{Hui05} Pan Hui, Augustin Chaintreau, James Scott, Richard Gass, Jon Crowcroft, and Christophe Diot. Pocket switched networks and human mobility in conference environments. In Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking (WDTN '05)
{Gonzalez08} Gonzalez, Marta C. and Hidalgo, Cesar A. and Barabasi, Albert-Laszlo. Understanding individual human mobility patterns. Nature, 2008, 453, 779-782 the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period; human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations
{Scellato11} Salvatore Scellato, Mirco Musolesi, Cecilia Mascolo, Vito Latora, and Andrew T. Campbell. NextPlace: A Spatio-Temporal Prediction Framework for Pervasive Systems. Proc. of Pervasive, 2011. nonlinear time series analysis; predict when the future visits to all significant locations will start and for how long they will last
{Rhee11} Injong Rhee, Minsu Shin, Seongik Hong, Kyunghan Lee, Seong Joon Kim, and Song Chong. On the Levy-Walk Nature of Human Mobility. IEEE/ACM Transactions on Networking, 2011, 19(3): 630-643. truncated Levy walk mobility (TLW) model; Pareto distributions
{Do12} Trinh Minh Tri Do, Daniel Gatica-Perez. Contextual Conditional Models for Smartphone-based Human Mobility Prediction. Proc. of ACM UbiComp, 2012.
{Sadilek12} Adam Sadilek, John Krumm. Far Out: Predicting Long-Term Human Mobility. Proc. of AAAI, 2012. Predict the most likely location at any given time in the future; eigenday
{Silva14} Thiago H Silva, Pedro O. S. Vaz de Melo, Jussara M. Almeida, Juliana Salles, Antonio A. F. Loureiro. Revealing the City that We Cannot See. ACM Transactions on Internet Technology (TOIT). 2014, 14(4), Article 26.
{Wang15} Huandong Wang, Fengli Xu, Yong Li, Pengyu Zhang, Depeng Jin. Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment. Proc. of ACM IMC, 2015.
{Xu16} Fengli Xu, Jie Feng, Pengyu Zhang, Yong Li. Context-aware Real-time Population Estimation for Metropolis. Proc. of ACM UbiComp, 2016. real-time and finegrained population estimation; a context-aware and dynamic model
{Xu17} Fengli Xu, Zhen Tu, Yong Li, Pengyu Zhang, Xiaoming Fu and Depeng Jin. Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data. Proc. of WWW, 2017.
{Zhang17} Chao Zhang, Keyang Zhang, Quan Yuan, Haoruo Peng, Yu Zheng, Tim Hanratty, Shaowen Wang and Jiawei Han. Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning. Proc. of WWW, 2017. detect spatiotemporal hotspots underlying people’s activities | capture the correlations among the units by encoding their co-occurrence and neighborhood relationships, and learn low-dimensional representations to preserve such correlations
{Feng18} Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. DeepMove: Predicting Human Mobility with Attentional Recurrent Networks. Proc. of WWW, 2018.