The new generation of artificial intelligence (AI), called AI 2.0, has recently become a
research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and …

Traffic state estimation (TSE) refers to the process of the inference of traffic state variables
(ie, flow, density, speed and other equivalent variables) on road segments using partially …

Traffic problems continue to deteriorate because of increasing population in urban areas
that rely on many modes of transportation, the transportation infrastructure has achieved …

Tree based ensemble methods have reached a celebrity status in prediction field. By
combining simple regression trees with 'poor'performance, they usually produce high …

Ensemble forecasting is a modeling approach that combines data sources, models of
different types, with alternative assumptions, using distinct pattern recognition methods. The …

In this paper, we present an ensemble learning approach for better understanding
ridesplitting behavior of passengers of ridesourcing companies who provide prearranged …

The accurate prediction of real-time traffic flow is indispensable to intelligent transport
systems. However, the short-term prediction remains a thorny issue, due to the complexity …

Traffic flow prediction is an important precondition to alleviate traffic congestion in large-
scale urban areas. Recently, some estimation and prediction methods have been proposed …

The success of intelligent transportation systems relies heavily on accurate traffic prediction,
in which how to model the underlying spatial-temporal information from traffic data has come …

Short-term traffic flow forecasting is a key element in Intelligent Transport Systems (ITS) to
provide proactive traffic state information to road network operators. A variety of methods to …