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Method for automated detection of outliers in crash simulations
Framework for development of the Scheduler for Activities, Locations, and Travel (SALT) model
Integration of machine learning and statistical models for crash frequency modeling
Integrating data-driven and simulation models to predict traffic state affected by road incidents
GraphCast for solving the air transportation nexus among safety, efficiency, and resilience
Incorporating congestion patterns into spatio-temporal deep learning algorithms
Assessing influential factors for lane change behavior using full real-world vehicle-by-vehicle data
Predicting incident duration using random forests
Sharkeye: Real-Time Autonomous Personal Shark Alerting via Aerial Surveillance
Arterial corridor travel time prediction under non-recurring conditions
Estimating cycle-level real-time traffic movements at signalized intersections
Probabilistic traffic breakdown forecasting through Bayesian approximation using variational LSTMs
Driver Identification with OBD-II Public Data