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dc.contributor.advisorChakrabarty, Dr. Amitabha
dc.contributor.authorOrthy, Marzia Khan
dc.contributor.authorSharmin, Suraiya
dc.contributor.authorReshad, Ashraful Islam
dc.date.accessioned2017-12-26T06:16:51Z
dc.date.available2017-12-26T06:16:51Z
dc.date.copyright2017
dc.date.issued2017-08-21
dc.identifier.otherID 12101049
dc.identifier.otherID 12101057
dc.identifier.otherID 12101059
dc.identifier.urihttp://hdl.handle.net/10361/8700
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 47).
dc.description.abstractOne of the most detrimental effects to our economy currently is most certainly Traffic jam. Be it in a public vehicle or private, valuable work hours (around 3.2 million per day) is wasted everyday while waiting in the traffic. While this problem cannot be overcome without proper urban planning and traffic management, there are definitely ways of providing the commuters with an idea about how long they might be needing for their route. Often, this information might decide between rescheduling a meeting or missing it altogether. Keeping this in mind, the Dhaka Real Traffic project has been taken. It aims to provide travel time predictions based on machine learning of crowd sourced real commuting data. Data mining was done by means of a data collection app and also via Google form. The collected data was classed and trained by means of Python coding. From an initial choice between SVM, KNN & ANN, ANN was selected as the machine learning algorithm due to its lowest mean square errors among all three. Using Java and XML, the frontend Android App name Dhaka Real Traffic (DRT) was created with backend server learning. Due to machine learning, DRT will continue to upgrade its database to provide the most realistic travel time estimateen_US
dc.description.statementofresponsibilityMarzia Khan Orthy
dc.description.statementofresponsibilitySuraiya Sharmin
dc.description.statementofresponsibilityAshraful Islam Reshad
dc.format.extent47 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis 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.subjectTraffic jamen_US
dc.subjectTravel timeen_US
dc.subjectPython codingen_US
dc.subjectReal trafficen_US
dc.subjectMachine learningen_US
dc.subjectAlgorithm
dc.subjectPrediction
dc.titleCrowed source based traffic analysis using machine learning algorithmen_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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