Cognitive Accessible Area on Mobility Hub

Offer a complete description of the experimental design and methods used to acquire these data. Please provide any programs or code files used for filtering and analysing these data. It is very important that this section is as comprehensive as possible. If you are submitting via another Elsevier journal (a co-submission) you are encouraged to provide more detail than in your accompanying research article. There is no character limit for this section; however, no insight, interpretation, or background should be included in this section.
The experimental design was generated using the Design of Experiments (DoE) module of the Radiant package in R, which provides an interactive interface for constructing efficient experimental designs. Radiant’s DoE module was used to define attributes and their respective levels and to generate an efficient subset of choice scenarios from the full factorial design space. Given the large number of possible combinations, a full factorial design was not feasible. Instead, a fractional factorial design was constructed using D-efficiency criteria.
Specifically, the DoE procedure in Radiant:
1. Allows specification of attribute levels and constraints
2. Generates candidate design matrices based on combinations of attribute levels
3. Applies D-efficiency optimization to select a subset of scenarios that minimizes parameter variance
4. Produces balanced and orthogonal designs suitable for discrete choice experiments
Using this approach, the final design included 24 choice scenarios for private car users, 36 choice scenarios for public transportation users. The resulting designs achieved D-efficiency values of 0.863 and 0.845, respectively, indicating high statistical efficiency.
To reduce respondent burden, the survey was divided into three questionnaire versions:
1. 8 choice tasks per respondent for private car users
2. 12 choice tasks per respondent for public transportation users