# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
## 2 Related Work  
Studies on location prediction have gained increasing popularity. The most direct manner is to derive the next location by speed and direction according to the past locations. This may not be an accurate method, because the next location is affected by various factors such as traffic condition, weather, and the behaviors of the objects. In recent years, many location prediction methods focus on mining historical track data. The movements of individuals may show regularity. For example, in Figure 1, couriers deliver the packages from one neighborhood to another, following the same routes and visiting orders every day. Buses travel along the same routes every day. The combination of historical movements and the latest few locations of the objects can provide accurate prediction result. In addition to trajectory data, location prediction methods based on other spatial-temporal data such as check in data and event-triggered data are also valuable as references. Related works in the area of location prediction are summarized in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Studies** & **Data Type** & **Model Type** & **Specialities** \\ \hline \hline \multirow{8}{*}{Location Recommender} & [PERSON] [1] & Event data & & Matrix Factorization \\ \cline{2-5}  & [PERSON] [2] & Event data & & Tensor Factorization \\ \cline{2-5}  & [PERSON] [3] & Trajectory & & Spatial Factorization \\ \cline{2-5}  & [PERSON] [4] & Check-in data & Simple models & Spatial-temporal Tensor \\ \cline{2-5}  & [PERSON] [5] & Event data & & Geo-location, time \\ \cline{2-5}  & \multirow{2}{*}{[PERSON] [6]} & \multirow{2}{*}{Trajectory} & & Using crowds to infer personal behavior \\ \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of location prediction methods. The next location is highly related to the interest and intentions of an object. Recommendation algorithms are used to find the most possible place the user is interested in visiting. The matrix factorization-based method by [PERSON] [1] factorizes a user-item rating matrix considering multiple features including the geo-location. [PERSON] [2] extended it as tensor factorization (TF) to be time-aware, by treating time bins as another dimension when factorizing. [PERSON] [3] modeled spatial information into factorization models. Moreover, [PERSON] [4] included both temporal and spatial aspects in TF as two separated dimensions and make location more predictable. [PERSON] [5] proposed a recommender that leverages rich context signals from mobile device such as geo-location and time. However, it is hard for factorization-based models to generate movements that have never or seldom appeared in the training data. [PERSON] [6] proposed a hybrid method considering both the user's own data and crowds that have similar behaviors. Recommendation-based algorithms do not consider current locations of users and the order of the movements, which may lead to a low precision of prediction. Movement pattern mining techniques find the regularity of movements of objects and combine current movements with historical data for prediction. By transforming trajectories into cells, [PERSON] [7] studied trajectories of taxis and found they move in flight behaviors. [PERSON] [8] forecasted the future locations of a user by predefined motion functions and linear or nonlinear models, then movement patterns are extracted by an a priori algorithm. [PERSON] [9] utilized an improved a priori algorithm to find association rules with support and confidence. These frequent patterns reveal the co-occurrences of locations. [PERSON] [10] developed a modified PrefixSpan algorithm to discover both the relevance of locations and the order of location sequences. Moreover, sequential pattern methods can be improved by adding temporal information. [PERSON] [11] extended travel time to location sequences and generated spatial-temporal patterns. [PERSON] [12,13] proposed two kinds of trajectory patterns: the periodic behavior pattern and the swarm pattern.