# Analyzing Parking Demand Characteristics Using a Bayesian Model Averaging
## 2 Data Preparation  
The parking demand dataset was collected in the parking lot of a High-tech International Plaza in Chengdu from 1 to 31 January 2015. The plaza is a business office space with a total area of \(2.3\times 10^{5}\) m\({}^{2}\) and 632 parking spots. The dataset provides details including the username and specific time of entry and exit from the parking lot. The parking lot users can be divided into two types: temporary users and long-term users. The temporary users primarily comprise individuals needing short-term parking, such as shoppers and occasional visitors, whereas the majority of long-term users are employees working in this plaza. For a business office space, the parking demand is affected by whether it is a working day. Therefore, the data collected under holiday and moderate/heavy rain conditions were removed.  
For exploring the characteristics of parking demand, the time-varying curves depicting parking arrival and departure patterns are illustrated in Figure 1. Note that the statistical time interval is 30 min, and 48 data points are obtained per day. The curves indicate the fluctuation trends of the mean value of parking arrival and departure vehicles over statistical days during the time interval, and the shaded regions denote the upper and lower range. It can be seen that the daily patterns of parking arrival and departure present similar trends. For the parking arrival pattern, the majority of long-term users arrive between 8:00 and 10:00 for work. The curve of temporary users demonstrates a bimodal trend, with peak hours occurring between 9:00 to 11:00 and 13:00 to 16:00. Regarding the parking departure pattern, long-term users' peak hours are from 17:00 to 19:00, whereas temporary users' peak periods are from 11:00 to 12:00 and 14:00 to 18:00. These patterns reveal that the parking duration of long-term users tends to exceed that of temporary users, and the predominant work hours of long-term users typically commence at 9:00 and conclude at 18:00. This observation aligns with real-world practices.  
For a better understanding of the characteristics of the parking arrivals and departures, several metrics (e.g., minimum, maximum, mean, etc.) are summarized in Table 1 to depict the statistical characteristics. Note that the metrics are derived from data points between 8:00 a.m. and 8:00 p.m. using mean values, as data points from other time periods frequently include a considerable number of zeros. From Table 1, it can be observed that the statistical characteristics of the parking arrival and departures for temporary users exhibit similar trends, with comparable minimum, maximum, mean, and median values. Similarly, these characteristics are also observed for long-term users. However, the standard deviation for the parking departure is smaller than that for the parking arrival (the standard deviations of temporary users are 10.307 for departure and 15.723 for arrival, and those of long-term users are 11.411 and 15.723 for departure and arrival, respectively), which can be attributed to the more dispersed nature of the parking arrival.