為因應供不應求的停車空間，各國致力於制定先進且良好的停車管理策略。先進停車管理包含車位監測、車位預約及車位指引。由宏碁智通設計並應用於台南市的智慧路邊停車收費系統，透過智慧停車柱進行自動車輛偵測、車牌辨識，再將數據上傳雲端管理平台，以進行更有效率的停車管理。預約制度的引進可以降低停車時產生的不確定性，進而提升停車效率。但過往較少文獻探討駕駛對車位預約之使用意圖。因此本研究欲探討駕駛對智慧停車柱車位預約功能使用意圖之潛在影響因素，並結合客觀屬性變數進一步分析駕駛之停車選擇行為。
本研究以科技接受模式結合創新抵制理論作為研究架構，藉此探討影響駕駛使用智慧停車柱之心理主觀因素。再透過個體選擇模式，探討社會經濟變數及金錢成本、車位保留時間、尋找車位時間及步行至目地距離等4項客觀屬性變數，並將情境設計分析駕駛之選擇行為。透過Hybrid選擇模式將潛在變數納入選擇模式中，了解心理因素影響使用者停車選擇行為的程度。
研究結果顯示，主觀潛在變數方面「感知有用性」、「感知易用性」會正向影響「使用意願」，「使用障礙」及「風險障礙」會負向影響「感知易用性」，「形象障礙」會負向影響「感知有用性」；客觀屬性變數部分，在Hybrid選擇模式中，「金錢成本」、「車位保留時間」、「尋找車位時間」及「步行至目地距離」皆會顯著影響駕駛對智慧停車柱預約功能之選擇效用；最後透過彈性分析得出，4項屬性變數中，「金錢成本」對駕駛使用智慧停車柱預約功能的負向影響最大，「車位保留時間」對駕駛使用智慧停車柱預約功能的正向影響最大。
本研究結合主觀屬性變數及客觀心理因素，藉此深入探討旅客對於智慧停車柱車位預約功能之選擇行為，並期望研究結果能提供政府單位制訂停車策略及業者制定營運策略時作參考。

To address traffic congestion, increased collision risks, and air pollution stemming from insufficient parking spaces, many countries have adopted intelligent parking systems. Parking reservation is one aspect of these systems, and past research has mainly focused on improving reservation systems. However, studies on users' willingness to use these systems and potential influencing factors are limited. Tainan City has installed numerous smart parking meters on the streets. If these meters were to offer parking reservation capabilities, they could enhance operational efficiency and improve users' parking efficiency. Therefore, this study applies the Technology Acceptance Model combined with Innovation Resistance Theory to examine both positive and negative potential influences on the intention to use smart parking meter reservations. We use a stated preference method to establish a mode choice scenario and explore the impact of objective attributes on choice behavior. Finally, we employ a hybrid choice model to estimate both potential and objective factors simultaneously. Data collected in Taiwan in 2023 show that perceived usefulness and intention to use significantly influence mode choice behavior, while attributes such as cost, expiration times, time cost and walking distance also significantly affect mode choice intentions. The conclusions of this study are expected to provide theoretical contributions and practical implications for smart parking meter operators and governments.

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