Competition and Cooperation of Autonomous Ridepooling Services: Game-Based Simulation of a Broker Concept

TL;DR

This paper develops a game-based simulation framework to evaluate how competition and cooperation among autonomous ridepooling services affect efficiency, profit, and user experience, highlighting the benefits of regulated broker platforms.

Contribution

It introduces a novel simulation framework comparing different interaction scenarios among ridepooling providers, including a regulated broker platform that optimizes system performance.

Findings

Operators profit highest with a regulated broker platform.

Pooling efficiency nearly matches single-operator performance.

Regulated competition benefits operators, cities, and customers.

Abstract

Autonomous mobility on demand services have the potential to disrupt the future mobility system landscape. Ridepooling services in particular can decrease land consumption and increase transportation efficiency by increasing the average vehicle occupancy. Nevertheless, because ridepooling services require a sufficient user base for pooling to take effect, their performance can suffer if multiple operators offer such a service and must split the demand. This study presents a simulation framework for evaluating the impact of competition and cooperation among multiple ridepooling providers. Two different kinds of interaction via a broker platform are compared with the base cases of a single monopolistic operator and two independent operators with divided demand. In the first, the broker presents trip offers from all operators to customers (similar to a mobility-as-a-service platform), who can then freely choose an operator. In the second, a regulated broker platform can manipulate operator offers with the goal of shifting the customer-operator assignment from a user equilibrium towards a system optimum. To model adoptions of the service design depending on the different interaction scenario, a game setting is introduced. Within alternating turns between operators, operators can adapt parameters of their service (fleet size and objective function) to maximize profit. Results for a case study based on Manhattan taxi data, show that operators generate the highest profit in the broker setting while operating the largest fleet. Additionally, pooling efficiency can nearly be maintained compared to a single operator. With the resulting increased service rate, the regulated competition benefits not only operators (profit) and cities (increased pooling efficiency), but also customers. Contrarily, when users can decide freely, the lowest pooling efficiency and operator profit is observed.