Recently, the population density in cities has increased at a higher pace. According to the
United Nations Population Fund, cities accommodated 3.3 billion people (54%) of the global …

Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

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 …

Correlated traffic data refers to a collection of time series recorded simultaneously in
different regions throughout the same transportation network route. Due to the presence of …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

Taxi is one of the important means of transportation for people's daily travel activities, and it
is one of the important research objects of intelligent transportation system. Taxi demand …

Traffic networks exhibit complex spatial-temporal dependencies, and accurately capturing
such dependencies is critical to improving prediction accuracy. Recently, many deep …

Reliable traffic volume forecasting remains a significant challenge due to the unstable
variation patterns of traffic volume and the irregular spatial structure of traffic network. Most of …

Shared mobility has emerged as a sustainable alternative to both private transportation and
traditional public transport, promising to reduce the number of private vehicles on roads …

Historical cities around the world have serious traffic congestions due to old infrastructure
and urbanization. To mitigate traffic problems in such cities, infrastructure investments are …