20 citations · 64 across the 22 of their papers we have counts for
22 papers
Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning
Michael Villarreal, Bibek Poudel, Weizi Li
The surge in Reinforcement Learning (RL) applications in Intelligent Transportation Systems (ITS) has contributed to its growth as well as highlighted key challenges. However, defi…
Long-term Microscopic Traffic Simulation with History-Masked Multi-agent Imitation Learning
Ke Guo, Wei Jing, Lingping Gao +3
A realistic long-term microscopic traffic simulator is necessary for understanding how microscopic changes affect traffic patterns at a larger scale. Traditional simulators that mo…
Efficient Quality-Diversity Optimization through Diverse Quality Species
Ryan Wickman, Bibek Poudel, Michael Villarreal +2
A prevalent limitation of optimizing over a single objective is that it can be misguided, becoming trapped in local optimum. This can be rectified by Quality-Diversity (QD) algorit…
Mixed Traffic Control and Coordination from Pixels
Michael Villarreal, Bibek Poudel, Jia Pan +1
Traffic congestion is a persistent problem in our society. Previous methods for traffic control have proven futile in alleviating current congestion levels leading researchers to e…
Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersections
Dawei Wang, Weizi Li, Lei Zhu +1
Intersections are essential road infrastructures for traffic in modern metropolises. However, they can also be the bottleneck of traffic flows as a result of traffic incidents or t…
Urban Socio-Technical Systems: An Autonomy and Mobility Perspective
Weizi Li
The future of the human race is urban. The world's population is projected to grow an additional 2.5 billion by 2050, with all expected to live in urban areas. This will increase t…
Understanding and Developing Equitable and Fair Transportation Systems
Weizi Li
The transportation system is an interplay between infrastructure, vehicles, and policy. During the past century, the rapid expansion of the road network, blended with increasing ve…
AutoJoin: Efficient Adversarial Training against Gradient-Free Perturbations for Robust Maneuvering via Denoising Autoencoder and Joint Learning
Michael Villarreal, Bibek Poudel, Ryan Wickman +2
With the growing use of machine learning algorithms and ubiquitous sensors, many `perception-to-control' systems are being developed and deployed. To ensure their trustworthiness,…
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…
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…
Simulation and Learning for Urban Mobility: City-scale Traffic Reconstruction and Autonomous Driving
Weizi Li
Traffic congestion has become one of the most critical issues worldwide. The costs due to traffic gridlock and jams are approximately $160 billion in the United States, more than £…
ADAPS: Autonomous Driving Via Principled Simulations
Weizi Li, David Wolinski, Ming C. Lin
Autonomous driving has gained significant advancements in recent years. However, obtaining a robust control policy for driving remains challenging as it requires training data from…
Capture and Recovery of Connected Vehicle Data: A Compressive Sensing Approach
Lei Lin, Weizi Li, Srinivas Peeta
Connected vehicles (CVs) can capture and transmit detailed data through vehicle-to-vehicle and vehicle-to-infrastructure communications, which bring new opportunities to improve th…
Estimating Traffic Conditions At Metropolitan Scale Using Traffic Flow Theory
Weizi Li, Meilei Jiang, Yaoyu Chen +1
The rapid urbanization and increasing traffic have serious social, economic, and environmental impact on metropolitan areas worldwide. It is of a great importance to understand the…
A Compressive Sensing Approach for Connected Vehicle Data Capture and Recovery and its Impact on Travel Time Estimation
Lei Lin, Weizi Li, Srinivas Peeta
Connected vehicles (CVs) can capture and transmit detailed data such as vehicle position and speed through vehicle-to-vehicle and vehicle-to-infrastructure communications. The weal…