為因應高鐵的營運衝擊，台鐵以捷運化之方法進行轉型。在高鐵尚未通車前，台鐵仍然以長途運輸為主。捷運化後簡易車站佈設以及密集通勤電車運行等，都可能影響長途列車之運行。為了台鐵在現行營運制度、設備與經費的限制下，能夠順利的轉型。分析轉型後可能面臨的問題有其重要性。

　　本研究將針對台鐵現行營運的相關資料，以及捷運化後的運行模式，設定欲分析之台鐵捷運化之相關課題，如車站的佈設、服務的車輛數、服務車站的範圍、每輛列車間的最小時間間距。對於每一個欲分析之課題設定若干變動之因素，如設定不同的服務列車數。針對每一個台鐵捷運化之相關課題，設計各種不同的情境。並以隨機的方式建立期望列車運行之班表，再使用列車排點最佳化之模式求解各種情境下最接近期望班表之運行班表。根據求解結果先整理出每一個求解班表與期望班表之差異，再以欲探討之相關課題，整理相關之求解結果，分析在不同因素下之趨勢。本研究將所有相關之因素結合，形成一龐大測試例集合，可以分析所有因素變動下之趨勢。

　　本研究所使用之列車排點最佳化之求解模式，乃引用文獻[8]之研究理論為基礎。由於文獻[8]所提出之求解流程難以在本研究之測試例中解到最佳解，故提出2種改善求解效能之求解流程：1.單純使用線性搜尋2.在二分搜尋後繼續使用線性搜尋。並將兩種求解流程與文獻[8]所發展之求解流程做比較。

　　本研究依據模式之求解結果整理以下結論：1.在最規模小時，列車最小時間間距、捷運區間服務範圍以及簡易車站旁軌佈設形式均無明顯影響。2.在規模小時，其影響程度最大者，多發生在最小時間間距為最小之情況。3.當營運規模越大時，限制最嚴格的例子開始出現求解結果與期望班表有最大差異之情況。

　　In response to the upcoming High speed railway, Taiwan Railway is in the process of transforming in part into an urban rapid transit system. Before the high speed railway actually starts, Taiwan Railway still mainly aims at inter-city transportation. The newly added transit system will offer frequent train services, some on simplified stations. These new services will inevidently impact the operation of inter-city trains. In order to enable Taiwan Railway transform successfully subject to its current system, facility and funding, it is thus important to analyze the problems Taiwan Railway may face after the tranformation.

　　This research is aimed at related information of Taiwan railway working system and working system that after transform into rapid transit system. We set related discussion of Taiwan railway transformation for analyizing. For example, station layout, amount of service trains, range of service station and headway for each train. We set different factors for each related issue. For example, setting different service train number. To each related discussion of Taiwan railway transformation set various situation. We randomly generate an ideal timetable and use a train dispatching model to solve for a working timetable closest to the ideal timetable under various situations. According to the solution we adjust the difference between each working timetable and ideal timetable, then analyize the trend under different synarios. This research combines all the related factors to form a huge set of test cases to analyize the trend related to all factors.

　　The train dispatching optimization model and the solution method in this research is borrowed from an earlier research. Because the model in its original form is hard to solve to optimal, we propose two better solving procedures:1. using linear search only, and 2. using linear search after binary search. Comparison with predecessor’s solving procedures is offered.

　　This research proposes 3 conclusions:1. in the smallest case, there is no apparent effect to headway for each train, range of service station and layout of side track in simple stations, 2. for small cases, the largest difference between solving timetable and ideal timetable usually happen in the situation which headway for each train is the smallest, and 3.for large cases, the largest difference between solving timetable and ideal timetable is going to occur to the most strickly case constraint.

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