運用資料探勘預測旅遊需求

Data Mining for Predicting Travel Demand

魏佳美(Jia-Mei Wei)

指導教授 ： 劉敦仁

國立交通大學/管理學院/管理學院資訊管理學程/碩士(2018年)

摘要

隨著全球人口成長、交通運輸的成熟，旅遊已經變成現代人不可或缺的休閒活動，旅遊產業能替全球經濟帶來直接與間接的收益，因此預測旅遊需求為重要議題。旅遊需求預測可考量多變數因子，使用時間序列、資料探勘不同的演算法去預測評估。本研究透過使用變數「景氣分數」、「匯率」、「人口總數」、「季節指數」來預測「國人至日本旅遊人次」，比較幾種資料探勘演算法對於預測旅遊需求之效果，實驗結果顯示「類神經網路」的預測效果較好。

關鍵字

旅遊需求預測 ； 資料探勘 ； 線性回歸 ； 支援向量回歸 ； 梯度提升回歸樹 ； 類神經網路

並列摘要

With the global population growth and the mature development in traffic transport industry, travel activity had become Indispensable for people. Travel industry can bring the development of global economic directly and indirectly. Accordingly, the forecasting of travel demand is an important issue. There are several methods, including time series and data mining methods that can be used to predict the travel demand by considering multiple factors. This research applies serval data mining algorithms to predict the travel demand by considering the factors, including “Boom score”, “exchange rate” , “total population” , “seasonal ratio”. The experiment result shows that the neuro network approach can achieve the best performance.

並列關鍵字

Travel demand forecasting ； Data miming ； Linear Regression ； SVR ； GBR ； ANN

參考文獻

參考文獻 (英)：

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