捷運系統為運輸工具中最省能的運具之一。列車的行駛所需要的電力主要供給牽引馬達轉為動能，令列車加速或使之有足夠的動力克服各種阻力。實務上在列車的運行模式上並沒有特別的規劃，除了遵守速限、不超過列車機械性能極限以及準時到站外，對於駕駛方式如何變換並不做太多控制。以往以人做為駕駛員時確實是難以針對行車駕駛有精準的控制，然而現今以電腦來計算行車速率的機房卻能夠突破限制，精確控制駕駛模式的轉換。選擇列車出力或不出力關係了牽引馬達的電力消耗，同一軌道同樣的運行時間下，不同的駕駛決策都可令一輛列車行駛電力的消耗有不同的結果，本研究目的就在於就已知的軌道線型與運行時間，設計適用的演算法，規劃列車最節能的運行模式。
本演算法結合真實的軌道條件與列車行駛規範，由模擬列車行駛開始，建立描述列車運行的計算過程，並計算兩輛列車行駛之安全係數確保列車間不致追撞後建立啟發式演算法。此演算法的求解過程首先以簡單的方式產生初始解集合，而後演算法之選解機制隨機從此集合中選出一解做五百次的擾動改善其品質，並以改善過的解判斷是否取代解集合中品質最差的解，重複五百個回合之後挑選出解集合中品質最佳的解，此解即為最省能之速率曲線，並用Optimal control的方式將速率曲線轉換為一連串的列車運行模式。解得不同站間不同運行時間下各別的最佳速率曲線後，再進一步擬定策略決定如何分配多餘的行車時間。

Rapid transit system is one of the most energy-efficient means of transportation. These trains mainly require energy to drive the motor so that it is possible to speed up and overcome resistance. Practically, there has been no particular planning on the operation mode of the train, except that it should comply with certain speed limit based on its mechanical properties and arrive on time. Furthermore, it should also have flexible driving methods that will not be affected by various circumstances. Formerly, the train was man operated and hence it was difficult to have precise control over speed and time of the train. However, now it is possible to use computer to help precisely calculate train speed and to have control over changing driving modes and traction power to save energy. Furthermore, for the same track and driving time, different operational strategies will result in different power consumption. This research aims to investigate on an algorithm that helps calculate the most energy-efficient operational mode for certain rail lines and running time.
The proposed algorithm takes railway alignment and operational rules into consideration. Using simulation as the major approach, we established a method to describe the operation of a train along a track, and developed an algorithm to estimate the safety factor between a leading train and another following train on the same track. Another algorithm solves for the minimum-energy consumption speed curve for a given operation time on a given track segment. It uses a simple rule to generate the first solution, and then attempts to improve gradually by a neighborhood search heuristic. The obtained speed curve is then converted into a set of driving modes with an optimal control-based approach. After establishing the relationship between minimum energy consumption and operation time, one can then proceed to optimize how extra run-time reserve can be optimally allocated among track segments.

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