Chi Xie

TL;DR: A bi-level network optimization model is formulated, in which the upper level aims at optimizing the network evacuation performance subject to the lane-reversal and crossing-elimination constraints and the lower level conveys a cell transmission-based dynamic traffic assignment problem.

TL;DR: The prediction results show that the two data mining models offer comparable but slightly better performances than the MNL model in terms of the modeling results, while the DT model demonstrates the highest estimation efficiency and most explicit interpretability, and the NN model gives a superior prediction performance in most cases.

TL;DR: Results indicate that for an absolute error in estimated average link speed to be less than 5 km/hr at least 95% of the time, the network needs to have 4% to 5% of active probe vehicles, or at least ten probe vehicles must passed through a link within the sampling period.

TL;DR: In this article, the authors proposed an enhanced hybrid ensemble ML approach called RS-MultiBoosting by incorporating two classic ensemble ML approaches, random subspace (RS) and MultiBoosting, to improve the accuracy of forecasting SMEs' credit risk.

TL;DR: In this paper, the authors proposed an extreme risk spillover network for analysing the intimate value at risk (VaR) and the Granger causality risk test (GRLT) to quantify the risk of spillovers.