## 3 Modeling Method  
Taipei is one of the world's most famous cities, which contains a variety of commercial activity areas, and various commercial activities are frequently carried out 24 h a day. Therefore, this city as a sampling reference for this study is sufficient to cover most possible parking scenarios in Asia. In Taipei, are at least 1000 parking lots. It obviously requires a lot of computing resources to retrieve the available parking spaces from each parking lot and then use a neural network for modeling and estimation. Therefore, such an approach struggles to achieve the goal of a city-scale dynamic pricing system for regional parking fees. Therefore, to quickly process the estimation of the number of available parking spaces of many parking lots in a specific area, we use a fast and efficient rule-based modeling method. This study does not focus on the changes in parking conditions in the time series but instead explores its characteristic points. Although the prediction is also based on big data, compared with the neural network operation using deep learning, this research adopts a statistical verification method and cooperates with effective operation rules. This is so that data utilization is no longer a black-box operation, and it can also reduce the influence of data overfitting. After reviewing various estimation algorithms, we know that the number of available parking spaces in a parking lot is often not changed by linear averaging. After careful examination, the following vital rules for modeling can be obtained:  
**Important Rule 1:**  
Even in the same parking lot, the number of available parking spaces in the same period often differs from day to day. However, the number of available parking spaces in the two hours before and after is often very similar. Therefore, if the numbers of available parking spaces for three consecutive hours are respectively called \"number of available parking spaces_current,\" \"number of available parking spaces_before one hour,\" and \"number of available parking spaces_before two hours,\" then the three should have the following relationship:  
\[\text{Available}_{\text{before two hours}}:\text{Available}_{\text{before one hour}}=\text{Available}_{\text{before one hour}}:\text{Available}_{\text{current}}\]Thus,  
\[\text{Available}_{\text{current}}=\text{Available}_{\text{before one hour*}}\]  
Available before one hour  
**Important Rule 2:**  
If the current hour happens to be a working time or a popular time, obviously, the number at this time will not change with the ratio of the previous two hours, and the number at the same time of each day will not be the same. However, the rate of change between this time of day and the previous hour is often still the same. As long as the average daily rate of change of the field for this period is obtained, it can be estimated based on the number of the previous hour:  
Thus,  
\[\text{Available}_{\text{current}}=\text{Available}_{\text{before one hour}}*Avg \bigg{(}\frac{\text{Available}_{\text{current}}}{\text{Available before one hour}}\bigg{)}\]  
If one wants to estimate for any period of the day, as long as one can obtain the historical data for the first two consecutive hours from that period, one can calculate for that period. The \"AVG\" referred to here is just a very simple average. For example, if we have the ratio of available parking spaces for a specific period in the past month, we can simply average the ratio of each day in this month. This average value is usually not affected by the conditions of the day and is always fixed. This study also uses this important feature to make predictions. However, there are still two issues that need to be addressed. First, as mentioned in Important Rule 2 above, we know that some periods have different ratios of change. Since the calculation formula of the period is different, how do we know whether the period to be calculated belongs to the standard period or the change period?