77 citations · 158 across the 6 of their papers we have counts for
6 papers
Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting
Seongjin Choi, Nicolas Saunier, Vincent Zhihao Zheng +2
Deep learning-based multivariate and multistep-ahead traffic forecasting models are typically trained with the mean squared error (MSE) or mean absolute error (MAE) as the loss fun…
Bayesian calibration of traffic flow fundamental diagrams using Gaussian processes
Zhanhong Cheng, Xudong Wang, Xinyuan Chen +2
Modeling the relationship between vehicle speed and density on the road is a fundamental problem in traffic flow theory. Recent research found that using the least-squares (LS) met…
Probabilistic forecasting of bus travel time with a Bayesian Gaussian mixture model
Xiaoxu Chen, Zhanhong Cheng, Jian Gang Jin +2
Accurate forecasting of bus travel time and its uncertainty is critical to service quality and operation of transit systems; for example, it can help passengers make better decisio…
Real-time forecasting of metro origin-destination matrices with high-order weighted dynamic mode decomposition
Zhanhong Cheng, Martin Trepanier, Lijun Sun
Forecasting the short-term ridership among origin-destination pairs (OD matrix) of a metro system is crucial in real-time metro operation. However, this problem is notoriously diff…
Incorporating travel behavior regularity into passenger flow forecasting
Zhanhong Cheng, Martin Trepanier, Lijun Sun
Accurate forecasting of passenger flow (i.e., ridership) is critical to the operation of urban metro systems. Previous studies mainly model passenger flow as time series by aggrega…
Solving the clustered traveling salesman problem with d-relaxed priority rule
Hoa Nguyen Phuong, Huyen Tran Ngoc Nhat, Minh Hoàng Hà +2
The Clustered Traveling Salesman Problem with a Prespecified Order on the Clusters, a variant of the well-known traveling salesman problem is studied in literature. In this problem…