考量碳排放之電動公車補助策略，及購置計畫之雙層最佳化模式

A Bi-level Model for Optimizing Subsidizing Strategies Considering CO2 Emissions and Purchase/Deployment Plans of Electric Buses

為改善空氣品質並建構低碳的永續運輸環境，公車電動化是近年政府極力推廣的政策。然而，電動公車價格昂貴且有續航里程限制，公車電動化政策的步伐相當緩慢。因此，如何透過提供有效、合理的補貼來鼓勵公車業者之車隊朝電動化發展至關重要。在此基礎上，本研究提出雙層數學規劃模型，根據公車業者購買和配置電動公車之購置計畫，求解碳排減量最大化之最佳政府補助策略。由於模型的複雜性，本研究以遺傳演算法(GAs)用於近似求解模型。結果顯示，在班次密集及路線長度較短的公車路線中，將優先選擇快充公車。此外，取消柴油公車的補貼，電動公車比柴油公車在經濟上更具優勢，若電動公車價格較低且續航里程變長，電動公車的經濟優勢更加明顯。

"In order to improve air quality and create a low-carbon sustainable transportation environment, the government proactively accelerates bus fleet electrification in recent years. However, due to the high price of electric buses along with their insufficient recharging range, the pace of bus electrification policy is rather sluggish. Therefore, how to encourage bus companies to electrify their bus fleets by providing effective and reasonable subsidy is essential. Based on this, this study proposes a Bi-level mathematic programming model for determining the optimal government subsidizing strategy towards maximal CO2 reduction subject to the electric buses purchase and deployment decisions of bus companies. Due to the complexity of the proposed model, genetic algorithms (GAs) are used to solve the model approximately. The results show that on-route recharging buses are favored for the bus routes with higher frequency and short route. However, for the subsidizing strategies with no subsidy for diesel buses, electric buses become more economically advantageous than diesel buses. Moreover, if the price of electric buses is lower and recharging range becomes longer, then economical advantage of electric buses is even more obvious."