… and forecasting are central components in dynamic traffic management and … traffic state
estimation approach based on an improved formulation of the traditional Ensemble Kalman filter (…

… The purpose of this paper is to develop a methodology for the design of real-time traffic
state estimators providing formal differential privacy guarantees [7], as defined in Section 2. …

… Abstract—Traffic state estimation is a challenging problem for the transportation community
due to the limited deployment of sensing infrastructure. However, recent trends in the mobile …

… its application in freeway traffic state estimation in this paper. … existing traffic state evolution
models that we may ensemble. … traffic state estimation based on extended Kalman filter: A …

… In this paper, a real-time road traffic state prediction based on autoregressive integrated
moving average (ARIMA) and the Kalman filter is proposed. First, an ARIMA model of road traffic …

… estimate the traffic state and traffic incidents in real–time on a freeway segment using a multiple
model nonlinear filter… , the traffic evolution equation used for the hybrid state estimation is …

… Traffic State Estimation (TSE) refers to the estimation of density, speed, or other parameters
of vehicular traffic on … It can be used as a component in applications such as traffic control …

… Traffic state estimation (TSE) refers to the process of the inference of traffic state variables
(ie, flow, density, speed and other equivalent variables) on road … ensemble Kalman filter …

… techniques that rely on sampling to produce the estimates (eg, ensemble Kalman filtering).
These techniques can be computationally cumbersome and preclude real-time applications. …

… The basic methodologies that are used for traffic state estimation are Kalman filtering,
Bayesian estimation, maximum likelihood, particle filtering, and data-driven machine learning …