A Dynamic Model of Profit Maximization for Carsharing Services: Astana, Republic of Kazakhstan

Abstract

This study considers building a dynamic model of profit maximization for a carsharing system and its verification based on the case of implementing such a system in Astana, Republic of Kazakhstan. The region, bounded by the administrative boundaries of Astana, was divided into subregions that covered the region with regular hexagons placed side by side. A dataset was built with information on 1168 trips to Astana from January to March 2023. The Kepler visualization service constructed maps of the beginning and end of the trips to the region and a map of trips binding to the hexagonal grid cells. Each cell of the grid corresponds to a specific subregion, for which the quantitative parameters necessary for solving the profit maximization problem in the carsharing system are calculated. Stations with cars are placed in the cells of the grid, which are available to carsharing service customers. Based on the collected data, dynamic (four periods per day) and static profit maximization models in the carsharing system were built. Modeling was carried out based on the built models in the case of Astana. It was established that using a dynamic profit maximization model in the carsharing system increases profit by 3.7%. The obtained results are important for the development of the infrastructure of the capital of Kazakhstan and for finding a solution to the problems of urban science in this region.