兩步驟類神經網路車輛偵測器遺漏資料之填補及其應用

Two-Stage Data Imputation for Missing Value of Vehicle Detectors and Its Applications Using Artificial Neural Networks

10.6402/TPJ.201103.0001

吳健生(Jiann-Sheng Wu)；廖梓淋(Tzu-Lin Liao)；林鈺翔(Yu-Shiang Lin)

兩步驟資料填補 ； K-means法 ； 回饋式類神經網路 ； 填補績效 ； 偵測器佈設間距 ； Two-stage data imputation ； K-means ； Recurrent neural netwok ； Imputation performance ； Installation spacing of vehicle detectors

運輸計劃季刊

40卷1期（2011 / 03 / 30）

1 - 29

繁體中文

本研究採用兩步驟資料填補方式，針對雪山隧道路段車輛偵測器遺漏資料之填補進行實證分析，以期找出其中最為適用之填補方法，並發展其可能之應用。於資料填補時，首先採用K-means法將資料分群，而後再以最具代表性之三種類神經網絡分別進行填補。測試結果發現，將資料分為兩群，並採用回饋式類神經網路進行填補時可獲得最高之填補績效。最後，依據填補績效發展兩種可能之應用，即遺漏資料填補及偵測器佈設間距。在遺漏資料填補方面，速率填補之績效最高，無論是以上、下游任何一對偵測器資料作為輸入，準確度均高達97.5%以上。其次為流率，其準確度可達90%以上，並可以上、下游2或10對偵測器資料作為群1或群2填補之輸入。最差者為占有率，僅當準確度門檻降至80%時，群1資料方能進行填補，群2資料則無比限制。在偵測器佈設間距方面，若合併考量流率、速率與占有率三者，則佈設間距由填補績效最差之占有率決定。僅在整體準確度降至85%以下時，方可將現行之350m佈設間距擴增至3,500m。若僅考慮隨機性較低之群2資料，則在準確度高達90%以上時，即可將佈設間距增加至4,200m。

Using a two-stage data imputation method based on artificial neural networks, we carried out, in this study, an empirical analysis of the missing value of vehicle detectors in Hshehshan Tunnel to search for the optimal alternative, and developed its possible applications accordingly. By testing data imputation, we, at first, clustered all the data into groups using K-means, and then chose three typical artificial neural networks to impute the missing data. The result shows that two-group data clustering combined with a recurrent neural network can achieve the highest imputation performance. We, finally, developed two possible applications based on it, including data imputation and installation spacing of vehicle detectors. In respect to data imputation, speed performed the best with an accuracy of greater than 97.5%, and all pairs of vehicle detectors could be input for imputation. Flow performed the second best with an accuracy of over 90%, and the nearest two or ten pairs of detectors up-and downstream could be input for the imputation of data group 1 or 2, respectively Occupancy performed the worst. Only by an accuracy threshold lowered to 80%, data points in group 1 could be imputed, and those in group 2 were not restricted, nevertheless. In respect to installation spacing, occupancy would dominate due to its relatively poor performance by considering all the three traffic attributes. Only when the overall accuracy decreased to fewer than 85% could we extend the current spacing of 350 m to 3,500 m. If only considering data group 2, we could extend it to 4,200 m with an accuracy of over 90% due to lower randomness.

1. 吳冠宏、吳信宏、郭廣洋(2006)。應用分群技術於交通事故資料分析。品質學報，13(3)，305-312。
   連結：
2. 吳健生、廖梓淋(2010)。利用資料填補概念探討車輛偵測器佈設間距。運輸學刊，22(3)，307-326。
   連結：
3. 張堂賢、黃宏仁(2008)。車輛偵測器資料遺失之在線插補技術研究。運輸學刊，20(4)，377-404。
   連結：
4. Chen, C.,Kwon, J.,Rice, J.,Skabardonis, A.,Varaiya, P.(2003).Detecting Errors and Imputing Missing Data for Single Loop Surveillance Systems.Transportation Research Record,1855,160-167.
5. Chen, D.,Muller, S. G.,Mussone, L.,Montgomey, F.(2001).A Study of Hybrid Neural Network Approaches and the Effects of Missing Data on Traffic Forecastin.Neural Computing & Applications,10,277-286.
6. Daganzo, C.(1997).Fundamentals of Transportation and Traffic Operation.Oxford, U.K.:Pergamon Elsevier.
7. Delurgio, S. A.(1998).Forecasting Principles and Applications.New York:McGraw-Hill.
8. Gold, D. L.,Turner, S. M.,Gajewski, B. J.,Spiegelman, C.(2001).Imputing Missing Values in ITS Data Archives for Intervals under 5 Minutes.80th Annual Meeting, Transportation Research Board,Washington, D.C.:
9. Huang, C. C.,Lee, H. M.(2004).A Grey-Based Nearest Neighbor Approach for Missing Attribute Value Prediction.Applied Intelligent,20(3),239-252.
10. Huang, X. L.,Zhu, Q. M.(2002).A Pseudo-Nearest-Neighbor Approach for Missing Data Recovery on Gaussian Random Data Sets.Pattern Recognition Letters,23,1613-1622.
11. Little, R. J. A,Rubin, D. B.(1987).Statistical Analysis with Missing Data.New York:John Wiley & Sons.
12. Sgarma, S.(1995).Applied Multivariate Techniques, Strategies and Case Studies.New York:John Wiley & Sons.
13. Vanajakshi, L.,Rilett, L. R.(2006).System Wide Data Quality Control of Inductance Loop Data Using Nonlinear Optimization.Journal of Computing In Civil Engineering,20(3),187-197.
14. 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.
15. Zhong, M.,Lingras, P.,Sharma, S.(2004).Estimation of Missing Traffic Counts Using Factor, Genetic, Neural, and Regression Techniques.Transportation Research Part C,12,139-166.
16. 交通部(2006)。,未出版
17. 周文賢(2002)。多變量統計分析。臺灣:智勝文化。
18. 葉怡成(2000)。類神經網路模式應用與實作。臺北:儒林圖書有限公司。