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016 7 a| 0190316812| Uk;

020 a| 0128129700q| (pbk.);

020 a| 9780128129708q| (pbk.);

020 z| 9780128129715 (ePub ebook);

035 a| (WaSeSS)ssj0002515542;

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050 00 a| HE305b| .M64 2019;

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245 00 a| Mobility patterns, big data and transport analyticsh| [electronic resource] :b| tools and applications for modeling /c| edited by Constantinos Antoniou, Loukas Dimitriou, Francisco Pereira.;

260 a| Amsterdam, Netherlands ;a| Cambridge, MA :b| Elsevier,c| [2019];

300 a| xix, 432 pages :b| illustrations (some color) ;c| 23 cm;

504 a| Includes bibliographical references and index.;

505 00 a| Machine generated contents note:g| 1.t| Big Data and Transport Analytics: An Introduction /r| Francisco Camara Pereira --g| 1.t| Introduction --g| 2.t| Book Structure --t| Special Acknowledgments --t| References --t| Further Reading --g| pt. It| Methodological --g| 2.t| Machine Learning Fundamentals /r| Stanislav S. Borysov --g| 1.t| Introduction --g| 2.t| A Little Bit of History --g| 3.t| Deep Neural Networks and Optimization --g| 4.t| Bayesian Models --g| 5.t| Basics of Machine Learning Experiments --g| 6.t| Concluding Remarks --t| References --t| Further Reading --g| 3.t| Using Semantic Signatures for Social Sensing in Urban Environments /r| Song Gao --g| 1.t| Introduction --g| 2.t| Spatial Signatures --g| 2.1.t| Spatial Point Pattern --g| 2.2.t| Spatial Autocorrelations --g| 2.3.t| Spatial Interactions With Other Geographic features --g| 2.4.t| Place-Based Statistics --g| 3.t| Temporal Signatures --g| 4.t| Thematic Signatures --g| 5.t| Examples --g| 5.1.t| Comparing Place Types --g| 5.2.t| Coreference Resolution Across Gazetteers --g| 5.3.t| Ceoprivacy --g| 5.4.t| Temporally Enhanced Geolocation --g| 5.5.t| Regional Variation --g| 5.6.t| Extraction of Urban Functional Regions --g| 6.t| Summary --t| References --g| 4.t| Geographic Space as a Living Structure for Predicting Human Activities Using Big Data /r| Zheng Ren --g| 1.t| Introduction --g| 2.t| Living Structure and the Topological Representation --g| 3.t| Data and Data Processing --g| 4.t| Prediction of Tweet Locations Through Living Structure --g| 4.1.t| Correlations at the Scale of Thiessen Polygons --g| 4.2.t| Correlations at the Scale of Natural Cities --g| 4.3.t| Degrees of Wholeness or Life or Beauty --g| 5.t| Implications on the Topological Representation and Living Structure --g| 6.t| Conclusion --t| Acknowledgments --t| References --g| 5.t| Data Preparation /r| Francisco Camara Pereira --g| 1.t| Introduction --g| 2.t| Tools and Techniques --g| 2.1.t| Scripting and Statistical Analysis Software --g| 2.2.t| Database Management Software --g| 2.3.t| Working With Web Data --g| 3.t| Probe Vehicle Traffic Data --g| 3.1.t| Formats and Protocols --g| 3.2.t| Data Characteristics --g| 3.3.t| Challenges --g| 3.4.t| Data Preparation and Quality Control --g| 4.t| Context Data --g| 4.1.t| The Role of Context Data --g| 4.2.t| Types of Context Data --g| 4.3.t| Formats and Data Collection --g| 4.4.t| Data Cleaning and Preparation --t| References --g| 6.t| Data Science and Data Visualization /r| Constantinos Antoniou --g| 1.t| Introduction --g| 2.t| Structured Visualization --g| 3.t| Multidimensional Data Visualization Techniques --g| 3.1.t| Parallel Coordinates --g| 3.2.t| Multidimensional Scaling (MDS) --g| 3.3.t| t-Distributed Stochastic Neighbor Embedding for High-Dimensional Data Sets (t-SNE) --g| 4.t| Case Studies --g| 4.1.t| Experimental Setup --g| 4.2.t| Car Characteristics Data Set --g| 4.3.t| Congestion on 195 --g| 4.4.t| Dimensionality Reduction on NYC Taxi Flows --g| 4.5.t| Dimensionality Reduction on the NYC Turnstile Data Set --g| 5.t| Conclusions --t| References --t| Further Reading --g| 7.t| Model-Based Machine Learning for Transportation /r| Francisco Camara Pereira --g| 1.t| Introduction --g| 1.1.t| Background Concepts --g| 1.2.t| Notation --g| 2.t| Case Study 1: Taxi Demand in New York City --g| 2.1.t| Initial Probabilistic Model: Linear Regression --g| 2.2.t| Key Components of MBML --g| 2.3.t| Inference --g| 2.4.t| Model Improvements --g| 3.t| Case Study 2: Travel Mode Choices --g| 3.1.t| Improvement: Hierarchical Modeling --g| 4.t| Case Study 3: Freeway Occupancy in San Francisco --g| 4.1.t| Autoregressive Model --g| 4.2.t| State-Space Model --g| 4.3.t| Linear Dynamical Systems --g| 4.4.t| Common Enhancements to LDS --g| 4.5.t| NonLinear Variations on LDS --g| 5.t| Case Study 4: Incident Duration Prediction --g| 5.1.t| Preprocessing --g| 5.2.t| Bag-of-Words Encoding --g| 5.3.t| Latent Dirichlet Allocation --g| 6.t| Summary --g| 6.1.t| Further Reading --t| References --g| 8.t| Textual Data in Transportation Research: Techniques and Opportunities /r| Werner Rothengatter --g| 1.t| Introduction --g| 2.t| Big Textual Data, Text Sources, and Text Mining --g| 2.1.t| Meaning of Text in the Context of Computational Linguistics --g| 2.2.t| Text Mining --g| 2.3.t| Text Mining Process Model --g| 2.4.t| Textual Data Sources in Transportation --g| 3.t| Fundamental Concepts and Techniques in Literature --g| 3.1.t| Topic Modeling --g| 3.2.t| Word2Vec -- Text Embeddings With Deep Learning --g| 4.t| Application Examples of Big Textual Data in Transportation --g| 4.1.t| Developing Transportation and Logistics Performance Classifiers Using NLTK and Naive Bayes --g| 4.2.t| Understanding the Public Opinion Toward Driverless Cars With Topic Modeling --g| 4.3.t| Predicting Taxi Demand in Special Events With Text Embeddings and Deep Learning --g| 5.t| Conclusions --t| References --t| Further Reading --g| pt. IIt| Applications --g| 9.t| Statewide Comparison of Origin-Destination Matrices Between California Travel Model and Twitter /r| Konstadinos G. Goulias --g| 1.t| Introduction --g| 2.t| California Statewide Travel Demand Model --g| 3.t| Twitter Data --g| 4.t| Trip Extraction Methods --g| 5.t| Models for Matrix Conversion --g| 5.1.t| Tobit Regression Model --g| 5.2.t| Latent Class Regression Model --g| 6.t| Summary and Conclusion --t| References --g| 10.t| Transit Data Analytics for Planning, Monitoring, Control, and Information /r| Yiwen Zhu --g| 1.t| Introduction --g| 2.t| Measuring System Performance From the Passenger's Point of View --g| 2.1.t| The Individual Reliability Buffer Time (IRBT) --g| 2.2.t| Denied Boarding --g| 3.t| Decision Support With Predictive Analytics --g| 3.1.t| Framework --g| 3.2.t| Application: Provision of Crowding Predictive Information --g| 4.t| Optimal Design of Transit Demand Management Strategies --g| 4.1.t| Framework and Problem Formulation --g| 4.2.t| Application: Prepeak Discount Design --g| 5.t| Conclusion --t| Acknowledgments --t| References --t| Further Reading --g| 11.t| Data-Driven Traffic Simulation Models: Mobility Patterns Using Machine Learning Techniques /r| Haris N. Koutsopoulos --g| 1.t| New Modeling Challenges and Data Opportunities --g| 1.1.t| New Modeling Requirements --g| 1.2.t| New Data Sources --g| 1.3.t| Future Challenges --g| 2.t| Background --g| 3.t| Data-Driven Traffic Performance Modeling: Overall Framework --g| 3.1.t| Modeling Approach --g| 3.2.t| Model Components --g| 4.t| Application to Mesoscopic Modeling --g| 4.1.t| Data and Experimental Design --g| 4.2.t| Case Study Setup --g| 4.3.t| Application and Results --g| 5.t| Application to Microscopic Traffic Modeling --g| 5.1.t| Data and Experimental Design --g| 5.2.t| Case Study Setup --g| 5.3.t| Application and Results --g| 6.t| Application to Weak Lane Discipline Modeling --g| 6.1.t| Data and Experimental Design --g| 6.2.t| Case Study Setup --g| 6.3.t| Application and Results --g| 7.t| Network-Wide Application --g| 7.1.t| Implementation Aspects --g| 7.2.t| Case Study Setup --g| 7.3.t| Results --g| 8.t| Conclusions --t| Acknowledgments --t| References --g| 12.t| Big Data and Road Safety: A Comprehensive Review /r| Mohamed Abdel-Aty --g| 1.t| Introduction --g| 2.t| The Role of Big Data in Traffic Safety Analysis --g| 2.1.t| Real-Time Crash Prediction --g| 2.2.t| Driving Behavior --g| 3.t| ADAS and Autonomous Vehicles (AVs) --g| 4.t| Conclusions --t| References --g| 13.t| A Back-Engineering Approach to Explore Human Mobility Patterns Across Megacities Using Online Traffic Maps /r| Loukas Dimitriou --g| 1.t| Introduction --g| 2.t| Data and Traffic Information Extraction Methods --g| 2.1.t| Cities Characteristics --g| 2.2.t| Data Gathering and Preprocessing --g| 2.3.t| Extracting Traffic Information by Image Processing --g| 3.t| Temporal and Spatiotemporal Mobility Patterns --g| 3.1.t| Temporal Patterns --g| 3.2.t| Spatiotemporal Patterns --g| 4.t| Dynamic Clustering and Propagation of Congestion --g| 5.t| Conclusions --t| References --g| 14.t| Pavement Patch Defects Detection and Classification Using Smartphones, Vibration Signals and Video Images /r| George Hadjidemetriou --g| 1.t| Introduction --g| 2.t| Brief Literature Review --g| 2.1.t| Vibration-Based Methods --g| 2.2.t| Vision-Based Methods --g| 3.t| Methodology --g| 3.1.t| Anomaly Detection Using ANNs and Timeseries Analysis of Vibration Signals --g| 3.2.t| Anomaly Detection Using Entropic-Filter Image Segmentation --g| 3.3.t| Patch Detection and Measurement Using Support Vector Machines (SVM) --g| 4.t| Conclusions --t| References --g| 15.t| Collaborative Positioning for Urban Intelligent Transportation Systems (ITS) and Personal Mobility (PM): Challenges and Perspectives /r| Allison Kealy --g| 1.t| Introduction --g| 2.t| C-ITS in Support of the Smart Cities Concept --g| 2.1.t| Scientific and Policy Perspectives of Urban C-ITS --g| 2.2.t| Taxonomy of Urban C-ITS Applications --g| 3.t| User Requirements for Urban C-ITS --g| 3.1.t| Requirements Overview --g| 3.2.t| Positioning Requirements and Parameters Definition --g| 4.t| Positioning Technologies for Urban ITS --g| 4.1.t| Radio Frequency-Based (RF) Technologies --g| 4.2.t| MEMS-Based Inertial Navigation --g| 4.3.t| Optical Technologies --g| 5.t| Measuring Types and Positioning Techniques --g| 5.1.t| Absolute Positioning Techniques --g| 5.2.t| Relative and Hybrid Positioning Techniques --g| 6.t| CP for C-ITS --g| 6.1.t| From Single Sens 0a| Urban transportationx| Mathematical models.;

506 a| Available only to authorized users.;

538 a| Mode of access: World Wide Web;

650 0 a| Urban transportationx| Mathematical models.=| ^A1023591;

650 0 a| Big data.=| ^A1157100;

650 7 a| Big data.2| fast0| (OCoLC)fst01892965;

650 7 a| Urban transportation.2| fast0| (OCoLC)fst01162609;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Antoniou, Constantinos.?| UNAUTHORIZED;

700 1 a| Dimitriou, Loukas.?| UNAUTHORIZED;

700 1 a| Pereira, Francisco Baptista.?| UNAUTHORIZED;

776 08 i| ebook version :z| 9780128129715;

856 40 z| Full text available from eBook - Social Sciences 2018u| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Fbook%2F9780128129708;

947 a| (OCoLC)on1064617417;

949 a| CLICK ON WEB ADDRESSw| ASISh| JOYNER188;

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949 a| CLICK ON WEB ADDRESSw| ASISh| JMUSIC60;

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999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5866172-1001l| JNETm| JOYNERr| Ys| Yt| JNESSBKu| 7/18/2022x| EBOOKz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5866172-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 7/18/2022x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5866172-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 7/18/2022x| EBOOKz| JERESOURCE;

Mobility patterns, big data and transport analytics tools and applications for modeling / edited by Constantinos Antoniou, Loukas Dimitriou, Francisco Pereira.

Other author	Antoniou, Constantinos.
Other author	Dimitriou, Loukas.
Other author	Pereira, Francisco Baptista.
Format	Electronic
Publication Info	Amsterdam, Netherlands ; Cambridge, MA : Elsevier, [2019]
Description	xix, 432 pages : illustrations (some color) ; 23 cm
Supplemental Content	Full text available from eBook - Social Sciences 2018
Subjects	Urban transportation--Mathematical models. Big data.

Contents	Machine generated contents note: 1. Big Data and Transport Analytics: An Introduction / Francisco Camara Pereira -- 1. Introduction -- 2. Book Structure -- Special Acknowledgments -- References -- Further Reading -- pt. I Methodological -- 2. Machine Learning Fundamentals / Stanislav S. Borysov -- 1. Introduction -- 2. A Little Bit of History -- 3. Deep Neural Networks and Optimization -- 4. Bayesian Models -- 5. Basics of Machine Learning Experiments -- 6. Concluding Remarks -- References -- Further Reading -- 3. Using Semantic Signatures for Social Sensing in Urban Environments / Song Gao -- 1. Introduction -- 2. Spatial Signatures -- 2.1. Spatial Point Pattern -- 2.2. Spatial Autocorrelations -- 2.3. Spatial Interactions With Other Geographic features -- 2.4. Place-Based Statistics -- 3. Temporal Signatures -- 4. Thematic Signatures -- 5. Examples -- 5.1. Comparing Place Types -- 5.2. Coreference Resolution Across Gazetteers -- 5.3. Ceoprivacy -- 5.4. Temporally Enhanced Geolocation -- 5.5. Regional Variation -- 5.6. Extraction of Urban Functional Regions -- 6. Summary -- References -- 4. Geographic Space as a Living Structure for Predicting Human Activities Using Big Data / Zheng Ren -- 1. Introduction -- 2. Living Structure and the Topological Representation -- 3. Data and Data Processing -- 4. Prediction of Tweet Locations Through Living Structure -- 4.1. Correlations at the Scale of Thiessen Polygons -- 4.2. Correlations at the Scale of Natural Cities -- 4.3. Degrees of Wholeness or Life or Beauty -- 5. Implications on the Topological Representation and Living Structure -- 6. Conclusion -- Acknowledgments -- References -- 5. Data Preparation / Francisco Camara Pereira -- 1. Introduction -- 2. Tools and Techniques -- 2.1. Scripting and Statistical Analysis Software -- 2.2. Database Management Software -- 2.3. Working With Web Data -- 3. Probe Vehicle Traffic Data -- 3.1. Formats and Protocols -- 3.2. Data Characteristics -- 3.3. Challenges -- 3.4. Data Preparation and Quality Control -- 4. Context Data -- 4.1. The Role of Context Data -- 4.2. Types of Context Data -- 4.3. Formats and Data Collection -- 4.4. Data Cleaning and Preparation -- References -- 6. Data Science and Data Visualization / Constantinos Antoniou -- 1. Introduction -- 2. Structured Visualization -- 3. Multidimensional Data Visualization Techniques -- 3.1. Parallel Coordinates -- 3.2. Multidimensional Scaling (MDS) -- 3.3. t-Distributed Stochastic Neighbor Embedding for High-Dimensional Data Sets (t-SNE) -- 4. Case Studies -- 4.1. Experimental Setup -- 4.2. Car Characteristics Data Set -- 4.3. Congestion on 195 -- 4.4. Dimensionality Reduction on NYC Taxi Flows -- 4.5. Dimensionality Reduction on the NYC Turnstile Data Set -- 5. Conclusions -- References -- Further Reading -- 7. Model-Based Machine Learning for Transportation / Francisco Camara Pereira -- 1. Introduction -- 1.1. Background Concepts -- 1.2. Notation -- 2. Case Study 1: Taxi Demand in New York City -- 2.1. Initial Probabilistic Model: Linear Regression -- 2.2. Key Components of MBML -- 2.3. Inference -- 2.4. Model Improvements -- 3. Case Study 2: Travel Mode Choices -- 3.1. Improvement: Hierarchical Modeling -- 4. Case Study 3: Freeway Occupancy in San Francisco -- 4.1. Autoregressive Model -- 4.2. State-Space Model -- 4.3. Linear Dynamical Systems -- 4.4. Common Enhancements to LDS -- 4.5. NonLinear Variations on LDS -- 5. Case Study 4: Incident Duration Prediction -- 5.1. Preprocessing -- 5.2. Bag-of-Words Encoding -- 5.3. Latent Dirichlet Allocation -- 6. Summary -- 6.1. Further Reading -- References -- 8. Textual Data in Transportation Research: Techniques and Opportunities / Werner Rothengatter -- 1. Introduction -- 2. Big Textual Data, Text Sources, and Text Mining -- 2.1. Meaning of Text in the Context of Computational Linguistics -- 2.2. Text Mining -- 2.3. Text Mining Process Model -- 2.4. Textual Data Sources in Transportation -- 3. Fundamental Concepts and Techniques in Literature -- 3.1. Topic Modeling -- 3.2. Word2Vec -- Text Embeddings With Deep Learning -- 4. Application Examples of Big Textual Data in Transportation -- 4.1. Developing Transportation and Logistics Performance Classifiers Using NLTK and Naive Bayes -- 4.2. Understanding the Public Opinion Toward Driverless Cars With Topic Modeling -- 4.3. Predicting Taxi Demand in Special Events With Text Embeddings and Deep Learning -- 5. Conclusions -- References -- Further Reading -- pt. II Applications -- 9. Statewide Comparison of Origin-Destination Matrices Between California Travel Model and Twitter / Konstadinos G. Goulias -- 1. Introduction -- 2. California Statewide Travel Demand Model -- 3. Twitter Data -- 4. Trip Extraction Methods -- 5. Models for Matrix Conversion -- 5.1. Tobit Regression Model -- 5.2. Latent Class Regression Model -- 6. Summary and Conclusion -- References -- 10. Transit Data Analytics for Planning, Monitoring, Control, and Information / Yiwen Zhu -- 1. Introduction -- 2. Measuring System Performance From the Passenger's Point of View -- 2.1. The Individual Reliability Buffer Time (IRBT) -- 2.2. Denied Boarding -- 3. Decision Support With Predictive Analytics -- 3.1. Framework -- 3.2. Application: Provision of Crowding Predictive Information -- 4. Optimal Design of Transit Demand Management Strategies -- 4.1. Framework and Problem Formulation -- 4.2. Application: Prepeak Discount Design -- 5. Conclusion -- Acknowledgments -- References -- Further Reading -- 11. Data-Driven Traffic Simulation Models: Mobility Patterns Using Machine Learning Techniques / Haris N. Koutsopoulos -- 1. New Modeling Challenges and Data Opportunities -- 1.1. New Modeling Requirements -- 1.2. New Data Sources -- 1.3. Future Challenges -- 2. Background -- 3. Data-Driven Traffic Performance Modeling: Overall Framework -- 3.1. Modeling Approach -- 3.2. Model Components -- 4. Application to Mesoscopic Modeling -- 4.1. Data and Experimental Design -- 4.2. Case Study Setup -- 4.3. Application and Results -- 5. Application to Microscopic Traffic Modeling -- 5.1. Data and Experimental Design -- 5.2. Case Study Setup -- 5.3. Application and Results -- 6. Application to Weak Lane Discipline Modeling -- 6.1. Data and Experimental Design -- 6.2. Case Study Setup -- 6.3. Application and Results -- 7. Network-Wide Application -- 7.1. Implementation Aspects -- 7.2. Case Study Setup -- 7.3. Results -- 8. Conclusions -- Acknowledgments -- References -- 12. Big Data and Road Safety: A Comprehensive Review / Mohamed Abdel-Aty -- 1. Introduction -- 2. The Role of Big Data in Traffic Safety Analysis -- 2.1. Real-Time Crash Prediction -- 2.2. Driving Behavior -- 3. ADAS and Autonomous Vehicles (AVs) -- 4. Conclusions -- References -- 13. A Back-Engineering Approach to Explore Human Mobility Patterns Across Megacities Using Online Traffic Maps / Loukas Dimitriou -- 1. Introduction -- 2. Data and Traffic Information Extraction Methods -- 2.1. Cities Characteristics -- 2.2. Data Gathering and Preprocessing -- 2.3. Extracting Traffic Information by Image Processing -- 3. Temporal and Spatiotemporal Mobility Patterns -- 3.1. Temporal Patterns -- 3.2. Spatiotemporal Patterns -- 4. Dynamic Clustering and Propagation of Congestion -- 5. Conclusions -- References -- 14. Pavement Patch Defects Detection and Classification Using Smartphones, Vibration Signals and Video Images / George Hadjidemetriou -- 1. Introduction -- 2. Brief Literature Review -- 2.1. Vibration-Based Methods -- 2.2. Vision-Based Methods -- 3. Methodology -- 3.1. Anomaly Detection Using ANNs and Timeseries Analysis of Vibration Signals -- 3.2. Anomaly Detection Using Entropic-Filter Image Segmentation -- 3.3. Patch Detection and Measurement Using Support Vector Machines (SVM) -- 4. Conclusions -- References -- 15. Collaborative Positioning for Urban Intelligent Transportation Systems (ITS) and Personal Mobility (PM): Challenges and Perspectives / Allison Kealy -- 1. Introduction -- 2. C-ITS in Support of the Smart Cities Concept -- 2.1. Scientific and Policy Perspectives of Urban C-ITS -- 2.2. Taxonomy of Urban C-ITS Applications -- 3. User Requirements for Urban C-ITS -- 3.1. Requirements Overview -- 3.2. Positioning Requirements and Parameters Definition -- 4. Positioning Technologies for Urban ITS -- 4.1. Radio Frequency-Based (RF) Technologies -- 4.2. MEMS-Based Inertial Navigation -- 4.3. Optical Technologies -- 5. Measuring Types and Positioning Techniques -- 5.1. Absolute Positioning Techniques -- 5.2. Relative and Hybrid Positioning Techniques -- 6. CP for C-ITS -- 6.1. From Single Sens 0 Urban transportation Mathematical models.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Issued in other form	ebook version : 9780128129715
Genre/form	Electronic books.
LCCN	2018963402
ISBN	0128129700 (pbk.)
ISBN	9780128129708 (pbk.)

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