節能減碳的議題在近年來漸漸受到重視，為了減少溫室氣體的排放，以綠色運輸來解決環境汙染及交通壅塞的概念因應而生，其中又以電力驅動的汽機車及近年來風行的公共自行車共享系統最受矚目。由於都會區交通擁擠且停車空間有限，單人開車浪費空間，多人共駕不夠方便且有安全疑慮，共享自行車不夠便捷又常因天候地形不佳而影響其使用率；反之，機車不但節省空間又便捷舒適，我們預期電動機車將會取代傳統燃油機車，成為通勤族使用捷運、公車等大眾運輸系統的最初及最後一哩路程之銜接工具，甚至可成為中短程通勤之代步工具。有鑑於此，本研究欲結合「電動機車」與「載具共享」兩大綠色運輸的概念，探討都會區中針對通勤需求而設計的公共電動機車共享系統之期初車輛佈署策略。
由於電動機車之續航力對共享系統之方便性影響甚鉅，本研究首度將「充電站」及「電池交換站」等兩類延長電池續航力之技術與設施納入共享系統的規劃考量，提出兩個線性混整數規劃模式來預估各租借站期初應擺放的車數，以滿足一定程度的租借需求，達到預設的服務水準。為使我們提出之數學規劃模式能更貼近現實及正確地反應使用者的租借行為與趨勢，讓更多租借需求的騎乘路線能有更多的電動機車租借流量，我們將電池充耗電速率列入考慮，並以不同起訖需求之相對比例關係來分配電動機車租借流量。
本研究利用最佳化軟體Gurobi求解混整數規劃模式，發現隨著模式規模擴大會增加求解時間，同時亦觀察到降低服務水準要求的模式求解更加耗時，因此本研究進一步發展兩個粒子群最佳化演算法PSOCP與PSOBE分別用以加速求解使用充電站與使用電池交換站的數學模式，並探討在不同情境下的期初車輛佈署策略，分析比較不同的電池續航力延伸技術以及不同的耗電與充電速率對服務水準與成本的影響，期望研究結果能作為未來相關政府部門及營運者之決策參考。

Green transportation has aroused more and more attentions recently, especially by the introduction to the concept of vehicle sharing that promotes shared vehicles to conserve energy, reduce carbon emissions, and improve traffic congestions. The bicycle sharing system has become the most poplar vehicle sharing systems so far. However, for some places with bad weather or topography not so convenient for biking, electric scooters (e-scooters) may serve better than bicycles as shared vehicles, since they are as mobile as bicycles, and can move even faster with ease.
This thesis focuses on the initial vehicle deployment at each rental site for a public e-scooter sharing system so that service requirement can be achieved with minimum total cost. In particular, we would like to put optimal number of e-scooters at each rental site in the beginning of each day so that the number of satisfied Origin-Destination demands attains specified service level requirement with minimum number of e-scooters. Two linear mixed integer programming models are proposed based on different ways of recharging batteries: one using the charging stations, and the other using the battery exchange stations. Both models assume uncapacitated rental sites, and available e-scooters are distributed fairly in proportion to their historical OD profile. They differ in how the battery power level changes, where an idle e-scooter recharged in a charging station model gains some battery power within a time period, whereas an idle e-scooter in a battery exchange station model does not gain any battery power, unless the power level is insufficient for one time period in which case the battery will be directly swapped to one with full power level.
We first solve these models by the Gurobi optimizer, and learned that models asking for lower service level requirements take longer time. We then design two particle swarm optimization algorithms, named PSOCP and PSOBE, respectively for eeach model. These proposed PSO algorithms can calculate good solutions in much shorter time than Gurobi. Finally, we conduct analyses on the effects in the service level requirements and total costs caused by different battery recharging models, as well as the effects caused by different battery consumption and recharging rates.

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網站資料
BP Statistical Review of World Energy：http://www.bp.com/sectionbodycopy.do?categoryId=7500&contentId=7068481
Electric Bikes Worldwide Reports(EBWR)：http：//www.ebwr.com/
IEK產業情報網：http：//ieknet.iek.org.tw/signoff.do
International Energy Agency(IEA)：http：//www.iea.org/
TES電動機車產業網站：http：//proj.moeaidb.gov.tw/lev/default.asp
台灣電動車產業聚落交流平台：http：//www.ev.org.tw/Home/Index
綠色能源產業資訊網：http：//www.taiwangreenenergy.org.tw/
電動機車聯合測試服務中心：http：//www.tes.org.tw/index.htm
交通部網站：http：//www.motc.gov.tw/
經濟部工業局網站：http：//www.moeaidb.gov.tw/external/view/tw/index.html
經濟部能源局網站： http：//www.moeaboe.gov.tw/