## 3 Application and Innovation  
Based on the above research directions, in recent years, many researchers have applied novel approaches to solving parking problems in specific contexts. Currently, the main directions of existing parking research and the review of applied models are summarized as follows. In Table 2, the typical approaches and main contexts of various parking strategy and pricing studies in recent years are listed. In terms of research objectives, most of the research mainly focuses on on-street parking spaces and ancillary parking spaces, and there is insufficient research on parking in specific areas such as commercial districts, hospitals, schools, office areas, etc., which ignores the characteristics of the area and the characteristics of the user's needs. In terms of research direction, the main directions are pricing, policy, behavioral choices, and demand, and the relationship between the factors should be considered comprehensively in the research process. In terms of research methodology, more researchers have chosen MNL modeling, nonlinear programming, game modeling, and machine learning methods. From their studies, it can be found that the main optimization objectives tend to be travel costs, parking profits, parking occupancy, and parking patronage. This study will focus primarily on the pricing of reserved parking in commercial areas. Under this study, we assume a scenario in which one of the various commercial facility parking lots in a business district pioneers a relatively new reservation method of parking allocation. Accordingly, we assume that parking in commercial areas is sufficient to accommodate demand. In this case, how can the most reasonable booking price be adopted by the management of a parking lot, with the potential to increase profit and at the same time not lose customer flow to the commercial property as a result of adopting the new method? We first considered the characteristics and user needs of the commercial area and proposed a set of user-friendly reservation policies that could be adopted for this parking lot. Afterward, in this scenario, we sequentially analyzed the user's choice behavior with the responses of other parking lots in the business district and formulated the price adopted by the reservation parking lot based on the user's demand and the subsequent price adjustment strategies of other parking lots. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reservation Parking Pricing** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **This Paper** \\  & **(2017)** [15] & **(2021)** [16] & **(2021)** [17] & **This Paper** \\ \hline Possible reservation strategies & & \(\surd\) & \(\surd\) & \(\surd\) \\ Demand-led prices & \(\surd\) & & & \(\surd\) \\ User selection behavior & & \(\surd\) & & \(\surd\) \\ Dynamic pricing & \(\surd\) & & & \(\surd\) \\ Gaming with neighboring parking lots & \(\surd\) & & & \(\surd\) \\ Feasible pricing model & \(\surd\) & & & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Modeling factors comparison [15; 16; 17]. Among the methods and models, we adopted the research experience and chose the logit model and Stackberg game model. For a more refined research direction, we further innovatively used a nested logit model to predict users' travel modes and booking durations. Based on the two models, the objective function of maximizing the profit of the parking lot and increasing the passenger flow is proposed, and the optimal price for each hour is solved using nonlinear programming. By summarizing and generalizing the research results, we conclude this paper by proposing a parking reservation strategy where the reservation time is free, and the user can choose a combination of multiple reservation periods.