將閒置資源分享共用以達成雙贏的共享經濟近年來蓬勃發展，本研究首先針對都會區內新興的無樁式電動機車共享系統，在日間的車輛配置時時受到使用者行為影響之際，探討如何指派其員工移動閒置車輛，以滿足更多租車需求之「動態運補」問題，避免車輛配置不利於使用者的租借；再進一步分析該共享系統所存取之會員騎乘路線資料，以協助「眾包運送」平台找尋合適對象，進行推播媒合度較高的運送任務。
在動態運補部分，本研究先劃分營運區域為數個虛擬站點，每30分鐘為一期，考慮各虛擬站點在各期的歷史租借記錄、當下閒置車數與員工人數後，再建構滾動式的數學規劃模型，以指派各員工移車或移位，將電動機車與租借需求的時地配置最佳化。由於無樁式的營運模式不受到固定站點設施的限制，讓其共享載具的租還地點與使用者的真實需求十分貼近，且該類系統每數秒會將車輛即時的狀態與位置資訊上傳，這些使用者移動路徑巨量資料加以分析後，恰好可被諸如UberEATS或GOGOVAN等國內外新創的眾包運送平台用來精準行銷，針對各運送任務找出資料庫中較順路的會員，對其推播媒合度較高的任務。本研究將以路徑分群手法統整會員的騎乘習慣，建構具有推播數量限制的任務推送數學規劃模型，避免過多的推播成為無效的垃圾訊息，達到精準媒合運送任務的效果，徹底發揮電動載具、個人時間等閒置資源的共享綜效。

The sharing economy has created opportunities for idle asset sharing and increased asset utilization, with the vehicle sharing system as a successful application. This thesis focused on issues related to the emerging free-floating electric motorcycle sharing system. We first investigate the dynamic repositioning strategies, and then discuss how to analyze the historical user trajectory data which can then be used for a crowdsourced shipping platform to seek optimally matched riders to plan shipping tasks.
In the first part, we first divide the entire operating area as a set of regions, where each region is treated as a virtual station. An ideal inventory mathematical model is proposed to determine the optimal ideal initial inventory for each period at each virtual station. Two dynamic repositioning models which assign staff to move idle motorcycles are proposed: the vehicle ideal inventory repositioning model, and the maximum demand satisfaction repositioning model.
In the second part, we explore a good application using the historical user trajectory data in this vehicle sharing system. For each user at each period, we group his/her historical trajectory data by trajectory clustering. Then, for each delivery task appeared in a crowdsourced shipping platform, we calculate the best matched users whose clustered trajectories have smaller expected detour costs, then only push these matched tasks to those users. Our proposed task pushing model achieves more accurate push effects and avoids ineffective delivery task pushes.

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