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Transportation analytics in the era ...

Ukkusuri, Satish V.

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Transportation analytics in the era of big data

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Transportation analytics in the era of big data/ edited by Satish V. Ukkusuri, Chao Yang.
其他作者:	Ukkusuri, Satish V.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xi, 234 p. :ill. (some col.), maps (some col.), digital ;24 cm.
內容註:	Preface -- Chapter 1. Beyond Geotagged Tweets: Exploring the Geolocalisation of Tweets for Transportation Applications -- Chapter 2. Social Media in Transportation Research and Promising Applications -- Chapter 3. Ground transportation big data analytics and third party validation - solutions for a new era of regulation and private sector innovation -- Chapter 4. A privacy-preserving urban traffic estimation system. - Chapter 5. Data, Methods, and Applications of Traffic Source Prediction. - Chapter 6. Analyzing the spatial and temporal characteristics of subway passenger flow based on smart card data -- Chapter 7. An Initial Evaluation of the Impact of Location Obfuscation Mechanisms on Geospatial Analysis -- Chapter 8. PETRA: The Personal Transport Advisor Platform and Services -- Chapter 9. Mobility Pattern Identification Based on Mobile Phone Data.
Contained By:	Springer eBooks
標題:	Urban transportation - Data processing. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-75862-6
ISBN:	9783319758626

Transportation analytics in the era of big data
Transportation analytics in the era of big data[electronic resource] /edited by Satish V. Ukkusuri, Chao Yang. - Cham :Springer International Publishing :2019. - xi, 234 p. :ill. (some col.), maps (some col.), digital ;24 cm. - Complex networks and dynamic systems,v.42195-724X ;. - Complex networks and dynamic systems ;v.4..

Preface -- Chapter 1. Beyond Geotagged Tweets: Exploring the Geolocalisation of Tweets for Transportation Applications -- Chapter 2. Social Media in Transportation Research and Promising Applications -- Chapter 3. Ground transportation big data analytics and third party validation - solutions for a new era of regulation and private sector innovation -- Chapter 4. A privacy-preserving urban traffic estimation system. - Chapter 5. Data, Methods, and Applications of Traffic Source Prediction. - Chapter 6. Analyzing the spatial and temporal characteristics of subway passenger flow based on smart card data -- Chapter 7. An Initial Evaluation of the Impact of Location Obfuscation Mechanisms on Geospatial Analysis -- Chapter 8. PETRA: The Personal Transport Advisor Platform and Services -- Chapter 9. Mobility Pattern Identification Based on Mobile Phone Data.

This book presents papers based on the presentations and discussions at the international workshop on Big Data Smart Transportation Analytics held July 16 and 17, 2016 at Tongji University in Shanghai and chaired by Professors Ukkusuri and Yang. The book is intended to explore a multidisciplinary perspective to big data science in urban transportation, motivated by three critical observations: The rapid advances in the observability of assets, platforms for matching supply and demand, thereby allowing sharing networks previously unimaginable. The nearly universal agreement that data from multiple sources, such as cell phones, social media, taxis and transit systems can allow an understanding of infrastructure systems that is critically important to both quality of life and successful economic competition at the global, national, regional, and local levels. There is presently a lack of unifying principles and methodologies that approach big data urban systems. The workshop brought together varied perspectives from engineering, computational scientists, state and central government, social scientists, physicists, and network science experts to develop a unifying set of research challenges and methodologies that are likely to impact infrastructure systems with a particular focus on transportation issues. The book deals with the emerging topic of data science for cities, a central topic in the last five years that is expected to become critical in academia, industry, and the government in the future. There is currently limited literature for researchers to know the opportunities and state of the art in this emerging area, so this book fills a gap by synthesizing the state of the art from various scholars and help identify new research directions for further study.

ISBN: 9783319758626

Standard No.: 10.1007/978-3-319-75862-6doiSubjects--Topical Terms:

3378558
Urban transportation
--Data processing.

LC Class. No.: HE147.6

Dewey Class. No.: 388.0285

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520 $a This book presents papers based on the presentations and discussions at the international workshop on Big Data Smart Transportation Analytics held July 16 and 17, 2016 at Tongji University in Shanghai and chaired by Professors Ukkusuri and Yang. The book is intended to explore a multidisciplinary perspective to big data science in urban transportation, motivated by three critical observations: The rapid advances in the observability of assets, platforms for matching supply and demand, thereby allowing sharing networks previously unimaginable. The nearly universal agreement that data from multiple sources, such as cell phones, social media, taxis and transit systems can allow an understanding of infrastructure systems that is critically important to both quality of life and successful economic competition at the global, national, regional, and local levels. There is presently a lack of unifying principles and methodologies that approach big data urban systems. The workshop brought together varied perspectives from engineering, computational scientists, state and central government, social scientists, physicists, and network science experts to develop a unifying set of research challenges and methodologies that are likely to impact infrastructure systems with a particular focus on transportation issues. The book deals with the emerging topic of data science for cities, a central topic in the last five years that is expected to become critical in academia, industry, and the government in the future. There is currently limited literature for researchers to know the opportunities and state of the art in this emerging area, so this book fills a gap by synthesizing the state of the art from various scholars and help identify new research directions for further study.
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