新冠肺炎( COVID-19 )疫情的爆發對於台灣社會與日常經濟活動帶來極大的衝擊，本文使用2022年3月1日至2023年3月21日的台灣交通資料，包含經過全台高速公路電子道路收費系統各站點之客車流量、鐵路、雙北地區之捷運等運量統計，透過交通人流資料來衡量及探討人流趨勢對於新冠肺炎的傳播的影響程度。方法上，先是運用區域型空間自相關指標 ( Local Indicator of Spatial Association，LISA ) 來分析疫情數據的空間群集現象及分布情形，並進一步以空間追蹤資料模型( Spatial Panel Data Model )探討交通人流與疫情傳播的相關性。
本文在比較多種空間計量模型後，選定空間杜賓模型( Spatial Durbin Model ) 作為主要分析模型。研究結果顯示，本島除花東和離島以外的其他地區，客車流量與新冠肺炎確診數呈現正相關，且具有顯著的直接效果和間接效果；而針對雙北地區的分析顯示，鐵路與確診數呈現正相關，具有顯著的間接效果與總效果，在客車與捷運在交通人流上則可能因當地交通特性之故，有不同的影響模式。因此未來若遇到類似疫情時，對於不同縣市可就當地交通特性，採因地制宜的防疫相關交通政策。

This study utilizes Taiwan's transportation data from March 1, 2022, to March 21, 2023, to examine the influence of human mobility trends on the spread of COVID-19. The data includes traffic flow of cars from electronic toll collection stations on highways, and railway and MRT traffic volumes in the Taipei metropolitan area. Using the Local Indicator of Spatial Association (LISA) to analyze spatial clustering of pandemic data and the Spatial Panel Data Model to examine correlations, the Spatial Durbin Model (SDM) was selected as the primary analytical model.
Results show that in the main island regions, excluding Hualien, Taitung, and outlying islands, car flow is positively correlated with confirmed COVID-19 cases, exhibiting significant direct and indirect effects. In the Taipei metropolitan area, railway traffic correlates positively with confirmed cases. However, cars and MRTs display different impact patterns due to the unique traffic characteristics of the area. The findings suggest that locally tailored pandemic prevention policies should be implemented in future outbreaks, considering specific traffic characteristics of different regions.

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