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An Evaluation of Traffic Matrix Estimation Techniques for Large-Scale IP NetworksAdelani, Titus Olufemi 09 February 2010 (has links)
The information on the volume of traffic flowing between all possible origin and destination pairs in an IP network during a given period of time is generally referred to as traffic matrix (TM). This information, which is very important for various traffic engineering tasks, is very costly and difficult to obtain on large operational IP network, consequently it is often inferred from readily available link load measurements.
In this thesis, we evaluated 5 TM estimation techniques, namely Tomogravity (TG), Entropy Maximization (EM), Quadratic Programming (QP), Linear Programming (LP) and Neural Network (NN) with gravity and worst-case bound (WCB) initial estimates. We found that the EM technique performed best, consistently, in most of our simulations and that the gravity model yielded better initial estimates than the WCB model. A hybrid of these techniques did not result in considerable decrease in estimation errors. We, however, achieved most significant reduction in errors by combining iterative proportionally-fitted estimates with the EM technique. Therefore, we propose this technique as a viable approach for estimating the traffic matrix of large-scale IP networks.
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An Evaluation of Traffic Matrix Estimation Techniques for Large-Scale IP NetworksAdelani, Titus Olufemi 09 February 2010 (has links)
The information on the volume of traffic flowing between all possible origin and destination pairs in an IP network during a given period of time is generally referred to as traffic matrix (TM). This information, which is very important for various traffic engineering tasks, is very costly and difficult to obtain on large operational IP network, consequently it is often inferred from readily available link load measurements.
In this thesis, we evaluated 5 TM estimation techniques, namely Tomogravity (TG), Entropy Maximization (EM), Quadratic Programming (QP), Linear Programming (LP) and Neural Network (NN) with gravity and worst-case bound (WCB) initial estimates. We found that the EM technique performed best, consistently, in most of our simulations and that the gravity model yielded better initial estimates than the WCB model. A hybrid of these techniques did not result in considerable decrease in estimation errors. We, however, achieved most significant reduction in errors by combining iterative proportionally-fitted estimates with the EM technique. Therefore, we propose this technique as a viable approach for estimating the traffic matrix of large-scale IP networks.
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Empirical Assessment of the Iterative Proportional Fitting Method for Estimating Bus Route Passenger Origin-Destination FlowsStrohl, Brandon A. 15 January 2010 (has links)
No description available.
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Combining Small Samples of Direct Observations of Passenger Flows with Large Quantities of Automatic Passenger Count Data for Estimating Bus Transit Route Origin-Destination FlowsRoy, Raj January 2021 (has links)
No description available.
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Bus Transit Passenger Origin-Destination Flow Estimation: Capturing Terminal Carry-Over Movements Using the Iterative Proportional Fitting MethodChen, Aijing January 2020 (has links)
No description available.
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