共享單車已成為都市永續交通的重要工具，然而由於不同站點間的需求不均衡，系統常面臨「空站」與「滿站」問題，導致使用者無法順利借還車，影響服務品質與使用體驗。本研究以臺南市 YouBike 2.0 為案例，建構離散事件模擬模型，比較卡車補車（Truck-based Rebalancing）與使用者參與補車（User-based Incentive Rebalancing） 兩類再平衡策略之效能表現。
研究設計以三個代表性站點為模擬場域，分別反映高、中、低需求情境。模擬情境包含五種策略：無補車、卡車補車，以及三種不同誘因強度之使用者參與補車（低、中、高）。模型透過 30次模擬，從使用者體驗（等待時間、借還車失敗次數）、系統效率（空站與滿站時間、補車次數）與營運成本（卡車補車與誘因支出） 三個面向進行評估。
模擬結果顯示，單純依賴卡車補車雖能改善部分供需失衡，但受限於調度頻率，其效果有限，甚至在某些情境下仍出現高比例的空站問題。相比之下，使用者參與補車能顯著提升系統穩定性，降低空滿站持續時間，其中中等誘因強度在效益與成本間達到最佳平衡，既改善使用者體驗，又避免高額補貼造成的邊際效益遞減。過高的誘因雖進一步增加補車次數，但對使用者體驗的改善有限，反而提高了營運成本。
本研究的貢獻在於提供 集中式補車與分散式補車之比較證據，並驗證不同誘因強度下的系統敏感度，提出誘因與卡車互補的混合策略作為實務建議。研究成果除可供共享單車營運商制定再平衡政策之參考外，亦對中小型城市共享運輸系統的永續發展提供實證啟示。

Bike-sharing systems have become an essential component of sustainable urban mobility. However, imbalances in supply and demand across stations often lead to “empty stations” and “full stations,” reducing service availability and user satisfaction. This study develops a discrete event simulation model, using Tainan’s YouBike 2.0 as a case study, to compare the effectiveness of truck-based rebalancing and user-based incentive rebalancing strategies.
Three representative stations, reflecting high-, medium-, and low-demand conditions, were selected for simulation. Five experimental scenarios were designed: a baseline without rebalancing, truck-based rebalancing, and three levels of user incentive participation (low, medium, and high). Each scenario was simulated 30 times, and system performance was evaluated across three dimensions: user experience (waiting time and failure counts), system efficiency (empty/full station duration and rebalancing frequency), and operational costs (truck dispatching and incentive expenditures).
The results show that truck-based rebalancing can partially alleviate supply-demand imbalances, but its effectiveness is constrained by limited dispatch frequency, leading to persistent empty-station problems. In contrast, user-based incentive rebalancing significantly improves system stability and reduces empty/full station occurrences. Among the tested levels, medium incentives provide the optimal balance between performance and cost efficiency, as excessive rewards yield diminishing returns while increasing expenditures.
This research contributes by offering empirical evidence on the comparative performance of centralized and decentralized rebalancing strategies. It highlights the potential of hybrid approaches that integrate truck operations with well-calibrated user incentives, providing actionable insights for bike-sharing operators and policymakers. The findings also extend to the sustainable management of shared mobility systems in small-and medium-sized urban contexts.

劉宜青（2012）。《以模擬最佳化求解公共自行車共享系統之初始車輛配置策略》。國立成功大學工業與資訊管理系碩士論文。
黃政逸（2016）。《以系統模擬探討公共自行車之運補策略-以臺北市公共自行車為例》。國立成功大學工業工程與系統管理學系碩士論文。
臺南市交通局（2023）《統計通報－綠色運輸，臺南市YouBike好行》臺南市政府交通局。
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