Data Science for Transport: A Self-Study Guide with Computer Exercises - Brossura
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Informazioni sull?autore
Dr. Charles Fox is a University Academic Fellow in Vehicle and Road Automation at the Institute for Transport Studies, University of Leeds. He researches autonomous off-road, on-road, and road-side perception, control and data analytics systems, using primarily Bayesian methods. Recent projects include IBEX2 off-road autonomous agricultural vehicles, featured in The Times and on the Discovery Channel; INTERACT pedestrian detection analytics for autonomous vehicles, with BMW; UDRIVE data mining of manual car driving big data to identify causes of dangerous driving, with Volvo; and Automated Number Plate Recognition analytics for Mouchel and the Highways Agency.
Dr. Fox holds a first class MA degree in Computer Science from the University of Cambridge, MSc with Distinction in Informatics from the University of Edinburgh, and a DPhil in Pattern Analysis and Machine Learning from the Robotics Research Group at the University of Oxford.
He has worked as a researcher in robotics and data-driven speech recognition at the University of Sheffield for users including the BBC, NHS and GCHQ, and as a high frequency data-driven trader for London hedge fund Algometrics Ltd.
He has published 50 conference and journal papers cited 800 times and has h-index 15. He is a director of Ibex Automation Ltd which advises hedge fund and venture capital clients and is available for consultancy and R&D work in Data Science and Robotics.
Dalla quarta di copertina
This book offers a unique introduction to the application of data science for transport professionals and students of transport studies. Based on a course taught by the Leeds Institute for Transport Studies, the world’s leading center for training transport professionals, it represents the first textbook in this new area.
As transportation planning has become increasingly data-driven, all graduate students and transport professionals urgently need to update their skills to include databases, machine learning, Bayesian statistics, geographic information system (GIS), and big data tools. Similarly, transport professionals including national and local government planners, transport consultants, and car company engineers are called upon to integrate these disparate areas with a specific focus on transportation issues, such as maps.
The textbook also features a downloadable software package with all of the open source tools and libraries used in code examples throughout the book, including Python, Spyder, PostGIS, PyMC and GPy installations. As such, it offers a unique resource for graduate/advanced undergraduate students and instructors in transportation studies, urban and regional planning, engineering and geography, as well as transportation professionals.
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Data Science for Transport
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Springer Nature Switzerland AG, CH, 2019
ISBN 10: 3030102912
ISBN 13: 9783030102913
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Paperback. Condizione: New. The quantity, diversity and availability of transport data is increasing rapidly, requiring new skills in the management and interrogation of data and databases. Recent years have seen a new wave of 'big data', 'Data Science', and 'smart cities' changing the world, with the Harvard Business Review describing Data Science as the "sexiest job of the 21st century". Transportation professionals and researchers need to be able to use data and databases in order to establish quantitative, empirical facts, and to validate and challenge their mathematical models, whose axioms have traditionally often been assumed rather than rigorously tested against data. This book takes a highly practical approach to learning about Data Science tools and their application to investigating transport issues. The focus is principally on practical, professional work with real data and tools, including business and ethical issues."Transport modeling practice was developed in a data poor world, and many of our current techniques and skills are building on that sparsity. In a new data rich world, the required tools are different and the ethical questions around data and privacy are definitely different. I am not sure whether current professionals have these skills; and I am certainly not convinced that our current transport modeling tools will survive in a data rich environment. This is an exciting time to be a data scientist in the transport field. We are trying to get to grips with the opportunities that big data sources offer; but at the same time such data skills need to be fused with an understanding of transport, and of transport modeling. Those with these combined skills can be instrumental at providing better, faster, cheaper data for transport decision- making; and ultimately contribute to innovative, efficient, data driven modeling techniques of the future. It is not surprising that this course, this book, has been authored by the Institute for Transport Studies. To do this well, you need a blend of academic rigor and practical pragmatism. There are few educational or research establishments better equipped to do that than ITS Leeds". - Tom van Vuren, Divisional Director, Mott MacDonald"WSP is proud to be a thought leader in the world of transport modelling, planning and economics, and has a wide range of opportunities for people with skills in these areas. The evidence base and forecasts we deliver to effectively implement strategies and schemes are ever more data and technology focused a trend we have helped shape since the 1970's, but with particular disruption and opportunity in recent years. As a result of these trends, and to suitably skill the next generation of transport modellers, we asked the world-leading Institute for Transport Studies, to boost skills in these areas, and they have responded with a new MSc programme which you toocan now study via this book." - Leighton Cardwell, Technical Director, WSP."From processing and analysing large datasets, to au Softcover Reprint of the Original 1st 2018 ed. Codice articolo LU-9783030102913
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