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研究生(外文):Pin-Hsuan Wu
論文名稱(外文):Using Time Series Features with Random Forest Algorithm to Predict Public Bike Stations of Traffic
外文關鍵詞:Machine learningRandom ForestYouBike
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In recent years, energy conservation and carbon reduction have become the mainstay of the development of various countries. In order to reduce carbon emissions and solve the problem of traffic congestion, the public bicycle rental system appeared to be used with public transportation, becoming the "first mile" and "last mile" transfer tools. YouBike started its trial operation in March 2009 and started in Xinyi District, Taipei City. It initially provided 11 stations and 500 public bicycles. Due to its small scale, it could not be effectively combined with public transportation, resulting in poor turnover. In 2012, the Taipei City Hall signed a contract with Giant Manufacturing Co. Ltd.. It is expected to build 163 sites and provide 5,350 public bicycles within seven years, opening YouBike's new history in public bicycles.
Due to the small scale of YouBike in the initial stage and the low volume of transportation, it can provide high-quality services in terms of maintenance and scheduling. With the expansion of the station, to achieve high-quality vehicle maintenance, and a better experience of borrowing and returning, how to provide a situation where the bike can be borrowed and returned at any time, the problem of station traffic has become the focus of this paper.
This paper takes YouBike as an example. We hope to use the information of the rental and return, with the weather factors and air pollution quality that affect the rental and return, with 3 kinds of time series features, and use the random forest algorithm to effectively calculate the traffic demand of bike station in time period. Furthermore, it provides reference value of vehicle demand data for front-line dispatchers. With the assistance of basic weather and air pollution information, the R2-Score can reach about 0.7, and the R2-Score after adding time series features can reach 0.75. The parameter adjustment, experimental results show that R2-Score can be increased by 5%-6% to 0.8. Therefore, both new recruits and experienced dispatchers can use this system to quickly and accurately grasp the vehicle needs of each station at each time.
摘要 i
Abstract ii
目次 iii
圖目錄 v
表目錄 vi
第一章 緒論 1
1.1 研究背景與動機 1
1.2 論文貢獻 3
1.3 論文架構 4
第二章 相關研究 5
2.1 公共自行車的演進 5
2.2 相關文獻研究與討論 6
第三章 研究方法 8
3.1 機器學習概述 8
3.2 機器學習的分類與迴歸 9
3.3 機器學習演算法 9
3.3.1 支持向量迴歸 (Support Vector Regression, SVR) 11
3.3.2 極限梯度提升 (eXtreme Gradient Boosting, XGBoost) 12
3.3.3 隨機森林 (Random Forest, RF) 13
3.4 評估指標 17
3.4.1 均方誤差 (Mean Squared Error, MSE) 17
3.4.2 均方根誤差 (Root Mean Squared Error, RMSE) 18
3.4.3 平均絕對誤差 (Mean Absolute Error, MAE) 18
3.4.4 決定係數R2 (R-Square) 18
第四章 實驗結果與評估 20
4.1 流量預測區域選定 20
4.2 資料收集與預處理 21
4.3 場站借還資料分析 23
4.4 實驗設計 27
第五章 結論與未來展望 37
參考文獻 38
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