Reliability-based journey time prediction via two-stream deep learning with multi-source data
This paper presents a distribution-free reliability-based prediction approach for estimating
journey time intervals with multi-source data using a two-stream deep learning framework …
journey time intervals with multi-source data using a two-stream deep learning framework …
[HTML][HTML] A time-varying shockwave speed model for reconstructing trajectories on freeways using Lagrangian and Eulerian observations
Inference of detailed vehicle trajectories is crucial for applications such as traffic flow
modeling, energy consumption estimation, and traffic flow optimization. Static sensors can …
modeling, energy consumption estimation, and traffic flow optimization. Static sensors can …
Network-Wide Traffic Flow Dynamics Prediction Leveraging Macroscopic Traffic Flow Model and Deep Neural Networks
Obtaining future traffic state evolution information is critical to traffic control algorithms design
and further to intelligent transportation systems. However, accurately predicting traffic state …
and further to intelligent transportation systems. However, accurately predicting traffic state …