51,600 rated or chosen profiles from 2,150 respondents, in up to 6 tasks of 4 profiles varying on 9 attributes.

About this table

Description	Stated-preference experiment on parking choice among 2150 licensed drivers in 27 Ecuadorian cities (online): in each of six fixed scenarios respondents chose among regulated on-street parking, private parking, irregular parking and leaving the car at home, described by walking distance, cost, probability of finding a space, search time, fine probability and amount, security and surveillance.
Reference	Garcia-Ramirez, Y. (2026). Data and R script for: Parking choice behavior in Ecuadorian cities: A national stated preference analysis with revealed preference validation, technology adoption, and inter-city heterogeneity [Data set]. Zenodo.
Licence	CC BY 4.0
Source data	https://doi.org/10.5281/zenodo.20272342

Notes

Conjoint table (irw_conjoint_2). Choice among four labelled parking options (regulated on-street, private, irregular, leave the car at home) in six fixed scenarios shown identically to all 2150 respondents (fixed design); the labelled options and their eight attribute values are taken from the design matrix in the authors' R script, with units added. Leaving the car at home is a profile with its own attributes (some '(not shown)'); no opt-out. Task order is the column order (inferred).

These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.

Size and shape

Respondents	2,150
Rows (one per respondent, task and profile)	51,600
Tasks per respondent (most)	6
Profiles per task	4
Attributes	9
Tasks with no profile chosen	0

Design

Topic	climate energy environment
A profile represents	product service
Country	EC
Language shown	es
Language of stored levels	en
Task order	inferred from row order
Profile position	recorded in the deposit

Task order or profile position was rebuilt from row order; analyses of position or task order should leave it out.

Outcomes:

choice	Imagine that you need to park your vehicle for 2 hours in a congested commercial area on a Tuesday at 10 AM. Which option would you choose? (translated) [choice, forced]

Columns

attr_fine_amountattr_parking_cost_2hattr_parking_optionattr_prob_finding_spaceattr_prob_fineattr_search_timeattr_securityattr_surveillanceattr_walking_distancechoicecov_age_groupcov_citycov_educationcov_gendercov_household_incomecov_household_vehiclecov_provinceidprofiletask

The layout (one row per respondent, task and profile; choice/rating outcomes; attr_ columns holding each attribute level as displayed) is defined in the IRW conjoint standard. What the outcomes asked is under Design above.

Get the data

Download CSVno account needed Browse on Redivisexplore and query Croissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("garciaramirez_2026_parking_national", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("garciaramirez_2026_parking_national", source="conj")

How to cite

@misc{https://doi.org/10.5281/zenodo.20272342,
  doi = {10.5281/ZENODO.20272342},
  url = {https://zenodo.org/doi/10.5281/zenodo.20272342},
  author = {García-Ramírez, Yasmany},
  title = {Data and R Script for: Parking Choice Behavior in Ecuadorian Cities — A National Stated Preference Analysis with Revealed Preference Validation and Inter-City Heterogeneity},
  publisher = {Zenodo},
  year = {2026},
  copyright = {Creative Commons Attribution 4.0 International}
}

Version and provenance

IRW version	v584
Redivis dataset	irw_conjoint_2 v1.4
Redivis dataset DOI	10.57761/hk97-wv30
Manifest pin for this IRW version	v1.4
Metadata source	irw_meta v44.0