|摘要: ||本研究認為要提升交通運輸服務的使用狀況，除了對服務屬性滿意度進行調查之外，首先應藉由系 統性回顧國內外對於交通主觀幸福感之研究，包含日常旅運行為與主觀幸福感關聯的相關文獻，並利 用主觀幸福感的要素對日常交通服務的主觀幸福感內涵進行探討，以形成理論模型基礎並從相關理論 當中發展量測工具，以此對於使用者是否滿足日常旅運活動需求、獲得交通主觀幸福感之相關影響因 子進行深入探討。其次為運用社群運算的概念，對使用者於社群媒體上的活動資料進行使用者行為分 析，並將日常旅運活動行為與交通主觀幸福感兩者，藉由發展適合之機器學習演算法驗證其關聯，以 獲得在社群媒體中的日常交通主觀幸福感模型，補強原有僅探討運輸服務屬性滿意度的不足之處，以 提供更有效益且更貼近交通使用者需求及主觀幸福感之分析工具。
Subjective Well-being (SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated daily travel data on social media. By utilizing users’ social media data with SWB labels, we train machine learning models that are able to “sense” individual SWB. Our proposed model, which attains the state-of-the-art prediction accuracy, can then be applied to identify large amount of social media users’ SWB in time with low cost. The proposed models, which attain the state-of-the-art prediction standard, have equivalent utility with well-designed psychological scales. This approach of psychological assessment, can predict one's daily travel SWB by automatically by analyzing his/her social media data in a non-invasive manner, and makes it feasible to assess users' psychological features, in large scale and timely. It is our will that the methods in this study can inspire subsequent research in the area of conventional psychology or social sciences. More empirical analysis on real data, leads to more reliable conclusion, and such conclusion can be used to improve the public welfare.