To illustrate the benefits of our dynamic pricing approach (C-ANT) over other benchmarks, we carried out several computational studies and a case study using Share Now data from the city of Vienna, Austria. We considered three benchmarks. The first is a unit price (BASE). Thus, the provider does not differentiate the price. The second is customer-centric myopic pricing (MYOP), which does not consider the expected future profit. The third is non-customer-centric (i.e., location-based) anticipative dynamic pricing (L-ANT).

In an extensive computational study, we examined the developed approach and the benchmarks in realistic settings with varying business area and fleet sizes as well as varying demand patterns and overall demand levels, indicated by the demand-supply-ratio (i.e., the maximum period demand divided by the fleet size).

Setting

We assumed a planning horizon of one day. The demand patterns we used replicate what is observed in practice: demand intensity varies over the course of the day with two peaks. There is also a spatial variation of demand (e.g., between the city center and outer areas). Across all settings and demand preferences, we considered three or five possible prices (in the case study), as car-sharing providers aim for a transparent, easy-to-communicate pricing mechanism. These prices are predefined based on typical prices in practice.

Profit Impact

We divided the results into two categories: profit and sustainability implications. The numerical results of our computational study show the C-ANT approach provides the highest profit for all settings and demand levels.

When compared to MYOP, we found that anticipative approaches (C-ANT and L-ANT) result in a higher fluctuation of prices across the business area. This is because they can consider future vehicle distribution and rentals in their pricing approach, allowing them to incentivize user-based relocations by varying prices in time and space.

For instance, in all peripheral areas, relatively low prices are set in the morning to incentivize customers to drive vehicles to the center, where demand is comparatively high. Furthermore, taking situation-specific customer information into account enables our approach to better adapt incentives to the customer’s choice behavior.

Due to its ability to anticipate the spatiotemporal demand asymmetries and incentivize user-based vehicle relocations, C-ANT leads to a distribution of vehicles that is better aligned with demand. Immediately before the afternoon peak at 17:30, C-ANT manages to have more vehicles in the center of the business area where demand is strongest at this time compared to the benchmarks.

Regarding possible interdependencies between vehicle distribution and profits, we concluded that very low prices, especially in the outer areas during morning hours, can be successfully used as a customer incentive and can lead to higher profits later in the day when the vehicle distribution is better synchronized with demand.

The results of the numerical study and the case study confirm the benefits of the customer-centric dynamic pricing approach, which outperforms all considered benchmarks significantly, particularly with regard to realized profits and the spatial distribution of vehicles. An analysis suggests that the anticipation of future states and profits, together with the implementation of customer-centricity, is the main driver of its performance.

Sustainability

Although profit maximization is the objective of the optimization problem and the most important metric from the perspective of car-sharing providers, the customer-centric dynamic pricing approach can make a significant contribution to a provider’s sustainability performance due to the reduced need for operator-based relocations.

Profit maximization does not have to be traded against sustainability considerations, as both targets can be considered by anticipating spatiotemporal demand variation in pricing, incentivizing customers to drive from low-demand to high-demand locations. The incentivization of user-based relocations via a customer-centric anticipative dynamic pricing approach improves profitability compared to alternative approaches while reducing the need for operator-based relocations. Providers not only save operational costs incurred by additional staff and equipment, they can reduce CO2 emissions and fuel consumption caused by “unnecessary” rentals or relocations.

To summarize, the customer-centric anticipative dynamic pricing approach for free-floating car-sharing systems performs considerably better in comparison to existing approaches in terms of relevant performance metrics and potential for improving sustainability.

[For more from the authors on this topic, see: “Dynamic Pricing for Car-Sharing Systems Reduces CO2 Emissions.”]