Publication

Specification, estimation and validation of a pedestrian walking behavior model

Michel Bierlaire
2007
Reports

Abstract

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 avoidance model. The spatial correlation between the alternatives is taken into account defining a cross nested logit model. The model is estimated by maximum likelihood estimation on a real data set of pedestrian trajectories, manually tracked from video sequences. The model is validated using a bi-directional flow data set, collected in controlled experimental conditions at Delft university.

Official source

https://infoscience.epfl.ch/entities/publication/9a81a5e2-fa63-423d-948d-e4badd70cde5

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Michel Bierlaire
2007
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/9a81a5e2-fa63-423d-948d-e4badd70cde5

About this result

Related concepts (29)

Discrete choice

In economics, discrete choice models, or qualitative choice models, describe, explain, and predict choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport. Such choices contrast with standard consumption models in which the quantity of each good consumed is assumed to be a continuous variable. In the continuous case, calculus methods (e.g. first-order conditions) can be used to determine the optimum amount chosen, and demand can be modeled empirically using regression analysis.

Maximum likelihood estimation

In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference.

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

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Specification, estimation and validation of a pedestrian walking behavior model

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