臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：應用格位傳送模式建構預測型高速公路動態起迄矩陣推估演算法
作者：曾群明
作者(外文)：Tseng, Chun-Ming
校院名稱：國立交通大學
系所名稱：交通運輸研究所
指導教授：藍武王
邱裕鈞
學位類別：博士
出版日期：2012
主題關鍵詞：交通量預測；基因規劃法；成長型階層式自我組織放射圖；滾動式自我建構交通模型；動態起迄矩陣推估；進階卡門濾波；格位傳送模式；Traffic prediction；Genetic programming；Growing hierarchical self-organizing map；Rolling self-structured traffic patterns；Dynamic origin-destination；Cell transmission model；Extended Kalman filtering
原始連結：連回原系統網址new window
相關次數：
  • 歷年統計：
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:13
正確和有效率的動態起迄矩陣推估對運輸規劃、交通管理及交通策略研擬是很重要的資訊，近年來隨著智慧型運輸系統之快速發展，動態起迄矩陣之推估及即時性交通控制之執行如路徑導引及號誌控制等績效均有顯著提昇。過去許多文獻利用所偵測到的高速公路主線與上、下匝道流量，進行高速公路動態起訖矩陣之推估，或以增加額外的假設或是外生的資訊，如路徑選擇行為或已知的依時性交通流量資料等提昇績效，然而，在此課題中，所面臨的挑戰是欲推估之參數數量遠大於資料所提供之訊息及如何處理旅行時間變異影響之問題。
基此，本研究提出一個預測型高速公路動態起迄矩陣推估方法，包括一個二階段交通量預測模式及一個整合型動態起迄矩陣推估演算法，交通量預測模式係以滾動式自我建構交通模型來預測中、長期交通特性，其中，包含以成長型階層式自我組織放射圖(GHSOM)將所有交通量樣本分成若干群，並針對每一群以基因規劃法(GP)建構相對應之非線性交通量預測模式去預測交通特性。而整合型動態起迄矩陣推估演算法係為了能在不同的交通情況下，有效率且準確的獲取車輛到達型態，藉由結合格位傳送模式(Cell Transmission Model，CTM)及卡門濾波 (Extended Kalman Filtering，EKF)，來建構遞迴式動態起迄矩陣推估演算法，由CTM模擬車輛運行行為，預測各依時起迄對之到達率，再以EKF推估動態起迄矩陣。
為驗證此演算法之績效及實用性，本文以所設計之6個起迄對之小型路網推估90分鐘的起迄矩陣作為驗證範例。同時為比較本模式於旅行時間預測之績效，以Greenshields巨觀模式假設在進入路網之車輛會於兩時階範圍內到達迄點之條件下，預測車輛旅行時間。結果顯示本模式推估結果之RMSE為0.069遠較Greenshields之0.145為優。在實例應用上，本文先以國道1號泰山收費站至楊梅交流道計6個交流道36公里長之3車道高速公路中型路網進行實驗驗證，結果顯示格位傳送模式在自由流至擁擠流等不同情境交通狀況下，有效模擬車輛到達率，而本模式並以相當低的RMSE績效，精確推估動態起迄矩陣。
最後，本研究再以國道1號頭份交流道至北斗交流道計15個交流道長110公里之3車道高速公路大型路網作為實例驗證，結果也證明本文所提二階段交通量預測模式的績效及整合型動態起迄矩陣推估演算法的實用性。另外，本文亦比較所提二階段交通量預測模式之績效優於傳統ARIMA模式。並進一步針對交通量樣本長度進行敏感度分析，發現5分鐘為一時階之交通量樣本長度在120個時階（10小時），即能達到相當之預測績效。
Accurate and effective dynamic origin-destination (O-D) matrices estimation is important for transport planning, traffic management and strategic planning. Recently, the rapid development of intelligent transport systems has enhanced accurate dynamic O-D information and the implementation of real-time traffic control, such as real-time route guidance and signal control. Numerous studies have devoted to developing estimation algorithms based on observable mainline and ramp flow rates, with constraints dependent on time series traffic flow and extra system equations and using recursive or non-recursive system solution techniques to estimate O-D matrices. However, this dynamic O-D matrices estimation issue remains challenging in that the number of parameters to be estimated is always far greater than the available information, and the impact of travel time variability on the time-varying O-D matrices.
In light of this, the study proposes a novel approach to estimate medium-to long- term freeway dynamic O-D matrices. The proposed approach includes a two-stage prediction model with an integrated algorithm. The traffic prediction model predicts medium- to long-term traffic features based on rolling self-structured traffic patterns. The rationales include using the growing hierarchical self-organizing map model (GHSOM) to partition unlabeled traffic patterns into clusters and then developing an associated genetic programming (GP) model to predict the traffic features in each cluster. And then, the integration algorithm, which combined cell transmission model (CTM) with extended Kalman filtering (EKF) to respectively and iteratively estimate the arrival distributions and O-D proportions.
To demonstrate the performance and applicability of the proposed approach, a seminal example with six O-D pairs of 90 minutes estimation is designed. The performance of this mode in terms of travel time prediction is compared to the Greenshields macroscopic model prediction. The results showed that the propose approach is better than the Greenshields model. In the field study, a medium-scale networks and a large-scale network of on-ramp traffic patterns on a freeway are examined. The medium-scale network covers a section of Taiwan No.1 Freeway (Taishan toll station to Yangmei toll station), a 36 km three-lane freeway section with 6 interchanges, and the results showed that the CTM can accurately capture the degree of traffic dispersion under traffic scenarios ranging from free-flow to congested-flow conditions and that the proposed EKF algorithm can accurately estimate the O-D proportions with rather low RMSE.
For the large-scale network, 15 interchanges from Toufen interchange to Beidou interchange, a 110-kilometer stretch of Taiwan No.1 Freeway, were tested and the results indicated the practical applicability of the proposed algorithm. In addition, the proposed method has performed much better than the conventional ARIMA model. The sensitive analysis has also revealed the necessity of acquiring five-minute traffic patterns longer than 120 time intervals (10 hours) in order to achieve sufficient high prediction accuracy.
曾國雄、卓訓榮、周幼珍、江勁毅 (1997)，「動態流量推估動態O-D方法之研究」，運輸計劃季刊，第二十六卷第四期，頁615-638。
陳敦基 (2010)，高速公路按里程電子收費通行費率之研究，交通部臺灣區國道高速公路局委託研究計畫。
邱裕鈞、藍武王、許珮珊、曾群明(2011)，「應用格位傳送模式建構高速公路動態起迄矩陣推估演算法」，運輸學刊，第二十三卷第一期， 頁 97-128。
Afzal, W., Torkar, R. (2011) On the application of genetic programming for software engineering predictivemodeling: A systematic review. Expert Systems with Applications 38, 11984–11997.
Asa, B.H., David, H., Hava, T. I., Vapnik, V. (2001) Support vector clustering. Journal of Machine Learning Research 21, 25-137.
Ashok, K., Ben-Akiva, M. (2000) Alternative approaches for real-time estimation and prediction of time-dependent original-destination flows. Transportation Science 34, 21-36.
Ashok, K., Ben-Akiva, M. (2002) Estimation and prediction of time-dependent original-destination flows with a stochastic mapping to path flows and link flows. Transportation Science 36, 184-198.
Barlović, R., Santen, L., Schadschneider, A., Schreckenberg, M. (1998) Metastable states in cellular automata for traffic flow. European Physical Journal 5B, 793–800.
Baykasog˘lu, A., Llu, H.G., Anakc,H.C., zbakr, L.O. (2008) Prediction of compressive and tensile strength of limestone via genetic programming. Expert Systems with Applications 35, 111-123.
Bell, M.G.H. (1983) The estimation of an origin-destination matrix from traffic counts. Transportation Science 17, 198-217.
Bell, M.G.H. (1991) The estimation of origin-destination matrices by constrained generalized least squares. Transportation Research 25B, 13-22.
Bick, J. H., Newll, G.F. (1960) A continuum model for two-directional traffic flow. Applied Mathematics 18, 191-204.
Boel, R., Mihaylova, L. (2006) A compositional stochastic model for real time freeway traffic simulation. Transportation Research 40B, 319-334.
Brezocnik, M., Kovacic, M., Ficko, M. (2004) Prediction of surface roughness with genetic programming. Journal of Materials Processing Technology 157, 28–36.
Brucker, P., (1978) On the complexity of clustering problems. In Optimization and Operation Research, 45-54.
Chandler, R.E., Herman, R., Montroll, E. (1958) Traffic dynamics: Studies in car following. Operations Research 6, 165–184.
Chang, G.L., Wu, J. (1994) Recursive estimation of time-varying origin-destination distributions with dynamic screenline flows. Transportation Research 34B, 277-290.
Chang, G.L., Tao, X. (1996) Estimation of dynamic network O-D distribution. Proceedings of 13th Symposium on Transportation and Traffic Flow Theory, Elsevier Science.
Chang, G.L., Miaou, S.P. (1999) Real-time prediction of traffic flows using dynamic generalized linear models. Transportation Research Record 1678, 168-178.
Chang, G.L., Tao, X. (1999) An integrated model for estimating time-varying network O-D distribution. Transportation Research 33A, 381-399.
Chang, S.C., Lai , H.C., Yu, H.C. (2005) A variable P value rolling grey forecasting model for Taiwan semiconductor industry production. Technological Forecasting & Social Change 72, 623–640.
Chen, H., Grant-Muller, S., (2001) Use of sequential learning for short-term traffic flow forecasting. Transportation Research 9C, 319-336.
Chen, K.Y. (2007) Forecasting systems reliability based on support vector regression with genetic algorithms. Reliability Engineering and System Safety 92, 423-432.
Chiou, Y.C., Lan, L.W. (2001) Genetic clustering algorithms. European Journal of Operational Research 135(2), 413-427.
Chiou, Y.C., Ai, C.M. (2007) Rolling grey forecasting models for short-term traffics. Journal of the Eastern Asia Society for Transportation Studies 7, 2486-2501.
Chiou, Y.C.,Chou, S.T. (2010) Ant clustering algorithms. International Journal of Applied Evolutionary Computation 1, 1-15.
Chiou, Y.C., Lan, L.W., Tseng, C.M. (2010) Estimation of dynamic freeway origin-destination matrices with cell-based arrival distribution modeling. Journal of Eastern Asia Society for Transportation Studies 7, 1-16.
Codina, E. (2004) Adjustment of O–D trip matrices from observed volumes: An algorithmic approach based on conjugate directions. European Journal of Operational Research 155, 535–557.
Cremer, M., Keller, H. (1987) A new class of dynamic methods for the identification of origin-destination flows. Transportation Research 21B, 117-132.
Daganzo, C.F. (1994) The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory. Transportation research 28B, 269-287.
Daganzo, C. F. (1995) The cell transmission model: network traffic. Transportation Research 29B, 79-93.
Daganzo, C. F. (1999) The lagged cell-transmission model. Inst. of Transportation Studies, University of California, Berkeley, California, USA.
Dervoort, M., Dougherty, M., Watson, S. (1996) Combining kohonen maps with ARIMA time series models to forecast traffic flow. Transportation Research 4C, 307-318.
Dia, H. (2001) An object-oriented neural network approach to short-term traffic forecasting. European Journal of Operational Research 131, 253-261.
Dimitriou, L., Tsekeris, T., Stathopoulos, A. (2008) Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow. Transportation Research 16C , 554-573.
Dougherty, M., Cobbett, M. (1997) Short-term inter-urban traffic forecasts using neural networks. International Journal of Forecasting 13, 21-31.
Frahling, G. (2005) A fast k-means implementation using corsets. Heinz Nixdorf Institute and Department of Computer ScienceUniversity of Paderborn.
Gaur, S., Deo, M.C. (2008) Real-time wave forecasting using genetic programming. Ocean Engineering 35, 1166-1172.
Gomes, G., Horowitz, R. (2006) Optimal freeway ramp metering using the asymmetric cell transmission model. Transportation Research 14C, 244-262.
Hazelton, M.L. (2001) Inference for origin-destination matrices: Estimation, prediction and reconstruction. Transportation Research 35B, 667-676.
Hazelton, M.L. (2008) Statistical inference for time varying origin-destination matrices. Transportation Research 42B, 542-552.
Ho, W.M. (2008) Comparisons of assignment-based and non-assignment-based time dependent O-D estimation models for general networks. Unpublished thesis, National Cheng Kung University (in Chinese).
Huang, S., Sadek, A.W. (2009) A novel forecasting approach inspired by human memory: The example of short-term traffic volume forecasting. Transportation Research 17C, 510-525.
Ishak, S., Baton, B. (2003) Optimization of dynamic neural networks performance for short-term traffic prediction. TRB 2003 Annual Meeting.
Kalman, R.E. (1960) A new approach to linear filtering and prediction problems. Transactions of the ASME Journal of Basic Engineering 82, 35-45.
Kantabutra, S., Naramittakapong, C., Kornpitak, P. (2000) Pipelined k-means algorithm on COWs. The Theory of Computation Group.
Kerner, B.S. (2004) The physics of traffic, Springer-Verlag Berlin Heidelberg.
Kerner, B.S. (2005) Control of spatiotemporal congested traffic patterns at highway bottlenecks. Physica 355A, 565-601.
Kirby, H., Dougherty, M., Watson, S. (1997) Should we use neural networks or statistical models for short term motorway traffic forecasting. International Journal of Forecasting 13, 43-50.
Knospe, W., Santen, L., Schadschneider, A., Schreckenberg, M. (1999) Disorder effects in cellular automata for two-lane traffic. Physica 265A, 614-633.
Knospe, W., Santen, L., Schadschneider, A., Schhreckenberg, M. (2000) Towards a realistic microscopic description of highway traffic. Journal of Physics 33A, 477-485.
Kohonen, T. (1982) Self-organized formation of topologically correct feature maps. Biological Cybernetics 43, 50-69.
Koza, J.R. (1992) Genetic programming: on the programming of computers by means of natural selection. The MIT Press.
Kühne, R. in: Gartner, N., Messner, C.J., Rathi, A.J. (1998) Traffic Flow Theory, 2nd ed. Transportation Research Board, Special Report 165.
Lam, W.H.K., Chan, K.S., Shi, J.W. (2002) A traffic flow simulator for short-term travel time forecasting. Journal of Advanced Transportation 36, 265-291.
Lam, W.H.K., Chan, K.S., Tam, M.L., Shi, J.W. (2005) Short-term travel time forecasts for transport information system in Hong Kong. Journal of Advanced Transportation, 39, 289-306.
Lam, W.H.K., Tang, Y.F., Tam, M.L. (2006) Comparison of two non-parametric models for daily traffic forecasting in Hong Kong. Journal of Forecasting 25, 173-192.
Lan, L.W., Sheu, J.B., Huang, Y.S. (2007) Prediction of short-interval traffic dynamics in multidimensional spaces. Journal of the Eastern Asia Society for Transportation Studies 7, 2353-2367.
Lan, L.W., Lin, F.Y., Kuo, A.Y. (2010) Three novel methods to predict traffic time series in reconstructed state spaces. International Journal of Applied Evolutionary Computation 1, 16-35.
Ledoux, C. (1997) An urban traffic flow model integrating neural network. Transportation Research Part 5C, 287-300.
Li, B., Moor, B.D. (1999) Recursive estimation based on the equality-constrained optimization for intersection origin-destination matrices. Transportation Research 33B, 203-214.
Lighthill, M. H., Whitham, G. B. (1955) On kinematic waves: a theory of traffic flow on long crowed roads. Proceedings of the Royal Society, Series 229A, 317-345.
Lin, P.W., Chang, G.L. (2006) Modeling measurement errors and missing initial values in freeway dynamic origin-destination estimation systems. Transportation Research 14C, 384-402.
Lin, P.W., Chang, G.L. (2005) A robust model for estimating freeway dynamic origin-destination matrix. Transportation Research Record 1923, 110-118.
Lin, P.W., Chang, G.L. (2007) A generalized model and solution algorithm for estimation of the dynamic freeway origin-destination matrix. Transportation Research 41B, 554-572.
Lin, W.H., Lo, H.K. (2003) A theoretical probe of a German experiment on stationary moving traffic jams. Transportation Research 37B, 251-261.
Liu, G., Lyrintzis, A. S., Michalopoulus, P. G. (1998) Improved high-order model for freeway traffic flow. Transportation Research Record 1644, 37-46.
Lo, H.K. (2001) A cell-based traffic control formulation: strategies and benefits of dynamic timing plans. Transportation Science 35.
Lo, H.K., Szeto, W. Y. (2002) A cell-based dynamic traffic assignment model: formulation and properties. Mathematical and Computer Modelling 35, 849-865.
Lo, H.K., Szeto, W.Y. (2002) A cell-based variational inequality formulation of the dynamic user optimal assignment problem. Transportation Research 36B, 421-443.
Lo, H.K., Zhang, N., Lam, W. (1996) Estimation of an origin-destination matrix with random link choice proportions: A statistical approach. Transportation Research 30B, 309-324.
Lo, H.P., Zhang, N. (1996) Estimation of an origin-destimation matrix with random with choice proportions : a statistical approach. Transportation Research 30B, 309-324.
Lo, H.P., Chan, C.P. (2003) Simultaneous estimation of an origin–destination matrix and link choice proportions using traffic counts. Transportation Research 37A, 771–788.
Lundgren, J.T., Peterson, A. (2008) A heuristic for the bi-level origin-destination matrix estimation problem. Transportation Research 42B, 339-354.
Nagel, K., Schreckenberg, M. (1992) A cellular automaton model for freeway traffic. Physics 2I, 2221-2229.
Pai, P.F., Lin, C.S. (2005) A hybrid ARIMA and support vector machines model in stock price forecasting, Omega 33, 497 – 505.
Pal, N.R. Pal, K., Keller, J.M., Bezdek, J.C. (2005) A possibilistic fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy Systems 13, 517-532.
Pampalk, E., Widmer, G., Chan, A. (2003) A new approach to hierarchical clustering and structuring of data with self-organizing maps. Technical Report OeFAI-TR-2003-09.
Payne, H. J. (1971) Model of freeway traffic and control. Mathematics of Public Systems 1, 51-61.
Pipes, L.A. (1953) An operational analysis of traffic dynamics. Journal of Applied Physics 24 (3), 74–281.
Pottmeier, A., Barlovic, R., Knospe, W., Schadschneider, A. (2002) Localized defects in a cellular automaton model for traffic flow with phase separation. Physica 308A, 471-482.
Rauber, A., Merkl, D., Dittenbach, M. (2002) The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. Neural Networks, IEEE Transactions 13(6), 1331-1341.
Ricardo, G.R. (2008) A column generation algorithm for the estimation of origin–destination matrices in congested traffic networks. European Journal of Operational Research 184, 860–878.
Richards, P. I. (1956) Shock waves on highway. Operations Research 4, 42-51.
Sherali, H.D. Park. T. (2001) Estimation of dynamic origin-destination trip tables for a general network. Transportation Research 35B, 217-235.
Smith, B.L., Williams, B.M., Oswald, R.K. (2002) Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research 10C, 303-321.
Stathopoulos, A., Karlaftis, M.G. (2003) A multivariate state space approach for urban traffic flow modeling and prediction. Transportation Research 11C, 121-135.
Sun, X., Munoz, L., Horowitz, R. (2003) Highway traffic state estimation using improved mixture kalman filters for effective ramp metering control. Proceedings 42nd IEEE Conference on Decision and Control, 6333-6338.
Szeto, W.Y., Lo, H.K. (2004) A cell-based simultaneous route and departure time choice model with elastic demand. Transportation Research 38B, 593-612.
Tangsripairoj, S., Samadzadeh, M.H. (2006) Organizing and visualizing software repositories using the growing hierarchical self-organizing map. Journal of Information Science and Engineering 22, 283- 295.
Tseng, F.M., Tzeng, G.H. (2005) A fuzzy seasonal ARIMA model for forecasting. Omega 33, 497-505.
Van Zulylen, H., Willumsen, L. G. (1980) The most likely trip matrix estimated from traffic counts. Transportation Research 14B, 281-293.
Vardi, Y. (1996) Network tomography: estimating source-destination traffic intensities from link data. Journal of the American Statistical Association 91, 365-377.
Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C. (2006) Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume. Transportation Research 14C, 351-367.
Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C. (2006) Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume, Transportation Research 14C, 351–367.
Wang, B.H., Wang, L., Hui, P.M., Hu, B. (2000) The asymptotic steady states of deterministic one-dimensioned traffic flow models. Physica 27B, 237-239.
Wang, S.C., Tong, C.O. (1998) Estimation of time-dependent origin-destination matrices for transit network. Transportation Research 32B, 35-48.
Wang, X. (2005) GLS estimation of O-D matrix with traffic counts and information from ATIS. Proceedings of the Eastern Asia Society for Transportation Studies 5, 1188-1196.
Welch, W.J. (1983) A mean squared error criterion for the design of experiment. Biometrika 70, 205-213.
Wolf, D.E. (1999) Cellular automata for traffic simulations. Physica 263A, 438-451.
Wong, G. C. K., Wong, S. C. (2002). A multi-class traffic flow model–an extension of LWR model with heterogeneous drivers. Transportation Research 36A, 827-841.
Wu, J. (1997) A real-time origin-destination matrix updating algorithm for on-line approach. Transportation Research 31B, 381-396.
Wu, J., Chang, G.L. (1996) Estimation of time-varying origin-destination distributions with dynamic screenline flows. Transportation Research 30B, 277-290.
Yang, H., Sasaki, T., Iida, Y., Akiyama, T. (1992) Estimation of origin-destination matrices from link traffic counts on congested networks. Transportation Research 26B, 417-434.
Yang, H. (1995) Heuristic algorithms for the bi-level origin-destination matrix estimation problem. Transportation Research 29B, 231-242.
Yang, H.T., Chen, S.C., Tsai, W.N. (2004) Classification of direct load control curves for performance evaluation. IEEE Transactions on Power Systems 19, 811-829.
Yang, H.T., Chen, S.C., Peng, P.C. (2005) Genetic k-means-algorithm-based classification of direct load-control curves. IEE Proc.-Gener. Transm. Distrib. 152, 32-49.
Yang, Y., Jiang, D., Xia, M. (2010) Using improved GHSOM for intrusion detection. Journal of Information Assurance and Security 5232-239.
Yao, L., Lin, C.C. (2009) Identification of nonlinear systems by the genetic programming-based Volterra filter. IET signal process 3, 93-105.
Yella, S., Gupta, N.K., Dougherty, M.S. (2007) Comparison of pattern recognition techniques for the classification of impact acoustic emissions. Transportation Research 15C, 345–360.
Yu, N., Zhang,H.M., Recker, W.W. (2005) Inferring origin–destination trip matrices with a decoupled GLS path flow estimator. Transportation Research 39B, 497-518.
Yu, F., Tang, J., Cai, R. (2007) Partially horizontal collaborative fuzzy c-means, International Journal of Fuzzy Systems 9, 13-28.
Zhang, G.P. (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159-175.
Zhang, H. M., Kim, T. (2005) A car-following theory for multiphase vehicular traffic flow. Transportation Research 39B, 385-399.
Zhang, H. M. (1998) A theory of nonequilibrium traffic flow. Transportation Research 32B, 485-498.
Zhang, X., Maher, M.J. (1998) The evaluation and application of a fully disaggregate method for trip matrix estimation with platoon dispersion, Transportation Research 32B, 261-276.
Zhou, X., Mahmassani, H.S. (2007) A structural state space model for real-time traffic origin-destination demand estimation and prediction in a day-to-day learning framework. Transportation Research 41B, 823-840.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
:::
無相關博士論文
 
無相關書籍
 
無相關著作
 
無相關點閱
 
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：593（臺灣：582，外國：11） ／ 本年度總使用人次：2795664 ／ 訪客人次（自102年09月）：84118719
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上