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dc.contributor.advisorChakrabarty, Dr. Amitabha
dc.contributor.authorKabir, Md. Miraz
dc.contributor.authorAkil, Farhan Sadik
dc.contributor.authorMahmood, Ohinur Makamay
dc.date.accessioned2018-05-09T05:30:43Z
dc.date.available2018-05-09T05:30:43Z
dc.date.copyright2018
dc.date.issued2018-04-17
dc.identifier.otherID 14201010
dc.identifier.otherID 14101155
dc.identifier.otherID 16341014
dc.identifier.urihttp://hdl.handle.net/10361/10098
dc.descriptionThis thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references.
dc.description.abstractTraffic Jam impact our day to day life in more ways than we can think of. Our system aims to work on that particular problem to reduce the number of vehicles on the road by encouraging more people to get on public transport to make public transport more efficient for the people who are less keen on taking them due to time delays. Our system uses regression analysis on a data set collected on a specific route to estimate the arrival time of a public transport at a desired travel time. This gives the user a clear idea of the time required to travel between points and thus can utilize their time in an efficient way by not having to wait for a transport. The system uses a compiled data set to analyze historical data and then use regression analysis to predict the arrival time for particular location of the user.en_US
dc.description.statementofresponsibilityMd. Miraz Kabir
dc.description.statementofresponsibilityFarhan Sadik Akil
dc.description.statementofresponsibilityOhinur Makamay Mahmood
dc.language.isoenen_US
dc.publisherBRAC Univeristyen_US
dc.rightsBRAC University thesis reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectMachine learningen_US
dc.titlePublic vehicle ETA system using machine learningen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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