Today's Internet is a massive, distributed network which continues to explode in size as e-
commerce and related activities grow. The heterogeneous and largely unregulated structure …

The wave function of a composite system is defined in relativity on a space–time surface. In
the explicitly covariant light-front dynamics, reviewed in the present article, the wave …

We develop a deep learning model to predict traffic flows. The main contribution is
development of an architecture that combines a linear model that is fitted using ℓ 1 …

Traffic forecasting is a fundamental and challenging task in the field of intelligent
transportation. Accurate forecasting not only depends on the historical traffic flow information …

The accurate short-term passenger flow prediction is of great significance for real-time public
transit management, timely emergency response as well as systematical medium and long …

In this research, we propose a methodology to develop OD matrices using mobile phone
Call Detail Records (CDR) and limited traffic counts. CDR, which consist of time stamped …

Networks and network analysis are arguably one of the largest growth areas of the early
twenty-first century in the quantitative sciences. Despite roots in social network analysis …

Focusing on Bayesian approaches and computations using simulation-based methods for
inference, Time Series: Modeling, Computation, and Inference integrates mainstream …

Network traffic arises from the superposition of Origin-Destination (OD) flows. Hence, a
thorough understanding of OD flows is essential for modeling network traffic, and for …

A matrix giving the traffic volumes between origin and destination in a network has
tremendously potential utility for network capacity planning and management. Unfortunately …