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Umfang: 1 online resource (454 pages)

ISBN: 0-12-812971-9

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

Weitere Ausg.: ISBN 9780128129708

Weitere Ausg.: ISBN 0-12-812970-0

Sprache: Englisch