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研究生： 張博鈞
Po-Chun Chang
論文名稱： 運用機器學習預測 Youbike2.0供需與優化運補路徑-公館區為例
Using Machine Learning for Demand Forecasting and Route Optimization: The Case Study of Gongguan Youbike 2.0 District
指導教授： 呂志豪
Shih-Hao Lu
口試委員: 黃政嘉
Jheng-Jia Huang
黃振皓
Chen-Hao Huang
學位類別： 碩士
Master
系所名稱： 管理學院 - 企業管理系
Department of Business Administration
論文出版年： 2023
畢業學年度： 112
語文別： 英文
論文頁數： 32
中文關鍵詞： 共享單車系統 、最佳化 、路徑規劃 、啟發式演算法 、機器學習 、隨機森林迴歸 、多元迴歸
外文關鍵詞： Shared Bicycle System, Youbike2.0, Optimization; Route Planning, Heuristic Algorithms, Machine Learning, Random Forest Regression, Multiple Regression, eXtreme Gradient Boosting
相關次數： 點閱：246  下載：18 
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    • 近年來，共享單車在各大城市迅速普及，成為解決城市交通擁堵和環境永續性問題的重要方法。共享單車環保與方便，受到了廣泛關注。然而，隨著共享單車的使用率不斷增加，出現了一些新的問題，最棘手的為站點之間供需不平衡的情況。在尖峰時段，一些熱門站點經常出現共享單車不足或者無位可還的問題。影響了民眾的使用體驗且降低了共享單車系統的使用效率。為了解決這一問題，優化共享單車的調度。本研究採用了機器學習預測每個站點在特定時刻的淨需求，這些預測模型使用天氣、時間等變數，以更準確地估計淨需求。通過預測淨需求，我們可以更容易了解哪些站點在未來的某個時刻出現共享單車不足的問題，從而提前補充共享單車。此外，本研究使用啟發式演算法於路線規劃，找尋最佳化的路線，使 Youbike 更有效地配送車輛，以緩解尖峰時段的問題。改善共享單車系統的運營效率，減少共享單車不足和停車位不足等問題。通過結合機器學習和最佳化路徑，更精準預測共享單車需求，提高使用效率。

    In recent years, shared bicycles have rapidly gained popularity in major cities, emerging as a vital solution to urban traffic congestion and environmental sustainability. Their eco-friendliness and convenience have garnered widespread attention. However, as the usage rate of shared bicycles continues to rise, new challenges have emerged, the most daunting being the imbalance of supply and demand between stations. During peak hours, popular stations often experience shortages of shared bicycles or lack of available parking spaces. This affects the user experience and reduces the efficiency of the shared bicycle system. To address this issue, efforts have been made to optimize the dispatch of shared bicycles. Our study employs machine learning to predict the net demand at each station at specific times. The predictive model takes into account various factors, such as weather and time, to estimate net demand more accurately. By forecasting net demand, we can better understand which stations may face a shortage of shared bicycles in the future and, therefore, restock in advance. Additionally, the study uses heuristic algorithms for route planning to find the most optimized routes, enabling more efficient distribution of vehicles for Youbike, thus alleviating problems during peak periods. The operational efficiency of the shared bicycle system is improved by reducing issues like bicycle shortages and insufficient parking. By combining machine learning with optimized routing, the demand for shared bicycles can be predicted more accurately, enhancing usage efficiency.

    摘要 I ABSTRACT II Contents III List of Figures V List of Tables VI CHAPTER 1 INTRODUCTION 1 1.1 Research Background 1 1.2 Research Motivation 2 1.3 Reseatch Scope 3 CHAPTER 2 LITERATURE REVIEW 4 2.1 Bike-Sharing system 4 2.2 Bike sharing demand prediction 6 2.3 Vehicle Routing Problem(VRP) 7 CHAPTER 3 DATA AND METHODOLOGY 8 3.1 Data Collection 8 3.2 Machine Learning Model 9 3.2.1 Linear regression 9 3.2.2 The Random Forest 9 3.2.2 eXtreme Gradient Boosting 9 3.2.3 Evaluation 10 3.3 Mathematical Model and Metaheuristics 12 3.3.1 Mathematical Model 12 3.3.2 Metaheuristics 13 CHAPTER 4 RESULTS 15 4.1 Data PreProcessing 15 4.2 Exploratory Data Analysis 17 4.2.1 Feature Selection 17 4.2.2 Time Series data 19 4.3 Model Result 21 4.4 Example 25 CHAPTER 5 CONCLUSION 28 5.1 Conclusion 28 5.2 Contributions 28 5.2.1 Academic Contributions 28 5.2.2 Practical Contributions 29 5.3 Future Research 30 REFERENCE 31

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