Traffic flow prediction is increasingly vital for the administration of metropolitan areas. Many
research on spatio-temporal networks have been explored but the impacts of both spatial …

Predicting urban flow is crucial for intelligent transportation systems (ITS), but it is not easy
due to several complicated elements (such as dynamic spatio-temporal dependencies …

Traffic flow forecasting is a critical input to intelligent transportation systems. Accurate traffic
flow forecasting can provide an effective reference for implementing traffic management …

Traffic flow prediction is the upstream problem of path planning, intelligent transportation
system, and other tasks. Many studies have been carried out on the traffic flow prediction of …

Reliable traffic flow forecasting is paramount in Intelligent Transportation Systems (ITS) as it
can effectively improve traffic efficiency and social security. Its vital challenge is to effectively …

Traffic flow forecasting has been a long-standing topic in intelligent transportation systems,
and a renewed interest has been seen in recent years due to the development of artificial …

Predicting urban traffic flow is a challenging task, due to the complicated spatio-temporal
dependencies on traffic networks. Urban traffic flow usually has both short-term neighboring …

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 …

In this paper, considering spatiotemporal correlations, we propose a novel short-term traffic
flow prediction method that is based on diverse ensemble deep learning. First, a new …

Accurate traffic flow prediction is essential to building a smart transportation city. Existing
research mainly uses a given single-graph structure as a model, only considers local and …