The paper tackles OD matrix estimation starting from the measures of flow on road network links and proposes the application of soft-computing techniques. The application scenarios are two: a trial network and the real rural network of the Province of Naples both simulated by a micro-simulator dynamically assigning known OD matrices. A PCA (Principal Component Analysis) technique was also used to reduce the input space of variables in order to achieve better significance for input data and to study the possible eigengraphs of the road networks.

OD Matrices Estimation From Link Flows By Neural Networks And PCA

MATTEUCCI, MATTEO;MUSSONE, LORENZO
2006-01-01

Abstract

The paper tackles OD matrix estimation starting from the measures of flow on road network links and proposes the application of soft-computing techniques. The application scenarios are two: a trial network and the real rural network of the Province of Naples both simulated by a micro-simulator dynamically assigning known OD matrices. A PCA (Principal Component Analysis) technique was also used to reduce the input space of variables in order to achieve better significance for input data and to study the possible eigengraphs of the road networks.
2006
11th IFAC Symposium on Control in Transportation Systems
Variance stabilizatio
Neural Networks
OD Estimation
Principal Component Analysis
Link flow measurements
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/272119
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