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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪英超 | zh_TW |
| dc.contributor.advisor | Ying-Chao Hung | en |
| dc.contributor.author | 朱彥奇 | zh_TW |
| dc.contributor.author | Yan-Chi Ju | en |
| dc.date.accessioned | 2025-09-10T16:14:28Z | - |
| dc.date.available | 2025-09-11 | - |
| dc.date.copyright | 2025-09-10 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-30 | - |
| dc.identifier.citation | [1] 台北市政府資料開放平台, YouBike 即時站點資訊資料集. 2024. https://data.taipei/dataset/detail?id=c6bc8aed-557d-41d5-bfb1-8da24f78f2fb
[2] Yang, Xu-Hua, et al., The impact of a public bicycle-sharing system on urban public transport networks. Transportation research part A: policy and practice, 2018. 107: p. 246–256. [3] Zheng, Lingwei and Li, Yan, The development, characteristics and impact of bike sharing systems: a literature review. International review for spatial planning and sustainable development, 2020. 8(2): p. 37–52. [4] Guo, Yuanyuan, Yang, Linchuan, and Chen, Yang, Bike share usage and the built environment: A review. Frontiers in public health, 2022. 10: p. 848169. [5] Reinsch, Christian H, Smoothing by spline functions. Numerische mathematik, 1967. 10(3): p. 177–183. [6] Wahba, Grace, Spline models for observational data. 1990: SIAM. [7] De Boor, Carl, A practical guide to splines. Vol. 27. 1978: springer New York. [8] Green, Peter J and Silverman, Bernard W, Nonparametric regression and generalized linear models: a roughness penalty approach. 1993: Crc Press. [9] Beran, Jan, Statistics for long-memory processes. 2017: Routledge. [10] Taqqu, Murad S, Fractional Brownian motion and long-range dependence. Theory and applications of long-range dependence, 2003: p. 5–38. [11] Bardet, Jean-Marc, et al., Semi-parametric estimation of the long-range dependence parameter: A survey. Theory and applications of long-range dependence, 2003. 557: p. 577. [12] Taqqu, Murad S and Teverovsky, Vadim, Semi-parametric graphical estimation techniques for long-memory data. in Athens Conference on Applied Probability and Time Series Analysis: Volume II: Time Series Analysis In Memory of EJ Hannan. 1996. Springer. [13] Shapiro, Samuel Sanford. and Wilk, Martin B, An analysis of variance test for normality (complete samples). Biometrika, 1965. 52(3-4): p. 591–611. [14] Mardia, Kanti V, Measures of multivariate skewness and kurtosis with applications. Biometrika, 1970. 57(3): p. 519–530. [15] Henze, Norbert and Zirkler, Bernd, A class of invariant consistent tests for multivariate normality. Communications in statistics-Theory and Methods, 1990. 19(10): p. 3595–3617. [16] Anderson, Theodore Wilbur et al., An introduction to multivariate statistical analysis. Vol. 2. 1958: Wiley New York. [17] Mandelbrot, Benoit B and Van Ness, John W, Fractional Brownian motions, fractional noises and applications. SIAM review, 1968. 10(4): p. 422–437. [18] Coeurjolly, Jean-François, Estimating the parameters of a fractional Brownian motion by discrete variations of its sample paths. Statistical Inference for stochastic processes, 2001. 4: p. 199–227. [19] Hung, Ying-Chao and Michailidis, George, Modeling and optimization of time-of-use electricity pricing systems. IEEE transactions on smart grid, 2018. 10(4): p. 4116–4127. [20] Achard, Sophie and Coeurjolly, Jean-François, Discrete variations of the fractional Brownian motion in the presence of outliers and an additive noise. 2010. [21] Cheridito, Patrick, Kawaguchi, Hideyuki, and Maejima, Makoto, Fractional ornstein-uhlenbeck processes. 2003. [22] Mishura, Yuliya and Mishura, I︠U︡lii︠a︡ S, Stochastic calculus for fractional Brownian motion and related processes. Vol. 1929. 2008: Springer Science & Business Media. [23] Comte, Fabienne and Renault, Eric, Long memory in continuous‐time stochastic volatility models. Mathematical finance, 1998. 8(4): p. 291–323. [24] Brouste, Alexandre and Kleptsyna, Marina, Asymptotic properties of MLE for partially observed fractional diffusion system. Statistical Inference for Stochastic Processes, 2010. 13: p. 1–13. [25] Hu, Yaozhong and Nualart, David, Parameter estimation for fractional Ornstein–Uhlenbeck processes. Statistics & probability letters, 2010. 80(11-12): p. 1030–1038. [26] Chronopoulou, Alexandra and Viens, Frederi G, Estimation and pricing under long-memory stochastic volatility. Annals of finance, 2012. 8(2): p. 379–403. [27] Wang, Xiaohu, Xiao, Weilin, and Yu, Jun, Modeling and forecasting realized volatility with the fractional Ornstein–Uhlenbeck process. Journal of Econometrics, 2023. 232(2): p. 389–415. [28] Kříž, Pavel and Szała, Leszek, Least-squares estimators of drift parameter for discretely observed fractional Ornstein–Uhlenbeck processes. Mathematics, 2020. 8(5): p. 716. [29] Efron, Bradley, Bootstrap methods: another look at the jackknife, in Breakthroughs in statistics: Methodology and distribution. 1992, Springer. p. 569–593. [30] Efron, Bradley and Tibshirani, Robert J, An introduction to the bootstrap. 1994: Chapman and Hall/CRC. [31] Hall, Peter., The bootstrap and Edgeworth expansion. 2013: Springer Science & Business Media. [32] Davison, Anthony Christopher and Hinkley, David Victor, Bootstrap methods and their application. 1997: Cambridge university press. [33] Lahiri, Soumendra Nath, Resampling methods for dependent data. 2013: Springer Science & Business Media. [34] Hosking, Jonathan RM, Modeling persistence in hydrological time series using fractional differencing. Water resources research, 1984. 20(12): p. 1898–1908. [35] Dieker, Ton., Simulation of fractional Brownian motion. 2004, Masters Thesis, Department of Mathematical Sciences, University of Twente …. [36] Ascione, Giacomo, Mishura, Yuliya, and Pirozzi, Enrica, Time-changed fractional Ornstein-Uhlenbeck process. Fractional Calculus and Applied Analysis, 2020. 23(2): p. 450–483. [37] 臺北市政府交通局, YouBike 2.0 擴點成果報告. 2023. https://www.dot.gov.taipei/News_Content.aspx?n=D739A9F6B5C0AB95&s=F53FED0F1782B1B1 [38] 嘉義市政府, 嘉義市公共自行車 YouBike 2.0 系統持續擴點. 2023. https://www.chiayi.gov.tw/News_Content.aspx?n=456&sms=9151&s=596202 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99422 | - |
| dc.description.abstract | 本研究針對單一共享單車站點之營運情境，提出一套基於分數歐斯坦–烏倫貝克過程（fractional Ornstein-Uhlenbeck process, fOU）之損益分析模型，隨著共享單車逐漸成為城市交通運輸系統中不可或缺的一環，個別站點因車輛數量分布不均勻所導致的營運挑戰日益顯著，不僅影響使用者滿意度，亦降低整體系統效能。為了因應此問題，本研究建構一套以 fOU 模型為基礎之隨機模擬方法，透過 Hurst 指數（H）刻畫需求波動的長期依賴性，並引入均值回復率（λ）與波動強度（σ）以模擬站點車輛數隨時間之動態調整行為。
本模型可結合實際觀測資料進行參數估計與驗證，並用以分析站點營運的關鍵績效指標，包括未滿足需求量、營運成本，以及潛在收益損失或增益等。雖然本研究以單一站點為對象，然而所提出之方法具高度擴展性，未來可應用於多站點系統，作為優化整體營運績效之決策支援工具。 | zh_TW |
| dc.description.abstract | This study presents a loss and revenue analysis model based on the fractional Ornstein–Uhlenbeck (fOU) process for the operation of a single bike-sharing station. As bike-sharing becomes an integral part of urban transportation systems, operational challenges—particularly the uneven distribution of bicycles at individual stations—have become increasingly prominent, impacting user satisfaction and overall system efficiency. To address these issues, we develop a stochastic simulation model using the fOU process, which incorporates the Hurst index (H) to capture long-range dependence in demand fluctuations, the mean-reversion rate (λ), and the volatility parameter (σ) to describe dynamic adjustments in bike inventory over time.
The model can be calibrated with real-world data and used to evaluate key performance indicators such as unmet demand, operational costs, and potential revenue loss or gain. Although the focus is on a single station, the proposed method is highly scalable and can be extended to multi-station systems, providing a comprehensive framework for optimizing system-wide performance. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-09-10T16:14:28Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-09-10T16:14:28Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Research Background and Motivation 1 1.2 Research Objectives 3 Chapter 2 Literature Review 4 2.1 Bike-Sharing Systems and Censor-Correction of Operational Data 4 2.2 Statistical Diagnostics for Long-Memory Models 5 2.3 Stochastic Modeling of Long-Memory Demand Dynamics 6 2.3.1 Fractional Brownian Motion 7 2.3.2 Fractional Ornstein–Uhlenbeck process 8 2.4 Simulation Framework 9 Chapter 3 Modeling and Simulation Framework of Bike Inventory Process 11 3.1 Bike Inventory Data Processing 11 3.1.1 Recovery of Unbounded Bike Inventory Process 13 3.1.2 Spline-Based Recovery of Unbounded Bike Inventory Process……..15 3.2 Model Validation Procedure 19 3.2.1 Estimating the Mean Inventory Process 19 3.2.2 Modeling of Customer Uncertainty………………………………….21 3.2.3 Validation of Stationarity for Residual Process………………………22 3.2.4 Validation of Normality (Gaussian Process) 25 3.2.5 Checking Dependence Structure of the Residual Process 29 3.2.6 Discussion……………………………………………..…………….32 3.3 Modeling the Daily Residual Process 33 3.3.1 Fractional Brownian Motion…………………………………………34 3.3.2 Fractional Ornstein-Uhlenbeck (fOU) Process………………………36 3.4 Parameter Estimation for the fOU Process…………………………………39 3.4.1 Estimation of the Hurst Index………………………………………..39 3.4.2 Robust Estimation Based on Trimmed Means………………………41 3.4.3 Conditional Maximum Likelihood Estimation for 𝝈 and 𝝀…………..42 3.4.4 Stochastic-Parameter fOU Process…………………………………..44 3.5 Simulation of Bike Unbounded Bike Inventory Process…………………..46 3.5.1 The Hosking Method: Principles and Implementation……………….47 3.5.2 Simulation Steps……………………………………………………..49 3.5.3 Capacity-Constrained Adjustment for Simulated Bike Inventory Process………………………………………………………………51 3.6 Problem of Optimal Capacity Allocation……………………………………53 3.6.1 Simulated Average Bike Inventory……………………………...…...55 3.6.2 Fare Structure………………………………………………………..55 3.6.3 Estimating Average Revenue per Bike……………………………….55 3.6.4 Cost and Penalty Design……………………………………………..56 Chapter 4 Numerical Investigations……………………………………………...58 4.1 Impact of Capacity Adjustment on Bike Inventory Dynamics………………..58 4.1.1 Simulated Bike Inventory under Different Capacity Limits………….58 4.2 Mean-Level Transformation of Daily Bike Inventory Curves………………..61 4.3 Revenue Optimization Results……………………………………………….62 4.3.1 Simulation and Grid Search Results………………………………….63 4.3.2 Comparison with Profit Under Original Station Capacity……………65 4.4 Demand-Driven Justification for Capacity Expansion……………………….67 4.4.1 Diagnosing Capacity Constraints…………………………………….67 4.4.2 Prioritization Criteria and Illustrative Example……………………...67 Chapter 5 Conclusions and Future Research Directions………………………...70 5.1 Conclusions………………………………………………………………….70 5.2 Future Research Directions…………………………………………………..71 REFERENCE……………………………………………………………………….....73 | - |
| dc.language.iso | en | - |
| dc.subject | 共享單車系統 | zh_TW |
| dc.subject | 分數歐斯坦–烏倫貝克過程 | zh_TW |
| dc.subject | Hurst 指數 | zh_TW |
| dc.subject | 均值回復 | zh_TW |
| dc.subject | 均值回復、成本與收益分析 | zh_TW |
| dc.subject | mean reversion | en |
| dc.subject | Bike-sharing system | en |
| dc.subject | fractional Ornstein–Uhlenbeck process | en |
| dc.subject | cost and revenue analysis | en |
| dc.subject | Hurst index | en |
| dc.title | 共享單車存量流程之隨機建模與利潤最佳化應用 | zh_TW |
| dc.title | Stochastic Modeling of Bike Inventory Process with Application to Profit Optimization | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 黃道宏;喻奉天 | zh_TW |
| dc.contributor.oralexamcommittee | Kevin Huang;Vincent F. Yu | en |
| dc.subject.keyword | 共享單車系統,分數歐斯坦–烏倫貝克過程,Hurst 指數,均值回復,均值回復、成本與收益分析, | zh_TW |
| dc.subject.keyword | Bike-sharing system,fractional Ornstein–Uhlenbeck process,Hurst index,mean reversion,cost and revenue analysis, | en |
| dc.relation.page | 76 | - |
| dc.identifier.doi | 10.6342/NTU202502754 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-07-31 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 工業工程學研究所 | - |
| dc.date.embargo-lift | 2030-07-28 | - |
| 顯示於系所單位： | 工業工程學研究所 | |
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