Fuzzy temporal logic based railway passenger flow forecast model
Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2014 |
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| Erschienen: |
2014 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2014 |
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| Enthalten in: |
Computational intelligence and neuroscience - 2014(2014) vom: 01., Seite 950371 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Dou, Fei [VerfasserIn] |
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| Anmerkungen: |
Date Completed 07.07.2015 Date Revised 01.12.2014 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1155/2014/950371 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models | ||
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| 700 | 1 | |a Xu, Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Yakun |e verfasserin |4 aut | |
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