This paper attempts to deal with traffic Origin Destination (OD) matrix estimation starting from the measurements of flow on road network links. It proposes a different approach from published articles to date, by applying multilayer feed-forward neural networks. Since the relationship between link flow and the related OD matrix is continuous, it is possible to use the well known approximation property of Neural Network models. The method is proposed for a real-time correction of the OD matrix. Two application scenarios were developed: a trial network and an actual rural network were both simulated by a micro-simulator that assigns known OD matrices. A Principal Component Analysis (PCA) technique was used to reduce the amount of variables and to achieve improved significance for input data. The estimated error was relatively low and, as opposed to analytical approaches, the processing time was almost in real time, making this approach suitable for applications in dynamic traffic management. Comparisons with results obtained by an OD estimation commercial program show better performance in the NN approach both as regards error and computing time

OD Matrices Network Estimation from Link Counts by Neural Networks

MUSSONE, LORENZO;MATTEUCCI, MATTEO
2013

Abstract

This paper attempts to deal with traffic Origin Destination (OD) matrix estimation starting from the measurements of flow on road network links. It proposes a different approach from published articles to date, by applying multilayer feed-forward neural networks. Since the relationship between link flow and the related OD matrix is continuous, it is possible to use the well known approximation property of Neural Network models. The method is proposed for a real-time correction of the OD matrix. Two application scenarios were developed: a trial network and an actual rural network were both simulated by a micro-simulator that assigns known OD matrices. A Principal Component Analysis (PCA) technique was used to reduce the amount of variables and to achieve improved significance for input data. The estimated error was relatively low and, as opposed to analytical approaches, the processing time was almost in real time, making this approach suitable for applications in dynamic traffic management. Comparisons with results obtained by an OD estimation commercial program show better performance in the NN approach both as regards error and computing time
urban traffic, OD matrix estimation; neural networks; PCA; link flow; variance stabilization
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/750804
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