山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (5): 48-56.doi: 10.6040/j.issn.1672-3961.0.2022.121
• 机器学习与数据挖掘 • 上一篇
那绪博,张莹*,李沐阳,陈元畅,华云鹏
NA Xubo, ZHANG Ying*, LI Muyang, CHEN Yuanchang, HUA Yunpeng
摘要： 为提高网约车需求预测的准确率,提出结合卷积神经网络(convolutional neural network, CNN)和卷积门控循环单元(convolutional gate recurrent unit, ConvGRU)的出发地-目的地需求预测分析(origin-destination demand prediction with CNN and ConvGRU, ODCG)模型。ODCG模型的网络结构分为局部空间特征(local spatial feature, LSF)提取模块、时间演化特征(time evolution feature, TEF)提取模块、全局关联模块(global association module, GCM)和输出层。LSF提取模块利用CNN分别处理出发地视图和目的地视图,得到网约车需求的局部空间依赖性;TEF提取模块将网约车需求的局部空间信息、天气信息和订单序列关联度信息整合到ConvGRU中,分析网约车的需求;GCM模块整合所有区域之间的相关性,通过将所有区域特征加权求和得到全局相关性,并将相应区域之间的相似度定义为权重。试验结果表明,ODCG模型在网约车需求预测中优于其他基线模型,同时提高了网约车需求预测的准确率。
中图分类号:
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