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Traffic speed prediction for intelligent transportation system based on a deep feature fusion model
Machine learning algorithms in ship design optimization
A robust machine learning structure for driving events recognition using smartphone motion sensors
Learning Drivers’ Behavior to Improve Adaptive Cruise Control
An automatic methodology to measure drivers’ behavior in public transport
Using reinforcement learning to minimize taxi idle times
Using kinematic driving data to detect sleep apnea treatment adherence
Estimating the effect of biofouling on ship shaft power based on sensor measurements
Traffic Safety at German Roundabouts—A Replication Study
Arterial corridor travel time prediction under non-recurring conditions
Estimating cycle-level real-time traffic movements at signalized intersections
Unobtrusive, natural support control of an adaptive industrial exoskeleton using force myography
Multimode trip information detection using personal trajectory data
Driver’s black box: a system for driver risk assessment using machine learning and fuzzy logic
Prediction of fatigue failure in small-scale butt-welded joints with explainable machine learning
Identifying traffic conditions from non-traffic related sources
A human-centric machine learning based personalized route choice prediction in navigation systems
Drowsy behavior detection based on driving information
Propeller optimization by interactive genetic algorithms and machine learning
Development of Indoor Wear Test Method for Passenger Car Tires Reflecting Road Driving Conditions
Road Type Identification Ahead of the Tire Using D-CNN and Reflected Ultrasonic Signals
Prediction of Driver’s Drowsiness Using Machine Learning Algorithms for Minimal Risk Condition
Filtered Integral Formulation of the Sparse Model Identification Problem
SHapley Additive exPlanations for Explaining Artificial Neural Network Based Mode Choice Models
Machine-Learning-Assisted Optimization of Aircraft Trajectories Under Realistic Constraints
Simulation-driven design of a fast monohull
Study on CO2 Emission Assessment of Heavy-Duty and Ultra-Heavy-Duty Vehicles Using Machine Learning
Supervised vs. Unsupervised Learning for Operator State Modeling in Unmanned Vehicle Settings