大數據分析為解決及研究交通數據的重要工具，其中交通旅次資料作為分析旅運需求趨勢的重要來源，傳統交通流聚類包含空間與時間聚類，而空間及時間聚類方法可找出空間或時間相似的旅次，進而得知主要的交通旅次方向及其集中出現的時間。本研究取用2019年臺南市T-Bike租借資料，探討空間及時間的聚集程度。由於空間聚類在旅次數較少且最鄰近旅次數(K值)較大時，易將距離較遠的旅次誤歸為同一聚類，因此本研究在空間聚類時添加了中點距離限制。而在時間聚類中，發現若該時間聚類中有租借時間較長之旅次，其旅次彼此間開始時間與結束時間相差亦較大。考量到T-Bike的資料型態與GPS不同，透過適當的方法，仍然可以發現一些具體的趨勢或提供有價值的建議，如辨認出特定時間範圍內，哪些景點或通勤熱點的T-Bike使用量顯著上升，進而提供針對性的調度或行銷建議，如美術館站、海安民族站等。另外，透過T-Bike的使用熱點和流動路徑也有助於發現吸引遊客的特定地區和時間，進而推薦相關景點加強設施或導覽活動，這些建議將有助於推動當地的觀光產業發展。

Big data analysis is an essential tool for addressing and studying traffic data, where transportation trip data serves as a crucial source for analyzing travel demand trends. Traditional traffic flow clustering involves spatial and temporal clustering methods, which can identify spatially or temporally similar trips, thereby revealing the main traffic travel directions and their concentrated occurrence times. In this study, we utilized the 2019 T-Bike rental data from Tainan City to explore the degree of spatial and temporal aggregation. Since spatial clustering may erroneously group distant trips as the same cluster when the number of trips is low and the value of the nearest neighbor (K value) is large, this study incorporated a midpoint distance constraint during spatial clustering. In temporal clustering, it was found that if there were trips with longer rental durations within a specific time cluster, the time differences between the start and end times of these trips were also greater. Despite the distinct data types of T-Bike and GPS, through appropriate methods, we can still identify specific trends and provide valuable insights. For instance, we can identify significant increases in T-Bike usage around specific tourist attractions or commuter hotspots during certain time periods, thereby offering targeted scheduling or marketing recommendations, such as at the Tainan Art Museum or Hai'an Minzu Rd. Intersection. Additionally, analyzing T-Bike usage hotspots and flow patterns can help identify specific areas and times that attract tourists, leading to recommendations for enhancing facilities or guided tours in these locations. These suggestions will contribute to the development of the local tourism industry.

英文文獻
1. Anbaroglu, B., Heydecker, B., & Cheng, T. (2014). Spatio-Temporal Clustering for Non-Recurrent Traffic Congestion Detection on Urban Road Networks. Transportation Research Part C: Emerging Technologies, 48, 47-65.
2. Andrienko, G., Andrienko, N., Fuchs, G., & Wood, J. (2016). Revealing Patterns and Trends of Mass Mobility Through Spatial and Temporal Abstraction of Origin-Destination Movement Data. IEEE Transactions on Visualization and Computer Graphics, 23(9), 2120-2136.
3. Asadi, R., & Regan, A. (2019). Spatio-Temporal Clustering of Traffic Data with Deep Embedded Clustering. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, 45-52.
4. Asadi, R., & Regan, A. C. (2020). A Spatio-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting. Applied Soft Computing, 87, 105963.
5. Ballari, D., Giraldo, R., Campozano, L., & Samaniego, E. (2018). Spatial Functional Data Analysis for Regionalizing Precipitation Seasonality and Intensity in a Sparsely Monitored Region: Unveiling the Spatio‐Temporal Dependencies of Precipitation in Ecuador. International Journal of Climatology, 38(8), 3337-3354.
6. Caggiani, L., Ottomanelli, M., Camporeale, R., & Binetti, M. (2016). Spatio-Temporal Clustering and Forecasting Method for Free-Floating Bike Sharing Systems. International Conference on Systems Science,
7. Cao, Y., & Shen, D. (2019). Contribution of Shared Bikes to Carbon Dioxide Emission Reduction and The Economy in Beijing. Sustainable Cities and Society, 51, 101749.
8. Du, Y., Deng, F., & Liao, F. (2019). A Model Framework for Discovering The Spatio-Temporal Usage Patterns of Public Free-Floating Bike-Sharing System. Transportation Research Part C: Emerging Technologies, 103, 39-55.
9. Fan, Y., Zhu, X., She, B., Guo, W., & Guo, T. (2018). Network-Constrained Spatio-Temporal Clustering Analysis of Traffic Collisions in Jianghan District of Wuhan, China. PLoS one, 13(4), e0195093.
10. Kisilevich, S., Mansmann, F., Nanni, M., & Rinzivillo, S. (2009). Spatio-Temporal Clustering. In Data Mining and Knowledge Discovery Handbook, 855-874. Springer.
11. Li, H., Liu, J., Wu, K., Yang, Z., Liu, R. W., & Xiong, N. (2018). Spatio-Temporal Vessel Trajectory Clustering Based on Data Mapping and Density. IEEE Access, 6, 58939-58954.
12. Liang, M., Liu, R. W., Zhan, Y., Li, H., Zhu, F., & Wang, F.-Y. (2022). Fine-Grained Vessel Traffic Flow Prediction with a Spatio-Temporal Multigraph Convolutional Network. IEEE Transactions on Intelligent Transportation Systems, 23(12), 23694-23707.
13. Miller, H. J., & Han, J. (2009). Geographic Data Mining and Knowledge Discovery. CRC Press.
14. Rempe, F., Huber, G., & Bogenberger, K. (2016). Spatio-Temporal Congestion Patterns in Urban Traffic Networks. Transportation Research Procedia, 15, 513-524.
15. Romanillos, G., Zaltz Austwick, M., Ettema, D., & De Kruijf, J. (2016). Big Data and Cycling. Transport Reviews, 36(1), 114-133.
16. Shaheen, S. A., Guzman, S., & Zhang, H. (2010). Bikesharing in Europe, The Americas, and Asia: Past, Present, and Future. Transportation Research Record, 2143(1), 159-167.
17. Song, J., Zhao, C., Zhong, S., Nielsen, T. A. S., & Prishchepov, A. V. (2019). Mapping Spatio-Temporal Patterns and Detecting The Factors of Traffic Congestion With Multi-Source Data Fusion and Mining Techniques. Computers, Environment and Urban Systems, 77, 101364.
18. Tian, Y., Zhang, X., Yang, B., Wang, J., & An, S. (2021). An Individual-Based Spatio-Temporal Travel Demand Mining Method and Its Application in Improving Rebalancing for Free-Floating Bike-Sharing System. Advanced Engineering Informatics, 50, 101365.
19. Von Landesberger, T., Brodkorb, F., Roskosch, P., Andrienko, N., Andrienko, G., & Kerren, A. (2015). MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering. IEEE Transactions on Visualization and Computer Graphics, 22(1), 11-20.
20. Woodcock, J., Tainio, M., Cheshire, J., O’Brien, O., & Goodman, A. (2014). Health Effects of The London Bicycle Sharing System: Health Impact Modelling Study. Bmj, 348.
21. Yao, X., Zhu, D., Gao, Y., Wu, L., Zhang, P., & Liu, Y. (2018). A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends. IEEE Access, 6, 44666-44675.
22. Yuan, H., & Li, G. (2021). A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation. Data Science and Engineering, 6(1), 63-85.
23. Zhang, K., Feng, Z., Chen, S., Huang, K., & Wang, G. (2016). A Framework for Passengers Demand Prediction and Recommendation. 2016 IEEE International Conference on Services Computing (SCC). IEEE, 340-347.
24. Zhang, M., Liu, J., Liu, Y., Hu, Z., & Yi, L. (2012). Recommending Pick-Up Points for Taxi-Drivers Based on Spatio-Temporal Clustering. 2012 Second International Conference on Cloud and Green Computing. IEEE, 67-72.
25. Zhang, Y., & Mi, Z. (2018). Environmental Benefits of Bike Sharing: A Big Data-Based Analysis. Applied Energy, 220, 296-301.

中文文獻
1. 柯宜伶（2020），共享自行車旅次之時空分析-以臺南市T-bike為例，國立成功大學交通管理科學系碩士在職專班碩士論文。
2. 臺南市政府交通局臺南市T-Bike公共自行車（2022），2022/11台南市公共自行車T-Bike營運成果，擷取日期：2022年12月9日，網站：https://tbike.tainan.gov.tw/Portal/zh-TW/News/Detail/2337