Functional Mixture Prediction Model for Passenger Flows at MRT Stations
碩士 === 國立中央大學 === 土木工程學系 === 106 === Traffic flow is important for traffic engineers. This study attempts to apply functional data analysis to passenger flows forecasting. The main research framework, the mixture prediction method, can be divided into three parts: (1) functional data clustering; (2)...
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| Format: | Others |
| Language: | en_US |
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2018
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| Online Access: | http://ndltd.ncl.edu.tw/handle/x944j6 |
| Summary: | 碩士 === 國立中央大學 === 土木工程學系 === 106 === Traffic flow is important for traffic engineers. This study attempts to apply functional data analysis to passenger flows forecasting. The main research framework, the mixture prediction method, can be divided into three parts: (1) functional data clustering; (2) functional data membership classification; (3) functional simple regression model; (4) mixture prediction method.
In this study, the number of people entering and leaving the MRT station on a single route was used as analytical data, and the data was cleaned for a total of 363 days (from April 2017 to March 2018).
The results show that the best predicted time zone is at 14 known time points (τ = 14) and the predicted CMAPE is 12.68%, it is enough to provide operators to assess whether or not to implement regulatory measures as a reference.
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