Dissertation/Thesis Abstract

Vehicle classification using machine learning algorithm
by Patel, Darshan D., M.S., California State University, Long Beach, 2015, 37; 1604876
Abstract (Summary)

Increasing traffic on roadways requires some real-time system that can collect traffic data and helps us to manage existing road infrastructure. For this purpose, we need a state of art system that can detect and classify vehicles into different categories. We developed an in-node microprocessor-based vehicle classification system to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. Our approach for vehicle classification utilizes J48 classification algorithm, which is implemented in machine learning software Weka. J48 is a Quinlan's C4.5 algorithm, an extension of decision tree machine learning based on ID3 algorithm. The decision tree model is generated from a set of features extracted from vehicles passing over the 3-axis sensor. The generated tree model can then be easily implemented on microprocessors. The result of our experiment shows that the vehicle classification system is effective and efficient with the very high accuracy at ~98%.

Indexing (document details)
Advisor: Mozumdar, Mohammad M.
Commitee: Ary, James, Tsang, Chit-Sang
School: California State University, Long Beach
Department: Electrical Engineering
School Location: United States -- California
Source: MAI 55/02M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Electrical engineering
Keywords:
Publication Number: 1604876
ISBN: 978-1-339-29273-1
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