Search Results for author:
Found 5 papers, 1 papers with code
Time-to-Green predictions for fully-actuated signal control systems with supervised learning
Recently, efforts have been made to standardize signal phase and timing (SPaT) messages.
Application of Modular Vehicle Technology to Mitigate Bus Bunching
We develop a simple bus-splitting strategy that directs a modular bus to decouple into individual units when it experiences a headway longer than a given threshold.
Learning Traffic Speed Dynamics from Visualizations
We present a deep learning method to learn the macroscopic traffic speed dynamics from these space-time visualizations, and demonstrate its application in the framework of traffic state estimation.
Incorporating Kinematic Wave Theory into a Deep Learning Method for High-Resolution Traffic Speed Estimation
The results demonstrate that anisotropic kernels significantly reduce model complexity and model over-fitting, and improve the physical correctness of the estimated speed fields.
Traffic Data Imputation using Deep Convolutional Neural Networks
We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information.
