| 研究生: |
方菀萍 Fang, Wan-Ping |
|---|---|
| 論文名稱: |
個體選擇模式選擇集合之研究
—以國道客運北高路線為例 Disaggregate Intercity Passenger Mode Choice Models Considering Choice Set Generation |
| 指導教授: |
段良雄
Duann, Liang-Siong |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 交通管理科學系 Department of Transportation and Communication Management Science |
| 論文出版年: | 2002 |
| 畢業學年度: | 90 |
| 語文別: | 中文 |
| 論文頁數: | 123 |
| 中文關鍵詞: | 國道客運 、選擇集合 、羅吉特模式 、個體異質性 |
| 外文關鍵詞: | incomplete pairwise comparison, heterogeneity, Logit model, choice set, highway passenger transport |
| 相關次數: | 點閱:107 下載:4 |
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本研究從選擇集合內生化之定義方式,尋找能合理解釋並可實際反映國道客運市場旅運者選擇行為之運具選擇模式,藉此瞭解國道客運旅運者對運具選擇之偏好,以期對未來營運者之行銷策略有所助益。
本研究利用不完全成對比較法,瞭解旅運者對客運公司各服務準則之感受度,以為不同選擇集合定義方式下個體選擇模式構建之基礎。模式構建包括決定論定義選擇集合之MNL模式、機率論定義選擇集合之CDM模式與PIAL模式。此外亦探討個體異質性問題,瞭解個體品味差異對選擇模式之影響。
研究結果可以發現機率論定義選擇集合之CDM模式與PIAL模式,其模式解釋力皆較決定論定義選擇集合之MNL模式佳,其中又以CDM模式更能呈現個體選擇行為。本研究除了利用機率論定義選擇集合方式考慮個體異質性外,亦透過隨機個體品味差異項進行個體異質性之探討。在本研究模式解釋變數結構下,模式校估結果可以發現各服務感受度可直接反映個體之品味差異。而考慮個體異質性,有助於模式對個體選擇行為之解釋能力,唯不同個體品味差異項之假設,對模式呈現個體選擇行為亦有所異同。
This paper aims to look for reasonable and reflective of travelers behavior in the highway passenger transport market from an aspect of endogenous choice set generation. Basing on this mode choice preference, we can assist the highway passenger transport proprietors in marketing strategy.
In this research, we can understand travelers’ conscious of service that highway passenger transport proprietors provide. Basing on this conscious of service, we can construct individual choice models under different definitions of choice set. We constructed several models under deterministic definitions of choice set, like MNL, or under probability definitions of choice set, like CDM or PIAL. Beside this consideration for the individual heterogeneity by probability definitions of choice set, we also discussed the heterogeneity with individual random taste difference.
The empirical results showed that CDM and PIAL under probability definitions of choice set were better than MNL under deterministic definitions of choice set, especially CDM. This result indicated that model under probability definitions of choice set could express actual travelers’ behavior. Under the explanation variable structures in this research, the results showed that the conscious of service could reflect individual random taste difference directly. In short, the consideration of individual heterogeneity could improve the explanation of models. Only while different individual random taste difference were hypothesized, there are different appearances of individual choice behavior in the model.
參考文獻
中文部分
1. 吳泰岳,「敘述偏好數據誤差項之探討」,國立成功大學交通管理研究所碩士論文,民國90年
2. 段良雄、楊志文,「考慮選擇集合之兩階段運具選擇模式」,中華民國運輸學會第十五屆學術研討會論文集,民國89年
3. 施鴻志、段良雄、凌瑞賢,「都市交通計畫—理論與實務」,茂昌圖書有限公司,民國73年
4. 楊國峰,「家戶運具選擇模式」,國立成功大學交通管理研究所碩士論文,民國77年
5. 陳順宇,「多變量分析」,華泰書局,民國89年
英文部分
6. Alba, K.W., Hutchinson, J.W. and Lynch Jr., J.G. (1991), “Memory and decision making ,” In: Robertson, T.S., Kassarjian, H.H., Handbook of Consumer Behavior, Prentice Hall, Englewood Cliffs, NK, 1-49.
7. Andrews, R.L. and Srinivansan, T.C. (1995), “Studying consideration effects in empirical models using scanner panel data,” Journal of Marketing Research, 32, 30-41.
8. Ben Akiva, M. (1977), “Choice Models with Simple Choice Set Generating Processes,” working paper, Department of Civil Engineering, Massachusetts Institute of Technology, Cambridge, MA.
9. Bronnenberg, B.K. and Vanhonacker, W.R. (1996), “Limited choice sets, local price response, and implied measures of price competition,” Journal of Marketing Research, 33. 163-173.
10. Chamberlian, G.(1984), “Panel Data,” Handbook of Econometrics, Vol. 2, 1248-1318, North-Holland, Amsterdam.
11. Cho, H.J. and Kim, K.S.(1999), “Combined analysis of heteroscedasticity and correlation of repeated observations in SP data,” Proceeding of 27th European Transport Forum (PTRC), England.
12. Cogger, K.O. and Yu, P.L.(1985), “Eigen weight vectors and least distance approximation for revealed preference in pairwise weight ratios”, Journal of Optimization Theory and Applications, 46, 483-491
13. Crawford, G.. and Williams, C.A., “A note on the analysis of subjective judgment matrices”, Journal of Mathematical Psychology, 29, 387-405
14. Fotheringham, A.S. (1988), “Consumer Store choice and Choice Set Definition,” Marketing Science, 7, 299-310.
15. Hutchinson, J.W., Raman, K., and Mantrala, M.K. (1994), “Finding choice alternatives in memory: Probability models of brand name recall,” Journal of Marketing Research, 31, 441-461.
16. Gaudry, M.J.I. and Dagenais, M.G. (1979), “The Dogit model,” Transportation Research, Series B 13B, 105-111.
17. Gensch, D.H. (1987), “A two-stage disaggregate attribution choice model,” Marketing Science, 6, 223-231.
18. Koczkodaj, W. W.(1993), “A new definition of consistency of pairwise comparisons”, Mathematical and Computer Modelling, 18, 79-84
19. Koczkodaj, W. W.(1996), “Statistically accurate evidence of improved error rate by pairwise comparisons”, Perceptual and Motor Skills, 82, 43-38
20. Koczkodaj, W. W.(1998), “Testing the accuracy enhancement of pairwise comparisons by a Monte Carlo experiment”, Journal of Statistical Planning and Inference, 69, 21-31
21. Manrai, A.K. (1995), “Mathematical models of brand choice behavior,” European Journal of Operational Research, 82, 1-17.
22. Manrai, A.K. and Andrews, R.L. (1998), “Two-stage Discrete Choice Models for Scanner Panel Data: An assessment of Process and Assumptions,” European Journal of Operational Research, 111, 193-215.
23. Michael W.H. and Waldemar W.K. (1996), “A Monte Carlo study of pairwise comparison ”, Information Processing Letters, 57, 25-29.
24. Manski, C. (1977), “The structure of random utility models,” Theory and Decision, 8, 229-254.
25. Mitra, A. (1995), “Advertising and the stability of consideration sets over multiple purchase occasions,” International Journal of Research in Marketing, 12, 81-94.
26. Parkinson, T.L. and Michael, R. (1978), “An Information Processing Approach to Evoked Set Formation,” Advances in Consumer Research, 6 366-387.
27. Roberts, J.H. (1989), “A Grounded Model of Consideration Set Size and Composition,” Advances in Consumer Research, 16, 749-757.
28. Shocker, A.D., Ben-Akiva, M., Boccara, B. and Nedungadi, P. (1991), “Consideration set influences on consumer decision making and choice: Issues, models, suggestions,” Marketing Letters, 2, 181-197.
29. Siddarth, S., Bucklin, R.E. and Morrison, D.G, (1995), “Making the cut: Modeling and analyzing choice set restriction in scanner panel data,” Journal of Marketing Research, 32, 255-266.
30. Stigler, G. J. (1961), “The economics of information,” Journal of Pplitical Economy, 69, 213-225.
31. Swait, J.D. (1984), “Probabilistic choice set generation in transportation demand models,” Unpublished Ph.D. dissertation, Massachusetts Institute of Technology, Cambridge, MA
32. Swait, J.D. and Ben-Akiva, M. (1985), “Constrains on Individual Travel Behavior in a Brazilizn City,” Transportation Research Record, 1085, 75-85.
33. Swait, J.D. and Ben-Akiva, M. (1987a), “Incorporating random constraints in discrete models of choice set generation,” Transportation Research, Series B 21B, 91-102
34. Swait, J.D. and Ben-Akiva, M, (1987b), “Empirical test of a constrained choice discrete model: Mode choice in Sao Paulo, Brazil,” Transportation Research, Series B 21B, 103-115
35. Satty, T.L. (1977), “A scaling method for priorities in hierarchical structure”, Journal of Mathmematical Psychology , 15,234-281
36. Satty, T.L. and Vargas, L.G. (1984), “Comparison of Eigenvalue, Logarithmic Least Square and Least Square methods in estimating ratios”, Mathematical Modelling, 5, 309-324