52 citations · 107 across the 7 of their papers we have counts for
7 papers
Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems
Bilal Thonnam Thodi, Sai Venkata Ramana Ambadipudi, Saif Eddin Jabari
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…
Learning-based solutions to nonlinear hyperbolic PDEs: Empirical insights on generalization errors
Bilal Thonnam Thodi, Sai Venkata Ramana Ambadipudi, Saif Eddin Jabari
We study learning weak solutions to nonlinear hyperbolic partial differential equations (H-PDE), which have been difficult to learn due to discontinuities in their solutions. We us…
Generalized Adaptive Smoothing Using Matrix Completion for Traffic State Estimation
Chuhan Yang, Bilal Thonnam Thodi, Saif Eddin Jabari
The Adaptive Smoothing Method (ASM) is a data-driven approach for traffic state estimation. It interpolates unobserved traffic quantities by smoothing measurements along spatio-tem…
Learning Traffic Speed Dynamics from Visualizations
Bilal Thonnam Thodi, Zaid Saeed Khan, Saif Eddin Jabari +1
Space-time visualizations of macroscopic or microscopic traffic variables is a qualitative tool used by traffic engineers to understand and analyze different aspects of road traffi…
Incorporating Kinematic Wave Theory into a Deep Learning Method for High-Resolution Traffic Speed Estimation
Bilal Thonnam Thodi, Zaid Saeed Khan, Saif Eddin Jabari +1
We propose a kinematic wave-based Deep Convolutional Neural Network (Deep CNN) to estimate high-resolution traffic speed fields from sparse probe vehicle trajectories. We introduce…
Noticeability Versus Impact in Traffic Signal Tampering
Bilal Thonnam Thodi, Timothy Mulumba, Saif Eddin Jabari
This paper investigates the vulnerability of urban traffic networks to cyber-attacks on traffic lights. We model traffic signal tampering as a bi-objective optimization problem tha…
Traffic Data Imputation using Deep Convolutional Neural Networks
Ouafa Benkraouda, Bilal Thonnam Thodi, Hwasoo Yeo +2
We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, w…