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題名：以交通狀態為基礎之遺漏值補正策略
書刊名：運輸學刊
作者：汪進財 引用關係／邱孟佑
作者(外文)：Wong, Jinn-tsai／Chiu, Meng-yu
出版日期：2011
卷期：23:2
頁次：頁239-270
主題關鍵詞：遺漏值；分類迴歸樹；補值策略；Missing data；Classification and regression tree；Imputation strategy
原始連結：連回原系統網址new window
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  • 共同引用共同引用:2
  • 點閱點閱:28
交控中心通常藉由線圈偵測器或影像偵測器蒐集交通流量資料與交通狀況，以對旅行時間進行預測與推估；然而，任何一個即時的交通資料預測系統在實際運作時，遺漏值的處理是無可避免，當面臨遺漏值現象時，過去的補值策略往往並未詳細考慮車流續進與延滯之特性，僅以偵測器本身的歷史均值或是以移動平均方式填補遺漏值。為了考慮車流續進的過程以及彰顯路段相鄰偵測器間之交通狀態關係，本文除了採用基本歷史均值與移動平均模式進行遺漏值填補方法外，更引進資料採集分析技術設計出一套能考量相鄰偵測器之交通狀態之混合補值模式。首先，以集群分析方法對每個線圈偵測器之歷史資料作交通狀態分類處理，根據這些偵測器所代表之次路段交通狀態再結合整體路段之ETC旅行時間構建整體廻歸關係；接著以CART演算法構建各偵測點與其相鄰偵測器及路段ETC旅行時間所關聯之分類決策樹；最後，當某偵測點發生遺漏值時，則以該點對應之CART決策樹作為補值之預測依據。經過以實際有效樣本資料驗證結果顯示，透過交通狀態分類後之CART演算法可以有效提供長時窗遺漏值情況下的補值作業；另外，本文也發現在不同遺漏時窗數情境下，應以不同的補值策略進行補值，才能符合即時多變的偵測器遺漏值補正之需。
While loop or image detectors have been frequently adopted to collect traffic flow data as a basis for predicting and estimating travel time, missing values is an inevitable issue in real operations. Mean and moving average values based on historical data are common choices to replace missing values in past studies, which do not consider the features of vehicle flow continuation and lagging. To resolve this issue, this study proposes a novel approach which is based on data mining technique by combining the traffic information of traffic detector itself and its adjacent detectors. First, a regression model representing all road sections was developed based on the original historical traffic data of each loop detector. A decision tree was then established using Classification And Regression Tree (CART) to connect each detection point to the adjacent detectors and the Electronic Toll Collection (ETC) travel time on the associated road section. Finally, missing data were imputed based on the developed CART model. The empirical study showed that the CART imputation method based on traffic state works effectively to impute data with missing values, especially under the circumstance of long-period data missing. Moreover, it was found that under circumstances with different number of missing time-windows, hybrid imputation strategies fit better in meeting varying real-time needs.
期刊論文
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5.汪進財、邱孟佑(20100900)。以車流狀態為基礎之高速公路旅行時間預測模式。運輸學刊，22(3)，261-284。new window  延伸查詢new window
6.Mohanty, R.、Ravi, V.、Patra, M. R.(2010)。Web-services Classification Using Intelligent Techniques。Expert System with Applications，37(7)，5484-5490。  new window
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8.Bar-Gera, H.(2007)。Evaluation of a Cellular Phone-based System for Measurements of Traffic Speeds and Travel Times: A Case Study from Israel。Transportation Research Part C，15(6)，380-391。  new window
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12.Liu, P.(2009)。A Self-organizing Feature Maps and Data Mining Based Decision Support System for Liability Authentications of Traffic Crashes。Neurocomputing，72(13-15)，2902-2908。  new window
13.Loubesi, J.、Maza, E.、Lavielle, M.、Rodriguez, L.(2006)。Road Tracking Description and Short Term Travel Time Forecasting with a Classification Method。The Canadian Journal of Statistics，34(3)，475-491。  new window
14.Harper, P. R.(2004)。A Review and Comparison of Classification Algorithms for Medical Decision Making。Health Policy，71(3)，315-331。  new window
15.Huang, X.、Zhu, Q.(2002)。A Pseudo-nearest-neighbor Approach for Missing Data Recovery on Gaussian Random Data Sets。Pattern Recognition Letters，23(13)，1613-1622。  new window
16.Hyafile, L.、Rivest, R.(1976)。Constructing Optimal Binary Decision Trees is Np-complete。Information Processing Letters，5(1)，15-17。  new window
17.Otokita, T.、Hashiba, K.、Oda, T.(1998)。Travel Time Prediction Based on Pattern Extraction from Database。Papers of Technical Meeting on Transportation and Electric Railway，TER-99(11-20)，39-44。  new window
18.Reinhold, M.、Martin, M.,(2008)。Logistic Regression and CART in the Analysis of Multimarker Studies。Journal of Clinica Chimica Acta，394(1-2)，1-6。  new window
19.Rygielski, C.、Wang, J.-C.、Yen, D.(2002)。Data Mining Techniques for Customer Relationship Management。Technology in Society，24(4)，483-502。  new window
20.Breault, J. L.、Goodall, C. R.、Fos, P. J.(2002)。Data Mining a Diabetic Data Warehouse。Artificial Intelligence in Medicine，26(1-2)，37-54。  new window
21.Wei, C. H.、Lee, Y.(2007)。Development of Freeway Travel Time Forecasting Models by Integrating Different Sources of Traffic Data。IEEE Transactions on Vehicular Technology，56(6)，3682-3694。  new window
22.Wen, Y. H.、Lee, T. T.、Cho, H. T.(2005)。Missing Data Treatment and Data Fusion toward Travel Time Estimation for ATIS。Journal of the Eastern Asia Society for Transportation Studies，6，2546-2560。  new window
23.Zhang, X.、Rice, J. A.(2003)。Short-term Travel Time Prediction。Transportation Research Part C，11(3-4)，187-210。  new window
24.Brence, J. R.、Brown, D. E.(2002)。Data Mining Corrosion from Eddy Current Non-destructive Test。Computers and Industrial Engineering，43(4)，821-840。  new window
25.Chang, L. Y.、Chen, W. C.(2005)。Data Mining of Tree-based Models to Analyze Freeway Accident Frequency。Journal of Safety Research，36(4)，365-375。  new window
26.Choi, K.、Chung, Y.(2002)。A Data Fusion Algorithm for Estimating Link Travel Time。Journal of Intelligent Transportation Systems，7(3-4)，235-260。  new window
27.Dharia, A.、Adeli, H.(2003)。Neural Network Model for Rapid Forecasting of Freeway Link Travel Time。Engineering Applications of Artificial Intelligence，16(7)，607-613。  new window
28.Dia, H.(2001)。An Object-oriented Neural Network Approach to Short-term Traffic Forecasting。European Journal of Operational Research，131(2)，253-261。  new window
29.Fu, C. Y.(2004)。Combining Loglinear Model with Classification and Regression Tree (CART): An Application to Birth Data。Computational Statistics and Data Analysis，45(4)，865-874。  new window
會議論文
1.Lewis, R.(2000)。An Introduction to Classification and Regression Tree(CART) Analysis1-14。  new window
2.Wang, L.、Logendran, R.(2009)。Application of Regression Trees in Travel Time Estimation。  new window
3.Bartin, B.、Ozbay, K. l Iyigun, C.(2006)。A Clustering Based Methodology for Determining the Optimal Roadway Configuration of Detectors for Travel Time Estimation659-664。  new window
4.Bickel, P.、Chen, C.、Kwon, J.、Rice, J.、Varaiya, P.、van Zwet, E.(2005)。Traffic Flow on a Freeway Network。  new window
5.Fernandez, R.、Bertini, R.、Maier, D.(2008)。Developing an Imputation Strategy for an Archived Data User Service in Portland Oregon。  new window
6.Gold, D.、Turner, S.、Gajewski, B.、Spiegelman, C.(2001)。Imputing Missing Values in ITS Data Archives for Intervals under 5 Minutes。  new window
7.van Lint, J. W. C.、Hoogendorn, S. P.、van Zuylen, H. J.(2003)。Toward a Robust Framework for Freeway Travel Time Prediction: Experiments with Simple Imputation and State-space Neural Networks。  new window
8.Wen, Y. H.、Lee, T. T.(2005)。Hybrid Models toward Traffic Detector Data Treatment and Data Fusion525-530。  new window
圖書
1.Little, R. J. A.、Rubin, D. B.(1987)。Statistical Analysis with Missing Data。New York：John Wiley & Sons。  new window
 
 
 
 
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