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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ndltd-TW-106NCU050151132019-09-12T03:37:43Z http://ndltd.ncl.edu.tw/handle/x944j6 Functional Mixture Prediction Model for Passenger Flows at MRT Stations 應用函數混合模型預測捷運車站運量 YANG, BO-JUN 楊博鈞 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. 陳惠國 2018 學位論文 ; thesis 35 en_US |
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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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陳惠國 |
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陳惠國 YANG, BO-JUN 楊博鈞 |
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YANG, BO-JUN 楊博鈞 |
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YANG, BO-JUN 楊博鈞 Functional Mixture Prediction Model for Passenger Flows at MRT Stations |
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YANG, BO-JUN |
title |
Functional Mixture Prediction Model for Passenger Flows at MRT Stations |
title_short |
Functional Mixture Prediction Model for Passenger Flows at MRT Stations |
title_full |
Functional Mixture Prediction Model for Passenger Flows at MRT Stations |
title_fullStr |
Functional Mixture Prediction Model for Passenger Flows at MRT Stations |
title_full_unstemmed |
Functional Mixture Prediction Model for Passenger Flows at MRT Stations |
title_sort |
functional mixture prediction model for passenger flows at mrt stations |
publishDate |
2018 |
url |
http://ndltd.ncl.edu.tw/handle/x944j6 |
work_keys_str_mv |
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