高速公路旅行時間預測之研究--函數資料分析之應用

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：32

、訪客IP：18.191.154.132

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)

相關論文

★ 圖書館系統通閱移送書籍之車輛途程問題	★ 起迄對旅行時間目標下高速公路匝道儀控之研究
★ 結合限制規劃法與螞蟻演算法求解運動排程問題	★ 共同邊界資料包絡分析法在運輸業之應用-以國內航線之經營效率為例
★ 雙北市公車乘客知覺服務品質、知覺價值、滿意度、行為意向路線與乘客之跨層次中介效果與調節式中介效果	★ Investigating the influential factors of public bicycle system and cyclist heterogeneity
★ A Mixed Integer Programming Formulation for the Three-Dimensional Unit Load Device Packing Problem	★ Behavior Intention and its Influential Factors for Motorcycle Express Service
★ Inferring transportation modes (bus or vehicle) from mobile phone data using support vector machine and deep neural network.	★ 混合羅吉特模型於運具選擇之應用-以中央大學到桃園高鐵站為例
★ Preprocessing of mobile phone signal data for vehicle mode identification using map-matching technique	★ 含額外限制式動態用路人均衡模型之研究
★ 動態起迄旅次矩陣推估模型之研究	★ 動態號誌時制控制模型求解演算法之研究
★ 不同決策變數下動態用路人均衡路徑選擇模型之研究	★ 動態人口分布最佳化控制之研究-雙層規劃模型之應用

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

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。

摘要(英)

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

論文目次

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
3.1.2.1 Determination of number of principal components in each cluster 7
3.1.2.2 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

參考文獻

Anderson, T. K., 2009. Kernel density estimation and k-means clustering to profile road accident hotspots. Accident Analysis & Prevention, Vol. 41, Issue. 3, pp. 359-364.
Caceres, N., Romero, L. M., Benitez, F. G., 2012. Estimating traffic flow profiles according to a relative attractiveness factor. Procedia - Social and Behavioral Sciences, Vol. 54, pp. 1115-1124.
Chen, H. K., 2015. Travel time prediction for time-table-based vehicles traveling on known routes. Journal of the Eastern Asia Society for Transportation Studies, Vol. 11, pp. 1082-1096.
Chen, H. K., Wu, C. J., 2012. Travel time prediction using empirical mode decomposition and gray theory: Example of National Central University bus in Taiwan. Transportation Research Record: Journal of the Transportation Research Board, 2324, 11-19.
Chiou, J. M., 2012. Dynamical functional prediction and classification, with application to traffic flow prediction. The Annals of Applied Statistics, Vol. 6, No. 4, pp. 1588-1614.
Chiou, J. M., Li, P. L., 2007. Functional clustering and identifying substructures of longitudinal data. Journal of the Royal Statistical Society. Series B, Statistical Methodology, Vol. 69, No. 4, pp. 679-699.
Chiou, J. M., Müller, H. G., Wang, J. L., and Carey, J. R., 2003. A functional multiplicative effects model for longitudinal data, with application to reproductive histories of female medflies. Statistica Sinica, Vol. 13, No. 4, pp. 1119-1133.
Chiou, Y. C., 2006. Grey prediction models for freeway short-term traffic information.
Feng Chia University, MA, Taiwan.
Dai, X., Hadjipantelis, P. Z, Ji, H., Mueller, H.G., Wang, J. L., 2017. fdapace: Functional Data Analysis and Empirical Dynamics. R package version 0.3.0: CRAN https://github.com/functionaldata/tPACE/ (Accessed 25 Jan 2017)
Fan, J., 1993. Local linear regression smoothers and their minimax efficiencies. The Annals of Applied Statistics, Vol. 21, No. 1, pp. 196- 216.
Gaweł, P., Jaszkiewicza, A., 2011. Improving short-term travel time prediction for on-line car navigation by linearly transforming historical traffic patterns to fit the current traffic conditions. Procedia Social and Behavioral Sciences, Vol. 20, pp. 638-647.
Gurmu, Z. K., Nall, T., Fan, W., 2014. Artificial neural network travel time prediction model for buses using only GPS data. Journal of Public Transportation, Vol. 17, No. 2, pp. 45-65.
Guin, A., 2006. Travel time prediction using a seasonal autoregressive integrated moving average time series model. Proceedings of the 9th IEEE International Conference on Intelligence Transportation Systems, pp. 493-498.
Li, P. L., 2006. Clustering for longitudinal data. National Tsing Hua University, Ph.D, Taiwan.
Li, P. L., and Chiou, J. M., 2011. Identifying cluster number for subspace projected functional data clustering. Computational Statistics and Data Analysis, Vol. 55,No. 6, pp. 2090-2103.
Lewis, C. D., 1982. Industrial and business forecasting methods: A partial guide to exponential smoothing and curve fitting. Butterworth Scientific, London.
MacQueen, J. B., 1967. Some methods for classiﬁcation and analysis of multivariate observations. Mathematical Statistics and Probability, Vol. 1,No. 14, pp. 281–297.
Marzouk, M., Moselhi, O., 2004. Fuzzy clustering model estimating Haulers’ travel time. Journal of Construction Engineering and Management, Vol. 130, No. 3, pp. 878-886.
McCullah, P., and Nelder, J. A., 1983. Generalized Linear Models. Chapman & Hall, London.
MacQueen, J. B. (1967) Some methods for classiﬁcation and analysis of multivariate observations. In Proc. 5th
Berkeley Symp. Mathematical Statistics and Probability, vol. 1, pp. 281–297. Berkeley: University of California
Press
Müller, H. G., Chiou, J. M., and Leng, X., 2008. Inferring gene expression dynamics via functional regression analysis. BMC Bioinformatics, Vol. 9, No. 60, pp. 1-20.
Nath, R. P. D., Lee, H. J., Chowdhury, N. K., & Chang, J. W., 2010. Modified K-means clustering for travel time prediction based on historical traffic data. International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (pp. 511-521). Springer Berlin Heidelberg.
Ramsay, J. O., and Silverman, B. W., 1997. Functional Data Analysis. Springer, New York.
Rice, J., and Zwet, E. V., 2004. A simple and effective method for predicting
travel times on freeways. IEEE Intelligent Transportation Systems Society, Vol. 5, No. 3, pp. 200 - 207.
Taiwan Area National Freeway Bureau, 2005. Traffic Data Collection System, TDCS.
Ward, J. H., 1963. Hierarchical groupings to optimize an objective function. Journal of the American Statistical Association, Vol. 58, pp. 236-244.
Yao, F., Müller, H. G., and Wang, J. L., 2005a. Functional data analysis for sparse longitudinal data. Journal of the American Statistical Association, Vol. 100, No. 470, pp. 577-590.
Yao, F., Müller, H. G., and Wang, J. L. 2005b. Functional linear regression analysis for longitudinal data. The Annals of Statistics, Vol. 33, No. 6, pp. 2873-2903.

指導教授

陳惠國(Huey-Kuo Chen)

審核日期

2017-7-31

推文