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Updated: May 2, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

DADiffNet: Delay-aware diffusion networks with adaptive subgraphs for large scale traffic forecasting.

Yujie Fan¹, Jing Chen², Wenqiang Xu³

¹School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China.

Neural Networks : the Official Journal of the International Neural Network Society

|March 4, 2026

Summary

This summary is machine-generated.

This study introduces the Delay-Aware Diffusion Network (DADiffNet) for improved traffic forecasting. DADiffNet accurately predicts traffic flow by modeling perturbations and their delays, outperforming existing methods.

Keywords:

Diffusion networks Graph neural networks Propagation delay Spatiotemporal modeling Subgraph sampling

Related Experiment Videos

Last Updated: May 2, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Transportation Science
Data Science
Network Analysis

Background:

Accurate traffic forecasting is crucial for urban areas with increasing traffic demand.
Existing methods struggle to capture upstream traffic perturbations and their downstream propagation delays.

Purpose of the Study:

To develop a novel network model for enhanced spatiotemporal traffic forecasting.
To explicitly address the challenge of modeling delayed diffusion of traffic perturbations.

Main Methods:

Proposes the Delay-Aware Diffusion Network (DADiffNet) that reformulates spatiotemporal coupling.
Models traffic-flow increments and uses adaptive subgraph structures for efficient topology encoding.
Employs temporal differences to capture high-frequency perturbations and propagation delays.

Main Results:

DADiffNet consistently outperformed fifteen baseline methods across eight real-world datasets.
Achieved average improvements of 8.04% in MAE, 7.65% in RMSE, and 8.40% in MAPE.
Demonstrated reduced memory consumption, enhancing accuracy, efficiency, and interpretability.

Conclusions:

DADiffNet offers a superior approach to large-scale traffic forecasting by explicitly modeling delays and perturbations.
The model provides a better balance between predictive accuracy, computational efficiency, and interpretability.
This advancement is vital for managing traffic in rapidly expanding urban environments.