Destination Prediction A Deep Learning based Approach
Destination Prediction A Deep Learning based Approach
复制标题
目的地预测基于深度学习的方法
DOI：
10.1109/tkde.2019.2932984
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发表时间：
2019
影响因子：
8.9
通讯作者：
Lei Zhao
中科院分区：
文献类型：
--
作者：
Jiajie Xu;Jing Zhao;Rui Zhou;Chengfei Liu;Pengpeng Zhao;Lei Zhao
目的地预测是许多基于位置的服务（LBS）的重要问题。现有的解决方案通常采用概率模型或神经网络模型来预测子轨迹上的目的地，并采用标准注意机制来提高预测精度。然而，标准的注意力机制使用固定的特征表示，并且具有有限的能力来表示位置的不同特征。此外，现有的方法很少考虑空间和时间特性的轨迹的影响。由于数据稀疏性问题，它们在细粒度预测中的精度往往不能令人满意。因此，本文提出了一种精心设计的深度学习模型，称为LATL模型。它不仅采用自适应注意力网络来建模位置的独特特征，而且还将时间门和距离门实现到长短期记忆（LSTM）网络中，以捕获连续位置之间的时空关系。此外，为了更好地理解不同空间粒度下的移动模式，并探索多粒度学习能力的融合，进一步提出了一种分层模型，该模型利用多个空间粒度下的不同神经网络的定制组合。大量的实证研究表明，新提出的模型有效地执行和解决问题很好。
Destination prediction is known as an important problem for many location based services (LBSs). Existing solutions generally apply probabilistic models or neural network models to predict destinations over a subtrajectory, and adopt the standard attention mechanism to improve the prediction accuracy. However, the standard attention mechanism uses fixed feature representations, and has a limited ability to represent distinct features of locations. Besides, existing methods rarely take the impact of spatial and temporal characteristics of the trajectory into account. Their accuracies in fine-granularity prediction are always not satisfactory due to the data sparsity problem. Thus, in this paper, a carefully designed deep learning model called LATL model is presented. It not only adopts an adaptive attention network to model the distinct features of locations, but also implements time gates and distance gates into the Long Short-Term Memory (LSTM) network to capture the spatial-temporal relation between consecutive locations. Furthermore, to better understand the mobility patterns in different spatial granularities, and explore the fusion of multi-granularity learning capability, a hierarchical model that utilizes tailored combination of different neural networks under multiple spatial granularities is further proposed. Extensive empirical studies verify that the newly proposed models perform effectively and settle the problem nicely.