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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Crowd source based traffic analysis using machine learning algorithm

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    12101049,12101057,12101059_CSE.pdf (1.265Mb)
    Date
    2017-08-21
    Publisher
    BRAC Univeristy
    Author
    Orthy, Marzia Khan
    Sharmin, Suraiya
    Reshad, Ashraful Islam
    Metadata
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    URI
    http://hdl.handle.net/10361/9013
    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.
    Keywords
    Traffic analysis; Machine learning algorithm; Neural network
     
    Description
    This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (page 47)
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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