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: A secondary feature where a self-attention model assigns weights to these extracted features to prioritize the most relevant data points for predicting future longitude and latitude.

The core module of this model architecture focuses on processing subtrajectories to capture complex movement patterns: sc23026-WWv176447.part1.rar

: Analyzes the timing and duration of movements within the sliding window. : A secondary feature where a self-attention model

: Provides a higher level of motion analysis beyond simple 2D coordinates. sc23026-WWv176447.part1.rar

A primary "deep feature" of this system is its ability to extract from trajectory data through a dedicated representation layer. Key Deep Feature: Multi-Dimensional Extraction

: Captures the geometric and geographic positioning of a path.

A trajectory prediction model based on deep feature representation