Uncertainty quantification of sparse travel demand prediction with spatial-temporal graph neural networks
Origin-Destination (OD) travel demand prediction is a fundamental challenge in
transportation. Recently, spatial-temporal deep learning models demonstrate the …
transportation. Recently, spatial-temporal deep learning models demonstrate the …
Uncertainty quantification via spatial-temporal tweedie model for zero-inflated and long-tail travel demand prediction
Understanding Origin-Destination (OD) travel demand is crucial for transportation
management. However, traditional spatial-temporal deep learning models grapple with …
management. However, traditional spatial-temporal deep learning models grapple with …
Uncertainty quantification of spatiotemporal travel demand with probabilistic graph neural networks
Recent studies have significantly improved the prediction accuracy of travel demand using
graph neural networks. However, these studies largely ignored uncertainty that inevitably …
graph neural networks. However, these studies largely ignored uncertainty that inevitably …
On region-level travel demand forecasting using multi-task adaptive graph attention network
Accurate travel demand forecasting at the regional level benefits to urban traffic
management and service operations. Irregular regions can be naturally represented by …
management and service operations. Irregular regions can be naturally represented by …
Graph neural network for robust public transit demand prediction
Understanding and forecasting mobility patterns and travel demand are fundamental and
critical to efficient transport infrastructure planning and service operation. However, most …
critical to efficient transport infrastructure planning and service operation. However, most …
A dynamical spatial-temporal graph neural network for traffic demand prediction
F Huang, P Yi, J Wang, M Li, J Peng, X Xiong - Information Sciences, 2022 - Elsevier
Traffic demand prediction is significant and practical in the resource scheduling of
transportation application systems. Meanwhile, it remains a challenging topic due to the …
transportation application systems. Meanwhile, it remains a challenging topic due to the …
Long-term origin-destination demand prediction with graph deep learning
Accurate long-term origin-destination demand (OD) prediction can help understand traffic
flow dynamics, which plays an essential role in urban transportation planning. However, the …
flow dynamics, which plays an essential role in urban transportation planning. However, the …
Coupled layer-wise graph convolution for transportation demand prediction
Abstract Graph Convolutional Network (GCN) has been widely applied in transportation
demand prediction due to its excellent ability to capture non-Euclidean spatial dependence …
demand prediction due to its excellent ability to capture non-Euclidean spatial dependence …
Dynamic graph learning based on hierarchical memory for origin-destination demand prediction
Recent years have witnessed a rapid growth of applying deep spatiotemporal methods in
traffic forecasting. However, the prediction of origin-destination (OD) demands is still a …
traffic forecasting. However, the prediction of origin-destination (OD) demands is still a …
SAST-GNN: a self-attention based spatio-temporal graph neural network for traffic prediction
Y Xie, Y Xiong, Y Zhu - Database Systems for Advanced Applications: 25th …, 2020 - Springer
Traffic prediction, which aims at predicting future traffic conditions based on historical
observations, is of considerable significance in urban management. However, such tasks …
observations, is of considerable significance in urban management. However, such tasks …