Deep learning methods are emerging as popular computational tools for solving forward
and inverse problems in traffic flow. In this paper, we study a neural operator framework for …

It is challenging to predict a group of individuals' spatiotemporal trajectories in continuous
time and space, due to various environmental and intrinsic factors. Especially, social …

Traffic state imputation refers to the estimation of missing values of traffic variables, such as
flow rate and traffic density, using available data. It furnishes comprehensive traffic context …

This paper proposes a physics-informed neural operator (PINO) framework to solve a
system of coupled forward-backward partial differential equations (PDEs) arising from mean …

Traffic state estimation (TSE) refers to the inference of macroscopic traffic states, including
density, speed, and flow, based on partially observed traffic data and some prior knowledge …

This paper proposes a privacy-preserving data fusion method for traffic state estimation
(TSE). Unlike existing works that assume all data sources to be accessible by a single …

This paper proposes a scalable learning framework to solve a system of coupled forward–
backward partial differential equations (PDEs) arising from mean field games (MFGs). The …

This research contributes to the advancement of traffic state estimation methods by
leveraging the benefits of the nonlocal LWR model within a physics-informed deep learning …

Noise is inevitable in the collection of traffic data, which may cause accuracy and stability
issues in smart mobility applications. In the literature, most of the existing studies on traffic …

The adaptive smoothing method (ASM) is a standard data-driven technique used in traffic
state estimation. The ASM has free parameters which, in practice, are chosen to be some …