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Capturing interactions in pedestrian walking behavior in a discrete choice framework

Publications associées (36)

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Estimation of discrete choice models: extending BIOGEME

Michel Bierlaire, Mamy Nirina Fetiarison

BIOGEME is a free software package for estimating by maximum likelihood a broad range of random utility models. It can estimate particularly Multivariate Extreme Value (MEV) models including the logit model, the nested logit model, the cross-nested logit m ...

2009

Safe density ratio modeling

Kjell Konis

An important problem in logistic regression modeling is the existence of the maximum likelihood estimators. In particular, when the sample size is small, the maximum likelihood estimator of the regression parameters does not exist if the data are completel ...

2009

Properties, advantages, and drawbacks of the block logit model

Michel Bierlaire, Jeffrey Newman

This paper proposes a block logit (BL) model, which is an alternative approach to incorporating covariance between the random utilities of alternatives into a GEV random utility maximization model. The BL is similar to the nested logit (NL), in that it is ...

2009

Specification, estimation and validation of a pedestrian walking behavior model

Michel Bierlaire, Gianluca Antonini, Thomas Robin, Sabina Schneider

We propose and validate a model for pedestrian walking behavior, based on discrete choice modeling. Two main behaviors are identified: unconstrained and constrained. The constrained patterns are captured by a leader-follower model and by a collision avoida ...

2007

Specification, estimation and validation of a pedestrian walking behavior model

Michel Bierlaire, Gianluca Antonini, Javier Cruz Mota, Thomas Robin

We propose and validate a model for pedestrian walking behavior, based on discrete choice modeling. Two main types of behavior are identified: unconstrained and constrained. By unconstrained, we refer to behavior patterns which are independent from other i ...

2007

The estimation of multivariate extreme value models from choice-based samples

Michel Bierlaire

We consider an estimation procedure for discrete choice models in general and Multivariate Extreme Value (MEV) models in particular. It is based on a pseudo-likelihood function, generalizing the Conditional Maximum Likelihood (CML) estimator by Manski and ...

2007

Normalization and correlation of cross-nested logit models

Michel Bierlaire, Emmanuel Abbé

We address two fundamental issues associated with the use of cross-nested logit models. On the one hand, we justify the adequate normalization of the model proposed by Wen and Koppelman (2001). On the other hand, we provide an analysis of the correlation s ...

2007

Sparse Probabilistic Classifiers

The scores returned by support vector machines are often used as a confidence measures in the classification of new examples. However, there is no theoretical argument sustaining this practice. Thus, when classification uncertainty has to be assessed, it i ...

2007

Sparse Probabilistic Classifiers

IDIAP2007

Capturing interactioons in pedestrian walking behavior in a discrete choice framework

Michel Bierlaire

In this paper we propose a general framework for pedestrian walking behavior, based on discrete choice modeling. Two main behaviors are identified: unconstrained and constrained. The constrained patterns are further classified into attractive interactions ...

STRC 20062006