Machine Learning Baseed Trajectory Prediction
Abstract
This project presents a machine learningbased
approach for predicting the future trajectory
of users by analyzing their previous movement
patterns. Utilizing advanced algorithms such as
Long Short-Term Memory (LSTM) networks and
Sequence-to-Sequence (Seq2Seq) models, the
system aims to forecast the next location(s) of a
user based on historical GPS data. Accurate
location prediction has significant applications in
5G networks, where allocating the nearest cloud
resources to a user can drastically reduce latency
and enhance user experience. The Geolife dataset,
which consists of real-world GPS trajectories
including latitude, longitude, and user ID, was used
to train the model. Experimental results show that
the proposed method achieves high prediction
accuracy, with an LSTM-Seq2Seq model
outperforming traditional algorithms like GRU,
demonstrating lower mean squared error rates and
better trajectory forecasting performance.
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References
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