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

Short term traffic flow forecasting has received sustained attention for its ability to provide the
anticipatory traffic condition required for proactive traffic control and management. Recently …

Accurate metro ridership prediction can guide passengers in efficiently selecting their
departure time and transferring from station to station. An increasing number of deep …

Short-term traffic flow prediction is a critical aspect of Intelligent Transportation System.
Timely and accurate traffic forecasting results are necessary inputs for advanced traffic …

In order to reduce passenger delays and prevent severe overcrowding in the subway
system, it is necessary to accurately predict the short-term passenger flow during special …

Subway short-term ridership forecasting plays an important role in intelligent transportation
systems. However, limited efforts have been made to forecast the subway short-term …

Recent studies have significantly improved the prediction accuracy of travel demand using
graph neural networks. However, these studies largely ignored uncertainty that inevitably …

Because of the rapid changes in traffic conditions caused by various circumstances, such as
road construction and traffic jams, the distribution of the traffic volume data changes over …

Travel time prediction is important for freight transportation companies. Accurate travel time
prediction can help these companies make better planning and task scheduling. For several …

This work proposes a novel uncertainty quantification framework for long-term traffic flow
prediction (TFP) based on a sequential deep learning model. Quantifying the uncertainty of …