Systematic Analysis and Modelling of Profit Maximization on Carsharing

The success of carsharing as a relatively new and more sustainable way of traveling is moving private car ownership towards a service use model. Competitivity is an essential aspect of this service and ways to increase profit while offering the most appealing service are still getting explored. Among others, dynamic pricing strategies can be designed to increase profit by attracting more users, selling more rental hours, or maximizing fleet utilization. In this paper, we propose an experimental method aimed at developing a model for maximizing service profit. Using agent-based modeling to generate realistic scenarios, we analyze pricing as a function of the potential demand (i.e., number of members) and supply (hours of booking supplied). The process of reaching the maximum profit consists of testing various combinations of pricing-demand and pricing-supply ranges in order to find the values that maximize the profit for every demand and supply level. Once the optimal prices are known, a polynomial fitting and an optimization method are used to generate a functional form linking all the maximal profit obtaining the advised price to offer for any specific supply levels. Results show how the profit only slightly depends on the variability of the potential demand, while it strongly depends on the amount of supply. It is then shown how it is possible to obtain a linear relation that maximizes the profit in the function of the price offered once the supply is given.

Automated summary

This study proposes an experimental method to maximize profit in carsharing services through dynamic pricing strategies. The results indicate that profit is heavily dependent on supply, with little impact from variability in potential demand.