Back to the Future: Predicting Traffic Shockwave Formation and Propagation Using a Convolutional Encoder-Decoder Network
This addresses traffic management for urban planners and transportation engineers, but appears incremental as it applies a known deep learning architecture to a specific domain problem.
The study tackled predicting traffic shockwave propagation by using a convolutional encoder-decoder network on time-space diagrams, achieving predictions of future shockwave patterns.
This study proposes a deep learning methodology to predict the propagation of traffic shockwaves. The input to the deep neural network is time-space diagram of the study segment, and the output of the network is the predicted (future) propagation of the shockwave on the study segment in the form of time-space diagram. The main feature of the proposed methodology is the ability to extract the features embedded in the time-space diagram to predict the propagation of traffic shockwaves.