Kooperativer Bibliotheksverbund

UID:

Format: VII, 200 p. 88 illus., 71 illus. in color. , online resource.

Edition: 1st ed. 2023.

ISBN: 9789811998997

Content: This book summarizes the advanced intelligent pipeline management technologies. The text discusses the main challenges of how to define and reinvent data-driven intelligent pipeline systems by studying scheduling-operation- safety management systems. Additionally, within an all-around intelligent pipeline system technology development framework, this book characterizes the scientific problems of intelligent pipeline system services among different processes, such as scheduling, demand-side management, operation condition monitoring, safety analysis, fault detection, etc. This book also introduces the existing positive and successful intelligent pipeline system projects that can be identified in the studied domain, and how can they be best applied for practical success. The text is supported by informative illustrations and case studies so that practitioners can use the book as a toolbox to improve understanding in applying the novel technologies into intelligent pipeline system management and development.

Note: Chapter 1 - Overview for pipeline scheduling -- Chapter 2 - Advanced Modelling and algorithm for pipeline scheduling -- Chapter 3 - Demand Side Management in smart pipeline networks -- Chapter 4 - Operation condition monitoring for pipeline -- Chapter 5 - Operation condition prediction for pipeline -- Chapter 6 - Intelligent inspection for pipeline system -- Chapter 7 - Probabilistic Safety Analysis in complex pipeline systems -- Chapter 8 - Risk pre-warning method for pipeline systems -- Chapter 9 - Fault detection and diagnose method for pressurization devices -- Chapter 10 - Intelligent leakage detection for pipelines -- Chapter 11 - Smart emergency management of pipeline system -- Chapter 12 - Simulation of natural gas pipeline system.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9789811998980

Additional Edition: Printed edition: ISBN 9789811999000

Language: English

DOI: 10.1007/978-981-19-9899-7

URL: https://doi.org/10.1007/978-981-19-9899-7

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (244 pages)

ISBN: 9780443184239 , 0443184232

Content: Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations. The book introduces how to design MDM platforms that adapt to the evolving mobility environment—and new types of transportation and users—based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field. This third volume looks at various cases studies to illustrate and explore the methods introduced in the first two volumes, covering topics such as Intelligent Transportation Management, Smart Emergency Management—detailing cases such as the Fukushima earthquake, Hurricane Katrina, and COVID-19—and Urban Sustainability Development, covering bicycle and railway travel behavior, mobility inequality, and road and light pollution inequality.

Note: Front Cover -- Handbook of Mobility Data Mining -- Handbook of Mobility: Data Mining Mobility Data-Driven Applications -- Copyright -- Contents -- List of contributors -- Preface -- Acknowledgments -- One - Mobility data in bike-sharing systems -- 1. Introduction -- 2. Literature review -- 3. Case study -- 3.1 Case 1: Market-oriented subarea division -- 3.2 Case 2: Layout optimization -- 3.3 Case 3: System design optimization -- 4. Conclusion -- References -- Two - Improvement of an online ride-hailing system based on empirical GPS data -- 1. Introduction -- 2. Related works -- 3. Problem description -- 3.1 Emission performance and the user scale -- 3.1.1 Concept of emission performance -- 3.1.2 Emission performance and the user scale -- 3.1.3 Research framework -- 3.2 Ride-hailing dispatch based on prediction and optimization -- 3.2.1 Metric of evaluation -- 3.2.2 Research framework -- 4. Study case -- 5. Methodology -- 5.1 Emission performance and the user scale -- 5.1.1 Reassignment system -- 5.1.2 Gibbs Sampling for the generation of simulation samples -- 5.1.3 Crosssimulation module generation -- 5.1.4 Equation for result computation -- 5.2 Ride-hailing dispatch based on prediction and optimization -- 5.2.1 Time window division -- 5.2.2 Baseline: greedy algorithm -- 5.2.3 Prediction model -- 5.2.4 Optimization -- 6. Result analysis -- 6.1 Emission performance and the user scale -- 6.1.1 Void distance proportion analysis -- 6.1.2 Emission performance analysis -- 6.1.3 High-efficiency area computation under metric constraints -- 6.2 Ride-hailing dispatch based on prediction and optimization -- 6.2.1 Result of prediction -- 6.2.2 Result of dispatch -- 7. Conclusion and future work -- References -- Three - Research on vehicle routing problem and application scenarios -- 1. Introduction -- 2. Vehicle routing problem in MaaS shared-bus system. , 2.1 Introduction -- 2.2 Problem description -- 2.3 Methodology -- 2.4 Experiment of real world benchmark -- 2.4.1 Experimental set up -- 2.4.2 Experimental results and analysis -- 2.5 Conclusion -- 3. Application Scenario of Vehicle Routing Problem in Logistics Transportation -- 3.1 Introduction -- 3.2 Problem description -- 3.3 Methodologies -- 3.3.1 DBSCAN -- 3.3.2 Fast Unfolding -- 3.3.3 Experiment and result analysis -- 3.4 Conclusion -- 4. Summary and prospect -- References -- Four - Travel demand prediction model and applications -- 1. Introduction -- 2. Deep learning -- 2.1 Convolutional neural networks -- 2.2 Recurrent neural networks -- 3. Travel demand prediction model -- 4. Study case -- 5. Result and discussion -- 6. Technical potential analysis of travel demand prediction model -- 6.1 Scenarios -- 6.2 Indexes -- 6.2.1 Empty distance -- 6.2.2 Relative performance -- 6.3 Input and output -- 6.4 Ride-hailing dispatching system simulation -- 6.5 Result of ride-hailing dispatching system simulation -- 7. Conclusion -- References -- five - Railway usage behavior analysis based on mobile phone big data -- 1. Introduction -- 2. Literature review -- 3. Methodology -- 3.1 Framework -- 3.1.1 Dataset preparation -- 3.1.2 Quantifying usage behavior -- 3.1.3 Cluster analysis -- 3.1.4 Station attribution analysis -- 3.2 Deriving usage behavior probability -- 3.3 Station type detection -- 3.3.1 Usage behavior pattern clustering -- 3.3.2 Subtype detection -- 3.4 Identification of build environment indicators -- 4. Case study -- 4.1 Study area -- 4.2 Station type detection results -- 4.2.1 Parameter settings -- 4.2.2 Sensitivity Analysis -- 4.2.3 Usage behavior pattern clustering -- 4.2.4 Subtype detection -- 4.3 Built environment analysis -- 5. Discussion and conclusions -- References. , Six - An Origin-Destination matrix prediction-based road dynamic pricing optimization system -- 1. Introduction -- 2. Methodology -- 2.1 Karush-Kuhn-Tucker conditions -- 2.2 Link travel time function -- 2.3 Travel routes -- 2.4 Bi-level optimization model -- 3. Study case -- 4. Result and discussion -- 5. Conclusion -- References -- Seven - Blockchain for location-based big data-driven services -- 1. Introduction -- 1.1 Smart city -- 1.2 Location-based big data-driven services -- 1.3 Challenges in location-based big data-driven services -- 1.4 Blockchain for location-based big data-driven services -- 2. Background of blockchain -- 2.1 Blockchain -- 2.2 Smart contract -- 2.3 Consensus mechanism -- 3. Location-based big data-driven services -- 3.1 Shared mobility services -- 3.2 Charging services -- 3.3 Smart logistics services -- 4. Blockchain in location-based big data-driven services -- 4.1 Blockchian in shared mobility services -- 4.2 Blockchian in charging services -- 4.3 Blockchian in smart logistics services -- 5. Future trend of blockchain -- References -- Eight - Mobility data in urban road emission mitigation -- 1. Introduction -- 2. Literature review -- 3. Case studies -- 3.1 Case A: comparison between ride-hailing and taxi -- 3.1.1 Descriptions of the case study -- 3.1.2 Results -- 3.2 Case B: emission potential analysis of urban transit buses post-COVID-19 -- 3.2.1 Descriptions of the case study -- 3.2.2 Methodology -- 3.2.3 Results -- 3.3 Case C: spatio-temporal analysis on on-road braking emission -- 3.3.1 Descriptions of the case study -- 3.3.2 Methodology -- 3.3.3 Results -- 3.4 Case D: travel attraction and urban vehicle emissions in Japan -- 3.4.1 Descriptions of the case study -- 3.4.2 Methodology -- 3.4.3 Results -- 4. Conclusion -- References -- Nine - Living environment inequity analyses based on mobile phone big data. , 1. Introduction -- 1.1 Background -- 1.1.1 Environmental justice -- 1.1.2 Artificial light at night -- 1.1.3 TOD usage in the context of aging -- 1.2 Literature review -- 1.2.1 EJ research on ALAN -- 1.2.2 TOD typology and its extension -- 2. Framework and dataset -- 2.1 Framework for ALAN inequity analysis -- 2.2 Framework for TOD inequity analysis -- 2.3 Case study -- 2.4 Data description -- 3. Methodology -- 3.1 Methodology for ALAN inequity analysis -- 3.1.1 Quantification of NTL intensity -- 3.1.2 Inequity analysis of population groups -- 3.2 Methodology for TOD inequity analysis -- 3.2.1 TOD indicators -- 3.2.2 Inequity analysis: correlation coefficient analysis -- 3.2.3 Validation of inequity: cluster analysis -- 4. Results -- 4.1 Result of ALAN inequity analysis -- 4.1.1 Descriptive analysis -- 4.1.2 ALAN exposure among different age groups -- 4.1.3 ALAN exposure among different residence groups -- 4.2 Result of TOD inequity analysis -- 4.2.1 Correlation coefficient analysis -- 4.2.2 Clusters of TODs -- 5. Conclusion and limitation -- 5.1 Conclusion of ALAN equity -- 5.2 Conclusion of TOD equity -- References -- Further reading -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- K -- L -- M -- N -- O -- P -- R -- S -- T -- U -- V -- W -- Back Cover.

Additional Edition: Print version: Zhang, HaoRan Handbook of Mobility Data Mining, Volume 3 San Diego : Elsevier,c2023 ISBN 9780323958929

Language: English

Keywords: Case studies. ; Case studies.

UID:

Format: 1 online resource (xiii, 207 pages) : , illustrations (chiefly colour), colour maps

ISBN: 9780443184291 , 0443184291

Content: "Handbook of Mobility Data Mining, Volume One: Data Preprocessing and Visualization introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations.Further, the book introduces how to design MDM platforms that adapt to the evolving mobility environment, new types of transportation, and users based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field. This volume focuses on how to efficiently pre-process mobile big data to extract and utilize critical feature information of high-dimensional city people flow. The book first provides a conceptual theory and framework, then discusses data sources, trajectory map-matching, noise filtering, trajectory data segmentation, data quality assessment, and more, concluding with a chapter on privacy protection in mobile big data mining.Key Features: Introduces the characteristics of different mobility data sources, like GPS, CDR, and sensor-based mobility data. Summarizes existing visualization technologies of the current transportation system into a multi-view frame, covering the perspective of the three leading actors. Provides recommendations for practical open-source tools and libraries for system visualization. Stems from the editor’s strong network of global transport authorities and transport companies, providing a solid knowledge structure and data foundation as well as geographical and stakeholder coverage."--Provided by publisher.

Note: An overview of urban data variety and respective value to urban computing / , Quality assessment for big mobility data / , Noise filter method for mobiletrajectory data / , Modifiable areal unit problem in grided population density map / , Few-shot count estimation of mobility dynamics by scaling GPS / , Trip segmentation and mode detection for human mobility data / , Benchmark of travel mode detection with smartphone GPS trajectories / , Trajectory super-resolution methods / , Map-matching for low accuracy trajectory data / , Social information labeling for individual mobile phone user / , Web-based spatio-temporal datavisualization technology forurban digital twin /

Additional Edition: Print version: Handbook of mobility data mining. Volume 1, Data preprocessing and visualization. Amsterdam : Elsevier, [2023] ISBN 9780443184284

Language: English

DOI: 10.1016/C2022-0-00233-6

UID:

Format: 1 online resource (308 pages)

ISBN: 0-323-90170-0

Content: Big Data and Mobility as a Service explores MaaS platforms that can be adaptable to the ever-evolving mobility environment. It looks at multi-mode urban crowd data to assess urban mobility characteristics, their shared transportation potential, and their performance conditions and constraints. The book analyzes the roles of multimodality, travel behavior, urban mobility dynamics and participation. Combined with insights on using big data to analyze market and policy decisions, this book is an essential tool for urban transportation management researchers and practitioners.

Note: 1. Big Data and MaaS 2. MaaS system Development and APPs 3. Spatio-temporal Data Pre-processing Technologies 4. Travel Similarity Estimation and Clustering 5. Data Fusion Technologies for MaaS 6. Data-driven Optimization Technologies for MaaS 7. Data-driven Estimation for Urban Travel Shareability 8. MaaS system Data mining Technologies 9. IoT Technologies for MaaS 10. MaaS System Visualization 11. MaaS for Urban Sustainable Development , 2.2. Urban spatial structure.

Additional Edition: ISBN 0-323-90169-7

Language: English

UID:

Format: 1 online resource (212 pages)

ISBN: 9780443184253

Content: Handbook of Mobility Data Mining, Volume Two: Mobility Analytics and Prediction introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book introduces how to design MDM platforms that adapt to the evolving mobility environment and new types of transportation and users. This helpful guide provides a basis for how to simulate and predict mobility data. After an introductory theory chapter, the book then covers crucial topics such as long-term mobility pattern analytics, mobility data generators, user information inference, Grid-based population density prediction, and more. The book concludes with a chapter on graph-based mobility data analytics. The information in this work is crucial for researchers, engineers, operators, company administrators, and policymakers in related fields, to comprehensively understand current technologies' infra-knowledge structure and limitations.

Note: Front Cover -- Handbook of Mobility Data Mining -- Handbook of Mobility: Data Mining Mobility Analytics and Prediction -- Copyright -- Contents -- List of contributors -- Preface -- Acknowledgments -- One - Multi-data-based travel behavior analysis and prediction -- 1. Introduction -- 2. Description of mobility big data and travel behavior -- 2.1 Mobility data mining methods based on heterogenyeousmeans -- 2.1.1 Based on Bluetooth, WiFi, video detection -- 2.1.2 Based on GPS and POI -- 2.2 Definition and description of travel behavior -- 3. Travel behavior analysis based on mobility big data -- 3.1 Mobility data processing -- 3.1.1 Trajectory noise data processing -- 3.1.2 Analysis of stay point detection -- 3.1.3 Map matching -- 3.2 Analysis and prediction of travel behavior -- 3.2.1 Machine learning applications in activity-travel behavior research -- References -- Two - Mining individual significant places from historical trajectory data -- 1. Background -- 2. Related work -- 3. Methodology -- 3.1 Stay location extraction -- 3.2 Spatial clustering of stay location -- 3.3 Identify the semanteme of the significant places -- 4. Application -- 4.1 Preliminary setting -- 4.2 Case one: analysis of life pattern changes in the Great Tokyo area -- 4.3 Case two: analysis of population changes after the fukushima earthquake -- 4.4 Case three: analysis of the residential location of the park visitors in Tokyo and the surrounding area -- References -- Three - Mobility pattern clustering with big human mobility data -- 1. Introduction -- 2. Related works -- 3. Methods -- 3.1 Metagraph-based clustering method -- 3.1.1 Support graph and topology-attribute matrix construction -- 3.1.2 Structure constrained NMF and meta-graph space -- 3.2 Other methods -- Preliminary definition -- 4. Application -- 4.1 Application case -- 4.2 Algorithm performances. , 4.2.1 The computation efficiency -- 4.2.2 The representational capacity to the mobility pattern differences -- References -- Four - Change detection of travel behavior: a case study of COVID-19 -- 1. Introduction -- 1.1 Background -- 1.2 Related works -- 1.3 Objectives -- 2. Methodologies -- 2.1 Data preprocessing -- 2.2 Travel behavior pattern change detection -- 2.3 Data grading -- 3. Results and analysis -- 3.1 Individual level -- 3.2 Metropolitan level -- 4. Conclusion and discussion -- 4.1 Summary -- 4.2 Limitations and future direction -- References -- Five - User demographic characteristics inference based on big GPS trajectory data -- 1. Introduction -- 2. Preliminary -- 2.1 Definition -- 2.2 Solving barriers -- 3. Methodology -- 3.1 Framework -- 3.2 Variation inference theory -- 3.3 Variation inference model construction -- 3.4 PSO based method (baseline method 1) -- 3.5 Deep learning-based method (baseline method 2) -- 4. Case study: experiment in Tokyo, Japan -- 4.1 Data description -- 4.2 Baseline settings -- 4.3 Evaluation metrics -- 4.4 Overall results -- 4.5 Evaluation by time use survey data -- 4.6 Evaluation by built environment demographics -- 5. Conclusion -- References -- Further reading -- Six - Generative model for human mobility -- 1. Introduction -- 1.1 Background -- 1.2 Problem definition -- 1.3 Research objective -- 2. Methodology -- 2.1 Preliminary -- 2.2 Framework -- 3. Experiments -- 3.1 Descriptions of raw data -- 3.2 Data preprocessing -- 3.3 Experimental settings -- 3.4 Results and visualization -- 4. Conclusion -- 4.1 Discussion -- 4.2 Limitations -- References -- Further Reading -- Seven - Retrieval-based human trajectory generation -- 1. Introduction -- 1.1 Background -- 1.2 Research objective -- 2. Map-matching as postprocessing -- 2.1 Framework -- 2.2 Experiments -- 3. Metrics for assessment -- 3.1 Results. , 3.2 Discussion -- 4. Retrieval-based model -- 4.1 Preliminary -- 4.1.1 Bidirectional long-short term memory -- 5. K-dimensional tree -- 5.1 Framework -- 6. Experiments -- 6.1 Data description -- 6.2 Baseline methods and metrics -- 6.3 Results -- 7. Conclusion -- References -- Further reading -- Eight - Grid-based origin-destination matrix prediction: a deep learning method with vector graph transformation si ... -- 1. Introduction -- 2. Origin-destination matrices -- 3. Methodology -- 3.1 Deep learning model-based vector graph transformation loss function -- 3.2 Grid-based origin-destination matrix prediction model -- 4. Data generation and study area -- 5. Result and discussion -- 5.1 Result of deep learning model-based vector graph transformation loss function -- 5.2 Result of grid-based origin-destination matrix prediction model -- 6. Conclusion -- References -- Nine - MetaTraj: meta-learning for cross-scene cross-object trajectory prediction -- 1. Introduction -- 2. Related works -- 2.1 Social interactions for trajectory prediction -- 2.2 Multimodality of trajectory prediction -- 2.3 Meta learning on trajectory prediction -- 3. Problem description -- 4. MetaTraj -- 4.1 Overall architecture -- 4.2 Subtasks and meta-tasks -- 4.3 MetaTraj training -- 4.4 Loss function -- 4.5 Transformed trajectories -- 5. Experiments -- 5.1 Quantitative evaluation -- 5.2 Ablation studies -- 5.3 Qualitative evaluation -- 6. Conclusion -- References -- Ten - Social-DPF: socially acceptable distribution prediction of futures -- 1. Introduction -- 2. Related works -- 2.1 Social compliant trajectory prediction -- 2.1.1 Spatiotemporal graphs for trajectory prediction -- 2.1.2 Multimodal trajectory prediction -- 2.1.3 Loss functions for trajectory prediction -- 3. Problem formulation -- 4. Methodology -- 4.1 Overall architecture -- 4.2 Social memory -- 4.3 Path forecasting. , 4.4 Loss function -- 5. Experiments -- 5.1 Quantitative evaluation -- 5.2 Qualitative evaluation -- 6. Conclusion -- References -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- Back Cover.

Additional Edition: Print version: Zhang, HaoRan Handbook of Mobility Data Mining, Volume 2 San Diego : Elsevier,c2023 ISBN 9780443184246

Language: English

UID:

Format: 1 Online-Ressource (308 Seiten)

ISBN: 9780323901703

Note: 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

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

UID:

Format: [223] Seiten , 彩圖, 地圖 , 21 cm

Edition: 初版

Original writing title: 遇見花小香 : 來自深海的親善大使

Original writing person/organisation: 廖鴻基

Original writing publisher: 臺北市 : 有鹿文化事業有限公司

ISBN: 9789869756860 , 9869756867

Series Statement: Kan shi jie de fang fa 154

Language: Chinese

UID:

Format: 1 online resource (212 pages)

ISBN: 9780443184253

Content: Handbook of Mobility Data Mining, Volume Two: Mobility Analytics and Prediction introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book introduces how to design MDM platforms that adapt to the evolving mobility environment and new types of transportation and users. This helpful guide provides a basis for how to simulate and predict mobility data. After an introductory theory chapter, the book then covers crucial topics such as long-term mobility pattern analytics, mobility data generators, user information inference, Grid-based population density prediction, and more. The book concludes with a chapter on graph-based mobility data analytics. The information in this work is crucial for researchers, engineers, operators, company administrators, and policymakers in related fields, to comprehensively understand current technologies' infra-knowledge structure and limitations.

Note: Front Cover -- Handbook of Mobility Data Mining -- Handbook of Mobility: Data Mining Mobility Analytics and Prediction -- Copyright -- Contents -- List of contributors -- Preface -- Acknowledgments -- One - Multi-data-based travel behavior analysis and prediction -- 1. Introduction -- 2. Description of mobility big data and travel behavior -- 2.1 Mobility data mining methods based on heterogenyeousmeans -- 2.1.1 Based on Bluetooth, WiFi, video detection -- 2.1.2 Based on GPS and POI -- 2.2 Definition and description of travel behavior -- 3. Travel behavior analysis based on mobility big data -- 3.1 Mobility data processing -- 3.1.1 Trajectory noise data processing -- 3.1.2 Analysis of stay point detection -- 3.1.3 Map matching -- 3.2 Analysis and prediction of travel behavior -- 3.2.1 Machine learning applications in activity-travel behavior research -- References -- Two - Mining individual significant places from historical trajectory data -- 1. Background -- 2. Related work -- 3. Methodology -- 3.1 Stay location extraction -- 3.2 Spatial clustering of stay location -- 3.3 Identify the semanteme of the significant places -- 4. Application -- 4.1 Preliminary setting -- 4.2 Case one: analysis of life pattern changes in the Great Tokyo area -- 4.3 Case two: analysis of population changes after the fukushima earthquake -- 4.4 Case three: analysis of the residential location of the park visitors in Tokyo and the surrounding area -- References -- Three - Mobility pattern clustering with big human mobility data -- 1. Introduction -- 2. Related works -- 3. Methods -- 3.1 Metagraph-based clustering method -- 3.1.1 Support graph and topology-attribute matrix construction -- 3.1.2 Structure constrained NMF and meta-graph space -- 3.2 Other methods -- Preliminary definition -- 4. Application -- 4.1 Application case -- 4.2 Algorithm performances. , 4.2.1 The computation efficiency -- 4.2.2 The representational capacity to the mobility pattern differences -- References -- Four - Change detection of travel behavior: a case study of COVID-19 -- 1. Introduction -- 1.1 Background -- 1.2 Related works -- 1.3 Objectives -- 2. Methodologies -- 2.1 Data preprocessing -- 2.2 Travel behavior pattern change detection -- 2.3 Data grading -- 3. Results and analysis -- 3.1 Individual level -- 3.2 Metropolitan level -- 4. Conclusion and discussion -- 4.1 Summary -- 4.2 Limitations and future direction -- References -- Five - User demographic characteristics inference based on big GPS trajectory data -- 1. Introduction -- 2. Preliminary -- 2.1 Definition -- 2.2 Solving barriers -- 3. Methodology -- 3.1 Framework -- 3.2 Variation inference theory -- 3.3 Variation inference model construction -- 3.4 PSO based method (baseline method 1) -- 3.5 Deep learning-based method (baseline method 2) -- 4. Case study: experiment in Tokyo, Japan -- 4.1 Data description -- 4.2 Baseline settings -- 4.3 Evaluation metrics -- 4.4 Overall results -- 4.5 Evaluation by time use survey data -- 4.6 Evaluation by built environment demographics -- 5. Conclusion -- References -- Further reading -- Six - Generative model for human mobility -- 1. Introduction -- 1.1 Background -- 1.2 Problem definition -- 1.3 Research objective -- 2. Methodology -- 2.1 Preliminary -- 2.2 Framework -- 3. Experiments -- 3.1 Descriptions of raw data -- 3.2 Data preprocessing -- 3.3 Experimental settings -- 3.4 Results and visualization -- 4. Conclusion -- 4.1 Discussion -- 4.2 Limitations -- References -- Further Reading -- Seven - Retrieval-based human trajectory generation -- 1. Introduction -- 1.1 Background -- 1.2 Research objective -- 2. Map-matching as postprocessing -- 2.1 Framework -- 2.2 Experiments -- 3. Metrics for assessment -- 3.1 Results. , 3.2 Discussion -- 4. Retrieval-based model -- 4.1 Preliminary -- 4.1.1 Bidirectional long-short term memory -- 5. K-dimensional tree -- 5.1 Framework -- 6. Experiments -- 6.1 Data description -- 6.2 Baseline methods and metrics -- 6.3 Results -- 7. Conclusion -- References -- Further reading -- Eight - Grid-based origin-destination matrix prediction: a deep learning method with vector graph transformation si ... -- 1. Introduction -- 2. Origin-destination matrices -- 3. Methodology -- 3.1 Deep learning model-based vector graph transformation loss function -- 3.2 Grid-based origin-destination matrix prediction model -- 4. Data generation and study area -- 5. Result and discussion -- 5.1 Result of deep learning model-based vector graph transformation loss function -- 5.2 Result of grid-based origin-destination matrix prediction model -- 6. Conclusion -- References -- Nine - MetaTraj: meta-learning for cross-scene cross-object trajectory prediction -- 1. Introduction -- 2. Related works -- 2.1 Social interactions for trajectory prediction -- 2.2 Multimodality of trajectory prediction -- 2.3 Meta learning on trajectory prediction -- 3. Problem description -- 4. MetaTraj -- 4.1 Overall architecture -- 4.2 Subtasks and meta-tasks -- 4.3 MetaTraj training -- 4.4 Loss function -- 4.5 Transformed trajectories -- 5. Experiments -- 5.1 Quantitative evaluation -- 5.2 Ablation studies -- 5.3 Qualitative evaluation -- 6. Conclusion -- References -- Ten - Social-DPF: socially acceptable distribution prediction of futures -- 1. Introduction -- 2. Related works -- 2.1 Social compliant trajectory prediction -- 2.1.1 Spatiotemporal graphs for trajectory prediction -- 2.1.2 Multimodal trajectory prediction -- 2.1.3 Loss functions for trajectory prediction -- 3. Problem formulation -- 4. Methodology -- 4.1 Overall architecture -- 4.2 Social memory -- 4.3 Path forecasting. , 4.4 Loss function -- 5. Experiments -- 5.1 Quantitative evaluation -- 5.2 Qualitative evaluation -- 6. Conclusion -- References -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- Back Cover.

Additional Edition: Print version: Zhang, HaoRan Handbook of Mobility Data Mining, Volume 2 San Diego : Elsevier,c2023 ISBN 9780443184246

Language: English

UID:

Format: 1 online resource (xiii, 207 pages) : , illustrations (chiefly colour), colour maps

ISBN: 9780443184291 , 0443184291

Content: "Handbook of Mobility Data Mining, Volume One: Data Preprocessing and Visualization introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations.Further, the book introduces how to design MDM platforms that adapt to the evolving mobility environment, new types of transportation, and users based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field. This volume focuses on how to efficiently pre-process mobile big data to extract and utilize critical feature information of high-dimensional city people flow. The book first provides a conceptual theory and framework, then discusses data sources, trajectory map-matching, noise filtering, trajectory data segmentation, data quality assessment, and more, concluding with a chapter on privacy protection in mobile big data mining.Key Features: Introduces the characteristics of different mobility data sources, like GPS, CDR, and sensor-based mobility data. Summarizes existing visualization technologies of the current transportation system into a multi-view frame, covering the perspective of the three leading actors. Provides recommendations for practical open-source tools and libraries for system visualization. Stems from the editor’s strong network of global transport authorities and transport companies, providing a solid knowledge structure and data foundation as well as geographical and stakeholder coverage."--Provided by publisher.

Note: An overview of urban data variety and respective value to urban computing / , Quality assessment for big mobility data / , Noise filter method for mobiletrajectory data / , Modifiable areal unit problem in grided population density map / , Few-shot count estimation of mobility dynamics by scaling GPS / , Trip segmentation and mode detection for human mobility data / , Benchmark of travel mode detection with smartphone GPS trajectories / , Trajectory super-resolution methods / , Map-matching for low accuracy trajectory data / , Social information labeling for individual mobile phone user / , Web-based spatio-temporal datavisualization technology forurban digital twin /

Additional Edition: Print version: Handbook of mobility data mining. Volume 1, Data preprocessing and visualization. Amsterdam : Elsevier, [2023] ISBN 9780443184284

Language: English

DOI: 10.1016/C2022-0-00233-6

UID:

Format: 1 online resource (212 pages)

ISBN: 9780443184253

Content: Handbook of Mobility Data Mining, Volume Two: Mobility Analytics and Prediction introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book introduces how to design MDM platforms that adapt to the evolving mobility environment and new types of transportation and users. This helpful guide provides a basis for how to simulate and predict mobility data. After an introductory theory chapter, the book then covers crucial topics such as long-term mobility pattern analytics, mobility data generators, user information inference, Grid-based population density prediction, and more. The book concludes with a chapter on graph-based mobility data analytics. The information in this work is crucial for researchers, engineers, operators, company administrators, and policymakers in related fields, to comprehensively understand current technologies' infra-knowledge structure and limitations.

Note: Front Cover -- Handbook of Mobility Data Mining -- Handbook of Mobility: Data Mining Mobility Analytics and Prediction -- Copyright -- Contents -- List of contributors -- Preface -- Acknowledgments -- One - Multi-data-based travel behavior analysis and prediction -- 1. Introduction -- 2. Description of mobility big data and travel behavior -- 2.1 Mobility data mining methods based on heterogenyeousmeans -- 2.1.1 Based on Bluetooth, WiFi, video detection -- 2.1.2 Based on GPS and POI -- 2.2 Definition and description of travel behavior -- 3. Travel behavior analysis based on mobility big data -- 3.1 Mobility data processing -- 3.1.1 Trajectory noise data processing -- 3.1.2 Analysis of stay point detection -- 3.1.3 Map matching -- 3.2 Analysis and prediction of travel behavior -- 3.2.1 Machine learning applications in activity-travel behavior research -- References -- Two - Mining individual significant places from historical trajectory data -- 1. Background -- 2. Related work -- 3. Methodology -- 3.1 Stay location extraction -- 3.2 Spatial clustering of stay location -- 3.3 Identify the semanteme of the significant places -- 4. Application -- 4.1 Preliminary setting -- 4.2 Case one: analysis of life pattern changes in the Great Tokyo area -- 4.3 Case two: analysis of population changes after the fukushima earthquake -- 4.4 Case three: analysis of the residential location of the park visitors in Tokyo and the surrounding area -- References -- Three - Mobility pattern clustering with big human mobility data -- 1. Introduction -- 2. Related works -- 3. Methods -- 3.1 Metagraph-based clustering method -- 3.1.1 Support graph and topology-attribute matrix construction -- 3.1.2 Structure constrained NMF and meta-graph space -- 3.2 Other methods -- Preliminary definition -- 4. Application -- 4.1 Application case -- 4.2 Algorithm performances. , 4.2.1 The computation efficiency -- 4.2.2 The representational capacity to the mobility pattern differences -- References -- Four - Change detection of travel behavior: a case study of COVID-19 -- 1. Introduction -- 1.1 Background -- 1.2 Related works -- 1.3 Objectives -- 2. Methodologies -- 2.1 Data preprocessing -- 2.2 Travel behavior pattern change detection -- 2.3 Data grading -- 3. Results and analysis -- 3.1 Individual level -- 3.2 Metropolitan level -- 4. Conclusion and discussion -- 4.1 Summary -- 4.2 Limitations and future direction -- References -- Five - User demographic characteristics inference based on big GPS trajectory data -- 1. Introduction -- 2. Preliminary -- 2.1 Definition -- 2.2 Solving barriers -- 3. Methodology -- 3.1 Framework -- 3.2 Variation inference theory -- 3.3 Variation inference model construction -- 3.4 PSO based method (baseline method 1) -- 3.5 Deep learning-based method (baseline method 2) -- 4. Case study: experiment in Tokyo, Japan -- 4.1 Data description -- 4.2 Baseline settings -- 4.3 Evaluation metrics -- 4.4 Overall results -- 4.5 Evaluation by time use survey data -- 4.6 Evaluation by built environment demographics -- 5. Conclusion -- References -- Further reading -- Six - Generative model for human mobility -- 1. Introduction -- 1.1 Background -- 1.2 Problem definition -- 1.3 Research objective -- 2. Methodology -- 2.1 Preliminary -- 2.2 Framework -- 3. Experiments -- 3.1 Descriptions of raw data -- 3.2 Data preprocessing -- 3.3 Experimental settings -- 3.4 Results and visualization -- 4. Conclusion -- 4.1 Discussion -- 4.2 Limitations -- References -- Further Reading -- Seven - Retrieval-based human trajectory generation -- 1. Introduction -- 1.1 Background -- 1.2 Research objective -- 2. Map-matching as postprocessing -- 2.1 Framework -- 2.2 Experiments -- 3. Metrics for assessment -- 3.1 Results. , 3.2 Discussion -- 4. Retrieval-based model -- 4.1 Preliminary -- 4.1.1 Bidirectional long-short term memory -- 5. K-dimensional tree -- 5.1 Framework -- 6. Experiments -- 6.1 Data description -- 6.2 Baseline methods and metrics -- 6.3 Results -- 7. Conclusion -- References -- Further reading -- Eight - Grid-based origin-destination matrix prediction: a deep learning method with vector graph transformation si ... -- 1. Introduction -- 2. Origin-destination matrices -- 3. Methodology -- 3.1 Deep learning model-based vector graph transformation loss function -- 3.2 Grid-based origin-destination matrix prediction model -- 4. Data generation and study area -- 5. Result and discussion -- 5.1 Result of deep learning model-based vector graph transformation loss function -- 5.2 Result of grid-based origin-destination matrix prediction model -- 6. Conclusion -- References -- Nine - MetaTraj: meta-learning for cross-scene cross-object trajectory prediction -- 1. Introduction -- 2. Related works -- 2.1 Social interactions for trajectory prediction -- 2.2 Multimodality of trajectory prediction -- 2.3 Meta learning on trajectory prediction -- 3. Problem description -- 4. MetaTraj -- 4.1 Overall architecture -- 4.2 Subtasks and meta-tasks -- 4.3 MetaTraj training -- 4.4 Loss function -- 4.5 Transformed trajectories -- 5. Experiments -- 5.1 Quantitative evaluation -- 5.2 Ablation studies -- 5.3 Qualitative evaluation -- 6. Conclusion -- References -- Ten - Social-DPF: socially acceptable distribution prediction of futures -- 1. Introduction -- 2. Related works -- 2.1 Social compliant trajectory prediction -- 2.1.1 Spatiotemporal graphs for trajectory prediction -- 2.1.2 Multimodal trajectory prediction -- 2.1.3 Loss functions for trajectory prediction -- 3. Problem formulation -- 4. Methodology -- 4.1 Overall architecture -- 4.2 Social memory -- 4.3 Path forecasting. , 4.4 Loss function -- 5. Experiments -- 5.1 Quantitative evaluation -- 5.2 Qualitative evaluation -- 6. Conclusion -- References -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- Back Cover.

Additional Edition: Print version: Zhang, HaoRan Handbook of Mobility Data Mining, Volume 2 San Diego : Elsevier,c2023 ISBN 9780443184246

Language: English