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
Cruise dynamic pricing based on SARSA algorithm
Estimating the effect of biofouling on ship shaft power based on sensor measurements
Prediction of extent of damage in vehicle during crash using improved XGBoost model
Method for automated detection of outliers in crash simulations
Incorporating congestion patterns into spatio-temporal deep learning algorithms
Driver steering and muscle activity during a lane-change manoeuvre
Predicting incident duration using random forests
Arterial corridor travel time prediction under non-recurring conditions
Assessing influential factors for lane change behavior using full real-world vehicle-by-vehicle data
DLW-Net model for traffic flow prediction under adverse weather
Estimating cycle-level real-time traffic movements at signalized intersections
Probabilistic traffic breakdown forecasting through Bayesian approximation using variational LSTMs
Characterizing parking systems from sensor data through a data-driven approach