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Found 4 papers, 0 papers with code
Learning dynamic and hierarchical traffic spatiotemporal features with Transformer
However, the predefined fixed adjacent matrix is limited in reflecting the actual dependence of traffic flow.
Mining Truck Platooning Patterns Through Massive Trajectory Data
Truck platooning refers to a series of trucks driving in close proximity via communication technologies, and it is considered one of the most implementable systems of connected and automated vehicles, bringing huge energy savings and safety improvements.
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks
Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network.
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy.
