4. Conclusion

Discrete choice models are a valuable tool for analyzing the behavior of individuals when faced with a choice between mutually exclusive alternatives. They are based on the logic of economic rationality which aims at optimizing an objective function while taking into account both the socioeconomic characteristics of individuals and the technical-economic characteristics of the alternative to be chosen, as well as the uncertainty of the environment where the choice reigns.

Author details

Address all correspondence to: aloulouf@yahoo.fr

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board record. 1973; 526, Washington DC

Higher Institute of Transportation and Logistics, University of Sousse, Tunisia

http://master.is.free.fr/Ancien\_site/Documents/modele\_logit.pdf

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Foued Aloulou

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This objective function is conditional, discrete, and random. It is discrete because the problem of choice is no longer a continuum of possibilities but rather mutually exclusive alternatives, so that if the individual chooses a given alternative, he must renounce others. It is random in that the individual in question does not have perfect knowledge of the value of his objective function dependent on a given choice. This function is not observable. What is known is the choice of the user and not the value of this function. The objective function is conditional because it formalizes the satisfaction of the individual under the condition that he has already chosen the preferred alternative.

The multinomial logit model is the most used in empirical studies. It has the advantage of being able to treat the individual choice between a multitude of options and seeks to estimate the probability of having chosen a given alternative that better meets the requirements of the individual and the specific conditions characterizing the environment of choice.

It predicts the effects of modifying one of the characteristics of the alternative to choose or the individual's socioeconomic variables on the probability of making a relative decision of choice.

It allows better analysis of economic phenomena in relation to human behavior as a decisionmaking unit such as transport demand, accidentology, and valuation of nonmarket goods (transport time, membership of a given category population, etc.).

The objective of this chapter was to provide the reader with some essential elements for putting this multinomial logit model into practice by presenting in a first part its specificities and the interpretation of its estimated coefficients. In a second part, we tried to apply this model on two cases in relation to transport, one on modal choice and the other on accidentology.

Based on the results of these two applications, several pieces of information can be deduced which may be of great practical interest to individuals and public authorities involved in the transport. They constitute an important information base which guides these economic actors to the best choices of preventive actions and the orientation of the transport policy as well as in the matter of investment, pricing, road safety, etc. They offer us the possibility to calculate a specific time value to each individual according to their socioeconomic characteristics, their modal choice, and the conditions of travel (reason for travel, zone origin destination, time of departure, etc.), to propose the best preventive actions to accidentology, etc.
