PI-STGnet: Physics-integrated spatiotemporal graph neural network with fundamental diagram learner for highway traffic flow prediction
At present, traffic state prediction primarily relies on purely data-driven methods, ignoring the
incorporation of physical constraints within the field of traffic flow. Taking this as a starting …
incorporation of physical constraints within the field of traffic flow. Taking this as a starting …
[HTML][HTML] Koopman theory meets graph convolutional network: Learning the complex dynamics of non-stationary highway traffic flow for spatiotemporal prediction
Reliable and accurate traffic flow prediction is crucial for the construction and operation of
smart highways, supporting scientific traffic management and planning. However, accurately …
smart highways, supporting scientific traffic management and planning. However, accurately …
CDGNet: A Cross-Time Dynamic Graph-Based Deep Learning Model for Vehicle-Based Traffic Speed Forecasting
Vehicle-based traffic speed forecasting aims to predict the average speed of vehicles on the
road in the future, which is an essential side information in intelligent vehicles and beneficial …
road in the future, which is an essential side information in intelligent vehicles and beneficial …
SSGCRTN: a space-specific graph convolutional recurrent transformer network for traffic prediction
S Yang, Q Wu, Y Wang, T Lin - Applied Intelligence, 2024 - Springer
Current research often formalizes traffic prediction tasks as spatio-temporal graph modeling
problems. Despite some progress, this approach still has the following limitations. First …
problems. Despite some progress, this approach still has the following limitations. First …
Tunnel crash severity and congestion duration joint evaluation based on cross-stitch networks
Tunnels, with limited space and restricted widths/heights, increase the likelihood of crashes
and traffic congestion, where the severity and duration of one often exacerbate the other …
and traffic congestion, where the severity and duration of one often exacerbate the other …
Informer-FDR: A short-term vehicle speed prediction model in car-following scenario based on traffic environment
Q Xue, J Ma, X Zhao, R Liu, H Li, X Zhu - Expert Systems with Applications, 2025 - Elsevier
Drivers' car-following behaviors on urban roads are influenced by various factors, including
pedestrians, cyclists, adjacent vehicles, and roadside parking. However, few models …
pedestrians, cyclists, adjacent vehicles, and roadside parking. However, few models …
Weather Interaction-Aware Spatio-Temporal Attention Networks for Urban Traffic Flow Prediction
H Zhong, J Wang, C Chen, J Wang, D Li, K Guo - Buildings, 2024 - mdpi.com
As the cornerstone of intelligent transportation systems, accurate traffic prediction can
reduce the pressure of urban traffic, reduce the cost of residents' travel time, and provide a …
reduce the pressure of urban traffic, reduce the cost of residents' travel time, and provide a …