# Deep Learning applied to Road Traffic Speed forecasting

arXiv: https://arxiv.org/abs/1710.08266 · HTML: https://mlsanity.com/paper/1710.08266/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network](https://mlsanity.com/paper/2202.10508/) (2022)
- [A Deep Learning Approach for Network-wide Dynamic Traffic Prediction during Hurricane Evacuation](https://mlsanity.com/paper/2202.12505/) (2022)

## Abstract (arXiv)

In this paper, we propose deep learning architectures (FNN, CNN and LSTM) to forecast a regression model for time dependent data. These algorithm's are designed to handle Floating Car Data (FCD) historic speeds to predict road traffic data. For this we aggregate the speeds into the network inputs in an innovative way. We compare the RMSE thus obtained with the results of a simpler physical model, and show that the latter achieves better RMSE accuracy. We also propose a new indicator, which evaluates the algorithms improvement when compared to a benchmark prediction. We conclude by questioning the interest of using deep learning methods for this specific regression task.