Modellers are increasingly relying on the use of continuous random coefficients models, such as Mixed
Logit, for the representation of variations in tastes across individuals. In this paper, we provide an indepth
comparison of the performance of the Mixed Logit model with that of its far less commonly used
discrete mixture counterpart, making use of a combination of real and simulated datasets. The results not
only show significant computational advantages for the discrete mixture approach, but also highlight
greater flexibility, and show that, across a host of scenarios, the discrete mixture models are able to offer
comparable or indeed superior model performance.
