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研究生:黃宏仁
研究生(外文):Hung-Jen Huang
論文名稱:車輛偵測器數據補償與正規化研究
論文名稱(外文):Compensation and Regulation for Vehicle Detector Data
指導教授:張堂賢張堂賢引用關係
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:土木工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:99
中文關鍵詞:車輛偵測器漏失資料資料插補交通控制號誌時制計畫交通資料解調
外文關鍵詞:vehicle detectormissing datadata compensationtraffic controlsignal timing plantraffic data regulation
相關次數:
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  • 收藏至我的研究室書目清單書目收藏:1
智慧型運輸系統 (Intelligent Transportation Systems, ITS) 常依賴車輛偵測器 (Vehicle Detector) 自動化收集交通資料,供交通管理決策參考。所以偵測器本身是否設計精良,或是否被擺置於適當位置上,可能影響車輛偵測器資料的完整性,或可參考性。
本研究鑑於漏失資料有礙ITS子系統之先進交通管理系統(Advanced Traffic Management Systems, ATMS) 運轉順暢,特別是即時動態交通控制系統,所受影響最著。又參考價值低的車流資料,不僅可能導致所得之交通管理決策缺乏效率,更無法彰顯投資設置車輛偵測器之價值,故本研究利用中央極限定理 (Central Limit Theorem) 、傅立葉轉換 (Fourier Transforms) 、卡曼濾波器 (Kalman Filter) 及基因演算法 (Genetic Algorithm) 構建資料補償機制;其中插補估計值之計算,同時參考歷史資料及漏失資料發生稍早的資料走勢;並且,歷史資料符合大樣本集中趨勢。漏失資料插補實驗共計測試四種插補方法,其結果顯示:所實驗之四種方法,其插補績效大多控制在平均絕對標準誤 (Mean Absolute Percentage Error, MAPE) 小於 20% 之範圍內。如此結果,依統計學家Lewis (1982) 所提出的衡量標準,本研究之插補方法績效良好;而且,含有卡曼濾波器的方法,因其考量了漏失資料發生稍早以前的資料走勢,可提高資料漏失比率小於 30% 情況下的插補績效,達到精確的水準。
然而,再可靠的車輛偵測器,若未能佈設在合適的位置上,其所得之交通參數則可能帶有交通號誌控制的干擾,造成資料難以為系統求解交通號誌時制計畫或做成相關管理決策所使用。故本研究利用最佳控制理論 (Optimal Control Theory),發展車輛偵測器資料正規器 (Regulator),令車輛偵測器能自動感知其所量測到的資料是否存在交通號誌控制雜訊,並且自動化解調受干擾的資料。實驗分析結果顯示,比較解調前後之車流參數,速率資料之差異率最高達91.45%。如此差異將導致應用解調前後之速率資料所得之黃燈及全紅時段長度相差54% 之多。經檢定,由於經解調後之速率資料幾與未受號誌干擾之速率資料並無顯著差異;足見經解調之車流參數將令系統更能有效得出適合交通實況的交通號誌之黃燈、全紅、清道時間、損失時間及交通號誌時制週期。同時可利用解調後之速率與占有率資料反推未受號誌干擾之車流流率 (Flow)。
綜合上述,本研究嘗試克服二種偵測器於應用面上可能遭遇的問題。為交通資料時間數列提供了穩健的插補機制,支援動態的交通管理系統持續不斷的運作,也為落於受交通號誌控制干擾範圍內之車輛偵測器提供一套數據補救措施,以利動態交通控制系統得出適應交通狀況的交通號誌時制計或相關控制策略。期望所得結果能為先進交通管理系統和ITS做出貢獻。
Collecting traffic data for advanced use in Intelligent Transportation Systems (ITS) heavily relies on the performance of vehicle detectors. The completeness and the accuracy of data measurement depend on the quality of vehicle detector and whether it is installed at appropriate position. Missing data and inaccuracy traffic parameters prevent Real-time Traffic Control Systems (RTCS) from continuous working and decrease control performance,. These disadvantages decrease the value of vehicle detector investment.
This dissertation develops a novel compensation mechanism for missing data by integrating Central Limit Theorem, Fourier Transforms, Kalman Filter, and Genetic Algorithm. The interpolation considers the central tendency of historical data of large number and immediate past data in the current day. All experiment results show that the proposed compensation methods perform well under the Lewis (1989) criteria. In addition, the interpolation methods with Kalman Filter perform an accurate result when the missing rate is under 30% by fusing historical data and the immediate past data in the current day.
Even though the employed vehicle detectors can work reliably and reply traffic data continuously, installation at improper positions shall make the measurement disturbed by the nearby traffic signal. This dissertation utilizes optimal control theory to dynamically track whether the position of vehicle detector is proper and to automatically regulate the noised data to be similar to those collected in a proper site. The results show that the difference between regulated and non-regulated speed data is up to 91.45%. The difference leads yellow and all-red intervals obtained by non-regulated data differ from those obtained by regulated ones to 54%. The test proves that the regulated speed and occupancy do not significantly differ from those not disturbed by traffic signals. The findings reveal that the regulation is needed for the system to obtain real traffic conditions without the noise from signal control and then to decide the adaptive yellow and all-red intervals, clearance, lost time, and timing plan.
In summary, this dissertation proposes efficient methods to compensate missing data and regulate the data disturbed by traffic signal due to improper positions of vehicle detectors. The proposed methods will support RTCS to continuously obtain the data similar to those without the noise from traffic signal. The findings shall contribute to ITS.
誌 謝 I
摘 要 III
ABSTRACT V
目 錄 VII
表目錄 XI
圖目錄 XIII
第一章 緒論 1
1.1 研究背景 2
1.2 研究目的 5
1.3 研究方法 7
1.4 研究內容與流程 7
第二章 文獻回顧 11
2.1 車輛偵測器資料處理相關研究 12
2.1.1 漏失資料插補 12
2.1.2 車輛偵測器位置與資料正確性之相關性研究 15
2.1.3 綜合評析 16
2.2 本研究相關理論模式 18
2.2.1 中央極限定理 (Central Limit Theorem) 18
2.2.2 傅立葉轉換 (Fourier Transforms) 19
2.2.3 α-β-γ濾波器 (α-β-γ Filter) 21
2.2.4 基因演算法 (Genetic Algorithm, GA) 29
2.2.5 小結 32
第三章 車輛偵測器漏失資料即時插補技術 35
3.1 問題解析 35
3.2 即時資料插補技術 38
3.3 插補效率評估指標 42
第四章 車輛偵測器正規器及資料正規化方法 45
4.1 問題解析 45
4.2 資料正規器推導 50
4.2.1 偵測器最佳位置 50
4.2.2 正規器設計 53
4.2.3 偵測器資料正規化程序 56
第五章 模式評估 61
5.1 實驗設計 61
5.1.1 車輛偵測器漏失資料即時插補實驗 61
5.1.2 車輛偵測器資料正規化實驗 65
5.2 實驗分析 66
5.2.1 車輛偵測器即時漏失資料插補績效 66
5.2.2 車輛偵測器資料正規化作業績效 78
5.3 反推未受號誌控制干擾之車流到達率 88
第六章 結論與建議 91
6.1 結論 91
6.2 建議 93
參考文獻 95
黃宏仁(民 90),交叉路口車流衝突指標之研究,逢甲大學交通工程與管理學研究所碩士論文,頁50∼頁54。
林豐裕(民92),短期交通量變化混沌特性之探索,國立交通大學博士論文。
張堂賢,黃宏仁(民 97),車輛偵測器資料漏失之在線插補技術研究,運輸學刊,第二十卷,第四期,頁300∼頁320。
AASHTO (2001), A Policy on Geometric Design of Highways and Streets (Green book). 4th ed., Washington DC: American Association of State Highway and Transportation Officials.
Beveridge, S. (1992), “Least Squares Estimation of Missing Values in Time Series,” Communications in Statistics: Theory and Methods, Vol. 21, No. 12, pp. 3479-3496.
Bole, V., Cepar, D., Radalj, Z. (1990), “Estimating Missing Values in Time Series,” Methods of Operations Research, No. 62.
Cartwright, M. (1990), Fourier Methods for Mathematicians, Scientists, and Engineers, Chichester, UK: Ellis Horwood Ltd.
Cheng, D.X., Tian, Z.Z. and Messer, C.J. (2005), “Development of an Improved Cycle Length Model over the Highway Capacity Manual 2000 Quick Estimation Method,” Journal of Transportation Engineering, 131, pp. 890-897.
Coifman, B. (2001), “Improved Velocity Estimation Using Single Loop Detectors,” Transportation Research Part A, 35, pp. 863-880.
Coifman, B. (2003a), “Estimating Median Velocity Instead of Mean Velocity at Single Loop Detectors,” Transportation Research Part C, 11, pp. 211-222.
Coifman, B. and Ergueta, E. (2003b), “Improved Vehicle Reidentification and Travel Time Measurement on Congested Freeways,” Journal of Transportation Engineering, 129, pp. 475-483.
Coifman, B. and Dhoorjaty, S. (2004), “Event data-based traffic detector validation tests,” Journal of Transportation Engineering, 130, pp. 313-321.
Daily, D. J. (1999), “A Statistical Algorithm for Estimating Speed from Single Loop Volume and Occupancy Measurements,” Transportation Research Part B, 33, pp. 313-322.
FHWA (1976), Development and Testing of Incident Detection Algorithms, Volume 2: Research Methodology and Detailed Results, FHWA-RD-76-20, Federal Highway Administration, Department of Transportation, Washington, D.C.
FHWA (1990), Traffic Detector Handbook. 2nd edition, Washington, DC: USDOT, FHWA, pp. 69.
FHWA’s Scanning Program (1997), FHWA Study Tour for European Traffic Monitoring Programs and Technologies, Federal Highway Administration, Department of Transportation, Washington, D.C.
FHWA (2006), Traffic Detector Handbook:Third Edition—Volume II, Appendix L. Classification of Sensor Systems by Sensor Density Level, website: http://www.tfhrc.gov/its/pubs/06139/appendl.htm.
Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization & Machine Learning, Boston: Addison Wesley Longman Inc.
Gupta, A. and Lam, M. S. (1996), “Estimating Missing Values Using Neural Networks,” Journal of the Operational Research Society, Vol. 47, No.2, pp. 229-239.
Hollane, J.H. (1975), Adaptation in Natural and Artificial Systems, Ann Arbor, Michigan: University of Michigan Press.
Hellinga, B. R. (2002), “Improving Freeway Speed Estimates from Single-loop Detectors,” Journal of Transportation Engineering, 128, pp. 58-67.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C. and Liu, H. H. (1998), “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A (1998) 454, pp. 903-995.
Inoue, T., Hotta, M. and Yokota, T. (1999), “Characteristics of Vehicle Detector at Signalized Intersections,” Proceedings of the Sixth World Congress on Intelligent Transportation Systems, Toronto, Canada, pp. 628 – 633.
Jain, M. and Coifman, B. (2005), “Improved Speed Estimates from Freeway Traffic Detectors,” Journal of Transportation Engineering, 131, pp. 483-495.
ITE Technical Committee 4A-16 (1985). “Determining Vehicle Change Intervals,” Institute of Transportation Engineers (ITE) Journal, pp. 61–64.
Kreyszig, M. (1999), Advanced Engineering Mathematics, 8th ed., New York: Wiley and Sons.
Kuo, B.C. (1991), Automatic Control Systems. 6th ed., NJ: Prentice-Hall.
Lewis, C.D. (1982), Industrial and Business Forecasting Methods, Southampton: The Camelot Press Ltd.
Little, R. A. and Rubin, D. B. (1987), Statistical Analysis with Missing Data. New York: Wiley.
Luenberger, D.G. (1979), Introduction to Dynamic Systems: Theory, Models, & Applications. New York: John Wiley & Sons, 1979.
Martin, C. A. and Witt, S. F. (1989), “Forecasting Tourism Demand: A Comparison of the Accuracy of Several Quantitative Methods”, International Journal of Forecasting, Vol. 5, No. 1, pp. 7–19.
May, A. D. (1990), Traffic Flow Fundamentals. New Jersey: Prentice-Hall.
Michalewicz, Z. (1996), Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, 3rd Edition.
New Mexico State Highway and Transportation Department (1990), 1990 survey of traffic monitoring practices among state transportation agencies of the United States, Report No. FHWA-HRP-NM-90-05, Santa Fe, New Mexico.
Oh, S. (2003), “Optimal detector location for estimation link travel speed in urban arterial roads,” TRB 2003 Annual Meeting CD-ROM.
Peeta, S., Anastassopoulos, L. (2002), “Automatic Real-time Detection and Correction of Erroneous Detector Data with Fourier Transforms for Online Traffic Control Architectures,” Transportation Research Record 1811, pp. 1-11.
PTV (2005), VISSIM 4.10 User Manual. PTV Planung Transport Verkehr AG.
Redfern, E. J., Waston, S.M., Tight, M. R., and Clark, S. D. (1993), “A Comparative Assessment of Current and New Techniques for Detecting Outliers and Estimating Missing Values in Transport Related Time Series Data”, Proceedings of Highways and Planning Summer Annual Meeting, Institute of Science and Technology, U.K.
Retting, R. A., Chapline, J. F., and Williams A. F. (2002), “Changes in Crash Following Re-timing of Traffic Signal Change Intervals”, Accident Analysis and Prevention, Vol. 34, pp. 215-220.
Robins, J. M. Rotnitzky, A. and Zhao, L. P. (1994), “Estimation of Regression Coefficients When Some Regressions are not Always Observed," Journal of the American Statistical Association, Vol. 89, No. 427, pp. 846-866.
Robins, J. M. and Rotnitzky, A. (1995), “Semiparametric Efficiency in Multivariate Regression Models with Missing Data,” Journal of the American Statistical Association, Vol. 90, No. 429, pp. 122-129.
Roess, R. P., Prassas, E. S. and McShane, W. R. (2004), Traffic Engineering, 3rd Edition, Prentice Hall, pp. 515-524.
Rotnitzky, A. and Robins, J. (1995a), “Semiparametric Regression Estimation in the Presence of Dependent Censoring,” Biometrika, Vol. 82, No. 4, pp. 805-820.
Rotnitzky, A., and Robins, J. (1995b), “Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data,” Journal of the American Statistical Association, Vol. 90, No. 429, pp. 106-121.
Schafer, L. J. and Graham, W. J. (2002), “Missing Data: Our View of the State of the Art,” Psychological Methods, Vol. 7, No. 2, pp. 147-177.
Simon, D. (2006), Optimal State Estimation, New Jersey: John Wiley and Sons, Inc., Hoboken, pp.123-227.
Singh, V.P. and Harmancioglu, N.B. (1996), Estimation of missing values with use of entropy, NATO Advanced Research Workshop, Izmir, Turkey, pp. 267-274.
Thomas, G. B. (1999), “The Relationship between Detector Location and Travel Characteristics on Arterial Streets,” ITE Journal, 69, pp. 36-42.
TRB (2000), Highway Capacity Manual, 4th Edition, Transportation Research Board, Washington, DC.
Webster, F. V. (1958), “Traffic Signal Setting,” Road Search Paper No. 39, Department G, Scientific and Industrial Research, H. M. Stationery Office, London.
Wooldridge, J. (2002), “Inverse Probability Weighted M-estimators for Sample Selection, Attrition and Stratification,” Portuguese Economic Journal, Vol. 1, pp. 117-139.
Wright, P. M. (1993), “Filling in the Blanks: Multiple Imputation for Replacing Missing Values in Survey Data,” Proceedings of SAS 18th Annual Conference, New York.
Zhong, M., Lingras, P., and Sharma, S. (2004), “Estimation of Missing Traffic Counts Using Factor, Genetic, Neural, and Regression Techniques,” Transportation Research C, Vol. 12, pp. 139-166.
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