Promoting Collaborative Dispatching in the Ride-Sourcing Market With a Third-Party Integrator

The integrated ride-sourcing mode, developed by third-party integrators, is a feasible solution to market fragmentation because it integrates travel demand and vehicle supply. However, intense competition between platforms reduces the efficiency of the dispatching process. To tackle this issue, a two-stage dispatching framework is proposed, utilizing a partially observable Markov decision process (POMDP) to model the dispatching problem as a mixed cooperative-competitive reinforcement learning task. Within this framework, the Multi-Graph Hierarchical Multi-Head Attention-Deep Deterministic Policy Gradient (MGHMHA-DDPG) algorithm is proposed to determine the generalized values of driver-passenger pairs. A combinatorial optimization model is then formulated to identify the dispatching scheme that maximizes these values. Furthermore, the MGHMHA-DDPG algorithm incorporates a multi-graph convolutional module, a hierarchical multi-head attention module, and a gated recurrent module to model the global supply-demand distribution, the cooperation potential of vehicles, and the hidden features of the temporal dimension, respectively. Experiments using Beijing-based data demonstrate that the MGHMHA-DDPG algorithm outperforms benchmark methods in terms of market revenues and order response rates. This indicates that the MGHMHA-DDPG algorithm effectively mitigates dispatching conflicts between platforms and enhances overall market efficiency.