Multi-scale Traffic Volume Prediction via Nested Spatial-Temporal Modeling
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Solution Overview
Problem
Current traffic volume prediction methods face challenges in accurately forecasting traffic at fine-grained geographical regions due to high volatility, as they fail to effectively model spatial and temporal correlations across different scales.
Innovation Solution
A processor-implemented neural network system that models spatial correlations between fine and coarse spatial granularities to determine spatial feature vectors, and models temporal correlations across multiple temporal scales, enabling accurate traffic flow predictions by integrating both spatial and temporal dimensions using a multi-granularity, multi-graph convolution system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traffic volume prediction is performed at fine-grained geographical regions, then the precision of local traffic forecasting is improved, but the volatility and difficulty of accurate prediction increase
Solution Approach 1:
The patent implements a hierarchical prediction architecture where fine-grained site-level predictions are nested within coarse-grained region-level predictions. The region-level model captures macroscopic traffic patterns and temporal correlations, while site-level models capture local spatial correlations. These nested models work together to improve both precision and reliability of fine-grained traffic volume predictions by combining multiple scales of analysis.
Solution Approach 2:
The patent transitions from single-scale prediction to multi-scale prediction by adding the temporal dimension (multiple time scales) and spatial dimension (multiple geographical granularities). This multi-dimensional approach allows the system to capture traffic patterns at different levels of aggregation, improving prediction reliability while maintaining fine-grained precision through the integration of region-level and site-level models.
2Measurement precision
If multi-scale spatial-temporal correlations are modeled using neural networks, then the accuracy of traffic flow prediction is improved, but the computational complexity and system resource requirements increase
Solution Approach 1:
The patent segments the complex prediction task into distinct functional modules: a region-level temporal correlation model, a site-level spatial correlation model, and an integration mechanism. This segmentation allows each module to specialize in specific aspects of traffic pattern recognition, improving overall prediction accuracy while making the system more manageable and computationally efficient through modular architecture.
Solution Approach 2:
The patent merges region-level temporal predictions with site-level spatial predictions to produce integrated traffic flow forecasts. By combining the outputs of multiple specialized models through the nested doll architecture, the system achieves higher prediction accuracy than single-scale models while managing complexity through structured integration of complementary prediction layers.
Data Source
AI summary
Methods and systems for allocating network resources responsive to network traffic include modeling spatial correlations between fine spatial granularity traffic and coarse spatial granularity traffic for different sites and regions to determine spatial feature vectors for one or more sites in a network. Temporal correlations at a fine spatial granularity are modeled across multiple temporal scales, based on the spatial feature vectors. Temporal correlations at a coarse spatial granularity are modeled across multiple temporal scales, based on the spatial feature vectors. A traffic flow prediction is determined for the one or more sites in the network, based on the temporal correlations at the fine spatial granularity and the temporal correlations at the coarse spatial granularity. Network resources are provisioned at the one or more sites in accordance with the traffic flow prediction.