Abstract :

Trajectory Prediction Has Become A Crucial Task In Various Domains Such As Autonomous Driving, Robotics, Surveillance, And Human Activity Analysis, Where Anticipating Future Movement Patterns Is Essential For Decision-making And Safety. This Project Explores Trajectory Prediction Using Machine Learning Methods To Model And Forecast The Future Positions Of Moving Objects Based On Historical Data. Traditional Approaches Often Rely On Rule-based Or Physics-based Models, Which Fail To Capture Complex And Dynamic Movement Behaviors In Real-world Environments. To Overcome These Limitations, The Proposed System Leverages Machine Learning Algorithms Capable Of Learning Temporal And Spatial Patterns From Trajectory Data. The Methodology Involves Collecting Sequential Movement Data, Preprocessing It To Remove Noise, And Transforming It Into Structured Formats Suitable For Training Models. Algorithms Such As Linear Regression, Support Vector Machines, And Recurrent Neural Networks (RNNs), Including Long Short-Term Memory (LSTM), Are Utilized To Capture Dependencies In Sequential Data. The Models Are Trained To Predict Future Coordinates Based On Past Trajectories, Enabling Accurate Forecasting Of Movement Paths. Experimental Results Demonstrate That Deep Learning Models, Particularly LSTM, Outperform Traditional Machine Learning Methods In Handling Complex Temporal Dependencies And Nonlinear Patterns. However, Challenges Such As Data Sparsity, Variability In Movement Patterns, And Computational Complexity Remain. The System Shows Promising Performance In Applications Such As Pedestrian Tracking, Vehicle Navigation, And Crowd Behavior Analysis

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

2014-2020

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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