Format:
1 Online-Ressource (308 Seiten)
ISBN:
9780323901703
Note:
Description based on publisher supplied metadata and other sources
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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
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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
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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
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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
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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
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2.2. Urban spatial structure
Additional Edition:
Erscheint auch als Druck-Ausgabe Zhang, Haoran Big Data and Mobility As a Service San Diego : Elsevier,c2021 ISBN 9780323901697
Language:
English
Subjects:
Engineering
Keywords:
Big Data
;
Mobilitätsplattform
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