一种基于深度学习的即时配送时间预测模型

Instant delivery time prediction model based on deep learning

为了提高即时配送服务水平,文章针对即时配送时间预测问题,提出一种分特征学习的预测模型。首先考虑不同因素对配送过程的影响,使用地理哈希、图嵌入等方法对多种特征进行表示;然后使用多头自注意力模型(multi-head self-attention,MHSA)和残差连接组合的方法学习多种特征间存在的关联关系,同时利用卷积神经网络(convolutional neural network,CNN)对配送节点间存在的空间关系进行提取,实现对不同特征的学习;最后将提取的特征进行融合,输入多层感知机实现对配送时间的预测。在真实即时配送数据集上的对比实验表明,该文提出的预测模型能够有效学习各类特征及关联关系,预测效果更优。

Based on the goal of achieving higher quality of instant delivery services,a model for instant delivery time prediction is constructed with multi-feature learning.Firstly,the impact of different factors on the instant delivery process is fully considered,and multiple features are represented using geographic hashing and graph embedding to facilitate the input of different models subsequently.Then,the association relationships between multiple features are learned by a combination of multi-head self-attention(MHSA)and residual connections,and the spatial relationships between delivery nodes are extracted using convolutional neural network(CNN),thus achieving sufficient learning and extraction of different features.Finally,the features extracted by different modules are fused and input to the multilayer perceptron module to realize the prediction of instant delivery time.The comparative experiments on real instant delivery dataset show that the proposed prediction model can effectively learn various features and association relationships,and the prediction effect is better.