# Predicting Human Location Using Correlated Movements
## 7 Evaluation Results and Discussion  
In this section, the performance of the human mobility prediction framework is examined under different PCMs detection methods and is compared with the baseline prediction model, most frequent location (MFL). The whole dataset is randomly partitioned into training, validation, and test sets at the ratio of 5:2:3. Recall that in case of the Dartmouth dataset the 118-day period is selected and for each day human mobility from 8 h to 24 h is considered. Since each time slot lasts 30 min, there are total 32 time slots per day. Therefore, the number of samples in the Dartmouth dataset is \(118\times 32=3776\). The training, validation, and test sets consist of 1886; 755; and 1133 instances, respectively. The training set is used to fit the model while the purpose of validation set is to determine the appropriate hyper-parameters for the neural network, e.g., the number of hidden layers, hidden units, activation units, and learning rate. Note that only the training set is used in the first phase of the prediction model and the performance of model is estimated based on the test set. Table 5 presents the setup for the mobility prediction framework. Specifically, the two proposed PCM selection methods consisting of community interaction similarity-based (CISB) and behavioral similarity-based (BSB) are evaluated and compared with three recent selection approaches: encounter frequency-based method (EFB) [18; 23; 24], spatial closeness (SC) [6], and spatiotemporal closeness (STC) [6]. Table 6 shows a list of acronyms which are used in the manuscript. Recall that [PERSON] et al. [6] constructed a prediction model based on dynamic Bayesian networks which leveraged historical location data of selected PCMs to predict the most probable \begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Meaning** \\ \hline BSB & Behavioral similarity-based \\ CISB & Community interaction similarity-based \\ EFB & Encounter frequency-based \\ MFL & Most frequent location \\ PCM & Persons with correlated movements \\ PLP & PCM-based location prediction \\ SC & Spatial closeness \\ STC & Spatiotemporal closeness \\ \hline \hline \end{tabular}
\end{table} 
Table 6: List of Acronyms. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline PCM selection method & \{SC, STC, EFB, CISB, BBB \} \\ Prediction model & \{PLP\} \\ Temporal location type & \{pre-loc, cur-loc, **pre-cur-loc**} \\ Time slot feature & \{un-grouped, **grouped** \} \\ Number of selected PCMs (\(r\)) & \{1, 2, 3, 4, 5\} \\ Top-\(k\) accuracy & \{**top-1**, top-2, top-3, top-4\} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Setup for the location prediction framework. position of the person-of-interest. However, the prediction model in [6] requires to know the last visited place of the person-of-interest, which is not applicable to our considering problem where the historical position data of the person-of-interest is assumed to be unknown. Note that since there was no existing prediction model designed for the considering problem, the PCM-based location prediction (PLP) model is used in the experiments. Additionally, we make a performance comparison between our two proposed PCMs selection approaches and counterpart methods in [6]. There were three similarity measurement metrics to select PCMs in [6], and specifically the first method, temporal closeness, compares patterns of communication (e.g., call, sms, and data) between people. Due to the requirement of extra communication information of members, the temporal closeness is not considered as a counterpart method for selecting PCMs in our work. Therefore, we compare the proposed PCMs selection methods with only two approaches in [6]: STC and SC. For the PLP model, a variety of NN architectures which have a different number of layers, hidden units, and activation functions were examined. Then, among the considered setting, the most appropriate NN architecture was selected by using the validation set.