# Back to the Future: Predicting Traffic Shockwave Formation and Propagation Using a Convolutional Encoder-Decoder Network

## TL;DR

This paper introduces a deep learning approach using a convolutional encoder-decoder network to predict traffic shockwave propagation from time-space diagrams, enabling better traffic flow management.

## Contribution

It presents a novel deep learning model that extracts features from time-space diagrams to accurately forecast shockwave propagation in traffic segments.

## Key findings

- Effective prediction of shockwave propagation demonstrated.
- Model outperforms traditional methods in accuracy.
- Deep learning captures complex traffic dynamics.

## Abstract

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.