workflow of data preprocessing -- 3. Key technologies and corresponding application -- 3.1. Outlier removement -- 3.2. Stay location detection -- 3.3. Travel segmentation -- 3.4. Travel mode detection -- 3.5. Map matching -- 3.6. Summary -- 4. Case study -- 4.1. Stay location detection: Life pattern analysis -- 4.1.1. Introduction -- 4.1.2. Problem and methodology -- 4.1.3. Result illustration and analysis -- 4.1.4. Conclusion -- 4.2. Travel segmentation and mode detection: Ride-sharing potential analysis -- 4.2.1. Introduction -- 4.2.2. Problem and methodology 4.2.3. Result illustration and analysis -- 4.2.4. Conclusion -- 4.3. Map matching: Estimation of urban scale PM emission -- 4.3.1. Introduction -- 4.3.2. Problem and methodology -- 4.3.3. Result illustration and analysis -- 4.3.4. Conclusion -- 5. Conclusion -- References -- Chapter 3: Travel similarity estimation and clustering -- 1. Introduction -- 2. Trajectory similarity -- 2.1. Point-to-point distance metric -- 2.2. Similarity function of trajectory -- 2.3. Trajectory clustering -- 3. Travel pattern similarity -- 3.1. Travel pattern extraction -- 3.2. Travel pattern expression -- 3.3. Travel pattern clustering -- 4. Origin-destination matrix similarity -- 4.1. Volume difference focused OD similarity measure -- 4.2. Image-based OD similarity measure -- 4.3. Transforming distance-based OD similarity measure -- 4.4. OD tableau similarity measure: Mobsimilarity -- 5. Case study -- 5.1. CDR-based travel estimation accuracy analysis -- 5.2. Metro usage pattern clustering -- 6. Conclusion and future directions -- References -- Chapter 4: Data fusion technologies for MaaS -- 1. Introduction -- 2. Data formula -- 2.1. Attribute and event data -- 2.2. Trajectory data -- 2.3. Origin-destination (OD) trip data -- 2.4. Correlation network -- 2.5. Environmental data -- 3. Categories of data fusion methods in MaaS -- 4. Data fusion based on deep learning -- 4.1. Fundamental building units of deep learning network -- 4.1.1. CNN -- 4.1.2. RNN -- 4.1.3. ConvLSTM -- 4.1.4. Autoencoder (AE) -- 4.1.5. Convolution graph neural network (ConvGNN) -- 4.2. Fusion strategy -- 4.2.1. Concatenation -- 4.2.2. Sum & -- Hadamard product -- 4.2.3. Attention mechanism -- 4.2.4. Graph fusion -- 4.2.5. Output-input structure -- 5. Decomposition-based methods -- 6. Challenging problems of data fusion in MaaS -- 6.1. Data quality -- 6.2. Model complexity 6.3. Data fusion in comparative analysis -- 7. Conclusions -- Acknowledgments -- References -- Chapter 5: Data-driven optimization technologies for MaaS -- 1. Overview of data-driven optimization for the urban mobility system -- 1.1. Data-driven dispatching methods for on-demand ridesharing -- 1.2. Data-driven scheduling methods for public transit -- 1.3. Data-driven rebalancing methods for bicycle-sharing -- 2. Overview of the general concept in MaaS System -- 2.1. Overview of the MaaS systems -- 2.2. Overview of data in MaaS systems -- 3. Mobility resource allocation in MaaS system -- 3.1. Mobility resource allocation framework in MaaS -- 3.2. Data-driven online stochastic resource allocation problems -- 4. Data-driven optimization technologies for resource allocation in MaaS -- 4.1. Sample average approximation -- 4.2. Robust optimization -- 4.3. Predictive analysis and prescriptive analysis -- 4.4. Machine learning-based robust optimization -- 5. Real-world application and case study -- 5.1. Problem description -- 5.2. Methodology -- 5.3. Results and discussion -- 6. Conclusions -- References -- Chapter 6: Data-driven estimation for urban travel shareability -- 1. Introduction -- 1.1. The emergence of sharing transportation -- 1.2. The significance of shareability estimation -- 1.3. Chapter organization -- 2. Emerging sharing transportation mode -- 2.1. Bicycle sharing -- 2.2. Ride sharing and taxi sharing -- 2.3. Customized bus -- 2.4. Characteristics of sharing transportation modes -- 3. Background to traditional data and their limitations -- 4. New and emerging source of data -- 4.1. Track and trace data -- 4.1.1. Mobile phone data -- 4.1.2. Smart card data -- 4.1.3. Taxi GPS data -- 4.1.4. Bicycle-sharing data -- 4.2. Geographic information data -- 4.2.1. Transportation network -- 4.2.2. Vector data -- 4.2.3. Point of interest data 4.2.4. Navigation data -- 4.3. Advantages and disadvantages of new data sources -- 5. Emerging form of key technologies -- 5.1. Agent-based modeling -- 5.2. How ABM can be applied in shareability estimation -- 5.2.1. Level 1: ABM in macroscopic policy assessment -- 5.2.2. Level 2: ABM in microscopic strategy evaluation -- 5.2.3. Level 3: ABM in both macroscopic and microscopic strategy optimization -- 6. Case study of ABM in urban shareability estimation -- 6.1. Dynamic electric fence for bicycle sharing -- 6.2. ABM simulation -- 6.3. Data and study area -- 6.4. Result of simulation -- 6.5. Evaluation of the result -- 7. Opportunities and challenges -- 7.1. Data acquisition -- 7.2. Demand prediction -- 7.3. Design improvement of ABM -- 7.4. Acceleration of large-scale ABM -- 8. Conclusions -- Acknowledgment -- References -- Chapter 7: Data mining technologies for Mobility-as-a-Service (MaaS) -- 1. Introduction of data mining technologies in MaaS system -- 2. Data mining technologies in MaaS system -- 2.1. What is data mining? -- 2.2. Object of data mining -- 2.3. Classical steps of data mining -- 2.4. Types of transportation data -- 2.4.1. Static data -- 2.4.2. Fixed detector data -- 2.4.3. Mobile detector data -- 2.4.4. Operation data -- 3. Methodologies of data mining technologies used in MaaS system -- 3.1. Support vector machine -- 3.1.1. linear SVM in linearly separable case -- 3.1.2. linear SVM in linearly inseparable case -- 3.1.3. Nonlinear SVM -- 3.2. Linear regression -- 3.2.1. Least square method -- 3.2.2. Maximum likelihood estimation -- 3.3. Decision tree -- 3.3.1. The structure of decision tree -- 3.3.2. Attribute partition selection -- Information entropy -- Information gain -- Rate of information gain -- Gini index -- 3.4. Clustering analysis -- 3.4.1. Similarity measurement -- Numerical variable -- 3.4.2. Clustering algorithms K-means -- Objective function -- Hierarchical clustering -- Algorithm -- Density-based spatial clustering of applications with noise (DBSCAN) -- Algorithm -- Grid-based clustering -- Algorithm -- 4. Case study of data mining for MaaS: Bike sharing in Beijing during Covid-19 pandemic -- 5. Summary of chapter -- References -- Chapter 8: MaaS and IoT: Concepts, methodologies, and applications -- 1. Introduction -- 2. Overview of the concept -- 2.1. Overview of the general concept -- 2.2. Challenges of IoT application in MaaS -- 3. Key technologies and methodologies -- 3.1. Intelligent transportation equipment -- 3.2. Communication protocols for the Internet of Things -- 3.3. Microservices based on the Internet of Things -- 3.4. Cloud computing based on the Internet of Things -- 3.5. Edge computing -- 3.6. Security technologies for the Internet of Things -- 4. Application and case study -- 4.1. Background introduction -- 4.2. System framework -- 4.3. Core function -- 5. Conclusion and future directions -- References -- Chapter 9: MaaS system visualization -- 1. Overview of the general concept -- 2. The key visualization technologies in MaaS for different stakeholders -- 2.1. The perspective of demanders of mobility -- 2.2. The perspective of supplier of transportation service -- 2.2.1. Monitoring -- Object movement monitoring -- Operation status monitoring -- 2.2.2. Analysis and optimization -- 2.3. The perspective of city manager -- 3. Real-world application and case study -- 3.1. Case for demanders of mobility -- 3.2. Case for supplier of transportation service -- 3.3. Case for city manager -- 3.4. Open-source visualization tools and libraries -- 4. Conclusion and future directions -- References -- Chapter 10: MaaS for sustainable urban development -- 1. Introduction -- 2. MaaS interacted with urban traffic and space -- 2.1. Urban traffic structure 2.2. Urban spatial structure Big Data (DE-588)4802620-7 gnd Mobilitätsplattform (DE-588)1263605907 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)1263605907 |
| title | Big Data and Mobility As a Service |
| title_auth | Big Data and Mobility As a Service |
| title_exact_search | Big Data and Mobility As a Service |
| title_full | Big Data and Mobility As a Service |
| title_fullStr | Big Data and Mobility As a Service |
| title_full_unstemmed | Big Data and Mobility As a Service |
| title_short | Big Data and Mobility As a Service |
| title_sort | big data and mobility as a service |
| topic | Big Data (DE-588)4802620-7 gnd Mobilitätsplattform (DE-588)1263605907 gnd |
| topic_facet | Big Data Mobilitätsplattform |
| work_keys_str_mv | AT zhanghaoran bigdataandmobilityasaservice AT songxuan bigdataandmobilityasaservice AT shibasakiryosuke bigdataandmobilityasaservice |