As cities grow bigger and denser, the need for sustainable urban planning has increased
with climate change already impacting urban life. A well-functioning public transport network …

Traffic forecasting and prediction are crucial in urban planning, transportation management,
and decisionmaking. Traditional methods often struggle to capture traffic data's complex …

Graph convolution is a deep learning method for graph data, which can process
unstructured traffic network data, which is very suitable for traffic flow prediction tasks …

The significant increase in world population and urbanisation has brought several important
challenges, in particular regarding the sustainability, maintenance and planning of urban …

While deep learning has shown success in predicting traffic states, most methods treat it as a
general prediction task without considering transportation aspects. Recently, graph neural …

This study delves into the application of graph neural networks in the realm of traffic
forecasting, a crucial facet of intelligent transportation systems. Accurate traffic predictions …

Traffic forecasting is a crucial aspect of Intelligent Transportation Systems, as it has the
potential to improve the mobility and efficiency of transportation in cities while reducing costs …

Short-term traffic demand prediction is one of the crucial issues in intelligent transport
systems, which has attracted attention from the taxi industry and Mobility-on-Demand …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

Traffic prediction is a spatiotemporal predictive task that plays an essential role in intelligent
transportation systems. Today, graph convolutional neural networks (GCNNs) have become …