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, 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.
Edited by:
Constantinos Antoniou (Professor and Chair of Transportation Systems Engineering Technical University of Munich Germany),
Loukas Dimitriou (Lecturer,
Department of Civil and Environmental Engineering,
University of Cyprus and Head,
Lab. for Transport Engineering,
University of Cyprus,
Republic of Cyprus),
Francisco Pereira (Professor,
Technical University of Denmark,
Kongens Lyngby,
Denmark)
Imprint: Elsevier Science Publishing Co Inc
Country of Publication: United States
Dimensions:
Height: 229mm,
Width: 151mm,
Weight: 680g
ISBN: 9780128129708
ISBN 10: 0128129700
Pages: 452
Publication Date: 27 November 2018
Audience:
Professional and scholarly
,
Undergraduate
Replaced By: 9780443267895
Format: Paperback
Publisher's Status: Active
1. Big Data and Transport Analytics: An Introduction Constantinos Antoniou, Loukas Dimitriou and Francisco Camara Pereira 1 Introduction 2 Book Structure Part I: Methodological 2. Machine Learning Fundamentals Francisco Camara Pereira and Stanislav S. Borysov 3. Using Semantic Signatures for Social Sensing in Urban Environments Krzysztof Janowicz, Grant McKenzie, Yingjie Hu, Rui Zhu and Song Gao 4. Geographic Space as a Living Structure for Predicting Human Activities Using Big Data Bin Jiang and Zheng Ren 5. Data Preparation Kristian Henrickson, Filipe Rodrigues and Francisco Camara Pereira 6. Data Science and Data Visualization Michalis Xyntarakis and Constantinos Antoniou 7. Model-Based Machine Learning for Transportation Inon Peled, Filipe Rodrigues and Francisco Camara Pereira 8. Textual Data in Transportation Research: Techniques and Opportunities Aseem Kinra, Samaneh Beheshti Kashi, Francisco Camara Pereira, Francois Combes and Werner Rothengatter 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 10. Transit Data Analytics for Planning, Monitoring, Control, and Information Haris N. Koutsopoulos, Zhenliang Ma, Peyman Noursalehi and Yiwen Zhu 11. Data-Driven Traffic Simulation Models: Mobility Patterns Using Machine Learning Techniques Vasileia Papathanasopoulou, Constantinos Antoniou and Haris N. Koutsopoulos 12. Big Data and Road Safety: A Comprehensive Review 13. A Back-Engineering Approach to Explore Human Mobility Patterns Across Megacities Using Online Traffic Maps 14. Pavement Patch Defects Detection and Classification Using Smartphones, Vibration Signals and Video Images Symeon E. Christodoulou, Charalambos Kyriakou and George Hadjidemetriou 15. Collaborative Positioning for Urban Intelligent Transportation Systems (ITS) and Personal Mobility (PM): Challenges and Perspectives Vassilis Gikas, Guenther Retscher and Allison Kealy
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). 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. 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.
