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Mobility Patterns, Big Data and Transport Analytics
Tools and Applications for Modeling
- 1st Edition - November 27, 2018
- Editors: Constantinos Antoniou, Loukas Dimitriou, Francisco Pereira
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 2 9 7 0 - 8
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 1 2 9 7 1 - 5
Mobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizi… Read more
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Request a sales quoteMobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns - a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. Users will find a detailed, mobility ‘structural’ analysis and a look at the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications and transportation systems analysis that are related to complex processes and phenomena.
This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data’s impact on mobility and an introduction to the tools necessary to apply new techniques.
The book covers in detail, mobility ‘structural’ analysis (and its dynamics), the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications, and transportation systems analysis related to complex processes and phenomena. The book bridges the gap between big data, data science, and Transportation Systems Analysis with a study of big data’s impact on mobility, and an introduction to the tools necessary to apply new techniques.
- Guides readers through the paradigm-shifting opportunities and challenges of handling Big Data in transportation modeling and analytics
- Covers current analytical innovations focused on capturing, predicting, visualizing, and controlling mobility patterns, while discussing future trends
- Delivers an introduction to transportation-related information advances, providing a benchmark reference by world-leading experts in the field
- Captures and manages mobility patterns, covering multiple purposes and alternative transport modes, in a multi-disciplinary approach
- Companion website features videos showing the analyses performed, as well as test codes and data-sets, allowing readers to recreate the presented analyses and apply the highlighted techniques to their own data
Transport researchers, practitioners, and consultants, Undergraduate and graduate students in transportation programs, Transport policy makers
1. Big Data and Transport Analytics: An Introduction Constantinos Antoniou, Loukas Dimitriou and Francisco Camara Pereira
1 Introduction
2 Book Structure
References
Further Reading
Part I: Methodological
2. Machine Learning Fundamentals
Francisco C^amara Pereira and 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
Krzysztof Janowicz, Grant McKenzie, Yingjie Hu, Rui Zhu and 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 Geoprivacy
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
Bin Jiang and 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
Kristian Henrickson, Filipe Rodrigues and 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
Michalis Xyntarakis and 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 I95
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
Inon Peled, Filipe Rodrigues and 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
Aseem Kinra, Samaneh Beheshti Kashi, Francisco Camara Pereira, Francois Combes and 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 Naı¨ve 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
Part II: Applications
9. Statewide Comparison of Origin-Destination Matrices Between California Travel Model and Twitter
Jae Hyun Lee, Adam Davis, Elizabeth McBride and 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
Haris N. Koutsopoulos, Zhenliang Ma, Peyman Noursalehi and 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
Vasileia Papathanasopoulou, Constantinos Antoniou and 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
Katerina Stylianou, Loukas Dimitriou and 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
Vana Gkania and 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
Symeon E. Christodoulou, Charalambos Kyriakou and 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
Vassilis Gikas, Guenther Retscher and 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 Sensor Positioning to CP
6.2 Fusion Algorithms and Techniques for CP
7 Application Cases of Integrated Urban C-ITS
7.1 Case 1: Smart-Bike Systems as a Component of Urban C-ITS
7.2 Case 2: Smart Intersection for Traffic Control and Safety
8 Discussion, Perspectives, and Conclusions
References
Further Reading
Conclusions
1 Introduction
2 Book Structure
References
Further Reading
Part I: Methodological
2. Machine Learning Fundamentals
Francisco C^amara Pereira and 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
Krzysztof Janowicz, Grant McKenzie, Yingjie Hu, Rui Zhu and 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 Geoprivacy
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
Bin Jiang and 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
Kristian Henrickson, Filipe Rodrigues and 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
Michalis Xyntarakis and 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 I95
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
Inon Peled, Filipe Rodrigues and 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
Aseem Kinra, Samaneh Beheshti Kashi, Francisco Camara Pereira, Francois Combes and 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 Naı¨ve 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
Part II: Applications
9. Statewide Comparison of Origin-Destination Matrices Between California Travel Model and Twitter
Jae Hyun Lee, Adam Davis, Elizabeth McBride and 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
Haris N. Koutsopoulos, Zhenliang Ma, Peyman Noursalehi and 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
Vasileia Papathanasopoulou, Constantinos Antoniou and 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
Katerina Stylianou, Loukas Dimitriou and 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
Vana Gkania and 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
Symeon E. Christodoulou, Charalambos Kyriakou and 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
Vassilis Gikas, Guenther Retscher and 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 Sensor Positioning to CP
6.2 Fusion Algorithms and Techniques for CP
7 Application Cases of Integrated Urban C-ITS
7.1 Case 1: Smart-Bike Systems as a Component of Urban C-ITS
7.2 Case 2: Smart Intersection for Traffic Control and Safety
8 Discussion, Perspectives, and Conclusions
References
Further Reading
Conclusions
- No. of pages: 452
- Language: English
- Edition: 1
- Published: November 27, 2018
- Imprint: Elsevier
- Paperback ISBN: 9780128129708
- eBook ISBN: 9780128129715
CA
Constantinos Antoniou
Constantinos Antoniou is a Professor and Chair of Transportation Systems Engineering at the Technical University of Munich, Germany. He was previously an Associate Professor at the National Technical University of Athens, Greece. His research focuses on modelling and simulation of transportation systems, Intelligent Transport Systems (ITS), calibration and optimization applications, road safety and sustainable transport system. Antoniou has been involved in a large number of projects, primarily in Europe and the US, and has authored more than 500 scientific publications, including in Elsevier’s Transportation Research Part C: Emerging Technologies (for which he serves on the editorial board) and Transportation Research Part A: Policy and Practice (for which he serves as an Associate Editor).
Affiliations and expertise
Professor and Chair of Transportation Systems Engineering, Technical University of Munich, GermanyLD
Loukas Dimitriou
Loukas Dimitriou is an Assistant Professor in the Department of Civil and Environmental Engineering, University of Cyprus (UCY) and founder and head of the Lab for Transport Engineering, UCY. His research interests focus on the application of advanced computational intelligence methods, concepts and techniques for understanding the complex phenomena involved in realistic transport systems, and developing design and control strategies. The methodological paradigms that he proposes utilize elements from Data Science, behavioral analytics, complex systems modelling and advanced optimization, applied in traditional fields of transport, like demand modelling, travel behavior and systems organization, optimization and control. He has more than 100 publications in peer-reviewed journals, proceedings of conferences and book chapters, while he is an active member of international scientific organizations and committees.
Affiliations and expertise
Lecturer, Department of Civil and Environmental Engineering, University of Cyprus and Head, Lab. for Transport Engineering, University of Cyprus, Republic of CyprusFP
Francisco Pereira
Francisco Pereira is a Professor at the Technical University of Denmark, in Kongens Lyngby, Denmark, where he leads the Smart Mobility research group. Previously, he was Senior Research Scientist at MIT/CEE ITSLab, where he worked on real-time traffic prediction, behavior modeling, and advanced data collection technologies, both in Boston and Singapore, as part of the Singapore-MIT Alliance for Research and Technology, Future Urban Mobility project (SMART/FM). His main research focus is on applying machine learning and pattern recognition to the context of transportation systems with the purpose of understanding and predicting mobility behavior, and modeling and optimizing the transportation system as a whole. He has been published in many journals, including in Elsevier’s Transportation Research Part C: Emerging Technologies.
Affiliations and expertise
Professor, Technical University of Denmark, Kongens Lyngby, DenmarkRead Mobility Patterns, Big Data and Transport Analytics on ScienceDirect