A comparative study of a hybrid Logit–Fratar and neural network models for trip distribution: case of the city of Isfahan | Zendy

Shirmohammadli M. | Zendy; ShetabBushehri S. N. | Zendy; Poorzahedy H. | Zendy; Hejazi S. R. | Zendy

Open Access

A 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.

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