442 citations · 579 across the 20 of their papers we have counts for
21 papers
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…
Neural Process for Black-Box Model Optimization Under Bayesian Framework
Zhongkai Shangguan, Lei Lin, Wencheng Wu +1
There are a large number of optimization problems in physical models where the relationships between model parameters and outputs are unknown or hard to track. These models are nam…
Assessing Road Traffic Safety During COVID-19: Inequality, Irregularity, and Severity
Lei Lin, Feng Shi, Weizi Li
COVID-19 is affecting every social sector significantly, including human mobility and subsequently road traffic safety. In this study, we analyze the impact of the pandemic on traf…
Spatial-temporal Analysis of COVID-19's Impact on Human Mobility: the Case of the United States
Songhe Wang, Kangda Wei, Lei Lin +1
COVID-19 has been affecting every aspect of societal life including human mobility since December, 2019. In this paper, we study the impact of COVID-19 on human mobility patterns a…
Predicting Station-Level Bike-Sharing Demands Using Graph Convolutional Neural Network
Lei Lin, Weizi Li, Srinivas Peeta
This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations among stati…
MCFlow: Monte Carlo Flow Models for Data Imputation
Trevor W. Richardson, Wencheng Wu, Lei Lin +2
We consider the topic of data imputation, a foundational task in machine learning that addresses issues with missing data. To that end, we propose MCFlow, a deep framework for impu…