行人作為弱勢道路使用者，其安全議題長期受到國際關注，其中都市環境與車速亦被視為影響行人安全的關鍵因素。既有文獻指出，交通事故中行人之死傷程度與車速高度相關，特定的環境特徵亦可有效降低車速、提升安全性。因此，歐美日等先進國家積極推動「低速交通」相關計畫，如交通寧靜區與共享空間等，透過設計低速限之街道系統，以達成車速控制與營造安全步行環境。然欲有效營造低速交通環境，需深入理解街道周邊環境特徵與車速之關聯性，方能因應不同城市脈絡提出適切對策。
本研究旨在探討街道環境特徵與車速之關係，並特別關注「路側與周邊使用」對車速的影響，並彌補國內相關研究之不足，以作為未來人本交通規劃提供在地化且可操作的依據。研究以台南市舊城牆區域之次要道路為對象，結合 Google Maps API 及 OpenStreetMap 等開放資料，並應用語義分割 (Semantic Segmentation) 技術進行街景影像辨識，量化路側與周邊使用資訊，以取代傳統實地調查。最終建構多元迴歸模型，檢視各項環境特徵於不同時段對車速之影響。
研究結果顯示，路側與周邊使用特徵 (如 POI 密度、綠視率、建築物佔比)對車速具有顯著負向影響，並揭示環境特徵與車速之間的關聯性。此外，不同時段之環境特徵影響程度存在差異，離峰時段模型更顯示出環境因子對車速的解釋力較高。本研究主要貢獻包括：(1) 驗證並補充了國內街道周邊環境特徵與各時段車速之關係；(2) 建立結合圖像辨識之量化分析方法，拓展都市環境探勘應用；(3) 提供人本交通規劃建議，作為推動低速交通政策之參考。
惟本研究在資料取得上仍存有限制，部分數據來源受到 API 配額限制，且圖像辨識技術難以準確捕捉動態特徵，加上模型尚有改進空間。未來建議可補充更精確之交通流量與即時影像資料，以提升模型之穩健性與實務應用價值。

This study examines the relationship between street environmental features and vehicle speed in the historic core of Tainan City, with a specific focus on roadside and surrounding land use. To address the lack of related research in Taiwan and support human-centered traffic planning, the study combines open data sources such as Google Maps API and OpenStreetMap. Semantic segmentation techniques are applied to quantify visual elements in street view images, which are then analyzed using multiple regression models to assess their effects on vehicle speed across different time periods. The results show that POI density, green view index, and building coverage ratio are significantly and negatively associated with speed, with stronger effects observed during off-peak hours. Key contributions include: (1) supplementing domestic empirical research on urban speed and environment; (2) developing a visual-data-driven analytical approach; and (3) offering insights for low-speed traffic planning. However, limitations remain in capturing dynamic features through image-based methods. Future work should integrate traffic volume and real-time imagery to enhance model robustness and practical application.

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