Hierarchical Bayesian Traffic Prediction via Node Knowledge Transfer
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Solution Overview
Problem
Existing traffic prediction models fail to accurately predict real traffic conditions due to their reliance on ideal network conditions, ignoring abnormalities such as accidents and weather, which are common in real-life scenarios.
Innovation Solution
The development of systems and methods using Hierarchical Bayesian and Deep Learning models that transfer knowledge of traffic patterns from node to node, enabling robust and scalable probabilistic approaches to predict traffic patterns, even under abnormal conditions, by utilizing sensor data and hyperparameters to derive correlations and relationships between nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing traffic prediction models use ideal network conditions for training, then model simplicity is maintained, but prediction accuracy under real-world conditions deteriorates
Solution Approach 1:
The patent segments the traffic prediction problem into multiple levels: individual node models that capture local abnormal behaviors, and a hierarchical model that aggregates patterns across nodes. This segmentation allows the system to maintain simplicity at each node while achieving high overall accuracy through collaborative knowledge transfer.
Solution Approach 2:
The patent introduces a hierarchical dimension to traditional traffic prediction, moving from single-node models to multi-node hierarchical models. This dimensional expansion enables the system to capture both local abnormal patterns and global traffic dynamics, resolving the contradiction between model simplicity and prediction accuracy.
2Reliability
If traffic prediction models are trained with normal traffic data only, then training data quality is high, but adaptability to abnormal conditions deteriorates
Solution Approach 1:
The patent prepares node models in advance using normal traffic data, then enables these pre-trained models to adapt to abnormal conditions through hierarchical knowledge transfer. The preliminary training ensures high data quality, while the hierarchical framework provides adaptability when abnormalities occur.
Solution Approach 2:
The hierarchical model incorporates feedback mechanisms where prediction errors at individual nodes are aggregated and used to update the overall model, which then provides improved predictions back to individual nodes. This feedback loop enables continuous adaptation to abnormal conditions while maintaining training reliability.
3Measurement precision
If node-specific models are used to capture local traffic patterns, then local prediction accuracy improves, but system complexity deteriorates
Solution Approach 1:
The patent merges individual node models into a unified hierarchical framework where knowledge is transferred collaboratively across nodes. This merging maintains local prediction accuracy while reducing overall system complexity through shared parameters and centralized coordination.
Solution Approach 2:
The hierarchical model serves multiple functions: it trains individual node models, aggregates their predictions, detects abnormalities, and provides feedback for continuous improvement. This multi-functionality reduces the need for separate complex systems while maintaining high local prediction accuracy.
Data Source
AI summary
Disclosed are systems and methods for traffic pattern prediction under abnormal behavior through collaborative knowledge transferring from node to node. In one example, a system includes a processor and a memory having instructions that cause the processor to determine vehicle traffic flows at each of a plurality of nodes using a general model that utilizes hyperparameters that derive relationships between and within each node of the plurality of nodes and observed data from sensors monitoring the plurality of nodes. The observed data includes real-world traffic data affected by hidden parameters. Using an understandable algorithm, the general model derives correlations between the hidden parameters from the observed data at multiple levels.