大葉大學圖書館 |

Innovative applications of big data in the railway industry[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	385.0285/57
書名/作者:	Innovative applications of big data in the railway industry/ Shruti Kohli, A.V. Senthil Kumar, John M. Easton and Clive Roberts, editors.
其他作者:	Kohli, Shruti,
出版者:	Hershey, Pennsylvania : : IGI Global,, [2018]
面頁冊數:	1 online resource (xviii, 395 p.)
標題:	Railroads - Management.
標題:	Railroads - Technological innovations.
標題:	Big data.
ISBN:	9781522531777 (ebook)
ISBN:	9781522531760 (hardcover)
書目註:	Includes bibliographical references and index.
內容註:	Section 1. Concepts and approaches. Chapter 1. Big data in railway O&M: a dependability approach ; Chapter 2. Blockchains: a distributed data ledger for the rail industry ; Chapter 3. Visual and lidar data processing and fusion as an element of real time big data analysis for rail vehicle driver support systems ; Chapter 4. Wayside train monitoring systems: origin and application -- Section 2. Innovations and technologies. Chapter 5. Scalable software framework for real-time data processing in the railway environment ; Chapter 6. Predicting behavior of passengers using data collected through smart cards ; Chapter 7. Dynamic behavior analysis of railway passengers ; Chapter 8. Intelligent transport systems services in vanets and case study in urban environment -- Section 3. Big data and text mining. Chapter 9. Study and analysis of delay factors of Delhi metro using data sciences and social media: automatic delay prediction system for Delhi metro ; Chapter 10. Social media as a tool to understand behaviour on the railways ; Chapter 11. Big data and natural language processing for analysing railway safety: analysis of railway incident reports -- Section 4. Applications and use cases. Chapter 12. Evolution of Indian railways through IOT ; Chapter 13. Application of big data technologies for quantifying the key factors impacting passenger journey in a multi-modal transportation environment ; Chapter 14. Big data analytics for train delay prediction: a case study in the Italian railway network.
摘要、提要註:	"This book explores how big data technologies can address the operational challenges in the railway industry. It assists academics working in the big data domain to gain an understanding of the problems faced by the rail industry, enabling them to start developing their existing tools and technologies to meet the needs of this dynamic new application domain"--
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-3176-0

Innovative applications of big data in the railway industry[electronic resource] /
Innovative applications of big data in the railway industry[electronic resource] /Shruti Kohli, A.V. Senthil Kumar, John M. Easton and Clive Roberts, editors. - Hershey, Pennsylvania :IGI Global,[2018] - 1 online resource (xviii, 395 p.)

Includes bibliographical references and index.

Section 1. Concepts and approaches. Chapter 1. Big data in railway O&M: a dependability approach ; Chapter 2. Blockchains: a distributed data ledger for the rail industry ; Chapter 3. Visual and lidar data processing and fusion as an element of real time big data analysis for rail vehicle driver support systems ; Chapter 4. Wayside train monitoring systems: origin and application -- Section 2. Innovations and technologies. Chapter 5. Scalable software framework for real-time data processing in the railway environment ; Chapter 6. Predicting behavior of passengers using data collected through smart cards ; Chapter 7. Dynamic behavior analysis of railway passengers ; Chapter 8. Intelligent transport systems services in vanets and case study in urban environment -- Section 3. Big data and text mining. Chapter 9. Study and analysis of delay factors of Delhi metro using data sciences and social media: automatic delay prediction system for Delhi metro ; Chapter 10. Social media as a tool to understand behaviour on the railways ; Chapter 11. Big data and natural language processing for analysing railway safety: analysis of railway incident reports -- Section 4. Applications and use cases. Chapter 12. Evolution of Indian railways through IOT ; Chapter 13. Application of big data technologies for quantifying the key factors impacting passenger journey in a multi-modal transportation environment ; Chapter 14. Big data analytics for train delay prediction: a case study in the Italian railway network.

Restricted to subscribers or individual electronic text purchasers.

"This book explores how big data technologies can address the operational challenges in the railway industry. It assists academics working in the big data domain to gain an understanding of the problems faced by the rail industry, enabling them to start developing their existing tools and technologies to meet the needs of this dynamic new application domain"--

ISBN: 9781522531777 (ebook)Subjects--Topical Terms:

733263
Railroads
--Management.

LC Class. No.: TF507 / .I537 2018e

Dewey Class. No.: 385.0285/57

Innovative applications of big data in the railway industry[electronic resource] /
LDR:02926nmm a2200277 a 4500 001 512596
003 IGIG
005 20181029090948.0
006 m o d
007 cr cn
008 210927s2018 pau fob 001 0 eng d
010 $z 2017016448
020 $a 9781522531777 (ebook)
020 $a 9781522531760 (hardcover)
035 $a (OCoLC)1010954240
035 $a 1071025384
040 $a CaBNVSL $b eng $c CaBNVSL $d CaBNVSL
050 4 $a TF507 $b .I537 2018e
082 0 4 $a 385.0285/57 $2 23
245 0 0 $a Innovative applications of big data in the railway industry $h [electronic resource] / $c Shruti Kohli, A.V. Senthil Kumar, John M. Easton and Clive Roberts, editors.
260 $a Hershey, Pennsylvania : $b IGI Global, $c [2018]
300 $a 1 online resource (xviii, 395 p.)
504 $a Includes bibliographical references and index.
505 0 $a Section 1. Concepts and approaches. Chapter 1. Big data in railway O&M: a dependability approach ; Chapter 2. Blockchains: a distributed data ledger for the rail industry ; Chapter 3. Visual and lidar data processing and fusion as an element of real time big data analysis for rail vehicle driver support systems ; Chapter 4. Wayside train monitoring systems: origin and application -- Section 2. Innovations and technologies. Chapter 5. Scalable software framework for real-time data processing in the railway environment ; Chapter 6. Predicting behavior of passengers using data collected through smart cards ; Chapter 7. Dynamic behavior analysis of railway passengers ; Chapter 8. Intelligent transport systems services in vanets and case study in urban environment -- Section 3. Big data and text mining. Chapter 9. Study and analysis of delay factors of Delhi metro using data sciences and social media: automatic delay prediction system for Delhi metro ; Chapter 10. Social media as a tool to understand behaviour on the railways ; Chapter 11. Big data and natural language processing for analysing railway safety: analysis of railway incident reports -- Section 4. Applications and use cases. Chapter 12. Evolution of Indian railways through IOT ; Chapter 13. Application of big data technologies for quantifying the key factors impacting passenger journey in a multi-modal transportation environment ; Chapter 14. Big data analytics for train delay prediction: a case study in the Italian railway network.
506 $a Restricted to subscribers or individual electronic text purchasers.
520 3 $a "This book explores how big data technologies can address the operational challenges in the railway industry. It assists academics working in the big data domain to gain an understanding of the problems faced by the rail industry, enabling them to start developing their existing tools and technologies to meet the needs of this dynamic new application domain"-- $c Provided by publisher.
650 0 $a Railroads $x Management. $3 733263
650 0 $a Railroads $x Technological innovations. $3 437565
650 0 $a Big data. $3 571002
700 1 $a Kohli, Shruti, $d 1978- $e editor. $3 733260
700 1 $a Kumar, A. V. Senthil, $d 1966- $3 481200
700 1 $a Easton, John M., $e editor. $3 733261
700 1 $a Roberts, Clive, $e editor. $3 733262
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-3176-0