  The mixed multinomial logit model assumes constant preference parameters of a
decision-maker throughout different choice situations, which may be considered
too strong for certain choice modelling applications. This paper proposes an
effective approach to model context-dependent intra-respondent heterogeneity,
thereby introducing the concept of the Context-aware Bayesian mixed multinomial
logit model, where a neural network maps contextual information to
interpretable shifts in the preference parameters of each individual in each
choice occasion. The proposed model offers several key advantages. First, it
supports both continuous and discrete variables, as well as complex non-linear
interactions between both types of variables. Secondly, each context
specification is considered jointly as a whole by the neural network rather
than each variable being considered independently. Finally, since the neural
network parameters are shared across all decision-makers, it can leverage
information from other decision-makers to infer the effect of a particular
context on a particular decision-maker. Even though the context-aware Bayesian
mixed multinomial logit model allows for flexible interactions between
attributes, the increase in computational complexity is minor, compared to the
mixed multinomial logit model. We illustrate the concept and interpretation of
the proposed model in a simulation study. We furthermore present a real-world
case study from the travel behaviour domain - a bicycle route choice model,
based on a large-scale, crowdsourced dataset of GPS trajectories including
119,448 trips made by 8,555 cyclists.
