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

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

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

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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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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Lin Jiang, Yu Yang, Guang Wang ·

    HCRide: Harmonizing Passenger Fairness and Driver Preference for Human-Centered Ride-Hailing

    arXiv:2508.04811v2 Announce Type: replace Abstract: Order dispatch systems play a vital role in ride-hailing services, which directly influence operator revenue, driver profit, and passenger experience. Most existing work focuses on improving system efficiency in terms of operato…

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