高齡者搭乘捷運旅運之時空分布型態分析 –以雙北地區為例

我國預計於2026年邁入超高齡社會，面對逐漸增加之高齡人口，凸顯出高齡者之交通運輸問題。而在科技發展之下交通運輸與大數據之連結已變得密不可分，我國衛生福利部也提出運用大數據整合跨域資訊掌握高齡需求，創建對高齡者之友善樂活環境，且提出以TOD等手段，來保障高齡者的交通權益及基本民行需求，以全面提升高齡者生活品質。故本研究以搭乘台北捷運之高齡者為研究對象，並藉由捷運大數據資料的分析，探討高齡者在不同時空下之分布，且透過熱點分析，以視覺化的方式呈現高齡者使用捷運的冷熱區分布情形。再者，藉由文獻回顧擷取出影響高齡者旅運行為之因素，並建立一般線性迴歸模型、經典地理加權迴歸及多尺度地理加權迴歸模型，探討各種因素對於高齡者搭乘捷運旅次量之影響，以釐清高齡者使用台北捷運之旅運特性。實證結果顯示，高齡者搭乘捷運時間乘高原狀，且會避開上班族尖峰時間，其使用熱區為中正區、萬華區、大同區。此外，建成環境中可抵達之公共設施數對於高齡者搭乘捷運旅次量具有顯著正向影響。最後，透過多尺度地理加權迴歸可瞭解各捷運站點不同因素影響高齡者搭乘捷運旅次量之程度，希冀以此給予未來政府、系統營運者改善之基準。

關鍵字

高齡者；大數據；旅運行為；多尺度地理加權迴歸

並列摘要

Taiwan officially turned into an elderly society in 2018, and is expected to enter a super-aged society in 2026. In the face of the gradual increase in the elderly population, the transportation problems of the elderly have also been highlighted. To this end, my country responds to the "Friendly Cities for the Elderly" proposed by the World Health Organization, assists county and city governments in reviewing the existing living environment of senior citizens, and proposes improvement plans and suggestions for the deficiencies of the city’s software and hardware, and uses TOD as a means. To protect the transportation rights and basic civilian needs of disadvantaged groups (including the elderly, disabled, children, etc.). In addition, ITDP published the "TOD Standard" version 3.0, which aims to build a people-oriented city and puts forward the principle of inclusiveness, so that people living in cities and suburbs can obtain opportunities and resources in the city. In addition, under the development of science and technology, the connection between transportation and big data has become inseparable. The Ministry of Health and Welfare of my country has also proposed to use big data to integrate cross-domain information to grasp the needs of the elderly, and to create a friendly and happy environment for the elderly to comprehensively improve The health and quality of life of the elderly. Therefore, this research takes the elderly who take the Taipei MRT as the research object, and uses the analysis of the MRT big data to explore the distribution of the elderly in different time and space, and through the hot spot analysis, the use of the elderly is presented in a visual way. The distribution of hot and cold areas of the MRT. In addition, through literature review, the factors that affect the travel of the elderly are extracted, and general linear regression models, classical geographically weighted regression and multi-scale geographically weighted regression models are established to explore the influence of various factors on the use of elderly people on the MRT. , In order to clarify the transportation characteristics of the Taipei MRT for the elderly. The empirical results show that elderly people take the MRT time to take the plateau shape and avoid the peak hours of office workers, and the hot spots used by the elderly are Zhongzheng District, Wanhua District, and Datong District. In addition, the number of public facilities that can be reached in the built environment has a significant positive impact on the number of elderly people taking the MRT. Finally, through multi-scale geographic weighted regression, we can understand the significant factors that influence the use of the MRT by the elderly at each MRT station.