Deep Graph Gaussian Process for Traffic Flow Forecasting
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Solution Overview
Problem
Existing deep spatiotemporal models are inadequate in extracting complex spatiotemporal features and quantifying uncertainty with a small number of traffic flow data samples, particularly in areas with scarce data resources.
Innovation Solution
A traffic flow forecasting method based on Deep Graph Gaussian Processes, which uses an attention kernel function to describe dynamic spatial dependencies and combines Aggregation Gaussian Processes with Temporal Convolutional Gaussian Processes to extract spatiotemporal features and quantify uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep spatiotemporal models are used to extract complex spatiotemporal features, then feature extraction capability is improved, but the requirement for data resources increases
Solution Approach 1:
The model segments the spatiotemporal feature extraction into distinct spatial and temporal components. The spatial graph convolutional network separately processes spatial dependencies among traffic nodes, while the temporal convolutional network separately processes temporal patterns. This segmentation allows each component to be optimized independently, reducing the overall data requirement compared to processing full spatiotemporal features simultaneously.
Solution Approach 2:
The patent transforms the traffic network into a graph structure where nodes represent traffic entities and edges represent spatial relationships. By introducing this graph dimension, the model can capture spatial dependencies without requiring complete spatiotemporal data cubes, effectively reducing the data dimensionality requirement while maintaining feature extraction capability.
2Measurement precision
If deep spatiotemporal models are used for traffic flow forecasting, then prediction accuracy is improved, but the model complexity increases
Solution Approach 1:
The deep spatiotemporal model is segmented into modular components: spatial graph convolutional layers for spatial feature extraction, temporal convolutional layers for temporal pattern recognition, and a prediction output layer. This modular segmentation reduces model complexity by allowing independent design, training, and optimization of each component, while maintaining high prediction accuracy through their coordinated operation.
Solution Approach 2:
The model dynamically adjusts the graph structure and convolutional kernels based on input data characteristics. The graph neural network dynamically learns node embeddings and edge weights, while the temporal convolution adapts to different traffic patterns at different time scales. This dynamic adaptability improves prediction accuracy without requiring a fixed complex structure.
3Device complexity
If traditional models are used with limited data, then model simplicity is maintained, but the ability to quantify uncertainty deteriorates
Solution Approach 1:
The patent introduces an uncertainty quantification module as an intermediary between the prediction model and the output. This module uses Monte Carlo dropout and ensemble methods to estimate prediction uncertainty without fundamentally changing the simplicity of the core forecasting model. The intermediary layer adds reliability by providing confidence intervals while maintaining the underlying model's simplicity.
Solution Approach 2:
The model changes its parameter representation to include uncertainty estimates alongside point predictions. By outputting both mean predictions and variance/standard deviation values, the model maintains its structural simplicity while providing comprehensive reliability information. This parameter expansion allows the same model architecture to serve both forecasting and uncertainty quantification functions.
Data Source
AI summary
A traffic flow forecasting method based on Deep graph Gaussian processes includes: S1, with respect to the dynamics existing in a spatial dependency, using an attention kernel function to describe a dynamic dependency among vertices on a topological graph, and using the attention kernel function as a covariance function in an Aggregation Gaussian process to extract dynamic spatial features; S2, obtaining a Temporal convolutional Gaussian process from weights at different times and a convolution function that obeys the Gaussian processes, and obtaining temporal features in traffic data by combining the Aggregation Gaussian process; S3, constructing a Deep graph Gaussian process method integrating a Gaussian process and a depth structure from the Aggregation Gaussian process, the Temporal convolutional Gaussian process and a Gaussian process with a linear kernel function, inputting a data sample to be forecasted into the Deep graph Gaussian process method to obtain a forecasted result.