Add to ORKGIn 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
This paper surveys the short-term road traffic forecast algorithms based on the long-short term memo...
In the past few years, Deep learning has re-emerged as a powerful tool to solve complex problems and...
Traffic speed forecasting in the short term is one of the most critical parts of any intelligent tra...
In this paper, we propose deep learning architectures (FNN, CNN and LSTM) to forecast a regression...
Traffic parameter forecasting is critical to effective traffic management but is a challenging task ...
Timely forecast of traffic is very much needed for smart cities, which allows travelers and governme...
Traffic speed prediction is known as an important but challenging problem. In this paper, we propose...
The amount of cars on the roads is increasing at a rapid pace, causing traffic jams to become common...
It is possible for routing and navigation applications to provide more accurate and more effective r...
This paper, titled "Revolutionizing Urban Mobility," focuses on data-driven traffic forecasting and ...
Short-term traffic speed prediction is a promising research topic in intelligent transportation syst...
Many methods of traffic prediction have been proposed over the years, from the time series models ov...
Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road...
Traffic information is of great importance for urban cities, and accurate prediction of urban traffi...
In this research, traffic data is formatted as a graph network problem and graph neural networks are...
This paper surveys the short-term road traffic forecast algorithms based on the long-short term memo...
In the past few years, Deep learning has re-emerged as a powerful tool to solve complex problems and...
Traffic speed forecasting in the short term is one of the most critical parts of any intelligent tra...
In this paper, we propose deep learning architectures (FNN, CNN and LSTM) to forecast a regression...
Traffic parameter forecasting is critical to effective traffic management but is a challenging task ...
Timely forecast of traffic is very much needed for smart cities, which allows travelers and governme...
Traffic speed prediction is known as an important but challenging problem. In this paper, we propose...
The amount of cars on the roads is increasing at a rapid pace, causing traffic jams to become common...
It is possible for routing and navigation applications to provide more accurate and more effective r...
This paper, titled "Revolutionizing Urban Mobility," focuses on data-driven traffic forecasting and ...
Short-term traffic speed prediction is a promising research topic in intelligent transportation syst...
Many methods of traffic prediction have been proposed over the years, from the time series models ov...
Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road...
Traffic information is of great importance for urban cities, and accurate prediction of urban traffi...
In this research, traffic data is formatted as a graph network problem and graph neural networks are...
This paper surveys the short-term road traffic forecast algorithms based on the long-short term memo...
In the past few years, Deep learning has re-emerged as a powerful tool to solve complex problems and...
Traffic speed forecasting in the short term is one of the most critical parts of any intelligent tra...