Deep Learning applied to Road Traffic Speed forecasting
arXiv:1710.08266
Abstract
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.
References in corpus (9)
- Adam: A Method for Stochastic Optimization
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- LSTM: A Search Space Odyssey
- Deep Residual Learning for Image Recognition
- Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
- Deep Learning with Limited Numerical Precision
- Learning Activation Functions to Improve Deep Neural Networks
- Explorations on high dimensional landscapes
- Deep learning: Technical introduction