# Predicting Human Location Using Correlated Movements
## 2 Related Work
### Human Mobility Prediction  
Several studies tried to predict where a person stays given the prior information on historical locations of that person [2; 6; 18; 29; 30; 31; 32]. Most of those studies are based on the Markov model. For instance, in order to predict the person's position in upcoming time slots, [PERSON] et al. [29] proposed a modified Markov model considering spatio-temporal information (i.e., sojourn time and location transition preference). The authors claimed that the modified Markov model achieves higher prediction accuracy than the original Markov model. [PERSON] et al. [6] constructed a prediction model based on dynamic Bayesian networks with the assumption of knowing the last visited location of the person-of-interest and historical position data of'strangers'. Strangers were defined as members who do not necessarily have a social link to the person-of-interest. Authors proposed three methods to determine strangers: temporal closeness, spatial closeness, and spatiotemporal closeness. In case of the temporal closeness approach, the members who have the most similar pattern of communication (e.g., call, sms, and data) to the person-of-interest were chosen. Meanwhile, the spatial closeness method selected the person with the most similar distribution of visited locations to the person-of-interest. The spatiotemporal closeness considered the chi squared test value to compute the closeness between two people \(p\) and \(q\). Specifically, the contingency table was first constructed where the element at row \(i\) and column \(j\) represents the number of times persons \(p\) and \(q\) concurrently stay at location \(i\) and \(j\), respectively. Then, the chi squared test value was used to measure the degree of association between two persons on the contingency table. In addition, [PERSON] et al. [32] first determined the missing data of human trajectories by using the Gibbs sampling algorithm. Then, a high-order Markov chain model was constructed to predict the most likely location of the person-of-interest. Meanwhile, [PERSON] et al. [2] considered two location prediction models based on linear regression and M5 model trees to address the problem of predicting the person's next location. Mobility features such as historical visits and temporal information were fed into the prediction model. The authors concluded that combining different mobility features achieved noticeably higher performance than using a single feature approach. Unlike models in [2; 6; 18; 29; 30; 31; 32], we design a prediction model which does not require a prior location history of the person-of-interest. In addition to historical visit information, a number of factors can be used to predict human mobility, such as social friendships, location preferences, and temporal information. Among these factors, the strong relationship between person movement and friends was revealed in some studies [8; 33; 34]. Consequently, a number of mobility prediction models were designed with the support of friendship information [8; 9; 10; 18; 35; 36]. For instance, [PERSON] et al. [8] decomposed human mobility into two parts: periodic and socially correlated movements. The authors demonstrated that short-distance travel is usually affected by periodic mobility, whereas friendship tends to influence long-range movement. They first developed a human mobility prediction model assuming that people's periodic travels follow a mixed Gaussian distribution of home and workplace. Then, with consideration for social friendships, the probability of being in a location is estimated as a function of the time period during which a friend stays in that location and the distance from the person to his/her friend. Finally, a mobility probability distribution combining periodic and socially correlated movement is formulated using [PERSON] theorem. Even though certain studies [8; 9] did not require a location history for people, their models only work with datasets of geographic locations, e.g., GPS traces.