Combining data gathering efficiency with behaviourally realistic modelling: A case of park-and-ride facility choice data gathered with a Sequential Best Worst Discrete Choice Experiment and estimated with a Random Regret Minimisation model

This research combines two relatively new additions to the field of discrete choice modelling: sequential best worst discrete choice experiments (SBWDCE) and random regret minimisation (RRM) modelling, with the hope of developing a more behaviourally realistic choice model. SBWDCEs are able to gather a larger number of stated choice observations from fewer respondents, while RRM models challenge the notion of fully compensatory behaviour implied by the traditional RUM model and suggest that consumers choose to minimise regret. According to image theory, best and worst choices are not made with the same kind of decision rule, so accounting for that variability using a RRM model would produce a more realistic model with better model fit. Estimating the combined model proves that people do in fact use a compensatory decision rule when selecting the best alternatives and a semi- to non-compensatory decision rule when selecting the worst. The results also show that the way choice set size variation is accounted for can greatly impact the scale parameters, as these and the choice set size constants are inversely related. Although a better model fit was achieved, using best-worst tasks is contested and researchers also warn against the lower reliability of additional choices in the same choice set. Nevertheless, SBWDCEs provide great benefits in fields with small population sizes and can potentially help in obtaining higher quality prior parameter values for use in efficient experimental design generation.Transport, Infrastructure and Logistic