Traffic Flow Inference Using Gravity Measures and Link Loads
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Solution Overview
Problem
Existing methods for determining traffic flow between nodes in a network based on link loads are inconsistent and fail to accurately account for nodes that primarily pass through traffic, leading to inaccuracies in network management and resource allocation.
Innovation Solution
A system that uses 'soft' gravity measures, based on demographics and user input, to estimate traffic flow between nodes, forming objective functions within constraints to optimize traffic flow while minimizing differences between specified and resulting gravity measures, presented via a graphic user interface for visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gravity measures based on edge node traffic are used, then traffic flow estimation can be performed, but inconsistencies and inaccuracies occur at nodes that primarily pass through traffic
Solution Approach 1:
The patent changes the parameter definition of gravity from being based on edge node traffic to being based on local subnetwork traffic characteristics. Specifically, gravity is redefined as the ratio of traffic originated/terminated at a node's local subnetwork to the total traffic passing through that node, which resolves the inconsistency problem for transit nodes while maintaining estimation accuracy.
2Measurement precision
If hard data from network devices is used for gravity measures, then objective traffic flow determination is achieved, but user expectations and soft information are not incorporated
Solution Approach 1:
The patent merges hard data from network devices (link loads, traffic statistics) with soft information from user input (demographics, expected traffic patterns) by using hard data to calculate objective gravity measures while allowing user input to define constraints and validate results, creating a hybrid approach that leverages both objective measurement and subjective expertise.
Solution Approach 2:
The patent introduces gravity measures as an intermediary that bridges hard network data and user expectations. The gravity calculation serves as a mediator that translates raw traffic statistics into meaningful indicators that can be compared against user-provided constraints, enabling reconciliation between objective measurements and subjective expectations.
3Measurement precision
If detailed traffic flow information is collected at each node, then accurate origin-destination data is obtained, but device complexity and cost increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for traffic flow determination (link loads and basic traffic statistics) from network devices, rather than collecting detailed origin-destination data at every node. This extraction approach obtains sufficient information for gravity calculation and traffic matrix determination while avoiding the complexity of comprehensive node-level monitoring.
Solution Approach 2:
The patent creates a simplified model of the network (traffic matrix representation) that copies only the essential traffic flow patterns between nodes rather than replicating detailed device-level information. This model copying approach captures the necessary traffic characteristics while reducing data complexity and processing requirements.
Data Source
AI summary
Traffic flow between each pair of nodes in a network may be modeled based on loads measured at each link and based on gravity measures associated with each node. Gravity measures correspond to a relative likelihood of the node being a source or a sink of traffic. Gravity objectives are assigned to nodes to serve as an objective for a node's performance. These gravity objectives may be based on qualitative characteristics associated with each node. Because the assigned gravity objectives may be subjective, the gravity measures are used to generate a quantitative function for determining whether a network can achieve these gravity objectives. In one embodiment, link loads are allocated to traffic flows between nodes and current gravity measures are determined. Changes to link loads and traffic flows may then be modeled to minimize a difference between the assigned gravity measures and the gravity measures.