Deep Learning applied to Road Traffic Speed forecasting
This work addresses traffic prediction for urban planning, but it is incremental as it shows limited gains over existing methods.
The authors tackled road traffic speed forecasting using deep learning architectures (FNN, CNN, LSTM) on Floating Car Data, but found that a simpler physical model achieved better RMSE accuracy, leading them to question the value of deep learning for this task.
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.