Open AccessA comparative study of a hybrid Logit–Fratar and neural network models for trip distribution: case of the city of Isfahan
Author(s) -
Shirmohammadli M.,
ShetabBushehri S. N.,
Poorzahedy H.,
Hejazi S. R.
Publication year - 2011
Publication title -
journal of advanced transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 46
eISSN - 2042-3195
pISSN - 0197-6729
DOI - 10.1002/atr.143
Subject(s) - artificial neural network , logit , logistic regression , mixed logit , econometrics , computer science , feature (linguistics) , distribution (mathematics) , operations research , engineering , economics , artificial intelligence , mathematics , machine learning , linguistics , philosophy , mathematical analysis
This paper introduces a new procedure to forecast the future O / D demand. It is a hybrid of logit and Fratar model. The hybrid model has the long run, policy sensitive, characteristic of a logit model, calibrated at sector‐level with little/no zero O / D cells. This feature, joint with a Fratar‐type operation at zonal level within a sector, gives a better performance to this model than either of the two types of the models alone. The performance of the hybrid model is contrasted with a neural network model, and shows encouraging results in a real case. Copyright © 2010 John Wiley & Sons, Ltd.