# Predicting Human Location Using Correlated Movements
## 1 Introduction
The first dataset provided cellular traces collected by the Massachusetts Institute of Technology (MIT) Media Lab [12], which recorded the time a mobile phone was associated with a base station. In a metropolitan area, small and short-range cells that cover distances of a few hundred meters are more popular [13]. Moreover, since people usually carry their phones all the time, the position of a base station can be used to represent a person's approximate location [14; 15; 16; 17; 10; 18]. The second dataset is the mobility data extracted from Wi-Fi traces collected in the Dartmouth university campus [19]. A log message was recorded whenever a mobile device associates or disassociates with an AP. Due to the relatively short range of the Wi-Fi technology, an AP can be used to reflect a person's location [20; 21; 22]. Experiments were performed in order to validate the proposed prediction model in various situations. We draw a comparison between the designed PLP model and the baseline one, called most frequent location (MFL), in which the most visited position is predicted as the location of the person-of-interest. Two proposed PCMs selection methods are also compared with three recent existing approaches: encounter frequency-based (EFB) [23; 18; 24], spatial closeness (SC) [6], and spatio-temporal closeness (STC) [6]. Specifically, EFB is rooted in encounter frequency, which is one of the natural characteristics of a friendship, i.e., two members who more frequently meet each other are likely to be closer friends. In SC, the persons who have the most similar distribution of visited locations to the person-of-interest are selected, meanwhile STC chooses members with the most synchronous movement with that of the person-of-interest. The main contributions of our work can be summarized as:  
* Our work considers a new problem in which human location should be estimated in a challenging situation, i.e., the recent historical location of the person-of-interest is not available. To address this challenge, a two-phase framework with low time and space complexity is proposed. Specifically, for a given person-of-interest, persons with correlated movements (PCMs) are first selected, then the location of the person-of-interest is estimated based on the spatio-temporal information of selected PCMs. * Two novel PCMs selection methods are proposed: CISB and BSB. CISB selects PCMs who have the strongest social tie with the person-of-interest. Because socially close people may have similar patterns of meeting other members in the community, CISB finds persons who have similar encounters with other members. * While the purpose of CISB is to find PCMs who have the strong social relationship with the person-of-interest, BSB aims at selecting PCMs who have similar behavior patterns with the person-of-interest, even though there may be no direct encounters or evident co-location between them. As a result, the selected PCMs under the BSB method may not have strong social tie with the person-of-interest. * A PCM-based location prediction model, which leverages the recent spatial data of selected PCMs, is developed. * Experimental results show that the performance of BSB substantially dominates other PCMs selection methods in terms of prediction accuracy. In addition, higher performance can be achieved when a larger number of PCMs are embedded into the model. In particular, a beneficial effect of a large number of PCMs is more clearly observed in case of BSB than other PCMs extraction methods. The rest of this manuscript is organized as follows. Sections 2 and 3 discuss related studies of human mobility prediction and the two considered datasets, respectively. Then, the proposed framework is described briefly in Section 4. In Section 5, two methods for selecting PCMs in the first phase are presented. Section 6 explains the PCM-based location prediction model in detail. Then, in Section 7, we analyze the evaluation results of the proposed model with different PCMs selection methods. Finally, the conclusions from this work are drawn in Section 8.