Short-term traffic forecasting is a vital part of intelligent transportation systems. Recently, the
combination of unprecedented data availability and the repaid development of machine …

Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …

Traffic flow prediction is crucial for public safety and traffic management, and remains a big
challenge because of many complicated factors, eg, multiple spatio-temporal dependencies …

With deepening urbanization and Internet of Vehicles (IoV) applications, the number of
private cars has been increasing in recent years. However, because the surging number of …

Traffic prediction methods on a single-source data have achieved excellent results in recent
years, especially the Graph Convolutional Networks (GCN) based models with spatio …

Predicting vehicle flows is of great importance to traffic management and public safety in
smart cities, and very challenging as it is affected by many complex factors, such as spatio …

In this paper, we address the problem of travel time prediction of bus journeys which consist
of bus riding times (may involve multiple bus services) and also the waiting times at transfer …

In smart cities, region-based prediction (eg traffic flow and electricity flow) is of great
importance to city management and public safety, and it remains a daunting challenge that …

Accurate representation of the temporal dynamics of traffic flow traveling in the road network
is the key to traffic prediction, it is therefore important to model the spatial dependence of the …

The objective of the study is to predict traffic flow under unusual conditions by using a deep
learning model. Conventionally, machine‐learning‐based traffic prediction is frequently …