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
Traditional bi-level origin-destination (OD) matrix estimation process adjusts the matrix (at the up...
Origin-Destination (OD) trip matrices describe the patterns of traffic behavior across the network a...
A method has been developed to estimate Origin Destination (OD) matrices using a neural network (NN)...
The paper tackles OD matrix estimation starting from the measures of flow on road network links and ...
This paper attempts to deal with traffic Origin Destination (OD) matrix estimation starting from the...
Given a road network, a fundamental object of interest is the matrix of origin destination (OD) flow...
The paper discusses the application of Neural Networks to OD estimation and assignment problems. Err...
OD flows provide important information for traffic management and planning. The prediction of dynami...
In previous work, we have explored the idea of dimensionality reduction and approximation of OD dema...
The idea behind this report is based on the fact that it is not only the number of users in the traf...
Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A c...
The fundamental challenge of the origin-destination (OD) matrix estimation problem is that it is sev...
A method has been developed to estimate Origin Destination (OD) matrices using a neural network (NN)...
In many OD estimation methods a linear relationship expressed by the assignment matrix is chosen. Ho...
In this paper we explore the idea of dimensionality reduction and approximation of OD demand based o...
Traditional bi-level origin-destination (OD) matrix estimation process adjusts the matrix (at the up...
Origin-Destination (OD) trip matrices describe the patterns of traffic behavior across the network a...
A method has been developed to estimate Origin Destination (OD) matrices using a neural network (NN)...
The paper tackles OD matrix estimation starting from the measures of flow on road network links and ...
This paper attempts to deal with traffic Origin Destination (OD) matrix estimation starting from the...
Given a road network, a fundamental object of interest is the matrix of origin destination (OD) flow...
The paper discusses the application of Neural Networks to OD estimation and assignment problems. Err...
OD flows provide important information for traffic management and planning. The prediction of dynami...
In previous work, we have explored the idea of dimensionality reduction and approximation of OD dema...
The idea behind this report is based on the fact that it is not only the number of users in the traf...
Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A c...
The fundamental challenge of the origin-destination (OD) matrix estimation problem is that it is sev...
A method has been developed to estimate Origin Destination (OD) matrices using a neural network (NN)...
In many OD estimation methods a linear relationship expressed by the assignment matrix is chosen. Ho...
In this paper we explore the idea of dimensionality reduction and approximation of OD demand based o...
Traditional bi-level origin-destination (OD) matrix estimation process adjusts the matrix (at the up...
Origin-Destination (OD) trip matrices describe the patterns of traffic behavior across the network a...
A method has been developed to estimate Origin Destination (OD) matrices using a neural network (NN)...