應用函數混合模型預測捷運車站運量

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姓名

楊博鈞(YANG, BO-JUN) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

應用函數混合模型預測捷運車站運量
(Functional Mixture Prediction Model for Passenger Flows at MRT Stations)

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摘要(中)

交通流量對於系統管理者是一個相當重要的依據，本研究嘗試將函數資料分析方法應用到運量預測。其主要研究框架函數型混合預測模型可分成三部分: (1) 函數型資料分群； (2) 函數資料隸屬度分類； (3) 函數型簡單迴歸模型； (4) 混合預測模型。
本研究使用單一路線之捷運站點進出人數作為分析資料，經過資料清洗後共計363天(從2017年4月至2018年3月)。
其結果顯示最好的預測時區在以14個已知時點(τ=14)，預測之CMAPE為12.68%，可提供經營者作為人力指派或是否進行旅客疏導之參考數據。

摘要(英)

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.

關鍵字(中)

★ 函數型資料分析

關鍵字(英)

★ FDA

論文目次

1. INTRODUCTION pp.1
2. LITERATURE REVIEW pp.2
3. RESEARCH FRAMEWORK pp.3
3.1 Functional Clustering for Historical Data pp.3
3.1.1 Initial clustering with functional component analysis pp.3
3.1.2 Determination of the cluster numbers pp.4
3.2 Probabilistic Functional Classification for Newly Data pp.5
3.3 Linear Regression for Prediction of Traffic Counts pp.6
3.3.1 Functional linear regression for passenger flows trajectory pp.7
3.3.2 Parameter estimation for functional mixture prediction models pp.7
3.4 Functional Mixture Prediction Model for Passenger Flows pp.8
3.5 Algorithm of Functional Mixture Prediction pp.9
4. CASE STUDY pp.9
4.1 Data pp.10
4.2 Historical Passenger Flow Patterns pp.10
4.3 The Mean Function pp.13
4.4 Variance and Covariance pp.15
4.5 Eigenvector pp.16
4.6 The Prediction Results pp.18
4.6.1 Posterior probability related each cluster pp.18
4.6.2 Traffic counts prediction errors pp.19
4.6.3 Comparison of results pp.22
5. ASSUMPTION pp.23
5.1 Clustering with Intrinsic Mode Functions pp.23
5.2 Increase Data Points pp.24
6. CONCLUSIONS AND SUGGESTIONS pp.24
References pp.26

參考文獻

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指導教授

陳惠國

審核日期

2018-8-22

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