4 papers
Probabilistic Traffic Forecasting with Dynamic Regression
Vincent Zhihao Zheng, Seongjin Choi, Lijun Sun
This paper proposes a dynamic regression (DR) framework that enhances existing deep spatiotemporal models by incorporating structured learning for the error process in traffic fore…
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…
Better Batch for Deep Probabilistic Time Series Forecasting
Vincent Zhihao Zheng, Seongjin Choi, Lijun Sun
Deep probabilistic time series forecasting has gained attention for its ability to provide nonlinear approximation and valuable uncertainty quantification for decision-making. Howe…
A Real-time Evaluation Framework for Pedestrian's Potential Risk at Non-Signalized Intersections Based on Predicted Post-Encroachment Time
Tengfeng Lin, Zhixiong Jin, Seongjin Choi +1
Addressing pedestrian safety at intersections is one of the paramount concerns in the field of transportation research, driven by the urgency of reducing traffic-related injuries a…