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Cost effective Vehicle Classification using a single wireless magnetometer

Traffic monitoring systems play a key role in the transition toward smart cities and more efficient transportation systems. Roads and highways are now equipped with sensors to measure traffic flow, predict congestion, and adaptively control traffic routes. Traffic planning applications rely on sensors to provide accurate information about vehicles and infrastructure status. Vehicle class is an important parameter measured by most traffic monitoring systems, playing a key role in traffic planning and optimization. This paper introduces algorithms to classify vehicles using inexpensive, easy-to-install magnetometers. The system uses a battery-powered magnetometer and requires minimum installation. The proposed classification system uses Discrete Fourier Transform (DFT) and Principal Component Analysis (PCA) to automatically extract features from magnetic signatures and classify vehicles into three categories.