An evidential neural network (ENN) for predicting individual travel mode is presented. This model can be used to support management decision making and to build predictions under uncertainty related to changes in people's behavior, the economic context, or the environment and policy. The presented model uses individuals’ characteristics, transportation mode specifications, and data related to places of work and residence. The data set analyzed was taken from a survey conducted in 2007 and contains information on the daily mobility (e.g., from home to work) of individuals who either lived or worked in Luxembourg. Individual characteristics were extracted to relate daily mobility (journeys between home and work, in particular) to the characteristics of working individuals. Information about public transportation specification and some geographical particularities of residential areas and workplaces were used. Rates of successful prediction obtained by the ENN and several alternative approaches were compared by cross-validation. The results showed that the ENN was superior to the studied alternatives.
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