Influence of Sequence Length and Geographic Representation on Optimal Prediction Architectures for Stolen Vehicle Geolocation
DOI:
https://doi.org/10.5753/jisa.2025.5187Keywords:
Vehicle Prediction, External Sensor Trajectory, XGBoost, LSTM, Transformer and LSTM Entangled, TLE, Geographic Representation, Historical Sequence Length, Optimal Architecture ShiftAbstract
When predicting the next geolocation of a stolen vehicle using external sensor data, such as speed radars, the challenge extends beyond the prediction itself to include determining the most suitable prediction architecture. While existing studies provide data that influence prediction performance, there is no consensus on the optimal architecture. Therefore, adopting a broader perspective to identify key criteria influencing the choice of architecture is essential. This study evaluates the shift in the optimal architecture depending on the length of the historical sequence and the format of geographic representation. The results reveal a shift in the optimal architecture, with the shift point being influenced by the type of geographic representation.
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