Fast algorithms to generate individualized designs for the mixed logit choice model

•Promoting sequential individualized designs for estimating mixed logit models.•Comparison of DD-efficiency with Kullback–Leibler (KL) based design criteria.•Result 1: all criteria investigated yield equally efficient designs.•Result 2: KL based design criteria are much faster to compute.

The mixed logit choice model has become the common standard to analyze transport behavior. Moreover, more and more transport studies start to make use of stated preference data to obtain precise knowledge on travelers’ preferences. Accounting for the individual-specific coefficients in the mixed logit choice model, this research advocates an individualized design approach to generate these stated choice experiments. Individualized designs are sequentially generated for each person separately, using the answers from previous choice sets to select the next best set in a survey. In this way they are adapted to the specific preferences of an individual and therefore more efficient than an aggregate design. In order for individual sequential designs to be practicable, the speed of designing an additional choice set in an experiment is obviously a key issue. This paper introduces three design criteria used in optimal test design, based on Kullback–Leibler information, and compares them with the well known DD-efficiency criterion to obtain individually adapted choice designs for the mixed logit choice model. Being equally efficient to DD-efficiency and at the same time much faster, the Kullback–Leibler criteria are well suited for the design of individualized choice experiments.