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English(EN) Toward Equitable Low-Carbon Mobility: Fairness-Aware Demand Prediction for Expanding Bike-Sharing Systems

FairGIN模型改进共享单车需求预测和公平性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了FairGIN，这是一种新颖的图神经网络，旨在改进共享单车系统的需求预测，特别是在不断扩展的城市网络中。该模型解决了两个关键挑战：新部署站点 the cold-start problem（冷启动问题）以及历史数据编码社会经济不平等的问题。FairGIN在训练中融入了模拟网络扩展、针对新站点的知识转移技术以及公平感知优化，以促进公平的资源分配。在纽约市和西雅图进行的实验表明，FairGIN不仅实现了高预测精度，而且在不牺牲整体效率的情况下显著减少了基于收入的服务差异。 AI

影响 这项研究通过改进共享交通系统的部署和资源分配，有可能带来更公平的城市出行解决方案。

排序理由 该集群包含一篇详细介绍新模型及其实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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FairGIN模型改进共享单车需求预测和公平性

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Man Luo, Yixuan Zhao ·

    迈向公平的低碳出行：用于扩展共享单车系统的公平感知需求预测

    arXiv:2608.26451v1 Announce Type: new Abstract: Bike-sharing systems are an important component of low-carbon urban mobility, but continued expansion creates challenges in both cold-start prediction and equitable resource allocation. Newly deployed stations lack historical riders…

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