.png){kind=icon}
返回
MuGIL: A Multi-Graph Interaction Learning Network for Multi-Task Traffic Prediction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2024.112709.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, multi-task traffic prediction has received increasing attention, as it enables knowledge sharing between heterogeneous variables or regions, thereby improving prediction accuracy while satisfying the prediction requirements of multi-source data in Intelligent Transportation Systems (ITS). However, current studies present two significant challenges. First, they often tend to construct specialized models for a limited set of predictive parameters, which results in a lack of generality. Second, modeling the graph-based multi-task interaction and message passing processes remains difficult due to the heterogeneity of graph structures arising from multi- source traffic data. To address these challenges, this paper proposes a Multi-Graph Interaction Learning Network (MuGIL), characterized by three key innovations: 1) A flexible end-to-end multi-task prediction framework that is generalizable for varied variables or scenarios; 2) A multi-source graph representation module that aligns heterogeneous information through semantic graphs; 3) A novel message passing mechanism for multi-task graph neural networks, which enables effective knowledge among tasks. The model is validated using data from California by comparing it with the state-of-the-art prediction models. The results show that the MuGIL model achieves better prediction performance than these baselines. Ablation experiments further highlight the critical role of the designed multi-source graph representation module and message passing mechanism in the model's success. The MuGIL model we have proposed is now open-sourced at the following link: https://github. com/trafficpre/MuGIL.
Keyword:
Multi-task learning
Graph neural networks
Message passing
Traffic prediction
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
A multi-task deep learning model for short-term taxi demand forecasting considering spatiotemporal dependences考虑时空依赖性的多任务深度学习短期出租车需求预测模型
OAAI
OAAI分享收藏Short-Term Traffic State Prediction Based on the Spatiotemporal Features of Critical Road Sections基于关键路段时空特征的短时交通状态预测.png)
SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏A deep learning based multitask model for network-wide traffic speed predictionNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting流量图卷积循环神经网络: 用于网络规模流量学习和预测的深度学习框架OAAI
OAAI分享收藏PCNN: Deep Convolutional Networks for Short-Term Traffic Congestion PredictionPCNN: 用于短期交通拥堵预测的深度卷积网络OAAI
OAAI分享收藏Spatial-temporal uncertainty-aware graph networks for promoting accuracy and reliability of traffic forecasting用于提高交通预测准确性和可靠性的时空不确定性感知图网络
PREAI
分享收藏A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting用于流量预测的分解动态图卷积递归网络PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容