# Predicting Human Location Using Correlated Movements
## 8 Concluding Remarks  
Human mobility prediction has been a key point for the success of a variety of potential applications. For instance, with the development of device-to-device communication technologies, being able to accurately predict human locations will facilitate the design of an efficient data  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **EFB** & **SC** & **STC** & **CISB** & **BSB** \\ \hline MIT & 11.01 & 11.66 & 15.07 & 11.03 & 15.19 \\ Dartmouth & 13.59 & 13.63 & 20.32 & 13.76 & 20.44 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of KL divergence values between PCMs selection methods.  
Figure 8: Comparison of time slot features in the human prediction framework in case of (**a**) the MIT dataset and (**b**) Dartmouth dataset.  
routing protocol in opportunistic networks. Moreover, the location prediction model inspires further applications such as urban data mining, location-based recommendation services, and contagious disease control. In addition, location estimation is required even when someone may not be willing to share their locations (e.g., geographic profiling of criminals).  
Therefore, in this work, we address the mobility prediction problem in which the current or next location of a person is estimated, especially without requiring the positional history of that person. Since the movement of a person is highly related to other people, a two-phase framework is proposed in which persons with correlated movements (PCMs) with the person-of-interest are determined in the first phase. Then, information of these selected PCMs is leveraged in the second phase to estimate the location of the person-of-interest. For selecting PCMs, the communication interaction similarity-based (CISB) method considers encounter interactions between people, whereas the behavioral similarity-based (BSB) scheme selects PCMs who have similar behavioral patterns, instead of considering co-location between people.  
For the considered problem, our approach is robust, because it can reduce overfitting by only using the location information of the selected PCMs to estimate the positions of the person-of-interest. Low time and space computational complexity is also achieved. Furthermore, geographic locations are not required in our model. Experimental results show that the BSB method gains significant improvement over other PCMs extraction approaches. In addition, the performance generally increases when more PCMs were embedded into the designed prediction model. In particular, a large number of PCMs is more beneficial under the BSB method than others.  
However, our PCMs selection methods are centralized approaches because of requiring mobility traces of all people. Therefore, the proposed PCMs extraction methods are restricted to small or medium networks. In addition, information assurance needs to be taken into account since mobility data of people is exchanged in the network. As a future work, to address the limitations of the centralized approaches, we plan to design a PCMs extraction method which does not require mobility traces of all people. Furthermore, datasets with more people will be used to examine the proposed prediction model in the future work.  
[PERSON] and [PERSON] designed the framework. The performance evaluation was conducted by [PERSON] and [PERSON]. All authors have written and approved the manuscript  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A3B03934617).  
The authors declare no conflict of interest.