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Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …
critical problem globally, resulting in negative consequences such as lost hours of additional …
Spatiotemporal traffic forecasting: review and proposed directions
This paper systematically reviews studies that forecast short-term traffic conditions using
spatial dependence between links. We extract and synthesise 130 research papers …
spatial dependence between links. We extract and synthesise 130 research papers …
Deep spatial–temporal 3D convolutional neural networks for traffic data forecasting
Reliable traffic prediction is critical to improve safety, stability, and efficiency of intelligent
transportation systems. However, traffic prediction is a very challenging problem because …
transportation systems. However, traffic prediction is a very challenging problem because …
Travel time estimation for urban road networks using low frequency probe vehicle data
The paper presents a statistical model for urban road network travel time estimation using
vehicle trajectories obtained from low frequency GPS probes as observations, where the …
vehicle trajectories obtained from low frequency GPS probes as observations, where the …
Learning the dynamics of arterial traffic from probe data using a dynamic Bayesian network
Estimating and predicting traffic conditions in arterial networks using probe data has proven
to be a substantial challenge. Sparse probe data represent the vast majority of the data …
to be a substantial challenge. Sparse probe data represent the vast majority of the data …
GraphSAGE-based dynamic spatial–temporal graph convolutional network for traffic prediction
T Liu, A Jiang, J Zhou, M Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Traffic networks exhibit complex spatial-temporal dependencies, and accurately capturing
such dependencies is critical to improving prediction accuracy. Recently, many deep …
such dependencies is critical to improving prediction accuracy. Recently, many deep …
Estimating arterial traffic conditions using sparse probe data
Estimating and predicting traffic conditions in arterial networks using probe data has proven
to be a substantial challenge. In the United States, sparse probe data represents the vast …
to be a substantial challenge. In the United States, sparse probe data represents the vast …
Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data
The accurate forecasting of traffic states is an essential application of intelligent
transportation system. Due to the periodic signal control at intersections, the traffic flow in an …
transportation system. Due to the periodic signal control at intersections, the traffic flow in an …
Spatio-temporal autocorrelation of road network data
Modelling autocorrelation structure among space–time observations is crucial in space–time
modelling and forecasting. The aim of this research is to examine the spatio-temporal …
modelling and forecasting. The aim of this research is to examine the spatio-temporal …
A novel residual graph convolution deep learning model for short-term network-based traffic forecasting
Short-term traffic forecasting on large street networks is significant in transportation and
urban management, such as real-time route guidance and congestion alleviation …
urban management, such as real-time route guidance and congestion alleviation …