  The mixed-logit model is a flexible tool in transportation choice analysis,
which provides valuable insights into inter and intra-individual behavioural
heterogeneity. However, applications of mixed-logit models are limited by the
high computational and data requirements for model estimation. When estimating
on small samples, the Bayesian estimation approach becomes vulnerable to over
and under-fitting. This is problematic for investigating the behaviour of
specific population sub-groups or market segments with low data availability.
Similar challenges arise when transferring an existing model to a new location
or time period, e.g., when estimating post-pandemic travel behaviour. We
propose an Early Stopping Bayesian Data Assimilation (ESBDA) simulator for
estimation of mixed-logit which combines a Bayesian statistical approach with
Machine Learning methodologies. The aim is to improve the transferability of
mixed-logit models and to enable the estimation of robust choice models with
low data availability. This approach can provide new insights into choice
behaviour where the traditional estimation of mixed-logit models was not
possible due to low data availability, and open up new opportunities for
investment and planning decisions support. The ESBDA estimator is benchmarked
against the Direct Application approach, a basic Bayesian simulator with random
starting parameter values and a Bayesian Data Assimilation (BDA) simulator
without early stopping. The ESBDA approach is found to effectively overcome
under and over-fitting and non-convergence issues in simulation. Its resulting
models clearly outperform those of the reference simulators in predictive
accuracy. Furthermore, models estimated with ESBDA tend to be more robust, with
significant parameters with signs and values consistent with behavioural
theory, even when estimated on small samples.
