Traffic Flow Inference Using Gravity Measures
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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 'soft' gravity measures, such as demographics and past experiences, leading to inaccuracies in traffic flow determination.
Innovation Solution
A system that uses 'soft' gravity measures to form objectives optimized within constraints, allowing for the determination of traffic flow between nodes while minimizing the difference between specified and resulting gravity measures, and presenting the results through a graphic user interface for visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional tomographic or gravity-based estimation techniques are used to determine traffic flow from link loads, then traffic flow can be estimated, but the results are inconsistent and inaccurate, particularly at nodes that primarily serve to pass data from one link to another
Solution Approach 1:
The patent implements an iterative optimization process where traffic flow estimates are continuously refined by comparing predicted link loads with actual measured link loads. The system adjusts traffic flow values in successive iterations to minimize the difference between predicted and actual link loads, thereby improving both accuracy and consistency of traffic flow determination.
Solution Approach 2:
The patent transforms the traffic flow determination problem from a direct estimation approach to an optimization problem by changing the parameters being optimized. Instead of directly estimating traffic flow from link loads, the system optimizes traffic flow values to minimize the objective function that measures the discrepancy between predicted and actual link loads, subject to constraints that ensure physical feasibility.
2Adaptability or versatility
If nodes are defined as edge nodes based on significant traffic reception or transmission, then gravity-based estimation can be applied, but this leads to outliers and inconsistencies at nodes that primarily pass data through
Solution Approach 1:
The patent applies different treatment to different nodes based on their local characteristics. The optimization process identifies and handles outlier nodes separately by detecting where the estimated gravity measures significantly deviate from expected values. This allows the system to maintain the gravity-based estimation approach for most nodes while correcting local inaccuracies at specific nodes that primarily serve as transit points.
3Productivity
If linear programming models are used to determine traffic flow based on hop constraints, then traffic flow can be determined, but the results may not be accurate for networks where traffic is not correlated with distance
Solution Approach 1:
The patent employs a dynamic optimization approach that adapts to the specific characteristics of the network being analyzed. The system does not rely on fixed assumptions about traffic patterns but instead dynamically adjusts traffic flow estimates based on actual measured link loads. The iterative process allows the solution to evolve and adapt to the actual network behavior, making it applicable to various network types regardless of whether traffic correlates with distance.
Data Source
AI summary
Traffic flow between each pair of nodes in a network are determined based on loads measured at each link and based on gravity measures. The gravity measures correspond to a likelihood of the node being a source or a sink of traffic and may be assigned based on characteristics associated with each node, such as the demographics of the region in which the node is located, prior sinking and sourcing statistics, and so on. The gravity measures are used to generate an objective function for solving a system of linear equations, rather than as criteria that must be satisfied in the solution. The measured link loads are allocated among the traffic flows between nodes to at least a given allocation efficiency criteria by solving a system of linear equations with an objective of minimizing a difference between the assigned gravities and the resultant gravities corresponding to the determined flows.