# Predicting Human Location Using Correlated Movements
## 7 Evaluation Results and Discussion
### Performance Comparison of PCM Selection Methods  
In this subsection, the performance results of PCMs extraction methods are discussed with different temporal features of selected PCMs. The location of a person during time \(t\) is predicted with the support of PCMs' positions during the time slot \((t-1)\) (denoted by pre-loc), time slot \(t\) (denoted by cur-loc), and both \((t-1)\) and \(t\) time slots (denoted by pre-cur-loc). Recall that the time slot lasts 15 min. The obtained results are shown in Figure 5.  
First, we evaluate the predictability of the baseline prediction model, most frequent location (MFL). As can be seen in Figure 5, the MFL model achieves much lower performance than the proposed PLP architecture, and specifically leads to top-1 accuracy of 38.62% and 70.92% with MIT and Dartmouth datasets, respectively. Since the MFL model does not use information of PCMs to predict where person \(p\) stays at time \(t\), the temporal feature of PCMs does not affect the Now, the performance of PLP model is analyzed with different PCM selection methods. In EFB and CISB methods, members with the most similar movement paths are selected as PCMs, i.e., people who stay the most frequently with a given person. The main difference between these two methods is that the EFB scheme weights the link of two people based on direct encounters between them, whereas the CISB method compares community interactions between persons to estimate their social correlation. Even though in some cases the CISB method obtains slightly higher accuracy than the EFB method, the two generally achieve similar performance. This indicates that a social relationship between two persons can be mainly reflected by direct interactions between them. In contrast, the BSB method aims at selecting PCMs with the most correlated behavioral patterns. Our work shows that discovering behavior patterns helps to significantly improve prediction accuracy of the model compared to the friendship-based method. In fact, for members who work in the same university or company, they tend to have similar lifestyles (e.g., behavioral pattern) owing to the common schedules of the workplace. Therefore, considering behavioral similarity between people can be helpful in selecting better PCMs for predicting people's locations. Specifically, when embedding pre-cur-loc information of PCMs selected by the BSB scheme into the PLP model, we achieve top-1 prediction accuracy as high as 71.00% and 92.31%, respectively, with MIT and Dartmouth datasets. In the SC scheme, regardless of encounters between two people, members who have similar spatial distribution, or longevity of visiting places are selected as PCMs. The SC method may choose PCMs who have different frequency or regularity of staying in locations with the person-of-interest. For example, if two people \(p\) and \(q\) spend 4 h at the library everyday, the SC method is likely to choose person \(q\) as a PCM of \(p\). However, if person \(p\) stays at the library in the whole afternoon while \(q\) spends 2 h in the morning and 2 h in the evening, using location information of \(q\) may not be appropriate to predict the mobility of person \(p\). Meanwhile, in case of the STC method, two people have a high closeness score if they move in a synchronous manner or their movement are highly dependent. Note that PCMs in both STC and BSB tend to move in a synchronous way with the person-of-interest. However, a fundamental difference between BSB and STC is that the STC method does not consider the temporal information when measuring the similarity between two people. Specifically, the STC approach computes the spatial distribution of person \(p\) given the location of person \(q\). Meanwhile, the BSB method investigates the spatial distribution of person \(p\) given both spatial and temporal information of person \(q\). By considering the temporal data when selecting PCMs, the BSB approach is able to measure movement association with both spatial and temporal aspects.