# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
## 2 Related Work
Trajectories are first clustered into reference spots and a Fourier-based algorithm is utilized to detect period. Then periodic patterns are mined by hierarchical clustering. The core step of pattern-based prediction methods is to cluster frequent places. However, due to the limitation of positioning devices, track points will be lost when the satellite signal is \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Methods**} & **Studies** & **Data Type** & **Model Type** & **Specialities** \\ \hline \multirow{6}{*}{\begin{tabular}{c} Movement Pattern \\ Mining \\ \end{tabular} } & \multirow{3}{*}{Mobile patterns} & \multirow{3}{*}{[PERSON] [7]} & \multirow{3}{*}{Trajectory} & \multirow{3}{*}{Simple model} & \multirow{3}{*}{\begin{tabular}{c} Location sequence, \\ flight pattern \\ \end{tabular} } \\ \cline{3-3} \cline{5-6}  & & & & & & \\ \cline{3-3} \cline{5-6}  & \multirow{3}{*}{Mobile patterns} & \multirow{3}{*}{Mobile patterns} & \multirow{3}{*}{Jeung [8]} & \multirow{3}{*}{Trajectory} & \multirow{3}{*}{\begin{tabular}{c} Motion functions, \\ a priori algorithm \\ \end{tabular} } \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & \multirow{3}{*}{Spatial-temporal patterns} & \multirow{3}{*}{[PERSON] [11]} & \multirow{3}{*}{\begin{tabular}{c} Synthetic \\ sequence \\ \end{tabular} } & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \hline \multirow{6}{*}{\begin{tabular}{c} Prediction Model \\ Building \\ \end{tabular} } & \multirow{3}{*}{Neighborhood-based model} & \multirow{3}{*}{Lathia [14]} & \multirow{3}{*}{Event data} & \multirow{3}{*}{\
\begin{tabular}{c} Unsupervised \\ learning \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} Time-aware \\ neighborhood \\ \end{tabular} } \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \cline{3-3} \cline{5-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._low. For example, when buses travel into tunnels or regions covered with tall buildings, the GPS signal is blocked and no points will be collected during that time, which causes the data sparsity problem during clustering. As shown in Figure 1, the courier also frequently visited the place at the bottom-right section of the map, which is not clustered. Only two frequent areas are gathered and trajectories between the frequent regions are abandoned. Clustering-based algorithms may lose a lot of information, leading to low coverage of prediction. After extracting the frequent regions from raw trajectories, various kinds of models can be used to make prediction. [PERSON] [14] proposed a time-aware neighborhood-based model paying more attention to recent locations and less to the past. [PERSON] [15] proposed a multi-center Gaussian model to calculate the distance between patterns. However, neighborhood-based methods do not consider the sequential factors in user's behaviors. The Markov chain (MC) model can take sequential features into consideration. [PERSON] [16] extended the MC with factorization of the transition matrix and calculated the probability of each behavior based on the past behaviors. [PERSON] [17] proposed a hybrid hidden Markov model (HMM) to transform the trajectory into clusters and train the HMM with them. [PERSON] [18] transferred trajectories into frequent regions by a cell partition algorithm, treating them as hidden states and observable states of the HMM. Accuracy of the prediction depends highly on the level of granularity of the cells.