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 segments using partially …

Freeway traffic state estimation and prediction are central components in real-time traffic
management and information applications. Model-based traffic state estimators consist of a …

We present a physics-informed deep learning (PIDL) approach to tackle the challenge of
data sparsity and sensor noise in traffic state estimation (TSE). PIDL strengthens a deep …

We consider the problem of sequential data assimilation for transportation networks using
optimal filtering with a scalar macroscopic traffic flow model. Properties of the distribution of …

Because of significant improvements in cost, accuracy, and coverage over dedicated traffic
infrastructures, GPS-enabled mobile devices are preferred for continuous collection of traffic …

We develop a Bayesian particle filter for tracking traffic flows that is capable of capturing non-
linearities and discontinuities present in flow dynamics. Our model includes a hidden state …

Transportation departments take actions to manage traffic flow and reduce travel times
based on estimated current and projected traffic conditions. Travel time estimates and …

The main motivation of this work is to assess the validity of a LWR traffic flow model to model
measurements obtained from trajectory data, and propose extensions of this model to …

This paper investigates the problem of estimating the state of discretized hyperbolic scalar
partial differential equations. It uses a Godunov scheme to discretize the so-called Lighthill …

We study the statistical properties of a cellular automata model of traffic flow with the look-
ahead potential. The model defines stochastic rules for the movement of cars on a lattice …