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Forecasting origin-destination matrices by using neural network approach: A comparison of testing performance between back propagation, variable learning rate and levenberg-marquardt algorithms

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References

1998Forecasting with artificial neural networks:4,225 cited
1987Recursive estimation of origin-destination matrices from input/output counts176 cited
1994A linear programming approach for synthesizing origin-destination trip tables from link traffic volumes124 cited
2000Trip distribution forecasting with multilayer perceptron neural networks: A critical evaluation120 cited
2004An approach to estimating and updating origin–destination matrices based upon traffic counts preserving the prior structure of a survey matrix103 cited
2004Inferring origin–destination trip matrices with a decoupled GLS path flow estimator89 cited
2003Estimation of origin–destination trip-tables based on a partial set of traffic link volumes57 cited
1998Forecasting travel demand: a comparison of logit and artificial neural network methods29 cited

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2013OD Matrix Estimation from Link Counts Using Artificial Neural Network10 cited
2014Revisiting the destination ranking procedure in development of an Intervening Opportunities Model for public transit trip distribution8 cited
2021Modelling Trip Distribution Using the Gravity Model and Fratar's Method6 cited

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