New HCRide system balances passenger fairness and driver preference in ride-hailing

Researchers have developed HCRide, a new system designed to balance passenger fairness and driver preferences in ride-hailing services. This system utilizes a novel multi-agent reinforcement learning algorithm called Habic, which incorporates a competition mechanism and dynamic actor-bi-critic networks. Evaluations on real-world datasets from Shenzhen and New York City demonstrate that HCRide effectively enhances system efficiency, passenger fairness, and driver preference compared to existing methods. AI

IMPACT This research could lead to more efficient and user-friendly ride-hailing platforms by better balancing the needs of passengers and drivers.

RANK_REASON The cluster contains an academic paper detailing a new algorithm and system for ride-hailing. [lever_c_demoted from research: ic=1 ai=0.7]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Habic
• HCRide
• Hugging Face
• IArxiv
• Lin Jiang
• New York City
• ScienceCast
• Shenzhen

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