| Pubbl/distr/stampa |
Amsterdam, Netherlands : , : Elsevier, , [2019]
|
| Descrizione fisica |
1 online resource (454 pages)
|
| Disciplina |
388.4
|
| Soggetto topico |
Enginyeria del trànsit
Transport urbà
Dades massives
Urban transportation
Urban transportation - Simulation methods
Traffic engineering
|
| ISBN |
0-12-812971-9
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
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.
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| Record Nr. | UNINA-9910583377803321 |
Info
Mobility patterns, big data and transport analytics : tools and applications for modeling / / edited by Constantinos Antoniou, Loukas Dimitriou, Francisco Pereira