Short-term traffic prediction allows Intelligent Transport Systems to proactively respond to
events before they happen. With the rapid increase in the amount, quality, and detail of traffic …

Traffic prediction plays an important role in evaluating the performance of
telecommunication networks and attracts intense research interests. A significant number of …

Because of the burstiness and uncertainty of network, the prediction for short-term network
traffic is a difficult problem. This paper proposes a real-time network traffic prediction model …

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents,
sports games, adverse weather, etc. We use data related to real-time traffic speed, jam …

Short-term traffic forecasting based on deep learning methods, especially long short-term
memory (LSTM) neural networks, has received much attention in recent years. However, the …

Traffic parameter forecasting is critical to effective traffic management but is a challenging
task due to the stochasticity of traffic flow characteristics, especially in urban road networks …

Predicting vehicle flows is of great importance to traffic management and public safety in
smart cities, and very challenging as it is affected by many complex factors, such as spatio …

Traffic flow prediction is important in modern traffic control and induction. Short-term traffic
flow prediction plays an important role in urban traffic navigation planning and traffic …

Travel time prediction has important applications within the field of intelligent transportation,
such as vehicle routing, congestion and traffic management. A challenging task in travel time …

This paper surveys the short-term road traffic forecast algorithms based on the long-short
term memory (LSTM) model of deep learning. The algorithms developed in the last three …