## 2 The Current Study  
In order to predict the available parking spaces more accurately, various advanced prediction methods have been proposed, such as deep learning-based CNN-LSTM [20], transfer learning framework [21], the bagging regression algorithm [22], random forest [23], the autoregressive moving average model ARIMA [24], voting-based hybrid ensemble classification [25], continuous-time Markov chain [26], hybrid space-time graph volume product network (HST-GCN) [27], and other methods. The various models described above can be used to estimate parking space occupancy rates, and they seem to have reached a good level of prediction. Although these models analyze factors such as weather, events, tolls, regions, time, etc., and establish some data-driven models, there are still some significant problems in these practices:  
* Learning through data can provide a model that fits the data, but this only means that the model is close to the data behavior to a certain extent, it does not mean that the model is explanatory, and it does not even guarantee that it is correct. Even if we put together the parking data of two different morphologies, day and night, we can still obtain a model. However, is such a model really suitable for our use? * Due to the field's characteristics, some parking lots' parking patterns may be regular, while the parking patterns of some parking lots may be irregular. Since we cannot predict the possible parking patterns of the parking lot in advance, even if it is expected to be a particular parking pattern at the beginning, this parking pattern may still change in the future. Using machine learning, however, is vulnerable to the shock of such a shift. Abnormal data or dirty data often make the model fit more and more biased. * When a parking lot is established in practical application scenarios, the prediction system usually needs to be used immediately. That is to say, the actual situation is that we cannot collect enough data for the system to learn, or there are no so-called historical data to learn. Therefore, it is necessary to try to reduce the amount of learning data required by the system, and at the same time, it can meet various types of field predictions. Below, we use some real case studies to observe the actual parking conditions of various types of parking lots. We made some observations on the situation of some parking lots through the open data of the Taipei City Government's parking information and obtained some comments: **Commercial office type locations (fixed entry and exit period)** Most of the parking lots belong to this type, mainly based on the parking needs of general office workers. They start to park during the morning work hours and gradually leave when they leave work in the evening, with no vehicles coming to park on weekends or holidays. The parking situation of this type of parking lot is the best estimate of all kinds, and the changes are relatively small. **Mall type locations (people are coming and going at any time)** These kinds of locations typically include department stores. Whether it is a weekday or a holiday, visitors are coming in and out at any time from morning to night, so the parking situation of the parking lot will also change correspondingly with the number of visitors and the average stay time of visitors. When businesses close for the day, the parking lot is usually back to high availability because all visitors have left. **Exhibition hall type locations (there will be visitors on unspecified dates)** Similar to the first type of commercial office locations, parking conditions are mainly divided into two types: exhibition days and non-exhibition days. As long as the type of day can be distinguished, the parking situation performance can be estimated directly according to the type. **Special business district type locations (some periods are prone to drastic changes)** Similar to the second type of mall location, this is also a type of location where visitors come and go throughout the day.