# Analyzing Parking Demand Characteristics Using a Bayesian Model Averaging
## 1 Introduction  
Amidst rapid socioeconomic development, there has been a notable upsurge in private car ownership. This surge, however, has not been met with a corresponding expansion in parking infrastructure. Consequently, the parking supply-demand imbalance is becoming increasingly acute, resulting in a pervasive issue of parking challenges [1, 2, 3]. Park-and-ride facilities provide a solution to parking problems in cities [4]. They are typically located at the outskirts of cities, which allows users to park and transfer to public transport to enter the city center. However, the choice of park-and-ride facilities is a complicated issue, influenced by numerous factors. [PERSON] et al. [5] attempted to identify and quantify the factors determining the choice of park-and-ride facilities using a multinomial logit model. Another solution to address the parking supply-demand disparity is to improve the requirement for comprehensive, secure, and delicate management of parking facilities [6, 7]. However, the delicacy of parking management necessitates more precise and comprehensive parking demand information. Therefore, accurately depicting parking demand characteristics, such as parking arrival and departure times, parking duration, parking peak ratio, etc., can provide crucial support for delicacy parking management, and has garnered much attention from scholars [8, 9, 10]. Parking duration, a crucial parking demand parameter, is defined as the amount of time a vehicle parks in a parking lot. Parking duration modeling serves as an essential procedure in parking demand analysis, as it allows for the discovery of intrinsic parking demand patterns [11; 12; 13]. Nevertheless, due to the sensitivity of parking duration, it is susceptible to various factors [14], such as the socioeconomic status of travelers, travel cost, parking fee, driving characteristics, etc. [PERSON] et al. [15] proposed an artificial neural network (ANN) to capture the interrelationship between driver characteristics and parking duration considering two land uses. These reveal that parking duration exhibits considerable uncertainty, and accurately capturing its statistical characteristics presents a significant challenge [16; 17; 18]. In addition, an accurate description of parking duration distribution can improve parking efficiency, parking facility planning and management, etc. In the context of analyzing parking characteristics, parking duration frequently serves as a metric to elucidate parking traits by delineating statistical distribution characteristics [19; 20]. To depict the parking characteristics exhibited by urban streets in commercial areas, [PERSON] et al. [21] conducted statistical modeling to analyze parking indexes, parking occupancy, parking duration, etc. [PERSON] et al. [22] used parking indexes, peak parking ratio, parking turnover rate, and parking duration to explore the parking characteristics of different types of land use (i.e., market, business-and-office-oriented, and food-and-drink-oriented areas) in central Shanghai city. [PERSON] et al. [23] conducted an investigation of parking duration and parking turnover in street parking, delving into the impact of parking pricing policies on street parking characteristics. [PERSON] et al. [24] focused on curb parking and designed a parking demand estimation framework to accurately predict the parking demand distribution over different parking durations for a road section. [PERSON] et al. [25] investigated the influence of parking time and duration on the choice of parking location and revealed increased turnover rates in parking lots that have higher parking fees. However, while these studies investigated the impacts of parking duration and described the parking demand characteristics, they do not provide a precise description of the parking duration distribution. In the area of statistical modeling of parking duration, several studies have endeavored to capture the distribution characteristics through both parametric and non-parametric approaches [26; 27]. [PERSON] et al.