Big data is becoming a research focus in intelligent transportation systems (ITS), which can
be seen in many projects around the world. Intelligent transportation systems will produce a …

Traffic prediction is a core problem in the intelligent transportation system and has broad
applications in the transportation management and planning, and the main challenge of this …

Short-term passenger demand forecasting is of great importance to the on-demand ride
service platform, which can incentivize vacant cars moving from over-supply regions to over …

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

Traffic forecasting is a challenging problem in the transportation research field as the
complexity and non-stationary changing of the traffic data, thus the key to the issue is how to …

The increasing development of urban centers brings serious challenges for traffic
management. In this paper, we introduce a smart visual sensor, developed for a pilot project …

We propose a novel approach for network-wide traffic state prediction where the statistical
time series model ARIMA is used to postprocess the residuals out of the fundamental …

This paper presents an integrated framework for physics-informed joint traffic state and
queue profile estimation (JSQE) on freeway corridors, utilizing heterogeneous data sources …

Recently, Graph Convolution Network (GCN) and Temporal Convolution Network (TCN) are
introduced into traffic prediction and achieve state-of-the-art performance due to their good …

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the
road network and provide great opportunities for enhanced short-term traffic predictions …