Summary of Forecasting Ferry Passenger Flow Using Long-short Term Memory Neural Networks, by Daniel Fesalbon
Forecasting Ferry Passenger Flow Using Long-Short Term Memory Neural Networks
by Daniel Fesalbon
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study aims to investigate an LSTM-based Neural Networks’ capability to forecast ferry passengers at two ports in the Philippines. The proposed model is evaluated using monthly passenger traffic data from 2016 to 2022 acquired from the Philippine Ports Authority (PPA). Mean Absolute Percentage Error (MAPE) is used as the primary metric to evaluate the model’s forecasting capability. The LSTM-based Neural Networks model achieved 72% and 74% forecasting accuracy for Batangas and Mindoro port ferry passenger data, respectively. The study concludes that the presented LSTM model demonstrates reasonable forecasting performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how well a special kind of computer program called an LSTM-based Neural Network can predict how many people will take ferries between two ports in the Philippines. They used data from 2016 to 2022 and looked at how close their predictions were to what actually happened. They found that the program was pretty good, predicting correctly about 72% to 74% of the time. |
Keywords
» Artificial intelligence » Lstm » Neural network
