Predicting the Number of Passengers in Public Transportation Areas Using the Deep Learning Model LSTM
Accurate predictions of the number of public transport passengers on buses in each region are crucial for operations. They are required by the planning and management authority for bus public transport. A deep learning-based LSTM prediction model is proposed to predict the number of passengers in 4...
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| Format: | Article |
| Language: | English |
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Udayana University, Institute for Research and Community Services
2025-01-01
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| Series: | Lontar Komputer |
| Online Access: | https://ojs.unud.ac.id/index.php/lontar/article/view/112651 |
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| author | Joko Siswanto Sri Yulianto Joko Prasetyo Sutarto Wijono Evi Maria Untung Rahardja |
| author_facet | Joko Siswanto Sri Yulianto Joko Prasetyo Sutarto Wijono Evi Maria Untung Rahardja |
| author_sort | Joko Siswanto |
| collection | DOAJ |
| description | Accurate predictions of the number of public transport passengers on buses in each region are crucial for operations. They are required by the planning and management authority for bus public transport. A deep learning-based LSTM prediction model is proposed to predict the number of passengers in 4 bus public transportation areas (central, north, south, and west), evaluated by MSLE, MAPE, and SMAPE with dropout, neuron, and train-test variations. The CSV dataset obtained from Auckland Transport(AT) New Zealand metro patronage report on bus performance(1/01/2019-31/07/2023) is used for evaluation. The best prediction model was obtained from the lowest evaluation value and relatively fast time with a dropout of 0.2, 32 neurons, and train-test 80-20. The prediction model on training and testing data improves with the suitability of tuning for four predictions for the next 12 months with mutual fluctuations. The strong negative correlation is central-south, while the strong positive correlation is north-west. Predictions are less closely interconnected and dependent, namely central-south. With its potential to significantly impact policy-making, this prediction model can increase public transport mobility in each region, leading to a more efficient and accessible public transport system and ultimately enhancing the public's daily lives. This research has practical implications for public transport authorities, as it can guide them in making informed decisions about service planning and resource allocation. |
| format | Article |
| id | doaj-art-0083de00dd5a432296faf5930c44185e |
| institution | Kabale University |
| issn | 2088-1541 2541-5832 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Udayana University, Institute for Research and Community Services |
| record_format | Article |
| series | Lontar Komputer |
| spelling | doaj-art-0083de00dd5a432296faf5930c44185e2025-01-31T23:56:26ZengUdayana University, Institute for Research and Community ServicesLontar Komputer2088-15412541-58322025-01-01150317318510.24843/LKJITI.2024.v15.i03.p03112651Predicting the Number of Passengers in Public Transportation Areas Using the Deep Learning Model LSTMJoko Siswanto0Sri Yulianto Joko Prasetyo1Sutarto Wijono2Evi Maria3Untung Rahardja4Politeknik Keselamatan Transportasi JalanFaculty of Information Technology, Satya Wacana Christian UniversityFaculty of Information Technology, Satya Wacana Christian UniversityFaculty of Information Technology, Satya Wacana Christian UniversityFaculty of Science and Technology, University of RaharjaAccurate predictions of the number of public transport passengers on buses in each region are crucial for operations. They are required by the planning and management authority for bus public transport. A deep learning-based LSTM prediction model is proposed to predict the number of passengers in 4 bus public transportation areas (central, north, south, and west), evaluated by MSLE, MAPE, and SMAPE with dropout, neuron, and train-test variations. The CSV dataset obtained from Auckland Transport(AT) New Zealand metro patronage report on bus performance(1/01/2019-31/07/2023) is used for evaluation. The best prediction model was obtained from the lowest evaluation value and relatively fast time with a dropout of 0.2, 32 neurons, and train-test 80-20. The prediction model on training and testing data improves with the suitability of tuning for four predictions for the next 12 months with mutual fluctuations. The strong negative correlation is central-south, while the strong positive correlation is north-west. Predictions are less closely interconnected and dependent, namely central-south. With its potential to significantly impact policy-making, this prediction model can increase public transport mobility in each region, leading to a more efficient and accessible public transport system and ultimately enhancing the public's daily lives. This research has practical implications for public transport authorities, as it can guide them in making informed decisions about service planning and resource allocation.https://ojs.unud.ac.id/index.php/lontar/article/view/112651 |
| spellingShingle | Joko Siswanto Sri Yulianto Joko Prasetyo Sutarto Wijono Evi Maria Untung Rahardja Predicting the Number of Passengers in Public Transportation Areas Using the Deep Learning Model LSTM Lontar Komputer |
| title | Predicting the Number of Passengers in Public Transportation Areas Using the Deep Learning Model LSTM |
| title_full | Predicting the Number of Passengers in Public Transportation Areas Using the Deep Learning Model LSTM |
| title_fullStr | Predicting the Number of Passengers in Public Transportation Areas Using the Deep Learning Model LSTM |
| title_full_unstemmed | Predicting the Number of Passengers in Public Transportation Areas Using the Deep Learning Model LSTM |
| title_short | Predicting the Number of Passengers in Public Transportation Areas Using the Deep Learning Model LSTM |
| title_sort | predicting the number of passengers in public transportation areas using the deep learning model lstm |
| url | https://ojs.unud.ac.id/index.php/lontar/article/view/112651 |
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