Analysis of paid parking service data to create an effective pricing system (using the example of Vladivostok)

The problem of allocation and operation of parking spaces is an important part of research in the field of intelligent transportation. In recent years, due to the sharp increase in the number of cars, the problem of limited parking space resources has been expressed. Effective parking management requires analysis of huge amounts of data and modeling to optimize the use of parking spaces. The implementation and operation of smart paid parking space in Vladivostok creates an interesting application area for data mining and machine learning. The study uses a large-scale data set of historical parking transactions in Vladivostok, including vehicle type, time, location, session duration, and more, to create a data model that reflects the relationship between parking prices, demand, and revenue. The article describes the mechanism for creating a data model that includes all important aspects of the functioning of paid parking lots and factors affecting occupancy. Using this model will allow for machine learning, application of models and evaluation of the effectiveness of their application. The study also identifies key factors influencing parking demand, such as time of day, day of week, location, etc. The data model and insights gained from this research can be used by governments and property owners to optimize the use of paid parking and improve traffic management in smart cities. The approach presented in this article can be applied to other cities to create data-driven pricing systems that meet the specific needs and characteristics of each city.

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