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A Missing Traffic Data Imputation Method Based on a Diffusion Convolutional Neural Network-Generative Adversarial

Chenchen Zhang¹, Lei Zhou^2,3, Xuemei Xiao¹

¹School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China.

Sensors (Basel, Switzerland)

|December 9, 2023

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This summary is machine-generated.

This study introduces a novel DCNN-GAN method to fill in missing traffic data, crucial for intelligent transportation systems (ITS). The approach effectively reconstructs road networks and imputes data, outperforming existing methods.

Area of Science:

Intelligent Transportation Systems
Data Science
Machine Learning

Background:

Intelligent transportation systems (ITS) rely on accurate traffic state data for optimal operation.
Environmental factors frequently cause missing values in traffic data collected by detectors.
Existing data imputation methods struggle with the complex spatiotemporal dependencies in traffic networks.

Purpose of the Study:

To propose a robust method for imputing missing traffic state data.
To leverage advanced deep learning techniques for enhanced traffic data reconstruction.
To improve the reliability of data used in intelligent transportation systems.

Main Methods:

A Diffusion Convolutional Neural Network-Generative Adversarial Network (DCNN-GAN) was developed for data imputation.

Keywords:

data imputation deepwalk diffusion convolutional neural networks generative adversarial network graph embedding

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Graph embedding algorithms were used to construct road network structures based on spatial correlations.

Generative Adversarial Networks (GANs) facilitated the generation of missing data through adversarial training.

DCNN was employed within the generator to extract spatiotemporal features for accurate imputation.

Main Results:

The proposed DCNN-GAN method demonstrated superior performance in imputing missing traffic data.
Validation on two real-world traffic datasets confirmed the model's effectiveness.
The method significantly outperformed comparative traditional and deep learning models.

Conclusions:

The DCNN-GAN approach offers a powerful solution for addressing missing traffic data in ITS.
The use of graph embedding and spatiotemporal feature extraction enhances imputation accuracy.
This method contributes to more reliable traffic state monitoring and management.