JointMatch integrates ride-sharing matching with graph neural networks

Researchers have developed JointMatch, a novel framework that unifies ride-sharing matching problems into a single graph neural network solver. This approach addresses the limitations of traditional sequential methods by considering both request pairing and vehicle assignment simultaneously. JointMatch demonstrates significant improvements in revenue and computational efficiency on New York City Yellow Taxi data, outperforming existing heuristics and two-stage GNN baselines. AI

影响 This unified approach could significantly improve efficiency and revenue for large-scale ride-sharing platforms.

排序理由 The cluster contains a research paper detailing a new method for ride-sharing matching using graph neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• arXiv
• graph neural network
• Hugging Face
• JointMatch
• New York City Yellow Taxi

• 论文
• 基础设施

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