A novel spatio-temporal generative inference network for predicting the long-term highway traffic speed
G Zou, Z Lai, C Ma, Y Li, T Wang - Transportation research part C: emerging …, 2023 - Elsevier
Accurately predicting the highway traffic speed can reduce traffic accidents and transit time,
which is of great significance to highway management. Three essential elements should be …
which is of great significance to highway management. Three essential elements should be …
A decentralized federated learning-based spatial–temporal model for freight traffic speed forecasting
Accurately understanding the spatial–temporal information of future freight traffic speed in
the metropolitan area is of vital importance to formulate freight-related traffic management …
the metropolitan area is of vital importance to formulate freight-related traffic management …
Spatial-temporal graph convolution network model with traffic fundamental diagram information informed for network traffic flow prediction
Accurate and fine-grained traffic state prediction has always been an important research
field. For long-term traffic flow prediction, the high-dimensional and coupled traffic feature …
field. For long-term traffic flow prediction, the high-dimensional and coupled traffic feature …
DAGCRN: Graph convolutional recurrent network for traffic forecasting with dynamic adjacency matrix
Accurate and real-time traffic forecasting is of great significance for urban traffic planning,
traffic control, and traffic management. However, the time-varying dynamic spatial relations …
traffic control, and traffic management. However, the time-varying dynamic spatial relations …
Modeling vehicle U-turning behavior near intersections: A deep learning approach based on TCN and multi-head attention
In U-turn bays near intersections, the conflict between U-turning vehicles and those going
straight-ahead results in traffic accidents since straight-ahead vehicles cannot reliably …
straight-ahead results in traffic accidents since straight-ahead vehicles cannot reliably …
FSTNet: Learning spatial–temporal correlations from fingerprints for indoor positioning
Q Ren, Y Wang, S Liu, X Lv - Ad Hoc Networks, 2023 - Elsevier
Due to the absence of a global positioning system (GPS) signal and pervasive deployment
of wireless local area networks (WLANs), Wireless Fidelity (WiFi) fingerprinting has been …
of wireless local area networks (WLANs), Wireless Fidelity (WiFi) fingerprinting has been …
GSTC-Unet: A U-shaped multi-scaled spatiotemporal graph convolutional network with channel self-attention mechanism for traffic flow forecasting
W Yu, X Huang, Y Qiu, S Zhang, Q Chen - Expert Systems with Applications, 2023 - Elsevier
Accurate forecasting of traffic flows remains a significant challenge owing to its complex
spatiotemporal dependencies. Although existing methods capture some spatiotemporal …
spatiotemporal dependencies. Although existing methods capture some spatiotemporal …
A deep learning-based smart service model for context-aware intelligent transportation system
Effective means for transportation form a critical city infrastructure, particularly for resource-
constrained smart cities. Rapid advancements in information and communication …
constrained smart cities. Rapid advancements in information and communication …
An adaptive deep multi-task learning approach for citywide travel time collaborative estimation
The provision of accurate travel time information holds paramount significance for optimizing
traffic operations and management. However, existing approaches typically address multiple …
traffic operations and management. However, existing approaches typically address multiple …
Multimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional network
Traffic flow prediction plays a crucial role in the management and operation of urban
transportation systems. While extensive research has been conducted on predictions for …
transportation systems. While extensive research has been conducted on predictions for …