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dc.contributor.author
Boufidis, Neofytos
en
dc.date.accessioned
2019-04-18T14:13:11Z
dc.date.available
2019-04-19T00:00:14Z
dc.date.issued
2019-04-18
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29396
dc.rights
Default License
dc.subject
Apache Spark
en
dc.subject
Traffic
en
dc.subject
Distributed systems
en
dc.title
Distributed traffic forecasting using Apache Spark
en
heal.type
masterThesis
en_US
heal.creatorID.email
n.boufidis@ihu.edu.gr
heal.creatorID.email
boufidisneo@certh.gr
heal.creatorID.email
boufidisneo@gmail.com
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in Data Science
en_US
heal.publicationDate
2019-04-18
heal.abstract
This dissertation was written as a part of the MSc in Data Science at the International Hellenic University. The exploding volume of available data in recent years render the development of distributed algorithms vital for most scientific fields, including Transportation and Intelligent Transport Systems. This thesis investigates the use of big data technologies for enhancing urban transportation and planning. A sophisticated traffic forecasting model is developed and tested utilizing a large-scale, real-world dataset produced by GPS sensors on a taxi fleet (i.e., floating car data) in the area of Thessaloniki, Greece. The model implementation, including parts of the data preprocessing steps, are implemented in PySpark, the Python API of Spark distributed programming language.
en
heal.advisorName
Papadopoulos, Apostolos
en
heal.committeeMemberName
Papadopoulos, Apostolos
en
heal.committeeMemberName
Berberidis, Christos
en
heal.committeeMemberName
Evangelidis, Georgios
en
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en_US


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