Pu Wang

TL;DR: In this article, a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutionsal network and the gated recurrent unit (GRU), is proposed.

TL;DR: Empirical data is used to show that the predictions of the CTRW models are in systematic conflict with the empirical results, and two principles that govern human trajectories are introduced, allowing for a statistically self-consistent microscopic model for individual human mobility.

TL;DR: The mean collective behavior at large scales is studied and it is shown that the interevent time of consecutive calls is heavy-tailed, which has implications for dynamics of spreading phenomena in social networks.

TL;DR: The mobility of mobile phone users is modeled in order to study the fundamental spreading patterns that characterize a mobile virus outbreak and it is found that although Bluetooth viruses can reach all susceptible handsets with time, they spread slowly because of human mobility, offering ample opportunities to deploy antiviral software.

TL;DR: This research proposes a methodology to develop OD matrices using mobile phone Call Detail Records (CDR) and limited traffic counts to determine the scaling factors that result best matches with the observed traffic counts.