机译:使用经常性神经网络预测车站水平需求在自行车共享系统中
Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei 10607 Taiwan;
Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei 10607 Taiwan|Acad Sinica Inst Informat Sci 128 Nankang Taipei Taiwan;
Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei 10607 Taiwan;
Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei 10607 Taiwan;
Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei 10607 Taiwan;
Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei 10607 Taiwan;
recurrent neural nets; transportation; bicycles; resource allocation; traffic engineering computing; bike shortage; bike station; load balancing strategies; recurrent neural network; New York Citi Bike dataset; global station levels; station level demand prediction; bike-sharing system; modern multimodal transportation system; public mobility; bike-sharing service; uneven bikes distribution; root mean percentage error; root mean squared logarithmic error; mean absolute error; root mean squared error;
机译:预测大型自行车共享网络中车站级别的小时需求:一种图形卷积神经网络方法
机译:开发站级需求预测和可视化工具,以支持自行车共享系统的运营商
机译:走向站立水平需求预测,以便在自行车共享系统中重新平衡
机译:基于递归神经网络的自行车共享系统中站位需求的预测
机译:预测经常性神经网络的重症监护记录中的住院内死亡率
机译:图卷积网络方法应用于考虑空间,时间和全局影响的每小时自行车共享需求预测
机译:预测大型自行车共享网络中的站级别的每小时需求:图形卷积神经网络方法