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研究生： 楊博鈞
YANG, BO-JUN
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
指導教授： 陳惠國
口試委員:
學位類別： 碩士
Master
系所名稱： 工學院 - 土木工程學系
Department of Civil Engineering
論文出版年： 2018
畢業學年度： 106
語文別： 英文
論文頁數： 35
中文關鍵詞： 函數型資料分析
外文關鍵詞： FDA
相關次數： 點閱：14  下載：0 
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    • 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。

    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.

    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

    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, 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, pp. 1119-1133.
    Chiou, J. M., Li, P. L., 2008. Correlation-based functional clustering via subspace projection. Journal of the American Statistical Association, Vol. 103, No. 484, pp. 990–999.
    Efron, B., Tibshirani, R., 1993. An Introduction to the Bootstrap. Chapman and Hall/CRC, London.
    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.
    Joe H. Ward Jr., 1963. Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association. Volume 58, Issue 301, pp. 236-244.
    Li, P. L., and Chiou, J. M., 2011. Identifying cluster number for subspace projected functional data clustering. Computational Statistics and Data Analysis, Vol. 55, pp. 2090-2103.
    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.
    MacQueen, JB. Some methods for classification and analysis of multivariate observations. Mathematical Statistics and Probability, Vol. 1, No. 14, 1967, pp. 281-297.
    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.

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