This paper presents a cost-effective, non-intrusive, and easy-to-deploy traffic count data collection method using two-dimensional light-detection and ranging (LiDAR) technology. The proposed method integrates a LiDAR sensor, continuous wavelet transform (CWT), and support vector machine (SVM) into a single framework for traffic count. LiDAR is adopted since the technology is economical and easily accessible. Moreover, its 360° visibility and accurate distance information make it more reliable compared with radar, which uses electromagnetic waves instead of light rays. The obtained distance data are converted into the signals. CWT is employed to detect any deviation in distance profile, because of its efficiency in detecting modest changes over a period of time. SVM is one of the supervised machine learning tools for data classification and regression. In the methodology, the SVM is applied to classify the distance data points obtained from the sensor into detection and non-detection cases, which are highly complex. Proof-of-concept (POC) test is conducted in three different places in Newark, New Jersey, to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances in vehicle count collection, resulting in 83–94% accuracy. It is discovered that the accuracy of the proposed method is affected by the color of the exterior surface of a vehicle.
Classification of Refrigerant Flow Noise of Air Conditioners Based on Continuous Wavelet Transform and Support Vector Machine
