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Big Data Analytics for Maintaining Transportation Systems
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0003-0734-0959
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0002-1938-0985
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Mechanical Engineering Department, Government College of Engineering and Technology, Jammu, Jammu, India.ORCID-id: 0000-0001-7229-4050
Maintenance Engineering Group, Design, Production and Management Department, University of Twente, Enschede, The Netherlands.
2019 (engelsk)Inngår i: Transportation Systems: Managing Performance through Advanced Maintenance Engineering / [ed] Sarbjeet Singh, Alberto Martinetti, Arnab Majumdar, Leo A. M. van Dongen, Springer , 2019, s. 73-91Kapittel i bok, del av antologi (Annet vitenskapelig)
Abstract [en]

Big Data Analytics (BDA) is becoming a research focus in transportation systems, which can be seen from many projects within the world. By using sensor and Internet of Things (IoT) technology in transportation system, huge amount of data is been generated from different sources. This data can be integrated, analyzed and visualized for efficient and effective decision-making for maintaining transportation systems. The key challenges that exist in managing Big Data are the designing of the systems, which would be able to handle huge amount of data efficiently and effectively and to filter the most significant information from all the collected data. This chapter will draw attention towards the present scenario and future projections of big data in transportation systems. It also presents big data tools and techniques and then presents one brief case study of BDA in each type of transportation system. In this chapter, a broad overview of Big Data definitions, its history, present, and future prospects are briefed. Several tools and technologies especially for transportation are pointed out for maintaining transportation systems. At the end of the chapter, a definitive case studies on each transportation area is demonstrated.

sted, utgiver, år, opplag, sider
Springer , 2019. s. 73-91
Serie
Asset Analytics, ISSN 2522-5162, E-ISSN 2522-5170
Emneord [en]
Big data analytics, Transportation system, Maintenance, Railway, Road, Aviation, Shipping
HSV kategori
Forskningsprogram
Drift och underhållsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-78438DOI: 10.1007/978-981-32-9323-6_6OAI: oai:DiVA.org:ltu-78438DiVA, id: diva2:1422965
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ISBN för värdpublikation: 978-981-32-9322-9, 978-981-32-9323-6

Tilgjengelig fra: 2020-04-10 Laget: 2020-04-10 Sist oppdatert: 2020-04-28bibliografisk kontrollert

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Kour, RavdeepThaduri, AdithyaSingh, Sarbjeet

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Totalt: 95 treff
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