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All Journal Jurnal Ilmiah Psikologi Terapan Astonjadro Automotive Experiences Jurnal Konstruksi dan Infrastruktur : Teknik Sipil dan Perencanaan Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi
Ainun Rahmawati
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Mechanical Engineering Social Sciences Transportation

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Journal : Automotive Experiences

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Short-Term Prediction of Bus Station Fleet Number Using a Combination of BiLSTM Models Siswanto, Joko; Rahmawati, Ainun; Rahardja, Untung; Putra, Nanda Dwi; Hakim, Muhammad Iman Nur; Pinandita, Tito; Prasetyo, Ilham Bagus
Automotive Experiences Vol 8 No 1 (2025)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.13402

Abstract

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, BiLSTM-CNN, BiLSTM-Transformer, BiLSTM-Informer, and BiLSTM-Reformer models. The dataset used is in the form of a CSV consisting of 6 types of arrivals and departures of the Giwangan City Yogyakarta type A bus station fleet from 01/01/2021 to 09/30/2023. The best prediction model was found in BiLSTM-Transformers based on a MAPE value of 0.2211 with a relatively fast time (00:00:52) compared to BiLSTM, BiLSTM-CNN, BiLSTM-Informer, and BiLSTM-Reformer. The BiLSTM-Transformer model can short-term predict 6 types of fleet arrivals and departures at the bus station in the next 30 days. The peak of the bar and curve is at 0 which means the proposed prediction model is very accurate. There is 1 strong positive correlation, 2 weak positive correlations, 2 strong negative correlations, 8 weak negative ones, and 2 uncorrelated ones. Prediction results can be used to support short-term decision making in fleet planning and management based on the dynamics of community mobility.
Co-Authors Barnard, Yvonne Benny Hamdi Rhoma Putra Danang Parikesit Dewanti Dewanti Faizal, Ar Rofiqi Mohamad Joko Siswanto Lestari, Astri Muhammad Iman Nur Hakim Prasetyo, Ilham Bagus Putra, Nanda Dwi Siti Shofiah Timms, Paul Tito Pinandita Untung Rahardja Yuni Nurhamida