Research on Parking Space Ratio under Mixed Parking Mode based on CRITIC Objective Weighting Method

In recent years, with the rapid development of urbanization and the surge in the number of cars, parking has become an urgent and complex challenge in urban governance. In the context of the data-driven intelligent era, parking reservation platforms have emerged, which have greatly improved parking efficiency by providing real-time parking information and reservation services. At present, some parking lots use first-come, first-served and first-booked, first-served mechanisms to allocate parking spaces. However, many studies have revealed the potential of auction reservation mechanisms in improving the economic benefits of parking management platforms, which optimize resource allocation through dynamic pricing strategies. However, this mechanism may cause social inequality because it is more biased towards high-income groups. In addition, given the diverse operating modes of parking lots, how to scientifically and reasonably allocate parking resources has become an urgent problem to be solved. Therefore, this study conducts an in-depth discussion on the parking ratio problem under the mixed parking mode. Based on the total revenue of the reservation parking platform, the per capita parking evaluation index of five types of residents, the vertical equity index and the social expected cost, a comprehensive evaluation system for parking ratio is established, and a comprehensive evaluation model for parking ratio in the mixed parking mode is constructed based on the CRITIC objective weighting method. Subsequently, the calculation steps of the model are further illustrated in combination with numerical data, providing a theoretical reference for parking lot data analysis and decision-making, and promoting the fairness and sustainable development of urban parking management.