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A bilevel model for public transport demand estimation
Author(s) -
Neïla Bhouri,
JeanPatrick Lebacque,
Pablo A. Lotito,
Victoria M. Orlando
Publication year - 2021
Publication title -
transportation research procedia
Language(s) - English
Resource type - Journals
eISSN - 2352-1465
pISSN - 2352-1457
DOI - 10.1016/j.trpro.2021.01.080
Subject(s) - bilevel optimization , mathematical optimization , computer science , optimization problem , disadvantage , focus (optics) , public transport , estimation , matrix (chemical analysis) , gradient descent , mathematics , economics , engineering , transport engineering , machine learning , artificial intelligence , artificial neural network , physics , materials science , optics , composite material , management
For the case of public transport, we consider the problem of demand estimation. Given an origin-destination matrix representing the public transport demand, the distribution of flow among different lines can be obtained assuming that it corresponds to a certain equilibrium characterized by an optimization problem. In particular we will focus on the assignment model proposed by Cepeda et al. (2006). However the knowledge of origin-destination matrix is expensive and sometimes unaffordable in practice. Traditionally, it is estimated using statistical or econometrical considerations. In this work, we explore the estimation through the numerical solution of a bilevel optimization problem. One disadvantage of this formulation is the difficulty of obtaining descent directions, hence, for the resolution of the optimization problem we use a derivative-free method. This method was applied for small networks getting good results.

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