{"id":332,"date":"2015-02-17T09:32:04","date_gmt":"2015-02-17T09:32:04","guid":{"rendered":"http:\/\/www.etfos.unios.hr\/ijeces-test\/?p=332"},"modified":"2017-03-23T08:04:47","modified_gmt":"2017-03-23T08:04:47","slug":"the-public-transportation-bigdata-clustering","status":"publish","type":"post","link":"http:\/\/www.etfos.unios.hr\/ijeces\/vol-4-no-1-2013\/the-public-transportation-bigdata-clustering\/","title":{"rendered":"The Public Transportation BigData Clustering"},"content":{"rendered":"
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Tomislav Galba, Zoran Balki\u0107, Goran Martinovi\u0107<\/h3>
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Abstract <\/strong><\/div>\n
An increase in the use of GPS modules and cell phones with location services has created a need for new ways of collecting and storing data. Considering a fairly large number of devices, data collected in such way in most cases take up a vast amount of space on servers while on the other hand, they represent a source of very useful information. A large number of companies use this method of data collection in order to create prediction models, reports and data analysis. As an object of observation, we use the database of a modern public transportation system which contains information about vehicle telemetry. In this paper, we will describe the application and result analysis of some well-known clustering algorithms in order to solve public transportation problems like traffic congestion, passenger transport, etc.<\/div>\n
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Keywords<\/strong><\/div>\n
analysis, big data, clustering, GPS, public transportation<\/div>\n
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