Optimizing Ride-Pooling Revenue: Pricing Strategies and Driver-Traveller Dynamics

TL;DR

The paper addresses how to price ride-pooling to balance platform revenue, driver participation, and traveler experience. It introduces an agent-based framework that fuses ExMAS for pool identification with MaaSSim for dynamic, two-sided market simulation in Delft, evaluating three pricing regimes: private rides, forced pooling, and profit maximization. Results show that profit-maximization boosts overall service rate and platform revenue but yields uneven driver earnings and longer traveler waiting times, whereas pooled rides offer more equitable driver income at the cost of total revenue; solo rides provide shorter waits but lower platform gains. These findings inform discriminative pricing strategies and policy considerations to achieve sustainable, multi-party benefits in ride-pooling platforms.

Abstract

Ride-pooling, to gain momentum, needs to be attractive for all the parties involved. This includes also drivers, who are naturally reluctant to serve pooled rides. This can be controlled by the platform's pricing strategy, which can stimulate drivers to serve pooled rides. Here, we propose an agent-based framework, where drivers serve rides that maximise their utility. We simulate a series of scenarios in Delft and compare three strategies. Our results show that drivers, when they maximize their profits, earn more than in both the solo-rides and only-pooled rides scenarios. This shows that serving pooled rides can be beneficial as well for drivers, yet typically not all pooled rides are attractive for drivers. The proposed framework may be further applied to propose discriminative pricing in which the full potential of ride-pooling is exploited, with benefits for the platform, travellers, and (which is novel here) to the drivers.

Figures (6)

• Figure 1: Consider a scenario where the trip requests are submitted to the platform, we first identify attractive pooled options for travellers (a). The platform then determines fares for each ride, depending on its current pricing policy (b). Drivers then are given the choice-set (c), from which they select the optimal ride to serve. In this particular case, driver selects to serve travellers A and B in a pooled ride with the profit-maximization policy (d) and the solo-ride of traveller (e) under the solo-rides policy (f). The objective is to identify such pricing that maximize the system performance from the perspectives of all the parties involved.
• Figure 2: We analyze the service rates (% of requests serviced) for three different strategies: profit maximization, pooled rides, and solo rides. On a grid of supply and demand Figure (a) shows the service rates for profit maximization, pooled rides, and solo rides with twenty minutes of patience time constraint. Profit maximization strategy efficiently utilizes available resources, resulting in a higher percentage of incoming ride requests being serviced. Under the constraint of a 3-minute patience time as shown in Figure (b), we have examined the service rates (% of requests serviced). Our analysis reveals that the profit maximization strategy consistently outperforms the other two approaches.
• Figure 3: The diagram shows the revenue distribution of profit maximization, pooled rides. These rides are associated with a total of five hundred travelers and fifty drivers. The x-axis and the y-axis shows the revenue distribution generated by each pricing strategy. Profit maximization generate highest revenue for the individual drivers but the the revenue is unevenly distributed. Pooled rides generate the least revenue overall and are also the most unevenly distributed. Solo rides generate less overall revenue, but are more evenly distributed, which can be beneficial to drivers.
• Figure 4: The diagrams illustrate the commission earnings garnered by a ride-sharing platform across three distinct pricing strategies: profit maximization, pooled rides, and solo rides. On the x-axis, the number of drivers adopting each pricing strategy is represented, while the y-axis portrays the platform's cumulative commission earnings corresponding to each strategy.
• Figure 5: An experiment was conducted involving 500 travellers and 50 drivers to analyze waiting times based on different pricing strategies. Among the strategies, the profit maximization approach exhibited the longest waiting times, followed by the pooled rides strategy, and finally, the solo rides strategy.