Francesco Piccialli1,2, Sara Amitrano3, Donato Cerciello3

  • 1Department of Mathematics and Applications "R. Caccioppoli", M.O.D.A.L. - Mathematical mOdelling and Data AnaLysis Research Group, University of Naples Federico II, Naples, Italy. francesco.piccialli@unina.it.

Nature communications
|October 23, 2025
PubMed
概括

本研究介绍了城市停车管理和流动性预测的数字框架,整合了用于预测建模的各种数据. 该框架通过优化停车和资源分配来增强城市流动性,在卡塞塔得到了验证.

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