在自駕車普及且大眾常以自駕作為主要交通手段的情況下，停車問題造成的危險、壅塞、空污及其他弊端不容小覷，若能有效進行停車需求預測，並透過合宜的價格機制來影響大眾的駕駛及停車行為，不僅能緩解停車問題，也可能改善收益。本研究提出一個基於停車需求制定停車場費率的模型框架，首先透過時間序列分群方法將每日之分時停車需求時間序列進行分群，以辨視可能影響停車需求之因素，接著建立含週間週末效應與天氣等外生變數的季節性整合移動平均自我迴歸模型（seasonal auto-regressive integrated moving average with exogenous factors, SARIMAX）以進行停車時間序列預測，最後並基於預測的需求，透過數學規畫方法優化不同時段的停車價格。本研究所提出之模型框架，可提供交通與停車管理單位決策之參考。

While the private transportation allows more flexibility on the time and route of transit, increasing use of private vehicles may cause serious problems of traffic congestion and air pollution. Better parking management that can increase overall accessibility and influence travel behavior is thus important. This study proposes a modeling framework that can be used to determine appropriate parking fees on the demand forecast. The framework includes a time series cluster model to identify factors that may affect demand, a seasonal auto-regressive integrated moving average with exogenous factors (SARIMAX) model that take the weather and day-of-the-week as exogenous factors to predict the utilization of a parking lot, and a mathematical programming model for decide the optimal parking fees for different periods of time. This framework was also empirically tested and verified by using the real-world parking data, and the transportation policy makers or managers of parking system can apply this framework to make better data-driven decisions.

1. Ajeng, C., Gim, T.H. (2018). Analyzing on-street parking duration and demand in a metropolitan city of a developing country: a case study of Yogyakarta City, Indonesia. Sustainability, 10 (3) (2018), p. 591
2. Amato G., Carrara F., Falchi F., Gennaro C., Meghini C., Vairo C. (2017). Deep learning for decentralized parking lot occupancy detection. Expert Systems with Applications, 72 (2017), pp. 327-334
3. Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J. M., Perona, I. (2013). An extensive comparative study of cluster validity indices. Pattern Recognition, 46(1), 243-256.
4. Azari, K. A., Arintono, S., Hamid, H., Rahmat, R. A. O. K. (2013). Modelling demand under parking and cordon pricing policy. Transport Policy, 25, 1-9.
5. Badii, C., Nesi, P., Paoli, I. (2018). Predicting available parking slots on critical and regular services by exploiting a range of open data. IEEE Access 6 (2018) 4 4059–4 4071.
6. Box, G. E. P., Hillmer, S. C., Tiao, G. C. (1978). Analysis and modelling of seasonal time series. A. Zellner (Ed.), Seasonal analysis of economic time series, US Dept. of Commerce-Bureau of the Census, Washington, DC (1978), pp. 309-334
7. Box, G. E. P., Jenkins, G. M. (1970) Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco.
8. Box, P. C. (2004). Curb-parking problems: Overview. Journal of Transportation Engineering, 130(1), 1-5.
9. Boyd, E., Kallesen, R. (2004). Practice Papers: The science of revenue management when passengers purchase the lowest available fare. J Revenue Pricing Manag 3, 171–177.
10. Bozkurt, Ö.Ö., Biricik, G., Tayşi, Z.C. (2017). Artificial neural network and sarima based models for power load forecasting in turkish electricity market. PloS One, 12 (4) (2017), p. e0175915
11. Chen, X. Y. (2009). Study of Structure Analysis of Yield Management System－A Case Study of Airlines Industry.
12. Fiez, T., Ratliff, L. J., Dowling, C., Zhang, B. (2018). Data driven spatio-temporal modeling of parking demand. In: 2018 Annual American Control Conference, (ACC), Milwauke (2018), p. 2018
13. Hensher, D. A., King, J., (2001). Parking demand and responsiveness to supply, pricing and location in the Sydney central business district. Transportation Research Part A, 35, 177-196.
14. Hyndman, R. J., Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(1), 1–22.
15. Kanafani, A. (1983). Transportation Demand Analysis, McGraw-Hill Book Company, New York, 1983.
16. Kim, M., Ramakrishna, R. S. (2005). New indices for cluster validity assessment. Pattern Recognition Letters, 26(15), 2353-2363.
17. Lee, T. C., Hersh, M. (1993). A Model for Dynamic Airline Seat Inventory Control with Multiple Seat Bookings. Transportation Science, Vol. 27, No. 3, 1993, pp. 252-265.
18. Liu, S., Guan, H., Yan, H., Yin, H. (2010). Unoccupied Parking Space Prediction of Chaotic Time Series. In: ICCTP 2010: Integrated Transportation Systems: Green, Intelligent, Reliable, pp. 2122–2131
19. Ma, W., Qian, Z. S. (2017). On the variance of recurrent traffic flow for statistical traffic assignment. Transportation Research Part C: Emerging Technologies, 81, 57–82.
20. Ma, W., Qian, Z.S. (2018). Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data. Transportation Research Part C: Emerging Technologies, 96, 96–121.
21. Millard-Ball, A., Weinberger, R.R., Hampshire, R.C. (2014). Is the curb 80% full or 20% empty? Assessing the impacts of San Francisco’s parking pricing experiment. Transportation Research Part A: Policy and Practice, 63, 76–92.
22. Ommeren, v. J., Wentink, D., Dekkers, J. (2011). The real price of parking policy. Journal of Urban Economics, 70(1), 25-31.
23. Parmar, J., Das, P., Azad, F. (2019). Evaluation of parking characteristics: a case study of Delhi. In: 15th World Conference on Transport Research, Mumbai, 2019.
24. Pi, X., Ma, W.,Qian, Z. S. (2019). A general formulation for multi-modal dynamic traffic assignment considering multi-class vehicles, public transit and parking. Transportation Research Part C: Emerging Technologies, 104, 369–389.
25. Pierce, G., Willson, H., Shoup, D. (2015). Optimizing the use of public garages: Pricing parking by demand. Transport Policy, 44 (2015), pp. 89-95
26. Pierce, G., Shoup, D. (2013). Getting the prices right: an evaluation of pricing parking by demand in San Francisco. Journal of the American Planning Association vol 79 (1), 67–81.
27. Provoost, J. C., Kamilaris, A., Wismans, L.J.J., van der Drift, S.J., van Keulen, M. (2020). Predicting parking occupancy via machine learning in the web of things. Internet Things 2020, 12, 100301.
28. Rabiner, L. R., Levinson, S. E. (1984). Isolated and connected word recognition-Theory and selected applications. IEEE Transactions on Communications, vol. 29, no. 5, pp. 621–659, May 1981.
29. Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53-65.
30. Saitta, S., Raphael, B., Smith, I. F. (2007). A bounded index for cluster validity. In International Workshop on Machine Learning and Data Mining in Pattern Recognition (pp. 174-187). Springer Berlin Heidelberg.
31. Sakoe, H., Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, Signal Processing, vol. ASSP-26, pp. 43-49, Feb. 1978.
32. Heffron Transportation, Inc. (2002). Seattle parking management study, available online (http://herbold.seattle.gov/wp-content/uploads/2017/10/SeattleParkingStudyFinalReport-2002.pdf).
33. Senin, P. (2008). Dynamic Time Warping Algorithm Review. Information and Computer Science Department, University of Hawaii at Manoa Honolulu, USA (December 2008)
34. Shen, Q. (1997). Urban transportation in Shanghai, China: problems and planning implications. International Journal of Urban and Regional Research, 21 (4) (1997), pp. 589-606
35. Shoup, D. C. (2006). Cruising for parking. Transport Policy, 13, pp. 479-486.
36. Spiliopoulou, C., Antoniou, C. (2012). Analysis of illegal parking behavior in Greece. Procedia - Social and Behavioral Sciences, 48, 1622-1631.
37. Tong, C. O., Wong, S. C., Leung, B. S. Y. (2004). Estimation of parking accumulation profiles from survey data. Transportation, 31(2), 183-202.
38. Yang, S., Qian, S. (2019). Understanding and predicting travel time with spatio-temporal features of network traffic flow, weather and incidents. IEEE Intelligent Transportation Systems Magazine 11 (3), 12–28.
39. Yu, F., Guo, J., Zhu, X., Shi, G. (2015). Real time prediction of unoccupied parking space using time series model. In: Transportation information and safety (ICTIS), 2015 International conference on. IEEE; 2015. p. 370–4.
40. Zhao, Z., Zhang, Y. (2020). A comparative study of parking occupancy prediction methods considering parking type and parking scale. Journal of Advanced Transportation, vol. 2020, pp. 1–12, Feb. 2020.
41. Zu, Z., Jin, Z., Zhao, Y. (2014). Exploration of Parking Problems in the Wujiang District. CICTP 2014: Safe, Smart, and Sustainable Multimodal Transportation Systems, 1945-1952.