Kernel-Based Optimization for Traffic Density Estimation in ITS

Efficiency of transportation systems is defined as relationship between costs and benefits. Congestion is a phenomena that increase utilization cost in different modes of transportation including the road networks. In this paper, a kernel-based density estimation method is utilized to extract the congestion spots in road networks based on collected position samples with time-stamp from floating car data. A probabilistic framework is developed to find optimized weights of kernels in an approximation function, centered at points-of-interest by minimizing the Cramer-von Mises distance between localized cumulative distributions of mixture of Dirac distributions of position samples and Gaussian mixtures of points-of-interest in a pre-defined time window. The approximation density function by optimized kernels' weights can be used to estimate the mobile vehicles density in a specific time and space. The proposed method can be significantly improved if we have a spatial-temporal model of floating car data.