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題名：類神經網路模式於短期列車旅運量需求預測之應用
作者：蔡宗憲
作者(外文)：Tsung-Hsien Tsai
校院名稱：國立成功大學
系所名稱：交通管理學系碩博士班
指導教授：李治綱
魏健宏
學位類別：博士
出版日期：2006
主題關鍵詞：局部遞迴；各個擊破；短期預測；類神經網路；模式整合；鐵路運輸；模式競爭；Competition and integration；Railway transportation；Short-term forecasting；Local recurrent；Artificial Neural Networks；Divide-and-conquer
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短期預測模式之重要性在於其預測結果可以提供後續短期營運規劃所用。舉例來說，短期需求預測模式為營收管理中一個重要的子模式。若短期需求預測模式的精確度夠佳，則對於後續營收管理系統中的艙位配置模式、超額預售模式以及票價模式都能提供有用的前端需求資訊。在預測的領域中，提升預測模式的績效一直以來皆為學者努力的目標之ㄧ。
此份論文的主要目標在於改善以類神經網路模式為基礎之預測模式的績效。近年來，類神經網路被應用來解決預測問題的實例愈來愈多，其模式潛力也被明確的指出。但是大部分的文章著重在於應用類神經網路建立預測系統，非常少的文章探討該如何針對問題之特性來設計類神經網路結構以提升預測系統之績效。更明確的說，此份論文主要針對研究資料之特性來設計類神經網路的結構，以有別於傳統之模式結構。本文的第一個貢獻在提供於類神經網路的架構下進行短期預測模式時可以參考使用的模式構建邏輯。在不同實務問題但具有類似資料特性時，這些模式構建邏輯可以被應用來提升模式的預測績效。本文的第二個貢獻在於提供有關鐵路運輸短期預測模式建立的相關討論。在文獻中，有關鐵路運輸預測模式的建立主要著重於中長期的總體預測模式，並無專文對短期預測模式之建立進行討論。本文提供以日為單位進行預測工作的實證經驗。
首先，收集的鐵路資料被仔細的分析以獲得重要的資料分佈特性。這些資料分佈特性可以做為後續模式構建時解釋變數選用的參考依據。此外本文探討三個研究課題。第一個研究課題探討如何根據資料之特性設計以類神經網路為基礎的時間特性模式(Neural network based temporal feature model)。在此部份，各個擊破(Divide-and-conquer)的概念被用來進行網路模式結構的設計。據此本文提出兩個新的網路模式結構，其一稱為多時間單位類神經網路模式(Multiple temporal units neural network)，另一為平行分工類神經網路模式(Parallel ensemble neural network)。在實證分析中這兩個新發展模式的預測績效都比傳統的多層感知機(Multi-layer Perceptron)預測績效來的佳。第二個研究課題則探討如何以時間序列(Time series)之角度來設計類神經網路預測模式。在此部份，局部遞迴(Local recurrent)的概念被使用來設計網路模式結構。據此本文提出一個新的網路模式結構，稱之輸入遞迴類神經網路模式(Input recurrent neural network)。同樣的在實證分析中輸入遞迴類神經網路模式比傳統結合輸入變數篩選方法以及多層感知機的預測模式獲得較佳的預測績效。第三個研究課題則進一步探討模式間的競爭以及整合。本文發現整合以類神經網路為基礎之時間特性模式以及時間序列模式，平均上來說可以比單獨使用單一類神經網路模式獲得更準確的預測結果。除此之外，本文也更進一步構建五個常見的統計模式以進行預測績效之比較。結果發現若更進一步整合類神經網路模式與統計模式之預測結果可以獲得更進一步平均績效上的改善。
Short-term forecasting is the base for short-term operational planning. For example, short-term forecasting is essential in revenue management. If short-term forecasting is accurate, then the following seat allocation, overbooking and pricing decisions in revenue management are reliable. In the literature, improving model performance is always an ongoing goal for forecasters.
The aim of this dissertation is to improve predicting performance of Artificial Neural Networks (ANN or NN). ANN has been found to be potential in dealing with short-term forecasting problems. Many applications have been addressed. However, very few papers have discussed about how to design network structures in terms of data features. This dissertation devotes to the issue of how to improve predicting performance via elaborate network designs. There are two major contributions. First, this study generates modeling logics for applying ANN to solve short-term forecasting problems. These modeling logics can be utilized for pursuing better performance when similar situations happen in other applications. Second, there is no discussion about short-term railway passenger demand forecasting in the literature. Some related researches are interested in macro forecasting, such as yearly or quarterly forecast. This study renders modeling experiences in daily forecast.
The dissertation starts from the extraction of railway data features. These features form the base for model construction. Three research issues are discussed. First, the dissertation addresses how to design NN based temporal feature model. The concept of divide-and-conquer is incorporated in designing neural network structure. Two novel structures, named multiple temporal units neural network and parallel ensemble neural network, are constructed and demonstrated to outperform conventional multi-layer perceptron. Second, the dissertation discusses about how to design NN based time series model. The concept of local recurrent is utilized to store historical information and simplify the process of model construction. One novel network structure, named input recurrent neural network, is constructed and demonstrated to outperform conventional NN based time series model. Third, model competition and model integration are implemented. This dissertation finds out that the integration of NN based temporal feature model and NN based time series model can averagely outperform individual NN models. In addition, five statistical models are also constructed for competition and integration purposes. The dissertation also verifies that integration between NN and statistical models can averagely upgrade predicting performance.
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