Predicting Human Location Using Correlated Movements

Abstract

This paper aims at estimating the current location, or predicting the next location, of a person when the recent location sequence of that person is unknown. Inspired by the fact that the behavior of an individual is greatly related to other people, a two-phase framework is proposed, which first finds persons who have highly correlated movements with a person-of-interest, then estimates the person’s location based on the position information for selected persons. For the first phase, we propose two methods: community interaction similarity-based (CISB) and behavioral similarity-based (BSB). The CISB method finds persons who have similar encounters with other members in the entire community. In the BSB method, members are selected if they show similar behavioral patterns with a given person, even though there are no direct encounters or evident co-locations between them. For the second phase, a neural network is considered in order to develop the prediction model based on the selected members. Evaluation results show that the proposed prediction model under the BSB scheme outperforms other methods, achieving top-1 accuracy of 71.13% and 69.36% for estimations of current and next locations, respectively, with the MIT dataset and 92.31% and 92.03% in case of the Dartmouth dataset.

The Impact of COVID-19 on Relative Changes in Aggregated Mobility Using Mobile-phone Data

“…The usage data of mobile-phones is used in a variety of different areas, such as during the COVID-19 pandemic (Pepe, Bajardi, Gauvin, Privitera, Lake, Cattuto, and Tizzoni 2020;Jia, Lu, Yuan, Xu, Christakis, Jia, and Nicholas 2020;Gao, Rao, Kang, Liang, Kruse, Doepfer, Sethi, Reyes, Patz, and Yandell 2020;Jeffrey, Walters, Ainslie, Eales, Ciavarella, Bhatia, Hayes, Baguelin, Boonyasiri, Brazeau et al 2020;Yabe, Tsubouchi, Fujiwara, Wada, Sekimoto, and Ukkusuri 2020;Vollmer, Mishra, Juliette et al 2020;Xu, Gutierrez, Mekaru, Sewalk, Goodwin, Loskill, Cohn, Hswen, Hill, Cobo, Zarebski, Li, Wu, Hulland, Morgan, Wang, O'Brien, Scarpino, Brownstein, Pybus, Pigott, and Kraemer 2020;Santamaria Serna Carlos, Sermi, Spyratos, Iacus, Annunziato, Tarchi, and Vespe 2020;Iacus Stefano, Serna, Sermi, Spyratos, Tarchi, and Vespe 2020a,b;Heuzroth 2020), customer segmentation (Aheleroff 2011), identification of personality traits and lifestyle (Chittaranjan, Jan, and Gatica-Perez 2011;Hillebrand, Khan, Peleja, and Oliver 2020), the analysis of large social networks (Aksu, Korpeoglu, and Ulusoy 2019;Al-Molhem, Rahal, and Dakkak 2019;Aledavood, Lehmann, and Saramäki 2018), hotspot detection (Nika, Ismail, Zhao, Gaito, Rossi, and Zheng 2016), prediction of movement (Dao, Le, and Yoon 2019), mode of transport identification (Zhao, Bucher, Martin, and Raubal 2020), credit scoring (Liu, Ma, Zhao, and Zou 2018), disaster recovery (Andrade, Layedra, Vaca, and Cruz 2019;Marzuoli and Liu 2019), analysis of sleeping behavior of the population (Monsivais, Bhattacharya, Ghosh, Dunbar, and Kaski 2017), migration (Isaacman, Frias-Martinez, and Frias-Martinez 2018) and land usage classification (Shi, Lv, Seng, Xing, and Chen 2019;Lenormand, Picornell, Cantú-Ros, Louail, Herranz, Barthelemy, Frías-Martínez, Miguel, and Ramasco 2015)...…”

The impact of COVID-19 on relative changes in aggregated mobility using mobile-phone data

“…The usage data of mobile-phones is used in a variety of different areas, such as during the COVID-19 pandemic [1]- [11], customer segmentation [12], identification of personality traits and lifestyle [13], [14], the analysis of large social networks [15]- [17], hotspot detection [18], prediction of movement [19], mode of transport identification [20], credit scoring [21], disaster recovery [22], [23], analysis of sleeping behavior of the population [24], migration [25] and land usage classification [26], [27].…”