… the potential of rich mobility data and deep learning on urban traffic prediction, and discuss
some pioneering attempts. Deep learning will advance traffic predictions through powerful …

… In a wireless network, given the mobile data traffic histories of network units and mobile
users’ mobility trajectories, our goal is to predict the mobile data traffic of network units in future …

… With the development of mobile … , traffic prediction plays an important role. In this paper,
we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data …

… mobile data traffic prediction, this paper focuses on the review of recently proposed ML models
to predict mobile data traffic in … of recently developed mobile data traffic prediction models …

… in citywide mobile traffic prediction with data scarcity. … on the traffic volumes for more
accurate mobile traffic prediction. … shift often exists in the mobile traffic data in different cities (eg…

… -series prediction algorithms in mobile network traffic prediction … effective algorithms to predict
future system characteristics. … predicting future behavior of mobile network traffic. Moreover, …

… traffic data collection, preprocessing and fusion chain to support real-time applications, whereas
data … -art sensory technology for traffic and generic mobility data collection is presented …

… data traffic volume into in-tower traffic and inter-tower traffic, and verify the importance of such
decomposition in cellular traffic prediction. … correlations of cellular traffic, and exploit graph …

… Here we give a formal definition of the urban mobility prediction problem: suppose dj …
urban mobility models for solving urban problems: 1) Traffic Forecasting: Urban traffic anomalies …

… traffic by 2021 and particularly, data traffic produced by smartphones will surpass 86 percent
of all the mobile data traffic as the emergence of various mobile … of the mobile users, an …