AI Summary & Key Takeaways
AI InsightFor all example data sets, the minimum mean square error of kernel weights is found to be less than 0.002 while error of mean values is found to be less than 0.261
📌 3 Key Takeaways
Abstract
As the number of vehicles in roads increases, information of traffic density becomes crucial to municipalities for making better decisions about road management and to the environment for reduced carbon emission. Here, the problem of traffic density estimation is addressed when there is continuous influx of vehicle data. First the traffic density is modeled by the clusters of the speed groups that are centered after Kernel Density Estimation technique is implemented for the probability density function of the speed data. Then, empirical cumulative distribution function of data is found by Kolmogorov-Smirnov Test. A peak detection algorithm is used to estimate speed centers of the clusters. Since the estimation model has linear and non-linear components, the estimation of variance values and kernel weights are found by a nonlinear Least Square approach with separation of parameters property. Finally, the tracking of former and latter estimations of a road is calculated by using Scalar Kalman Filtering with scalar state - scalar observation generality level. For all example data sets, the minimum mean square error of kernel weights is found to be less than 0.002 while error of mean values is found to be less than 0.261