Efficiency and Interventions of Strategic Driver Relocation for Ride-Hailing Platforms

Idle drivers’ spontaneous relocation, when provided with sufficient information, has the potential to mitigate the supply–demand imbalance in ride-hailing services. As this potential remains ambiguous, this study aims to address two fundamental questions and derive managerial insights: Q1: To what extent can drivers’ spontaneous relocation resolve the supply-demand imbalance? Q2: How can subsidies be designed to induce the platform optimum? We propose a leader-follower game-theoretic framework to investigate the dynamic relocation game on arbitrary service networks. The platform, as the leader, designs relocation subsidies to achieve service-oriented or interest-oriented goals, while idle drivers, as followers, decide relocation and compete for individual returns in a multi-stage context. Despite the inherent problem complexity, we investigate the existence, uniqueness, transformation, and solution of the driver equilibrium, providing insights into these critical questions. First, the platform should avoid issuing subsidies purely for relocation purposes when drivers’ actions align with platform goals, such as (1) when the supply and demand are sufficiently imbalanced in volume, eliminating drivers’ gambling behavior, and (2) when commission rates are low—a plausible case in practice—due to the tension between the platform and drivers. Second, we present the potential of the platform intervention in other cases, providing theoretical references for the platform expectations. Notably, due to the extra responsibility gap, the subsidy impact on completed trips exceeds that on platform profits, with the latter at most doubling. Third, we investigate the imbalance in demand that values the platform’s intervention. While a certain spatial imbalance or a demand surge in peak hours emphasizes subsidies for platform goals, the spatial and temporal imbalance can possibly complement each other, inducing coupled demand that directly achieves platform-optimal goals. These results comprehensively answer Q1 along with practical suggestions. Fourth, for Q2, we confirm its feasibility and provide actionable subsidy configurations that induce platform-optimal goals to aid platform management. Numerical examples demonstrate that sparse network structures can lead to larger realized gaps. Additionally, drivers with appropriately random behavior can enhance platform performance, suggesting careful information disclosure by platforms.