Nov 13, 2018 · We propose a generic learning framework to solve the problem by dealing with sparse matrices via matrix factorization and two graph ...

We propose a generic learning framework to solve the problem by dealing with sparse matrices via matrix factorization and two graph convolutional neural ...

Nov 13, 2018 · takes into account the correlations among the costs of different edges. Page 3. Recurrent Multi-Graph Neural Networks for Travel Cost Prediction.

May 27, 2024 · In this work, we focus on the challenge of traffic forecasting and review the recent development and application of graph neural networks (GNN) to this problem.

Dynamic multi-graph convolution recurrent neural network for traffic speed prediction[J]. ... Recurrent Multi-Graph Neural Networks for Travel Cost Prediction[J].

In this paper, we identify two essential spatial dependencies in traffic forecasting in addition to distance, direction and positional relationship.

In this article, we propose a novel traffic prediction framework, named Dynamic Graph Convolutional Recurrent Network (DGCRN).

Accurately predicting traffic flow ahead of time can help travelers to manage their trips reasonably, avoid rush hour, and reduce travel times and costs. In the ...

Sep 15, 2022 · In this paper, we investigate the modeling of travel times of bus systems from a new perspective, i.e., city-wide travel time prediction with ...

Furthermore, we divide the neighbors of each sensor into coarse-grained regions, and dynamically assign different weights to each region at different times.