台鐵過去原為主要城際運輸工具，但面臨公路運輸的競爭，導致長途客運運量萎縮，而使客運有集中在短途運輸的趨勢，近三年短途運輸(50公里以下)佔總客運量高達73%左右。為因應未來高鐵的競爭，台鐵將以都會區內短途運輸作為其重新定位，規劃在各大都會區內增設通勤車站，企圖藉由捷運化手段力求永續經營。此外，台鐵目前虧損嚴重，亦應以企業經營之觀念，利用其車站地利之便，追求多角化經營，以增加車站營收並減少虧損。由此可知，台鐵車站未來將扮演極為重要的角色。本研究企圖藉由分析台鐵車站營運狀況，來瞭解各站的定位與功能，以作為規劃的參考。首先利用資料包絡分析法(DEA)及隨機邊界分析法(SFA)衡量台鐵各車站之效率。之後建構Tobit迴歸模式探討影響效率差異之因素，其中並引入品質變數的概念，考量人力素質、車站自動化設備以及車站營運環境對效率改變的影響。
考慮到台鐵東西部幹線技術以及環境等不同，本研究將樣本分為西部幹線以及東部幹線。西部幹線包含台北、台中以及高雄三個運務段，而東部幹線則包含宜蘭與花蓮兩個運務段。實證結果顯示1）總體而言，等級較高之車站的平均效率值高於等級較低者。但部分等級較高車站之效率反而低於等級較低者。2）DEA之實證結果發現，以運輸收入為產出之模式所得的技術效率排名比較符合台鐵目前之車站等級。3）Tobit迴歸結果顯示車站營運環境變數仍是影響車站技術效率的主要因素。4）在西部幹線中，台北運務段與高雄運務段效率值相差不多，而台中運務段較差。在東部幹線中，宜蘭運務段比花蓮運務段好。5）不同車種之停靠率對於車站具有不同影響，自強號及莒光號列車之停靠率對車站收入有顯著影響，復興號及電聯車之停靠率則對旅客數有顯著影響。6）整體來說DEA法與SFA法的衡量結果頗為接近。

Taiwan Railway (TR) has played a major role in the long-distance ground transportation for past long periods, but its short-distance transportation has surpassed long-distance transportation and became the main passenger traffic source when facing the competition of highway transportation. In response to the competition of the High Speed Railroad, TR is in the process of transforming into part of the urban rapid transit system, repositioning into providing short-range transportation by increasing the construction of commuting stations. Furthermore, TP is presently in great deficit, thus under the concept of enterprise management, should utilize the advantageous location of its stations to pursue diversification management to increase revenue and decrease deficit. This study employs two methods – data envelopment analysis (DEA) and stochastic frontier analysis (SFA), to estimate the relative efficiency of each station’s operations. Then we construct Tobit regression models to analyze the factors that effect the efficiency. The factors used include quality variables such as human quality, automatic equipment, and operation environment
We divide the samples into western line and eastern line considering the different techniques and environments of the two. The western line includes three sections – Taipei, Taichung, and Kaohsiung. The eastern line includes two sections – Ilan and Hualien. The empirical results show that: 1) In overall, the average efficiency of the high-class stations are higher than the lower ones. But not all of the individual efficiencies of the high-class stations are better than the low-class ones. 2) The rank of station efficiency from the results of the DEA model which specify revenue as output is more identical to the current station status of TR. 3) The results of the Tobit regression found that the environment variable is the major factor effecting station efficiency. 4) In the western line, the efficiency of the Taipei section is close to Kaohsiung, and better than Taichung. In the eastern line, the Ilan section is better than Hualien. 5) The stop ratio of different train types effects the station differently. The stop ratio of the Tzu-Chiang train and Chu-Kuang train effects the revenue. The stop ratio of the Fu-Hsing train effects the passenger volume. 6) The empirical results of SFA are mostly identical to DEA.

1. 台灣鐵路管理局，台鐵統計月報。
2. 台灣鐵路管理局，台鐵統計年報。
3. 台灣鐵路管理局，台鐵站勢調查報告。
4. 台灣鐵路管理局，台鐵城際運輸及通勤運輸狀況之探討，民國90年8月，台灣鐵路管理局網站資料，http：//www.railway.gov.tw。
5. 交通部運輸研究所，公鐵路客運運價之研究，民國76年11月。
6. 交通部運輸研究所，台鐵經營管理之課題及改善策略，民國82年5月。
7. 交通部運輸研究所，台鐵組織、營運、財務及經營改善策略研究—工作報告，民國85年3月。
8. 交通部運輸研究所，鐵路票價檢討與研析，民國86年3月。
9. 游明敏、曹壽民，台灣民航機場隨機邊界生產函數及技術效率之分析，運輸學刊第十三卷第二期，頁1-頁26，民國90年6月。
10. 鄭雪萍，台鐵成本結構及生產力變化之研究，國立成功大學交通管理科學研究所碩士論文，民國91年6月。
11. 游明敏、徐世勳，考慮航空噪音下國內機場經營績效及投入擁擠現象之研究，2001台灣經濟學會年會論文集，民國90年12月。
12. 藍武王、林村基，鐵路運輸之生產效率分析：DEA與SFA方法之比較，第十屆校際運輸學術聯誼研討會，民國91年5月。
13. Aigner, D. J., Lovell, C. A. K. and Schmidt, P.（1977）, “Formulation and Estimation of Stochastic Frontier Production Function Models,” Journal of Economics, Vol. 6, pp.21-37.
14. Ali, A. I. and L. M. Seiford（1993）, ”The Mathematical Programming Approach to Efficiency Analysis,” in Fried, H. O., C. A. K. Lovell and S. S. Schmidt（Eds.） The Measurement of Productive Efficiency: Techniques and Applications, Oxford University Press, pp. 120-159.
15. Banker, R. D., Charns, A., Cooper, W. W. （1984）, “Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis,” Management Science, 30, No. 9, pp.1078-1092.
16. Battese, G. E. and Coelli, T. J.（1992）, “Frontier Production Functions, Technical Efficiency and Panel Data：With Application to Paddy Farmers in India,” Journal of Productivity Analysis, Vol. 3, pp.153-169.
17. Battese, G. E. and Coelli, T. J.（1995）, “A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data,” Empirical Economics, Vol. 20, pp.325-332.
18. Charns, A., Cooper, W. W. and Rhoades E.（1978）, “Measuring the Efficiency of Decision Making Units,” European Journal of Operational Research, 2, pp.429-444.
19. Colli, T. J.（1996）, “A guide to DEAP Version 2.1：A Data Envelopment Analysis（Computer）Program,” CEPA Working Paper No. 8/96, Department of Econometrics, University of New England.
20. Colli, T. J., D. S. P. Rao and G. Battese（1998）, An Introduction to Efficiency and Productivity Analysis, Kluwer Academic Publishers.
21. Cantos, P., Pastor, J. M. and Serrano, L.（1999）, “Productivity Efficiency and Technical Change in European Railways：A non-parametric approach,” Transportation, Vol. 26, pp.337-357.
22. Cantos, P. and Maudos, J.（2001）, “Regulation and Efficiency：The Case of European Railways,” Transportation Research Part A, 35, pp.459-472.
23. Cowie, J.（1999）, “The Technical Efficiency of Public and Private Onership in Railway Industry,” Journal of Transport Economics and Policy, 33, pp.241-252.
24. Farrell, M. J.（1957）, “The Measurement of Productive Efficiency,” Journal of the Royal Statistical Society, Series A, CXX, Part3, pp.253-290.
25. Gathon, H. J. and Pestieau, P.（1995）, “Decomposing Efficiency into it’s Managernal and it’s Regulatory Components：The Case of European Railways,” European Journal of Operational Research, pp.500-507.
26. Huang, C. and Liu, J. T.（1994）, “Estimation of a Non-Neutral Stochastic Frontier Production Function,” Journal of Productivity Analysis, Vol. 4, pp.171-180.
27. Loizides, J. and Tsionas, E. G.（2002）, “Productivity Growth in European Railways：A new approach,” Transportation Research Part A, pp.633-644.
28. Meeusen, W. van den Broeck, J.（1977）, “Efficiency Estimation from Cobb-Douglas Production Function with Composed Error,” International Economic Review, Vol. 18, pp.435-444.
29. Oum, T. H. and Yu, C.（1994）, “Economic Efficiency of Railways and Implications for Publicy,” Journal of Transport Economics and Policy, 28, pp.121-138.
30. Oum, T. H., WatersII, W. G. and Yu, C.（1999）, “A Survey of Productivity and Efficiency Measurement in Rail Transport,” Journal of Transport Economics and Policy, 33, Part 1, pp.9-42.