Optimization of two-passenger ride-pooling orders based on ST-GNN and path optimization.

ABSTRACT: Urban dynamic ride-pooling faces significant challenges in achieving efficient real-time order matching and path planning, primarily due to the complex spatio-temporal coupling of passenger demand and traffic conditions. Traditional algorithms often struggle to dynamically integrate these features and adapt to multi-objective optimization under real-world constraints. To address these limitations, this study proposes a novel dual-optimization framework that synergizes a Spatio-Temporal Graph Neural Network (ST-GNN) with a multi-objective path planning algorithm. Our approach begins by constructing a demand-adaptive urban spatial structure using Voronoi polygons. A spatio-temporal graph is then built upon this structure, where a graph neural network model, incorporating multi-head attention

SUBMITTER: Xing X 

PROVIDER: S-EPMC12688112 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature