Title page for 983202068


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Student Number 983202068
Author Wan-ling Tang(唐婉玲)
Author's Email Address No Public.
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Department Civil Engineering
Year 2010
Semester 2
Degree Master
Type of Document Master's Thesis
Language zh-TW.Big5 Chinese
Title A Study on the Installation Spacing of Vehicle Detectors on Road Section Using Data Imputation Based on Recurrent Neural Network
Date of Defense 2011-06-17
Page Count 107
Keyword
  • data imputation
  • installation spacing of vehicle detectors
  • K-means
  • recurrent neural netwok
  • Abstract The main purpose of this study is to use the concept of missing value treatment to investigate the maximum installation spacing of vehicle detectors on road sections. Assuming a vehicle detector undergoes data loss, data error or transmission distortion, we supplement its traffic data with those from its up and downstream detectors.By means of performance assessment, we identify the farthest effective detectors for supplement, and, hence, conclude the maximum possible installation spacing according to the distance between them.
    An empirical analysis for the missing value of vehicle detectors in Hshehshan tunnel, we, clustered all the data into groups using K-means, and then chose to recurrent neural network impute the missing data.We, finally, developed two possible applications based on imputation performance, including data imputation and installation spacing of vehicle detectors.The result shows that we could extend the current spacing of 350 m to 11,900 m by an accuracy of over 80%.
    Table of Content 摘要i
    Abstractii
    誌謝iii
    目錄iv
    圖目錄vi
    表目錄ix
    第一章緒論1
    1.1研究動機1
    1.2研究目的1
    1.3研究範圍2
    1.4研究方法2
    1.5研究流程3
    第二章文獻回顧5
    第三章佈設間距推估方法13
    3.1 佈設間距基本原則13
    3.2 遺漏值插補方法15
    3.2.1 聚類法K-means15
    3.2.2 回饋式類神經網路21
    3.2.3 線性內插法34
    3.2.4 類神經內插法35
    3.2.5 類神經二分法35
    3.2.6 類神經門檻選取法37
    3.3 評估指標39
    第四章實證分析45
    4.1 資料蒐集45
    4.2 權重插補測試52
    4.3 插補績效61
    4.4 佈設間距78
    4.5 佈設間距比較80
    第五章結論與建議89
    5.1 結論89
    5.2 建議91
    參考文獻92
    Reference 1.游裕昌,「運用基因群集技術於大型資料庫內遺失值之處理」,國立台灣科技大學電子工程系研究所碩士論文,2004。
    2.黃智建,「車輛偵測器不完整資訊推估旅行時間之研究」,逢甲大學交通工程與管理學系研究所碩士論文,2007。
    3.黃宏仁,「車輛偵測器數據補償與正規化研究」,國立臺灣大學土木工程學研究所博士論文,2009。
    4.廖梓淋,「利用資料插補概念探討車輛偵測器佈設間距」,國立中央大學土木工程學系研究所碩士論文,2009。
    5.林鈺翔,「利用時空資料推估車輛偵測器遺漏值之研究」,國立中央大學土木工程學系研究所碩士論文,2010。
    6.丁一賢、陳牧言,「資料探勘」,初版,滄海書局,2005。
    7.張斐章、張麗秋,「類神經網路導論:原理與應用」,初版,滄海書局,2010。
    8.廖述賢、溫志皓,「資料採礦與商業智慧」,初版,雙葉書廊,2009。
    9.葉怡成,「類神經網路模式應用與實作」,第八版,儒林書局,2003。
    10.張耀祖、白煌朗、方偉平,“數值分析Numerical Methods for Engineers”,松崗電腦圖書資料有限公司,1987。
    11.林丕靜著,「數值分析」,七版,格致圖書有限公司,1995。
    12.江大成譯,「數值分析」,初版,滄海書局,2006。
    13.林惠玲、陳正倉,「統計學方法與應用」,雙葉書廊,四版,2010。
    14.林豐博、蘇振維,「國道五號雪山隧道行車特性分析之研究」,運輸計畫季刊,第三十八卷,第一期,頁85-120,2009.
    15.高瑛穗,「雪山隧道行車特性分析」,國立中央大學土木工程學系研究所碩士論文,2009。
    16.Lewis, C.D., “Industrial and Business Forecasting Methods”, Southampton: The Camelot Press Ltd,1982.
    17.Batista, G. E. A. P. A. and Monard, M. C., “An Analysis of Four Missing Data Treatment Methods for Supervised Learning”, Applied Artificial Intelligence, vol. 17, no. 5-6, pp. 519-533, 2003.
    18.Rubin, D. B. and Little R. J., “Statistical Analysis with Missing Data.”John Wiley and Sons, New York, 1987.
    19.Rubin, D. B.,“Multiple imputations in sample surveys”Proc. Survey Res. Meth. Sec., Am. Statist. Assoc. 1978, 20-34, 1978.
    20.Gold, D. L., Turner, S. M., Gajewski, B. J. and Spiegelman, C., “Imputing Missing Values In ITS Data Archives For Intervals Under 5 Minutes,” Transportation Research Board 80th Annual Meeting January 7-11, Washington, D.C, 2001.
    21.Chen, D., Muller, S. G., Mussone, L. and Montgomey, F. , “A Study of Hybrid Neural Network Approaches and the Effects of Missing Data on Traffic Forecasting,” Neural Computing & Applications, pp. 277-286, 2001.
    22.Chen, C., Kwon, J., Rice, J., Skabardonis, A. and Varaiya, P. , “Detecting Errors And Imputing Missing Data For Single Loop Surveillance Systems,” Transportation Research Board January, Washington, D.C, 2002.
    23.Brian L. Smith, William T. Scherer, James H. Conklin,“Exploring Imputation Techniques for Missing Data in Transportation Management Systems”, Transportation Research Board, Vol. 1836, pp. 132-142, 2003.
    24.Satish Sharma, Pawan Lingras, Ming Zhong,“Effect of Missing Value Estimations on Traffic Parameters”, Transportation Planning and Technology vol. 27, no. 2, pp. 119-144, 2004.
    25.Huang, C. C. and Lee, H. M., “A Grey-based Nearest Neighbor Approach For Missing Attribute Value Prediction,” Applied Intelligent, Vol.20, No.3, pp. 239-252, 2004.
    26.Wen, Y. H., Lee, T. T. and Cho, H. T., “Missing Data Treatment And Data Fusion Toward Travel Time Estimation For ATIS,” Journal of the Eastern Asia Society for Transportation Studies, Vol.6, pp. 2546-2560, 2005.
    27.Mei Chen, Jingxin Xia, Rongfang Liu,“Developing a Strategy for Imputing Missing Traffic Volume Data”, JOURNAL of the TRANSPORTATION RESEARCH FORUM, Vol. 45, No. 3, pp. 57-76, 2006.
    28.Zhaobin Liu, Satish Sharma, Sandeep Datla,“Imputation of Missing Traffic Data during Holiday Periods”, Transportation Planning and Technology, Vol. 31, No. 5, pp. 525-544, 2008.
    29.Li Qu, Li Li, Yi Zhang, Jianming Hu,“PPCA-Based Missing Data Imputation for Traffic Flow Volume: A Systematical Approach”, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol. 10, No. 3, pp. 512-522, 2009.
    30.Hong, W. C.,“Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm”, Neurocomputing, pp. 2096-2107, 2011.
    Advisor
  • Jiann-sheng Wu(吳健生)
  • Files
  • 983202068.pdf
  • disapprove authorization
    Date of Submission 2011-07-02

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