Publication

Enhanced measurement equations for latent class choice models

Michel Bierlaire
2013
Talks

Abstract

Integrating psychometric indicators in latent class choice models allows to enhance the characterization of the latent classes. In this research, we consider measurement equations that include socio-economic indicators of the decision-makers. We show that such a specification increases the significance of the parameters relative to the class-membership relation and leads to a better interpretability of the behavior of individuals in the latent classes. The method is applied to a transportation mode choice case study in Switzerland.

Official source

https://infoscience.epfl.ch/entities/publication/fac4db5f-0a2d-4de4-b2d4-b51f437cad36

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Michel Bierlaire
2013
Talks

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/fac4db5f-0a2d-4de4-b2d4-b51f437cad36

About this result

Related concepts (32)

Mode choice

Mode choice analysis is the third step in the conventional four-step transportation forecasting model of transportation planning, following trip distribution and preceding route assignment. From origin-destination table inputs provided by trip distribution, mode choice analysis allows the modeler to determine probabilities that travelers will use a certain mode of transport. These probabilities are called the modal share, and can be used to produce an estimate of the amount of trips taken using each feasible mode.

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.

Transportation forecasting

Transportation forecasting is the attempt of estimating the number of vehicles or people that will use a specific transportation facility in the future. For instance, a forecast may estimate the number of vehicles on a planned road or bridge, the ridership on a railway line, the number of passengers visiting an airport, or the number of ships calling on a seaport. Traffic forecasting begins with the collection of data on current traffic. This traffic data is combined with other known data, such as population, employment, trip rates, travel costs, etc.