Short-Term Prediction of Bus Station Fleet Number Using a Combination of BiLSTM Models

Short-Term Prediction of Bus Station Fleet Number Using a Combination of BiLSTM Models

Predicting the number of bus station fleets requires a holistic approach, using sophisticated data analysis techniques and appropriate predictive modeling. Short-term predictions of bus station fleet numbers are proposed based on the best MAPE evaluation values ​​from the comparison of the Bi-LSTM,...

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Bibliographic Details
Main Authors: Joko Siswanto, Ainun Rahmwati, Untung Rahardja, Nanda Dwi Putra, Muhammad Iman Nur Hakim, Tito Pinandita, Ilham Bagus Prasetyo
Format: Article
Language:English
Published: Universitas Muhammadiyah Magelang 2025-04-01
Series:Automotive Experiences
Subjects:
Bus station
Fleet
Short-term
Prediction
BiLSTM
Online Access:https://journal.unimma.ac.id/index.php/AutomotiveExperiences/article/view/13402
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https://journal.unimma.ac.id/index.php/AutomotiveExperiences/article/view/13402

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