Speed Deviation Pattern

Speed deviation patterns, encompassing variations in vehicle or object speed from expected or average values, are a focus of research across diverse fields. Current investigations utilize various machine learning models, including neural networks and quantile regression, to analyze speed data from diverse sources like GPS trackers, traffic sensors, and even in-vehicle data to understand these deviations. This research aims to improve traffic prediction, enhance safety systems (e.g., crash avoidance), and diagnose equipment malfunctions (e.g., train bearing failures) by better understanding the underlying causes and characteristics of speed variations. The insights gained have implications for intelligent transportation systems, predictive maintenance, and improved safety in various transportation modes.