Hilmi Berk Celikoglu

TL;DR: Two different algorithms are employed to propose a new calibration process for travel mode choice analysis in a transportation modelling framework and results show both the surpassing of RBFNNs and GRNNs over frequently used FFBPNNs, and the superiority of neural network methods over a conventional statistical model, multivariate linear regression, during mode choice calibrations.

TL;DR: The employed GRNN method, generalized regression neural network, in comparison to both a frequently applied neural network training algorithm, feed-forward back-propagation, and a stochastic model of auto-regressive structure for the purpose of forecasting daily trip flows, which is an essential component in demand analysis.

TL;DR: A dynamic approach to specify flow pattern variations simulated by a multimode macroscopic flow model is followed, incorporating the neural network theory to reconstruct real-time traffic dynamics, returning promising results in capturing sudden changes on test stretch flow patterns that are simulated by the switching multimode discrete Macroscopic model.

TL;DR: A dynamic approach to specify flow pattern variations is proposed mainly concentrating on the incorporation of neural network theory to provide real‐time mapping for traffic density simultaneously in conjunction with a macroscopic traffic flow model.

TL;DR: The dynamic classification approach returned promising results in capturing sudden changes on test stretch flow patterns as well as performance compared to multivariate clustering, including incident detection and control and variable speed management.