摘要
移动轨迹目的地预测是基于位置服务的重要一环,现有的预测方法存在历史轨迹不能完全覆盖所有可能的查询轨迹(数据稀疏)问题,没有考虑前缀轨迹点对预测结果的影响差异(长期依赖问题)。为了解决上述问题,提出了轨迹分布式表示方法。首先,将轨迹进行网格划分,把表示位置的高维独热码向量进行降维处理,生成包含地理拓扑关系的低维嵌入向量。其次,对目的地进行聚类,把聚类中心作为簇中轨迹的标签,缩小相似轨迹的差异,放大不相似轨迹的特征,有效克服了数据稀疏问题。在目的地预测中,将自注意力机制引入长短期记忆网络,提出了基于长短期记忆网络的目的地预测模型SATN-LSTM,挖掘序列中的关键点并根据其重要程度分配权重,较好地解决了长期依赖问题。最后,在真实轨迹数据集上进行了多次实验,验证了模型的有效性,并与现有的模型进行对比,验证了本模型具有更好的准确性。
Destination prediction of moving trajectories is an important part of location-based ser-vices.The existing prediction methods have two problems:one is that the historical trajectory cannot completely cover all possible query traces(data sparse problem),and the other one is that the difference in the influence of prefix trajectory points on the prediction results is not taken into account(long-term dependence problem).As a result,a trajectory distributed representation method is proposed.Firstly,the trajectory sequence is divided into grids,and the high-dimensional one-hot code vectors representing the location is reduced to generate low-dimensional embedding vectors which contain geographical topo-logical relationships;Secondly,the destinations are clustered,and the cluster centers are used as the labels of the trajectories in the cluster,which reduces the difference of similar trajectories,highlights the characteristics of dissimilar trajectories,and effectively overcomes the problem of data sparseness.In the destination prediction,the self-attention mechanism is introduced into the LSTM network,and a destination prediction model(SATN-LSTM)based on the LSTM network are proposed.Mining the key points from the sequence and assigning weights according to their importance,which solves the long-term dependency problem better.Finally,several experiments are carried out on the real trajectory datasets to verify the effectiveness of our model.Compared with the existing models,it is verified that our model gets higher accuracy.
作者
晋广印
赵旭俊
龚艺璇
JIN Guang-yin;ZHAO Xu-jun;GONG Yi-xuan(School of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan 030024,China)
出处
《计算机工程与科学》
CSCD
北大核心
2024年第3期525-534,共10页
Computer Engineering & Science
基金
国家自然科学基金(61572343,U1931209)
国防科技重点实验室基金(JSY6142219202114)
山西省应用基础研究计划(20210302123223,202103021224275)。
关键词
目的地预测
网格划分
自注意力机制
移动轨迹
destination prediction
mesh division
self-attention mechanism
moving trajectory