Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators
The bus public transportation system has low reliability and ability to predict the number of passengers. The accuracy of predicting the number of passengers by public transport bus operators is still weak, which results in failure to implement solutions by operators. A prediction model with LSTM ba...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Department of Informatics, UIN Sunan Gunung Djati Bandung
2024-04-01
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| Series: | JOIN: Jurnal Online Informatika |
| Subjects: | |
| Online Access: | https://join.if.uinsgd.ac.id/index.php/join/article/view/1245 |
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| Summary: | The bus public transportation system has low reliability and ability to predict the number of passengers. The accuracy of predicting the number of passengers by public transport bus operators is still weak, which results in failure to implement solutions by operators. A prediction model with LSTM based on deep learning is proposed to predict passengers for 4 bus public transportation operators (Go Bus, New Zealand Bus, Pavlovich, and Ritchies) which are evaluated by MSLE, MAPE, and SMAPE with variations in epoch, batch size, and neurons. The dataset is a CSV performance report on Auckland Transport (AT) New Zealand metro patronage buses (01/01/2019-07/31/2023). The best prediction model was obtained from the lowest evaluation value and relatively fast time at variations of epoch 60, batch size 16, and neurons 32. The prediction results on training and testing data improved with the suitability of the model tuning. The proposed prediction model performs predictions 12 months later for 4 predictions simultaneously with predicted fluctuations occurring simultaneously. Strong negative correlation on New Zealand Bus-Pavlovich, strong positive correlation on Go Bus with Ritchies and Pavlovich. Predictions that are less closely related and dependent are New Zealand Bus against Go Bus, Pavlovich, and Ritchies. The proposed prediction modeling can be used as a basis for creating operator policies and strategies to deal with passenger fluctuations and for the development of new prediction models. |
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| ISSN: | 2528-1682 2527-9165 |