Publication

Sampling of alternatives using multidimensional analysis

Michel Bierlaire
2007
Reports

Abstract

Discrete choice models in general and random utility models in particular may be intractable when the number of alternatives is large. In the transportation context, it typically happens for route choice and destination choice models. In the specific case of the widely used multinomial logit model, it has been shown that the model could be estimated as if the choice was made among a subset of the alternatives. In this paper, we propose to design the sampling of alternatives based on a Principal Component Analysis and a Cluster Analysis of the actual data set, in order to increase the efficiency of the estimates. We present a case study of a destination choice model to empirically illustrate the added value of our approach.

Official source

https://infoscience.epfl.ch/entities/publication/eeb9edf0-7870-4309-865b-cde9a8b7c111

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Michel Bierlaire
2007
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/eeb9edf0-7870-4309-865b-cde9a8b7c111

About this result

Related concepts (32)

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.

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.

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.

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Sampling of alternatives using multidimensional analysis

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