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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/93318


    Title: 建立臺灣高鐵場站進出人次預測模型之初步研究-以台北、台中、高雄為例;Estbilshing the prediction model of passenger arrivals and departures of Taiwan′s high-speed rail stations - Taking Taipei, Taichung, and Kaohsiung station as examples
    Authors: 吳卓倫;Wu, Chuo-Lun
    Contributors: 土木工程學系
    Keywords: 台灣高鐵;指數平滑法;遺傳表達規劃法;High-speed railway;forecasting;GEP
    Date: 2023-12-18
    Issue Date: 2024-03-05 16:22:04 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 台灣高鐵於2007年通車,距今已17年,是一般民眾日常生活中相當依賴的交通工具。本研究為了預測高鐵旅客進站、出站,從沿線的12站中,挑選出其中3個場站,分別是台北、台中和高雄,先以資料收集方式,收集這3站進出站的運量,資料期間為2007年1月至2022年12月共72筆月資料。並運用指數平滑法、遺傳表達規劃法建立這3個場站旅客進、出站的預測模型,得到的結果以平均絕對百分誤差及判定係數來作評估。此外,建立2023年1~5月這3個場站進出站的預測模型,用來驗證預測的精準度。本研究以SPSS軟體建立指數平滑法、ARIMA的預測模型,從SPSS的運算過程中發現最佳模式為指數平滑法,並從結果觀察出高鐵台北站進站判定係數=0.92、平均絕對百分誤差5.1%和出站判定係數=0.92、平均絕對百分誤差5%,高鐵台中站進站判定係數=0.91、平均絕對百分誤差5.3%和出站判定係數=0.91、平均絕對百分誤差4.8%,高鐵高雄站進站判定係數=0.89、平均絕對百分誤差4.9%和出站判定係數=0.88、平均絕對百分誤差5.2%。從研究方法遺傳表達規劃法所預測出的結果發現,判定係數R2皆大於0.7,而且沒有過度訓練之現象,平均絕對百分誤差高鐵台北站分別是進站4%和出站7%、高鐵台中站進站10%和出站7%及高鐵高雄站進站11%和出站11%。比較GEP和指數平滑法2023年1~5月, 平均絕對百分誤差GEP 10%、指數平滑法15%,預測成效GEP優於指數平滑法,綜合以上結果,在本文獻中GEP是較適合的模式用於預測台灣高鐵旅客運量。;The Taiwan High-Speed Railway (THSR) has been in operation since 2007, the passenger of THSRs is increasing by the day and THSRs are already an important vehicle for Taiwan residents. The objective of this study is to forecast passengers′ departure and arrivals at the main stations including Taipei, Taichung, and Kaohsiung. Firstly, the passengers′ departure and arrivals data were collected and the date was from Jan. 2007 to Dec. 2022, and the data number by month is 72. Simple Smooth, ARIMA and Genetic Expression Programming (GEP) were adopted to establish those forecasting models of passengers′ departure and arrivals at the three main stations, mean absolute percent Error (MAPE) and Determination coefficient(R2) were used to evaluate the model performance. In addition, the passengers′ departures and arrivals of the three main stations Jan. 2023 to May 2023 were tested on the accuracy of the forecasting model. The SPSS was utilized to build up the Simple Smooth, and ARIMA model, and the best feasible model of SPSS was simple smooth. Based on the results of the Simple smooth using SPSS, the R2 and MAPE of the passengers′ arrivals model of Taipei were 0.92 and 5.1%, the R2 and MAPE of the passengers′ departure model of Taipei were 0.92 and 5%, the R2 and MAPE of the passengers′ arrivals model of Taichung were 0.91 and 5.3%, the R2 and MAPE of the passengers′ departure model of Taichung were 0.91 and 4.8%, the R2 and MAPE of the passengers′ arrivals model of Kaohsiung were 0.89 and 4.9%, and the R2 and MAPE of the passengers′ departure model of Kaohsiung were 0.88 and 5.2%. Based on the results of GEP, the training, and validation R2 of all models of the main stations were above 0.7 and there is no overfitting situation. The MAPE of the passengers′ departure and Arrivals model using GEP of Taipei were 4% and 7%, The MAPE of the passengers′ departure and Arrivals model using GEP of Taichung were 10% and 7%, and The MAPE of the passengers′ departure and Arrivals model using GEP of Kaohsiung were 11%. Comparing the prediction capacity between Simple smooth and GEP from Jan. 2023 to May 2023, the MAPE of the GEP Model is 10%, the MAPE of the Simple Smooth Model is 15%, and the forecasting performance of the GEP model is better than Simple Smooth model. Based on the above, the GEP is the better feasible method to build up a passenger forecasting model of THSR and it is worthy of further study.
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