By predicting the traffic load on network links, a network operator can effectively pre-dispose
resource-allocation strategies to early address, eg, an incoming congestion event. Traffic …

Modeling complex spatial and temporal correlations in the correlated time series data is
indispensable for understanding the traffic dynamics and predicting the future status of an …

Traffic forecasting is attracting considerable interest due to its widespread application in
intelligent transportation systems. Given the complex and dynamic traffic data, many …

Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …

This paper presents a review of the literature on network traffic prediction, while also serving
as a tutorial to the topic. We examine works based on autoregressive moving average …

Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of
transportation. However, it is very challenging since the traffic flows usually show high …

Graph convolutional neural networks (GCNN) have become an increasingly active field of
research. It models the spatial dependencies of nodes in a graph with a pre-defined …

Traffic prediction is an important and yet highly challenging problem due to the complexity
and constantly changing nature of traffic systems. To address the challenges, we propose a …

Short-term traffic forecasting on large street networks is significant in transportation and
urban management, such as real-time route guidance and congestion alleviation …

Traffic forecasting has attracted widespread attention recently. In reality, traffic data usually
contains missing values due to sensor or communication errors. The Spatio-temporal feature …