博碩士論文 104322082 詳細資訊

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姓名 郭豐瑋(Feng-Wei Guo)  查詢紙本館藏   畢業系所 土木工程學系
論文名稱 高速公路旅行時間預測之研究--函數資料分析之應用
(Travel Time Prediction on Freeways -- Application of Functional Data Analysis)
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摘要(中) 旅行時間預測在智慧運輸系統中是相當重要的,本研究使用函數資料分析方法進行分析及預測,以函數型混合預測模型作為研究的主要框架,可分成三部分; (1) 函數資料分群 (2) 函數資料機率分類 (3) 函數型線性迴歸模型。
本研究利用台灣高速公路局網站中的 ETC TDCS_M04A 資料進行驗證,以國道1號01F0155S (東湖)至01F0880S (竹北) 路段為研究範圍,資料經過處理後,範圍有80天(從2016/0901至2016/11/30,包含57天平日和23天假日)。
初步的結果顯示最好的預測時區組合為以3段已知時間段及2段預測時間段(ω=3, ν=2),預測之MAPE為7.26。這說明函數資料分析方法在高速公路旅行時間預測上是有如同Chiou (2012)預測交通流量的結果。此外,函數資料分析方法能夠輕易地分析長期追蹤型態資料,尤其是在分群部分,能夠應用在其他方法上,如:希爾伯特-黃轉換(HHT),增加旅行時間預測的精準度。
This research adopts a functional data analysis method that is mainly based on a mixture prediction method to analyze and predict travel times; such analysis and prediction constitute an essential component in Intelligent Transportation Systems applications. The mixture prediction method is developed through three major modules, i.e., functional clustering for historical functional travel time patterns, probabilistic functional classification for newly observed travel time trajectories, and linear regression model fitting for travel time prediction.
The research framework was demonstrated with data on 80 days of Electronic Toll Collection (ETC) travel times retrieved from the website under the database TDCS_M04A constructed between interchanges 01F0155S (Donghu) and 01F0880S (Chupei) on Taiwan Area National Freeway Bureau of Republic of China’s Ministry of Transportation and Communications. The demonstration encompassed 57 weekdays and 23 holidays from 2016/09/01 to 2016/11/30.
The preliminary result shows the best combination of observed time (ω) and unobserved (ν) time occurred at (ω=3, ν=2) with mean absolute percentage error (MAPE) equal to 7.26 and the usefulness of functional data analysis in analyzing and predicting the travel time trajectories on freeways is supported, similar to results for the traffic flow trajectories (Chiou, 2012). However, intensive research on different combination of (ω,ν) under various traffic conditions must be performed before a firm conclusion can be reached. Moreover, the merit of the functional data analysis, particularly the functional clustering method, can be readily employed by other “decomposition” type methods, such as Hilbert-Hwang Transform (HHT), to enhance their accuracy in prediction of travel times.
關鍵字(中) ★ 函數資料分析
★ 函數分群法
★ 事後群集隸屬度機率
★ 函數混合預測模型
★ ETC 旅行時間資料
關鍵字(英) ★ functional data analysis
★ functional clustering
★ posterior cluster membership probability
★ functional mixture prediction model
★ electronic toll collection travel times
Abstract i
中文摘要 ii
致謝 iii
Table of contents iv
List of Figures v
List of tables vi
1. Introduction 1
2. Literature Review 3
3. Research framework using functional data analysis 5
3.1 Functional clustering for historical functional travel time patterns 5
3.1.1 Initial clustering with functional component analysis 5
3.1.2 Iterative reclassification for probabilistic functional travel time trajectories 6 Determination of number of principal components in each cluster 7 Calculation of membership for each travel time trajectory in each cluster 8
3.1.3 Functional clustering steps for historical travel time patterns 11
3.2 Probabilistic functional classification for newly observed travel time trajectories 11
3.3 Linear regression model fitting for travel time prediction 12
3.3.1 Functional linear regression for travel time trajectory 13
3.3.2 Parameter estimation for functional mixture prediction models 14
3.3.3 Functional linear prediction model for future travel times 15
3.4 Algorithm for functional mixture prediction 15
4. Numerical results 17
4.1 Data collection and aggregation 17
4.2 Travel time prediction results 20
4.2.1 Travel time patterns 21
4.2.2 Functional mean 22
4.2.3 Eigenfunction 24
4.2.4 Covariance matrix 25
4.3 Travel time prediction results 26
4.3.1 Posterior probability corresponding to each cluster 26
4.3.2 Travel time prediction errors 27
5. Conclusions 31
References 33
Appendix A: Parameter estimating procedure 35

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指導教授 陳惠國(Huey-Kuo Chen) 審核日期 2017-7-31
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