Seattle, WA 98195
United States
University of Washington
Data poisoning attack, Transferability, Graph Neural Network, traffic forecasting
Data poisoning attacks, Traffic State Estimation and Prediction, Lipschitz analysis, Implicit function theorem
Connected and automated vehicles, Multiscale control, Mixed traffic flow
infrastructure-enabled, data poisoning attack, traffic state estimation and prediction, defense
impact evaluation, scenario-based, COVID-19, road network, transit
Data poisoning attack, Lipschitz Continuity , Intelligent Transportation Systems
Multimodal Transportation Data, Generative AI Models, Intelligent Transportation System, AI Foundation Model
Dynamic Traffic Assignment (DTA), Physics-Informed Deep Learning (PIDL), Neural Networks (NN), Data-Driven Traffic Assignment
Connected and automated vehicles, Multi-scale modeling and control, Signal-vehicle coupled control, Model predictive control, Stability analysis
big mobility data, public transit, COVID-19, data quality
Spatio-temporal traffic forecasting, time-series modeling, observation gaps, data fusion, traffic textual information
GPS spoofing, Infrastructure-enabled defense solution, Isolation forest, Roadside unit, Vehicle localization, Cybersecurity
Batch-based Vehicle-Tracking, Data Fusion and Information Integration, Mobile Sensing Data, Fixed-Location Data, Smart City
Keywords:Multimodal Signal Control, Connected and Automated Vehicles (CAVs), Active Transportation, Pedestrian and Cyclists, Vehicles with different energy types
