# Analyzing Parking Demand Characteristics Using a Bayesian Model Averaging
## 1 Introduction
[28] considered the impacts of COVID-19 using a non-parametric approach to characterize the parking arrival, departure, and duration distribution of an off-street parking area. [PERSON] et al. [29] sought to employ the gamma distribution and a gamma mixture model for delineating the parking duration distribution across distinct periods, with the additional objective of delving into the dynamic parking demand characteristics. [PERSON] et al. [30] used the parking duration distribution to estimate the probability of parking durations using survival analysis, thereby facilitating nighttime parking demand forecasting. Similarly, [PERSON] et al. [31] explored the distribution patterns of parking arrival and departure times, and proposed using the Markov birth-and-death process to model the distribution of short-term parking demand. [PERSON] et al. [32] investigated the parking patterns of trucks in urban freight loading zones by concurrently modeling vehicle arrival rates and parking duration, where the parking duration was characterized using survival analysis based on Weibull distribution. However, statistical distributions (e.g., log-normal distribution, log-logistic distribution, etc.) were not employed to depict the parking duration characteristics, and there was no specific distribution that could adequately fit the diverse parking duration data. In summary, previous studies have endeavored to characterize parking duration distribution patterns using statistical models like the gamma distribution and log-normal distribution. However, it is important to note that the appropriateness of these distributions may vary depending on the parking duration data collected from the various parking facilities. More specifically, because of the stochastic parking behavior and the diversity of parking facilities, distinct distributions may be more appropriate for representing parking duration, which is referred to as model uncertainty. Bayesian model averaging (BMA) is renowned for its capacity to mitigate model uncertainty by generating a set of candidate models and assigning weights to each component, where the weights reflect the contributions of candidate modes to the ensemble over the given data [33; 34; 35]. Previous studies have demonstrated that the BMA approach can offer a more dependable depiction of the overall predictive uncertainty and produce a more accurately derived probability density function and cumulative density function in probabilistic forecasts [36]. [PERSON] et al. [37] introduced a hybrid forecasting method for wind power, applying the BMA approach to amalgamate three distinct machine learning techniques, thereby enhancing predictive accuracy. [PERSON] et al. [38] employed the BMA approach to delineate the characteristics of time headway distribution in diverse traffic facilities, taking into account the model uncertainty. This study applies a BMA approach to depict the characteristics of parking duration distribution. The parking dataset was gathered within a commercial district, and it was categorized into two user groups--temporary users and long-term users--for comprehensive analysis. The rest of this paper is organized as follows. Section 2 provides a detailed description of the data preparation. Section 3 describes the methodology, encompassing the theory of the BMA approach and its implementation. Section 4 presents the experiments and results, and the conclusion is summarized in Section 5.