Traffic density estimation via KDE and nonlinear LS

Introduction to nonlinear LS estimation

Traffic Density Estimation from Highly

12 In this paper, we address the problem of how to accurately estimate the traffic 13 density of road segments from highly noisy image sources. Conventional traffic 14 density estimation techniques from camera feeds typically rely on high quality 15 images. Surprisingly, a large number of live feeds from traffic cameras in devel16 oping regions are highly noisy due to poor camera quality, poor ...

SIMO-OFDM Channel Estimation based on Nonlinear Complex LS-SVM

In this contribution, we propose a robust highly selective nonlinear channel estimator for Single -Input Multiple-Output (SIMO) Orthogonal Frequency Division Multiplexing (OFDM) system using complex Least Squares Support Vector Machines (LS-SVM) and applied to Long Term Evolution (LTE) downlink under high mobility conditions . The new method uses the information provided by the pilot signals to...

Multilane Traffic Density Estimation and Tracking

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 t...

Conditional Density Estimation via Least-Squares Density Ratio Estimation

Estimating the conditional mean of an inputoutput relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper, we propose a novel method of condition...