7 citations · 21 across the 6 of their papers we have counts for
6 papers
Urban traffic analysis and forecasting through shared Koopman eigenmodes
Chuhan Yang, Fares B. Mehouachi, Monica Menendez +1
Predicting traffic flow in data-scarce cities is challenging due to limited historical data. To address this, we leverage transfer learning by identifying periodic patterns common…
On the Role of Non-Localities in Fundamental Diagram Estimation
Jing Liu, Fangfang Zheng, Boxi Yu +1
We consider the role of non-localities in speed-density data used to fit fundamental diagrams from vehicle trajectories. We demonstrate that the use of anticipated densities result…
Generalized adaptive smoothing based neural network architecture for traffic state estimation
Chuhan Yang, Sai Venkata Ramana Ambadipudi, Saif Eddin Jabari
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 gene…
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…
Short-Term Traffic Forecasting Using High-Resolution Traffic Data
Wenqing Li, Chuhan Yang, Saif Eddin Jabari
This paper develops a data-driven toolkit for traffic forecasting using high-resolution (a.k.a. event-based) traffic data. This is the raw data obtained from fixed sensors in urban…
Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning
Wenqing Li, Chuhan Yang, Saif Eddin Jabari
This paper addresses the problem of short-term traffic prediction for signalized traffic operations management. Specifically, we focus on predicting sensor states in high-resolutio…