請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72851
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 楊曙榮(Shu-Jung Yang) | |
dc.contributor.author | Jhan-Hsiang Chen | en |
dc.contributor.author | 陳展詳 | zh_TW |
dc.date.accessioned | 2021-06-17T07:08:06Z | - |
dc.date.available | 2024-08-05 | |
dc.date.copyright | 2019-08-05 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-07-24 | |
dc.identifier.citation | Curran, A. (2008). Translink public bike system feasibility study. Quay Communications Inc., Vancouver.
DeMaio, P. (2009). Bike-sharing: History, impacts, models of provision, and future. Journal of public transportation, 12(4), 3. Fishman, E. (2016). Bikeshare: A review of recent literature. Transport Reviews, 36(1), 92-113. Gates, R. (2006). A mata Geweke–Hajivassiliou–Keane multivariate normal simulator. The Stata Journal, 6(2), 190-213. Genz, A. (1992). Numerical computation of multivariate normal probabilities. Journal of computational and graphical statistics, 1(2), 141-149. Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1-14. Lax, P. D. (1999). Change of variables in multiple integrals. The American mathematical monthly, 106(6), 497-501. Manski, C. F. (1977). The structure of random utility models. Theory and decision, 8(3), 229-254. Midgley, P. (2009). The role of smart bike-sharing systems in urban mobility. Journeys, 2(1), 23-31. Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods. O'Mahony, E. (2015). Smarter tools for (Citi) bike sharing. Pollak, R. A. (1971). Additive utility functions and linear Engel curves. The Review of Economic Studies, 38(4), 401-414 Rixey, R. A. (2013). Station-level forecasting of bikesharing ridership: station network effects in three US systems. Transportation Research Record, 2387(1), 46-55. Small, K. (2013). Urban transportation economics. Taylor & Francis. Yildirim, I. (2012a). Bayesian inference: Gibbs sampling. Technical Note, University of Rochester. Yildirim, I. (2012b). Bayesian inference: Metropolis-hastings sampling. Dept. of Brain and Cognitive Sciences, Univ. of Rochester, Rochester, NY. Borgnat, P., Abry, P., Flandrin, P., Robardet, C., Rouquier, J. B., & Fleury, E. (2011). Shared bicycles in a city: A signal processing and data analysis perspective. Advances in Complex Systems, 14(03), 415-438. Campbell, A. A., Cherry, C. R., Ryerson, M. S., & Yang, X. (2016). Factors influencing the choice of shared bicycles and shared electric bikes in Beijing. Transportation research part C: emerging technologies, 67, 399-414. Casella, G., & Berger, R. L. (1987). Reconciling Bayesian and frequentist evidence in the one-sided testing problem. Journal of the American Statistical Association, 82(397), 106-111. García-Palomares, J. C., Gutiérrez, J., & Latorre, M. (2012). Optimizing the location of stations in bike-sharing programs: A GIS approach. Applied Geography, 35(1-2), 235-246. Hajivassiliou, V. A., & Ruud, P. A. (1994). Classical estimation methods for LDV models using simulation. Handbook of econometrics, 4, 2383-2441. Hamari, J., Sjöklint, M., & Ukkonen, A. (2016). The sharing economy: Why people participate in collaborative consumption. Journal of the association for information science and technology, 67(9), 2047-2059. Henderson, H. V., Pukelsheim, F., & Searle, S. R. (1983). On the history of the Kronecker product. Linear and Multilinear Algebra, 14(2), 113-120. Hollins, B., & Shinkins, S. (2006). Managing service operations: Design and implementation. Sage Kaufman, S. M., Gordon-Koven, L., Levenson, N., & Moss, M. L. (2015). Citi bike: The first two years. Kemeny, J. G., & Snell, J. L. (1976). Markov Chains. Springer-Verlag, New York. Kim, J., Allenby, G. M., & Rossi, P. E. (2002). Modeling consumer demand for variety. Marketing Science, 21(3), 229-250. Maurer, H., & Zowe, J. (1979). First and second-order necessary and sufficient optimality conditions for infinite-dimensional programming problems. Mathematical programming, 16(1), 98-110. Plummer, M., Best, N., Cowles, K., & Vines, K. (2006). CODA: convergence diagnosis and output analysis for MCMC. R news, 6(1), 7-11. Winter, S., Sester, M., Wolfson, O., & Geers, G. (2011). Towards a computational transportation science. Journal of Spatial Information Science, 2011(2), 119-126. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72851 | - |
dc.description.abstract | 本文探討了空間性相關產品的產品選擇對使用者效用的影響。 本文的第一步是針對本研究的理論模型進行說明及推導,以租借站作為單位,分析每站使用者對於各個目標區域的效用以及如何通過貝葉斯分析方法來估計模型中的參數。 下一步,進行實證分析,我們對於台北市公共自行車租賃系統在2016年一月所記錄原始數據進行解析,將原始數據轉換後的變數資料導入效用模型中來生成相關參數。 在論文的最後步驟,探討如果選擇的特定目的地區域受到限制, 為了保持使用者的原始效用水準,評估應該為使用者補償多少費用。以此補償費用作為評估租借站是否做設備擴張的考量因素。 | zh_TW |
dc.description.abstract | This thesis explores the impact of product selection of spatial-related products on rider utility. The first step of the thesis is to develop the theoretical model. We use the rental station as a unit to analyze the utility of each station user for variety area and to explain how the parameters in the model are estimation via Bayesian analysis. Next step is to conduct the empirical analysis, the researcher converted the original data of Youbike in Taipei City in 2016, and then used the transformed data in the model to generate relevant parameters. Finally, in order to maintain rider utility, we measure how much money the rider should be compensated if the rider goes to the restricted destination area where the researcher selected. This compensation value will be used as a factor to evaluate whether the rental station is doing equipment expansion. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T07:08:06Z (GMT). No. of bitstreams: 1 ntu-108-R06741062-1.pdf: 4143056 bytes, checksum: 24f1ee69e1171d919da94804492c704e (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | Abstract ii
TABLE OF CONTENTS iii LIST OF FIGURES v LIST OF TABLES v 1. Introduction 1 2. Background 4 2.1 Shared bicycles 4 2.2 Youbike in Taipei city 5 3. Literature Review 7 3.1 Transportation Science and Service Operations research 7 3.2 Smart City and Bike Sharing research 7 4. Bayesian algorithm 9 4.1 Gibbs sampling algorithm 9 4.2 Metropolis-Hasting sampling algorithm 10 5. Model Development 11 5.1 Utility Structure 11 5.2 Statistical Specification 12 5.3 Heterogeneity 16 5.4 Parameter Estimate 17 5.5 Inference 18 6. Empirical Study 19 6.1 Data 19 6.2 Variables 22 6.3 Parameter Results 24 6.4 Policy Implication-Compensating Value 27 7. Concluding Remarks 34 References 36 Appendix 39 Appendix A: Estimation method 39 Appendix B: GHK implementation 41 Appendix C: Plot for MCMC 10,000 simulated draw for β 43 Appendix D: Plot for MCMC 10,000 simulated draw for ρ 45 Appendix E: Plot for MCMC 10,000 simulated draw for Dβ 47 Appendix F: Effective Sample Size for MCMC sampling 69 Appendix G: Parameter distribution for α and ψ 70 Appendix H: Code for study 72 H.1 Construct Markov transition matrix 72 H.2 Data process 73 H.3 Corner solution and internal solution counting 76 H.4 Heuristics method. 78 | |
dc.language.iso | en | |
dc.title | 空間相關產品多樣性的補償效果 | zh_TW |
dc.title | Effect of compensation on the variety of spatial-related products | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張景宏(Ching-Hung Chang),洪一薰(I-Hsuan Hong) | |
dc.subject.keyword | 貝葉斯分析,台北市公共自行車租賃系統,空間相關產品,共享單車, | zh_TW |
dc.subject.keyword | Bayesian analysis,Youbike,spatial-related products,shared bicycle, | en |
dc.relation.page | 81 | |
dc.identifier.doi | 10.6342/NTU201901762 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-07-24 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 商學研究所 | zh_TW |
顯示於系所單位: | 商學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-108-1.pdf 目前未授權公開取用 | 4.05 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。