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Crowed source based traffic analysis using machine learning algorithm

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dc.contributor.advisor Chakrabarty, Dr. Amitabha
dc.contributor.author Orthy, Marzia Khan
dc.contributor.author Sharmin, Suraiya
dc.contributor.author Reshad, Ashraful Islam
dc.date.accessioned 2017-12-26T06:16:51Z
dc.date.available 2017-12-26T06:16:51Z
dc.date.copyright 2017
dc.date.issued 2017-08-21
dc.identifier.other ID 12101049
dc.identifier.other ID 12101057
dc.identifier.other ID 12101059
dc.identifier.uri http://hdl.handle.net/10361/8700
dc.description This 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.description Cataloged from PDF version of thesis report.
dc.description Includes bibliographical references (page 47).
dc.description.abstract One 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 estimate en_US
dc.description.statementofresponsibility Marzia Khan Orthy
dc.description.statementofresponsibility Suraiya Sharmin
dc.description.statementofresponsibility Ashraful Islam Reshad
dc.format.extent 47 pages
dc.language.iso en en_US
dc.publisher BRAC University en_US
dc.rights BRAC 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.subject Traffic jam en_US
dc.subject Travel time en_US
dc.subject Python coding en_US
dc.subject Real traffic en_US
dc.subject Machine learning en_US
dc.subject Algorithm
dc.subject Prediction
dc.title Crowed source based traffic analysis using machine learning algorithm en_US
dc.type Thesis en_US
dc.contributor.department Department of Computer Science and Engineering, BRAC University
dc.description.degree B. Computer Science and Engineering


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