Yanru Zhang

TL;DR: The gradient boosting tree method strategically combines additional trees by correcting mistakes made by its previous base models, therefore, potentially improves prediction accuracy and model interpretability in freeway travel time prediction.

TL;DR: The experimental results demonstrate that the proposed method is able to unearth the underlying periodic characteristics and volatility nature of traffic flow data and show promising abilities in improving the accuracy and reliability of freeway traffic flow forecasting in multi-step ahead forecasting.

TL;DR: This paper can help management understand the dynamic volatility of the subway short-term ridership, and have the potential to disseminate more reliable subway information to travelers through the information systems.

TL;DR: Comparison of performances of the three models in different missing ratios and forecasting time intervals indicates that the accuracy of the VAR model is more sensitive to the missing ratio, while on average the GRNN model gives more robust and accurate forecasting with missing data, particularly when the missing data ratio is high.

TL;DR: The generalized autoregressive conditional heteroscedasticity (GARCH) model has proved to have the ability to model the uncertainty of traffic conditions as mentioned in this paper, which can forecast the unreliable traffic periods and enable the selection of proper strategies to avoid or release possible traffic congestion.