20 citations · 64 across the 22 of their papers we have counts for
6 papers · 1 filter
A Generic Graph Sparsification Framework using Deep Reinforcement Learning
Ryan Wickman, Xiaofei Zhang, Weizi Li
The interconnectedness and interdependence of modern graphs are growing ever more complex, causing enormous resources for processing, storage, communication, and decision-making of…
Network-wide Multi-step Traffic Volume Prediction using Graph Convolutional Gated Recurrent Neural Network
Lei Lin, Weizi Li, Lei Zhu
Accurate prediction of network-wide traffic conditions is essential for intelligent transportation systems. In the last decade, machine learning techniques have been widely used fo…
Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models
Bibek Poudel, Weizi Li
Traffic state prediction is necessary for many Intelligent Transportation Systems applications. Recent developments of the topic have focused on network-wide, multi-step prediction…
Analyzing Effects of The COVID-19 Pandemic on Road Traffic Safety: The Cases of New York City, Los Angeles, and Boston
Lahari Karadla, Weizi Li
The COVID-19 pandemic has resulted in significant social and economic impacts throughout the world. In addition to the health consequences, the impacts on traffic behaviors have al…
Learning to Control DC Motor for Micromobility in Real Time with Reinforcement Learning
Bibek Poudel, Thomas Watson, Weizi Li
Autonomous micromobility has been attracting the attention of researchers and practitioners in recent years. A key component of many micro-transport vehicles is the DC motor, a com…
Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images
Yu Shen, Laura Zheng, Manli Shu +3
For safety of autonomous driving, vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments. These external and environ…