摘要
Predicting human mobility has great significance in Location based Social Network applications,while it is challenging due to the impact of historical mobility patterns and current trajectories.Among these challenges,historical patterns tend to be crucial in the prediction task.However,it is difficult to capture complex patterns from long historical trajectories.Motivated by recent success of Convolutional Neural Network(CNN)-based methods,we propose a Union ConvGRU(UCG)Net,which can capture long short-term patterns of historical trajectories and sequential impact of current trajectories.Specifically,we first incorporate historical trajectories into hidden states by a shared-weight layer,and then utilize a 1D CNN to capture short-term pattern of hidden states.Next,an average pooling method is involved to generate separated hidden states of historical trajectories,on which we use a Fully Connected(FC)layer to capture longterm pattern subsequently.Finally,we use a Recurrent Neural Net-work(RNN)to predict future trajectories by integrating current trajectories and long short-term patterns.Experiments demonstrate that UCG Net performs best in comparison with neural network-based methods.
基金
This research was supported in part by National Key Research and Development Plan Key Special Projects under Grant No.2018YFB2100303
Key Research and Development Plan Project of Shandong Province under Grant No.2016GGX101032
Program for Innovative Postdoctoral Talents in Shandong Province under Grant No.40618030001
National Natural Science Foundation of China under Grant No.61802216
Postdoctoral Science Foundation of China under Grant No.2018M642613.