Public Preferences for Automated Vehicles
Data from Four U.S. Deliberative Forums
Leah Kaplan
May 05, 2021
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As they move from test tracks to city streets, automated vehicles (AVs) have the potential to revolutionize transportation and transform communities in ways large and small. Even as developers continue working to improve AV technologies, the impacts of AVs will largely be determined by whether and how people decide to use them.
What are individuals’ hopes and concerns when thinking about the future of transportation? Would AVs actually help or hurt people’s daily transportation experiences? Do people trust these vehicles enough to ride in them?
Autonomous vehicle companies, transportation planners, and policymakers are all working to plan for and shape the future of transportation. Understanding public preferences for AVs can help decision makers prepare for an autonomous future, directing AV impacts toward desired outcomes and away from undesired ones.
Research Questions
- What effects do people think AVs will have on the future of transportation?
- How comfortable are people with the idea of riding in an AV?
- Do public attitudes towards AVs vary by city?
To provide a platform for community members to engage with decision makers about AVs and to better understand public values and preferences for AVs, the Consortium for Science, Policy & Outcomes (CSPO) at Arizona State University and the Paris-based Missions Publiques launched an unprecedented program of public consultation. The “Our Driverless Futures” project was part of an international collaboration led by Missions Publiques to bring together the voices of hundreds of individuals in public forums across cities in North America, Europe, and Asia. Informed, deliberative, and neutral, the forums generated critical information for decision makers to prepare for the future of autonomous vehicles.
The following video shares some highlights from the international project, including reactions from forum participants:
Source: Missions Publiques
In the United States, CSPO partnered with universities, local governments, federal agencies, and philanthropies to engage with members of the public and address issues of relevance for cities across the country. Using their Participatory Technology Assessment (pTA) Method of public deliberation, CSPO hosted four forums in Boston, MA; Washington, DC; Phoenix, AZ; and Buffalo, NY in Summer 2019. The four forums brought together over 300 individuals who represented cross-sections of their local communities.
How did the forums work?
For the U.S. forums, participants were recruited through email lists, social and traditional media, institutional partnering, and face-to-face canvassing and were offered an 100 dollar stipend for participating. Participants applied via an online application and about 100 individuals were selected from each city to represent the demographic diversity of their region. These individuals were sent educational information about AVs prior to the forums to brief them on how AVs work.
On the day of the forum, participants sat at tables in small groups with a facilitator, watched short thematic videos, and engaged in multiple rounds of discussion about topics related to AVs. Throughout the day, participants completed worksheets individually and as a group. The forum day was divided into 5 main sessions. Four of the sessions were the same across all of the international sites but the fifth session was designed by each city to address local issues. Each session followed the same general format: watching a short video recapping information from the briefing guide, engaging in group discussion, and completing a group activity and individual worksheet. At the end of the day, participants also had an opportunity to share some of their table discussions with the whole group.
Forum agenda and the structure of each forum session. Source: ASU Consortium for Science, Policy & Outcomes
Data Collection
During the forums, participants completed worksheets individually and as table groups. The worksheets included a mix of quantitative questions (e.g, “Please rate your level of agreement on a 5-point scale.”) and qualitative questions (e.g., “Please explain your rationale.”). All of the data from these worksheets were later input into an online database system called Sphinx. Participants also completed pre- and post-surveys via SurveyMonkey to gauge their learning and attitude change as a result of participating in the forums. All participants had unique participant codes that they used on all of their worksheets and on the pre-and post-surveys. These codes allowed the research team to link participants to the worksheets they completed throughout the forum, while also keeping the responses anonymous.
Data Processing
Along with other researchers at Arizona State University and the University of Maryland, I conducted preliminary analyses on the data from the four U.S. forums. The research team kept all of the hard copies of the pre- and post-surveys and the worksheets to verify participant ID codes and correct typos as needed. Only the registration form linked participants’ names to their codes. During the data processing phase, all identifying information was removed from the data set, though demographic information was kept. The analysis for this report is based on these processed data.
What will an autonomous future look like?
During the first forum session, we asked participants to reflect on their current transportation routines and then consider what effects autonomous vehicles might have on those routines. Could autonomous vehicles help solve many of the transportation challenges they face or might they make them worse? Individuals who don’t see any benefits from AVs may be less interested in using AVs in the future. If members of the public don’t see any value-added by AVs, AVs could simply be a solution in search of a problem.
dataPath <- here::here('data_processed', 'combinedData.csv')
session1Data <- read_csv(dataPath) %>%
select(
accessibilityCar:timeAvailability
) %>%
filter(
!is.na(accessibilityCar),
!is.na(accessibilityTransit),
!is.na(travelTimeCar),
!is.na(travelTimeTransit),
!is.na(cityCongestion),
!is.na(hwCongestion),
!is.na(transportOptions),
!is.na(mobilityAreas),
!is.na(harmTrafficAccidents),
!is.na(transitCost),
!is.na(ownershipCost),
!is.na(locationAccess),
!is.na(timeAvailability)
)
## Reorder
session1Data$accessibilityCar = factor(session1Data$accessibilityCar,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$accessibilityTransit = factor(session1Data$accessibilityTransit,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$travelTimeCar = factor(session1Data$travelTimeCar,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$travelTimeTransit = factor(session1Data$travelTimeTransit,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$cityCongestion = factor(session1Data$cityCongestion,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$hwCongestion = factor(session1Data$hwCongestion,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$transportOptions = factor(session1Data$transportOptions,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$mobilityAreas = factor(session1Data$mobilityAreas,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$harmTrafficAccidents = factor(session1Data$harmTrafficAccidents,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$transitCost = factor(session1Data$transitCost,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$ownershipCost = factor(session1Data$ownershipCost,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$locationAccess = factor(session1Data$locationAccess,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$timeAvailability = factor(session1Data$timeAvailability,
level = c("-1", "0", "1"),
order = TRUE)
# Adjusting labels and names
levels(session1Data$accessibilityCar) <- c("Make worse", "No effect", "Improve")
levels(session1Data$accessibilityTransit) <- c("Make worse", "No effect", "Improve")
levels(session1Data$travelTimeCar) <- c("Make worse", "No effect", "Improve")
levels(session1Data$travelTimeTransit) <- c("Make worse", "No effect", "Improve")
levels(session1Data$cityCongestion) <- c("Make worse", "No effect", "Improve")
levels(session1Data$hwCongestion) <- c("Make worse", "No effect", "Improve")
levels(session1Data$transportOptions) <- c("Make worse", "No effect", "Improve")
levels(session1Data$mobilityAreas) <- c("Make worse", "No effect", "Improve")
levels(session1Data$harmTrafficAccidents) <- c("Make worse", "No effect", "Improve")
levels(session1Data$transitCost) <- c("Make worse", "No effect", "Improve")
levels(session1Data$ownershipCost) <- c("Make worse", "No effect", "Improve")
levels(session1Data$locationAccess) <- c("Make worse", "No effect", "Improve")
levels(session1Data$timeAvailability) <- c("Make worse", "No effect", "Improve")
names(session1Data) = c("Accessibility (physical access)\nof transportation by car",
"Accessibility (physical access) of public transportation",
"Travel time by car",
"Travel time by public transportation",
"Traffic congestion in cities",
"Traffic congestion on highways",
"Availability of transportation options",
"Areas used for other mobility methods\n(e.g., biking, walking)",
"Harm occurring from traffic accidents",
"Total cost of public transportation",
"Total cost of individual car ownership",
"Getting to and from places that\nare not easy to access",
"Availability of my time to do things\nwhile traveling (work, sleep, read, etc)")
Overall, forum participants felt that AVs would positively impact many elements of their current transportation experiences. Interestingly, harm occurring from traffic accidents had mixed responses. Many AV developers assert that automated vehicles would significantly increase road safety by eliminating human driving errors. Perhaps forum participants don’t trust the technology to exceed human capabilities at this point.
Forum participants anticipate cost increases for both private and public transportation as a potential negative effect of an AV future. Indeed, luxury car brands like Tesla, BMW, and Cadillac are all working on models of private autonomous vehicles that may be out of the price ranges for most individuals.
The impacts of AVs on public transportation costs are currently uncertain. Some transportation planners see automation as a way to cut transportation costs by eliminating the cost of a driver. If other AV models like automated ridehailing services become more popular however, they could compete with public transportation and drive up costs for riders who continue using transit. Whether or not high costs materialize, perceived high costs could nevertheless pose a barrier for lower income individuals in an AV future.
How comfortable are people with vehicle automation?
Vehicle automation is already a part of many people’s transportation experiences. Many new cars come with features like automatic braking and lane-keeping technologies, and some higher-end vehicles even have features like self-parking. The Society for Automotive Engineers (SAE) came up with a framework to describe different levels of vehicle automation.
Given that the forum participants may have experience with some automation features, we would expect most people to be fairly comfortable with level 2 vehicles but that they would feel less comfortable with higher levels of automation.
## Loading in data and filtering for Session 2 results
dataPath <- here::here('data_processed', 'combinedData.csv')
session2 <- read_csv(dataPath) %>%
select(
level2Comfort,
level3Comfort,
level4Comfort,
level5Comfort
) %>%
filter(
!is.na(level2Comfort),
!is.na(level3Comfort),
!is.na(level4Comfort),
!is.na(level5Comfort)
)
## Creating a second data frame to allow for grouping by city
session2Cities <- read_csv(dataPath) %>%
select(
city,
level2Comfort,
level3Comfort,
level4Comfort,
level5Comfort
) %>%
filter(
!is.na(level2Comfort),
!is.na(level3Comfort),
!is.na(level4Comfort),
!is.na(level5Comfort)
)
### Change variables to factors and specifying levels
session2$level2Comfort = factor(session2$level2Comfort,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
session2$level3Comfort = factor(session2$level3Comfort,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
session2$level4Comfort = factor(session2$level4Comfort,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
session2$level5Comfort = factor(session2$level5Comfort,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
session2Cities$city = factor(session2Cities$city,
level = c("Boston", "Buffalo", "Phoenix", "Washington"))
### Changing to full question names and adjusting factor labels for clarity
levels(session2$level2Comfort) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
levels(session2$level3Comfort) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
levels(session2$level4Comfort) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
levels(session2$level5Comfort) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
names(session2) = c("Level 2 automation",
"Level 3 automation",
"Level 4 automation",
"Level 5 automation")
# Creating Graph
plotColors = c('sienna','#e2a8a6' , 'grey60','lightsteelblue', 'steelblue')
result <- likert(as.data.frame(session2))
session2Bar <- plot(result,
type = "bar",
colors = plotColors,
ordered = FALSE,
wrap = 40)
session2Bar$layers[[2]]$geom_params$width = 0.4
session2Bar$layers[[3]]$geom_params$width = 0.4
base = 7
session2Bar +
theme_minimal_vgrid(
font_family = 'Helvetica',
font_size = base * 1.5) +
theme(
plot.title = element_text(
family = "Roboto Black",
size = base * 2)
) +
labs(
title = "Participants felt most comfortable with lower levels of automation",
subtitle = '"I would feel comfortable riding in a vehicle with..."',
y = "Percent of Participants") 
As expected, forum participants expressed the highest levels of comfort with lower levels of automation. We can see a jump in expressed discomfort when moving from level 2 vehicles to level 3 vehicles. This jump is actually in alignment with the views of many AV experts who have concerns about “mixed automation” levels that only occasionally require human intervention.
Why? Well, humans are actually pretty bad at suddenly snapping to attention. Experts worry that people would not be able to effectively take control of a vehicle if suddenly prompted to do so. Also, people are apt to push technologies beyond the limits to which they’re actually designed. For example, misuse of Tesla’s “Autopilot feature” (a name for which the company has been criticized) was involved in a recent fatal crash.
The worksheets for Session 2 asked participants to explain their rationales for their level of comfort ratings. Based on preliminary qualitative coding (i.e. looking for common themes), some of the following themes emerged:
| Theme | Sample Quote |
|---|---|
| Human-vehicle interactions | "I feel better with autonomous vehicles as opposed to these halfway things [that] are in level 3. Level 2 is ok because the driver still knows he has to do most of it. |
| Trust of the technology | “I’m convinced on the accuracy of ADAS [Advanced Driver-Assistance Systems] but not on fully automated vehicles. I would like some control.” |
| Relative safety | “The machine is less prone to error than humans.” |
| Desire for evidence | “I would feel comfortable in any level of driverless vehicle as long as the technology is ready, tested, and proven. Many years down the road for level 4&5.” |
| Retaining control | “I always want the option of taking over the vehicle if necessary.” |
Comfort with Automation in Different Cities
Given that the transportation landscapes are different in different cities, we can look to see if participants from the four forum locations expressed different attitudes towards vehicle automation. In cities where people are used to taking public transportation or using ridehailing services frequently, for instance, individuals may feel more comfortable giving up control of a vehicle.
level2 <- read_csv(here::here('data_processed', 'level2Comfort.csv')) %>%
drop_na() %>%
mutate(
Boston = factor(Boston,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Buffalo = factor(Buffalo,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Phoenix = factor(Phoenix,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Washington = factor(Washington,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
)
level2l <- likert(as.data.frame(level2))
level2Plot <- plot(level2l,
type = "bar",
colors = plotColors)
level2Plot$layers[[2]]$geom_params$width = 0.5
level2Plot$layers[[3]]$geom_params$width = 0.5
base = 10
level3 <- read_csv(here::here('data_processed', 'level3Comfort.csv')) %>%
drop_na() %>%
mutate(
Boston = factor(Boston,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Buffalo = factor(Buffalo,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Phoenix = factor(Phoenix,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Washington = factor(Washington,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
)
level3l <- likert(as.data.frame(level3))
level3Plot <- plot(level3l,
type = "bar",
colors = plotColors)
level3Plot$layers[[2]]$geom_params$width = 0.5
level3Plot$layers[[3]]$geom_params$width = 0.5
level4 <- read_csv(here::here('data_processed', 'level4Comfort.csv')) %>%
drop_na() %>%
mutate(
Boston = factor(Boston,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Buffalo = factor(Buffalo,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Phoenix = factor(Phoenix,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Washington = factor(Washington,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
)
level4l <- likert(as.data.frame(level4))
level4Plot <- plot(level4l,
type = "bar",
colors = plotColors)
level4Plot$layers[[2]]$geom_params$width = 0.5
level4Plot$layers[[3]]$geom_params$width = 0.5
level5 <- read_csv(here::here('data_processed', 'level5Comfort.csv')) %>%
drop_na() %>%
mutate(
Boston = factor(Boston,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Buffalo = factor(Buffalo,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Phoenix = factor(Phoenix,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Washington = factor(Washington,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
)
# Adjusting labels
levels(level5$Boston) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
levels(level5$Buffalo) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
levels(level5$Phoenix) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
levels(level5$Washington) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
level5l <- likert(as.data.frame(level5))
level5Plot <- plot(level5l,
type = "bar",
colors = plotColors)
level5Plot$layers[[2]]$geom_params$width = 0.5
level5Plot$layers[[3]]$geom_params$width = 0.5
# Creating the plots
l2p <- level2Plot +
theme_minimal_vgrid(
font_family = 'Helvetica',
font_size = base * 1.05) +
theme(
plot.title = element_text(
family = "Roboto Black",
size = base * 1.5),
legend.position = "none"
) +
labs(
title = "Participants in Boston felt most comfortable with\nhigh levels of vehicle automation",
x = "Level 2",
y = NULL)
l3p <- level3Plot +
theme_minimal_vgrid(
font_family = 'Helvetica',
font_size = base * 1.05) +
theme(
plot.title = element_text(
family = "Roboto Black",
size = base * 2),
legend.position = "none"
) +
labs(
x = "Level 3",
y = NULL)
l4p <- level4Plot +
theme_minimal_vgrid(
font_family = 'Helvetica',
font_size = base * 1.05) +
theme(
plot.title = element_text(
family = "Roboto Black",
size = base * 2),
legend.position = "none"
) +
labs(
x = "Level 4",
y = NULL)
l5p <- level5Plot +
theme_minimal_vgrid(
font_family = 'Helvetica',
font_size = base * 1.05) +
theme(
plot.title = element_text(
family = "Roboto Black",
size = base * 2),
legend.position = 'bottom'
) +
labs(
x = "Level 5",
y = "Percent of Participants")
plot_grid(
l2p,
l3p,
l4p,
l5p,
ncol = 1
)
Participants in Boston expressed the highest levels of comfort with riding in vehicles with higher levels of automation, followed by participants in Phoenix. Based on these results, we can hypothesize about why this might be the case. Boston, especially the Cambridge area where the forum was hosted, is a fairly tech-savvy region. Perhaps living in close proximity to universities like MIT has made Boston residents more comfortable with emerging technologies.
Similarly, the AV company Waymo has been conducting testing in Tempe, AZ (adjacent to Phoenix) for a few years. The Phoenix forum residents may have grown accustomed to seeing Waymo test vehicles, and may feel more comfortable with riding in an AV as a result. More detailed qualitative coding of participant rationales would be necessary to better understand the ways in which residents from different cities think about automated vehicles.
Conclusion
The four forums produced a wealth of data to be explored. From the few questions explored, we can begin to see some general patterns regarding what levels of automation people feel most comfortable with and the ways individuals expect AVs to impact their lives. We are also able to notice some potential differences in these attitudes. While we can speculate about why this might be the case, deeper analysis of the qualitative written rationales and further studies about public AV preferences fielded across the U.S. are necessary.
As researchers continue their work, AV developers continue to move forward on the road to vehicle automation. Perhaps AVs will be hitting your streets in the near future. If so, buckle up.
Acknowledgements
I would like to thank Mahmud Farooque, Associate Director of the ASU Consortium for Science, Policy & Outcomes (CSPO), for allowing me to use the data from the four forums CSPO hosted. For more information about the “Our Driverless Futures” project and CSPO’s other public deliberation work, please contact Mahmud Farooque.
Appendix
Question Rating Scales
Note: The forums used the term “driverless mobility” to refer to automated vehicle systems.
| Session | Question | Scale |
|---|---|---|
| Session 1 | What effect do you think driverless mobility would have on your transportation experiences? | -1 = Driverless mobility would make worse, 0 = Driverless mobility would have no effect, 1 = Driverless mobility would improve |
| Session 2 | Rating level of agreement | 1 = strongly disagree, 2 = Disagree, 3 = Unsure/I don’t know, 4 = Agree, 5 = Strongly Agree |
Data Dictionaries
The following data dictionaries only describes variables used in this report.
For combinedData.csv:
| Variable | Description |
|---|---|
| city | One of the four U.S. forum sites |
| ID | Unique participant ID |
| accessibilityCar | Response for: Accessibility (physical access) of transportation by car |
| accessibilityTransit | Response for: Accessibility (physical access) of public transportation |
| travelTimeCar | Response for: Travel time by car |
| travelTimeTransit | Response for: Travel time by public transportation |
| cityCongestion | Response for: Traffic congestion in cities |
| hwCongestion | Response for: Traffic congestion on highways |
| transportOptions | Response for: Availability of transportation options |
| mobilityAreas | Response for: Areas used for other mobility methods (e.g., biking, walking) |
| harmTrafficAccidents | Response for: Harm occurring from traffic accidents |
| transitCost | Response for: Total cost of public transportation |
| ownershipCost | Response for: Total cost of individual car ownership |
| locationAccess | Response for: Getting to and from places that are not easy to access |
| timeAvailability | Response for: Availability of my time to do things while traveling (work, sleep, read, etc) |
For level2Comfort.csv, level3Comfort.csv, level4Comfort.csv, and level5Comfort.csv:
| Variable | Description |
|---|---|
| Boston | Boston participant responses |
| Buffalo | Buffalo participant responses |
| Phoenix | Phoenix participant responses |
| Washington | Washington participant responses |
All Project Code
Reading in the Raw Data
bosPath <- here('data_raw', 'Our Driverless Futures Individual_BOSRaw.csv')
dfBOS <- read_csv(bosPath, skip = 2) %>%
filter(!is.na(Presence0)) %>%
clean_names()
#head(dfBOS)
#tail(dfBOS)
#dim(dfBOS) #Boston had 71 participants, this is correct
bufPath <- here('data_raw', 'Our Driverless Futures Individual_BUFRaw.csv')
dfBUF <- read_csv(bufPath, skip = 2) %>%
filter(!is.na(Presence0)) %>%
clean_names()
#head(dfBUF)
#tail(dfBUF)
#dim(dfBUF) #Buffalo had 96 participants, this is correct
dcPath <- here('data_raw', 'Our Driverless Futures Individual_DCRaw.csv')
dfDC <- read_csv(dcPath, skip = 2) %>%
filter(!is.na(Presence0)) %>%
clean_names()
#head(dfDC)
#tail(dfDC)
#dim(dfDC) #DC had 62 participants, this is correct
phxPath <- here('data_raw', 'Our Driverless Futures Individual_PHXRaw.csv')
dfPHX <- read_csv(phxPath, skip = 2) %>%
filter(!is.na(Presence0)) %>%
clean_names()
#head(dfPHX)
#tail(dfPHX)
#dim(dfPHX) #PHX had 88 participants, this is correctFormatting the Data
### Buffalo Formatting
dataBUF <- dfBUF %>%
select(
city = x1_2_location,
ID = cle,
#Session 0
initialAttitude = how_do_you_feel_about_the_idea_of_driverless_mobility_1_very_pessimistic_2_pessimistic_3_neutral_4_optimistic_5_very_optimistic,
#Session 1
accessibilityCar = accessibility_physical_access_of_transportation_by_car,
accessibilityTransit = accessibility_physical_access_of_public_transportation,
travelTimeCar = travel_time_by_car,
travelTimeTransit = travel_time_by_public_transportation,
cityCongestion = traffic_congestion_in_cities,
hwCongestion = traffic_congestion_on_highways,
transportOptions = availability_of_transportation_options,
mobilityAreas = areas_used_for_other_mobility_methods_e_g_biking_walking,
harmTrafficAccidents = harm_occurring_from_traffic_accidents,
transitCost = total_cost_of_public_transportation,
ownershipCost = total_cost_of_individual_car_ownership,
locationAccess = getting_to_and_from_places_that_are_not_easy_to_access,
timeAvailability = availability_of_my_time_to_do_things_while_traveling_work_sleep_read_etc,
otherCategory = other_please_list,
#Session 2
level2Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_2_automation,
level3Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_3_automation,
level4Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_4_automation,
level5Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_5_automation,
testingTestBeds = i_would_support_testing_of_driverless_vehicles_in_test_beds,
testingTestTrack = i_would_support_testing_of_driverless_vehicles_in_a_local_test_track,
testingLowPop = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_less_populated_areas,
testingCities = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_cities,
testingHW = i_would_support_testing_of_driverless_vehicles_on_highways,
cityDataManagement = it_is_acceptable_for_city_town_transportation_manaers_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataManagement = it_is_acceptable_for_private_companies_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataSelling = it_is_acceptable_for_data_about_passengers_generated_by_automated_mobility_systems_to_be_sold_to_companies,
citizenDataControl = individual_citizens_should_have_control_over_selling_their_data_or_forbidding_its_use_other_than_to_operate_the_system,
anonymizedDataUse = it_is_acceptable_for_the_anonymized_data_generated_by_the_operation_of_vehicles_to_be_freely_available_for_any_use,
#Session 3
currentSystemModel = improving_the_current_system,
individualOwnershipModel = individual_ownership,
rideShareModel = ride_share_model,
publicTransportationModel = public_transportation_model,
#Session 4
#Privacy Protection
nationalgov_PrivacyEffectiveness = national_government,
localgov_PrivacyEffectiveness = local_government,
nonprofit_PrivacyEffectiveness = non_profit_organizations,
transportationIndustry_PrivacyEffectiveness = transportation_industry,
insuranceCompanies_PrivacyEffectiveness = insurance_companies,
#Infrastructure
nationalgov_InfrastructureEffectiveness = national_government_1,
localgov_InfrastructureEffectiveness = local_government_1,
nonprofit_InfrastructureEffectiveness = non_profit_organizations_1,
transportationIndustry_InfrastructureEffectiveness = transportation_industry_1,
insuranceCompanies_InfrastructureEffectiveness = insurance_companies_1,
#Justice/Equity
nationalgov_EquityEffectiveness = national_government_2,
localgov_EquityEffectiveness = local_government_2,
nonprofit_EquityEffectiveness = non_profit_organizations_2,
transportationIndustry_EquityEffectiveness = transportation_industry_2,
insuranceCompanies_EquityEffectiveness = insurance_companies_2,
#Cybersecurity
nationalgov_CyberEffectiveness = national_government_3,
localgov_CyberEffectiveness = local_government_3,
nonprofit_CyberEffectiveness = non_profit_organizations_3,
transportationIndustry_CyberEffectiveness = transportation_industry_3,
insuranceCompanies_CyberEffectiveness = insurance_companies_3,
#Safety
nationalgov_SafetyEffectiveness = national_government_4,
localgov_SafetyEffectiveness = local_government_4,
nonprofit_SafetyEffectiveness = non_profit_organizations_4,
transportationIndustry_SafetyEffectiveness = transportation_industry_4,
insuranceCompanies_SafetyEffectiveness = insurance_companies_4,
#Environment
nationalgov_EnviroEffectiveness = national_government_5,
localgov_EnviroEffectiveness = local_government_5,
nonprofit_EnviroEffectiveness = non_profit_organizations_5,
transportationIndustry_EnviroEffectiveness = transportation_industry_5,
insuranceCompanies_EnviroEffectiveness = insurance_companies_5,
#Privacy Protection
nationalgov_PrivacyTrust = national_government_6,
localgov_PrivacyTrust = local_government_6,
nonprofit_PrivacyTrust = non_profit_organizations_6,
transportationIndustry_PrivacyTrust = transportation_industry_6,
insuranceCompanies_PrivacyTrust = insurance_companies_6,
#Infrastructure
nationalgov_InfrastructureTrust = national_government_7,
localgov_InfrastructureTrust = local_government_7,
nonprofit_InfrastructureTrust = non_profit_organizations_7,
transportationIndustry_InfrastructureTrust = transportation_industry_7,
insuranceCompanies_InfrastructureTrust = insurance_companies_7,
#Justice/Equity
nationalgov_EquityTrust = national_government_8,
localgov_EquityTrust = local_government_8,
nonprofit_EquityTrust = non_profit_organizations_8,
transportationIndustry_EquityTrust = transportation_industry_8,
insuranceCompanies_EquityTrust = insurance_companies_8,
#Cybersecurity
nationalgov_CyberTrust = national_government_9,
localgov_CyberTrust = local_government_9,
nonprofit_CyberTrust = non_profit_organizations_9,
transportationIndustry_CyberTrust = transportation_industry_9,
insuranceCompanies_CyberTrust = insurance_companies_9,
#Safety
nationalgov_SafetyTrust = national_government_10,
localgov_SafetyTrust = local_government_10,
nonprofit_SafetyTrust = non_profit_organizations_10,
transportationIndustry_SafetyTrust = transportation_industry_10,
insuranceCompanies_SafetyTrust = insurance_companies_10,
#Environment
nationalgov_EnviroTrust = national_government_11,
localgov_EnviroTrust = local_government_11,
nonprofit_EnviroTrust = non_profit_organizations_11,
transportationIndustry_EnviroTrust = transportation_industry_11,
insuranceCompanies_EnviroTrust = insurance_companies_11,
rank1 = x1,
rank2 = x2,
rank3 = x3,
rank4 = x4,
rank5 = x5,
rank6 = x6,
gender,
age,
ethnicity = what_is_your_ethnicity,
political_orientation,
residence,
area,
occupation,
education,
income = what_is_your_household_income_per_year
)
### Boston formatting
dataBOS <- dfBOS %>%
select(
city = x1_2_location,
ID = cle,
#Session 0
initialAttitude = how_do_you_feel_about_the_idea_of_driverless_mobility_1_very_pessimistic_2_pessimistic_3_neutral_4_optimistic_5_very_optimistic,
#Session 1
accessibilityCar = accessibility_physical_access_of_transportation_by_car,
accessibilityTransit = accessibility_physical_access_of_public_transportation,
travelTimeCar = travel_time_by_car,
travelTimeTransit = travel_time_by_public_transportation,
cityCongestion = traffic_congestion_in_cities,
hwCongestion = traffic_congestion_on_highways,
transportOptions = availability_of_transportation_options,
mobilityAreas = areas_used_for_other_mobility_methods_e_g_biking_walking,
harmTrafficAccidents = harm_occurring_from_traffic_accidents,
transitCost = total_cost_of_public_transportation,
ownershipCost = total_cost_of_individual_car_ownership,
locationAccess = getting_to_and_from_places_that_are_not_easy_to_access,
timeAvailability = availability_of_my_time_to_do_things_while_traveling_work_sleep_read_etc,
otherCategory = other_please_list,
#Session 2
level2Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_2_automation,
level3Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_3_automation,
level4Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_4_automation,
level5Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_5_automation,
testingTestBeds = i_would_support_testing_of_driverless_vehicles_in_test_beds,
testingTestTrack = i_would_support_testing_of_driverless_vehicles_in_a_local_test_track,
testingLowPop = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_less_populated_areas,
testingCities = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_cities,
testingHW = i_would_support_testing_of_driverless_vehicles_on_highways,
cityDataManagement = it_is_acceptable_for_city_town_transportation_manaers_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataManagement = it_is_acceptable_for_private_companies_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataSelling = it_is_acceptable_for_data_about_passengers_generated_by_automated_mobility_systems_to_be_sold_to_companies,
citizenDataControl = individual_citizens_should_have_control_over_selling_their_data_or_forbidding_its_use_other_than_to_operate_the_system,
anonymizedDataUse = it_is_acceptable_for_the_anonymized_data_generated_by_the_operation_of_vehicles_to_be_freely_available_for_any_use,
#Session 3
currentSystemModel = improving_the_current_system,
individualOwnershipModel = individual_ownership,
rideShareModel = ride_share_model,
publicTransportationModel = public_transportation_model,
#Session 4
#Privacy Protection
nationalgov_PrivacyEffectiveness = national_government,
localgov_PrivacyEffectiveness = local_government,
nonprofit_PrivacyEffectiveness = non_profit_organizations,
transportationIndustry_PrivacyEffectiveness = transportation_industry,
insuranceCompanies_PrivacyEffectiveness = insurance_companies,
#Infrastructure
nationalgov_InfrastructureEffectiveness = national_government_1,
localgov_InfrastructureEffectiveness = local_government_1,
nonprofit_InfrastructureEffectiveness = non_profit_organizations_1,
transportationIndustry_InfrastructureEffectiveness = transportation_industry_1,
insuranceCompanies_InfrastructureEffectiveness = insurance_companies_1,
#Justice/Equity
nationalgov_EquityEffectiveness = national_government_2,
localgov_EquityEffectiveness = local_government_2,
nonprofit_EquityEffectiveness = non_profit_organizations_2,
transportationIndustry_EquityEffectiveness = transportation_industry_2,
insuranceCompanies_EquityEffectiveness = insurance_companies_2,
#Cybersecurity
nationalgov_CyberEffectiveness = national_government_3,
localgov_CyberEffectiveness = local_government_3,
nonprofit_CyberEffectiveness = non_profit_organizations_3,
transportationIndustry_CyberEffectiveness = transportation_industry_3,
insuranceCompanies_CyberEffectiveness = insurance_companies_3,
#Safety
nationalgov_SafetyEffectiveness = national_government_4,
localgov_SafetyEffectiveness = local_government_4,
nonprofit_SafetyEffectiveness = non_profit_organizations_4,
transportationIndustry_SafetyEffectiveness = transportation_industry_4,
insuranceCompanies_SafetyEffectiveness = insurance_companies_4,
#Environment
nationalgov_EnviroEffectiveness = national_government_5,
localgov_EnviroEffectiveness = local_government_5,
nonprofit_EnviroEffectiveness = non_profit_organizations_5,
transportationIndustry_EnviroEffectiveness = transportation_industry_5,
insuranceCompanies_EnviroEffectiveness = insurance_companies_5,
#Privacy Protection
nationalgov_PrivacyTrust = national_government_6,
localgov_PrivacyTrust = local_government_6,
nonprofit_PrivacyTrust = non_profit_organizations_6,
transportationIndustry_PrivacyTrust = transportation_industry_6,
insuranceCompanies_PrivacyTrust = insurance_companies_6,
#Infrastructure
nationalgov_InfrastructureTrust = national_government_7,
localgov_InfrastructureTrust = local_government_7,
nonprofit_InfrastructureTrust = non_profit_organizations_7,
transportationIndustry_InfrastructureTrust = transportation_industry_7,
insuranceCompanies_InfrastructureTrust = insurance_companies_7,
#Justice/Equity
nationalgov_EquityTrust = national_government_8,
localgov_EquityTrust = local_government_8,
nonprofit_EquityTrust = non_profit_organizations_8,
transportationIndustry_EquityTrust = transportation_industry_8,
insuranceCompanies_EquityTrust = insurance_companies_8,
#Cybersecurity
nationalgov_CyberTrust = national_government_9,
localgov_CyberTrust = local_government_9,
nonprofit_CyberTrust = non_profit_organizations_9,
transportationIndustry_CyberTrust = transportation_industry_9,
insuranceCompanies_CyberTrust = insurance_companies_9,
#Safety
nationalgov_SafetyTrust = national_government_10,
localgov_SafetyTrust = local_government_10,
nonprofit_SafetyTrust = non_profit_organizations_10,
transportationIndustry_SafetyTrust = transportation_industry_10,
insuranceCompanies_SafetyTrust = insurance_companies_10,
#Environment
nationalgov_EnviroTrust = national_government_11,
localgov_EnviroTrust = local_government_11,
nonprofit_EnviroTrust = non_profit_organizations_11,
transportationIndustry_EnviroTrust = transportation_industry_11,
insuranceCompanies_EnviroTrust = insurance_companies_11,
rank1 = x1,
rank2 = x2,
rank3 = x3,
rank4 = x4,
rank5 = x5,
rank6 = x6,
gender,
age,
ethnicity,
residence,
area,
occupation,
education,
)dataDC <- dfDC %>%
select(
city = x1_2_location,
ID = cle,
#Session 0
initialAttitude = how_do_you_feel_about_the_idea_of_driverless_mobility_1_very_pessimistic_2_pessimistic_3_neutral_4_optimistic_5_very_optimistic,
#Session 1
accessibilityCar = accessibility_physical_access_of_transportation_by_car,
accessibilityTransit = accessibility_physical_access_of_public_transportation,
travelTimeCar = travel_time_by_car,
travelTimeTransit = travel_time_by_public_transportation,
cityCongestion = traffic_congestion_in_cities,
hwCongestion = traffic_congestion_on_highways,
transportOptions = availability_of_transportation_options,
mobilityAreas = areas_used_for_other_mobility_methods_e_g_biking_walking,
harmTrafficAccidents = harm_occurring_from_traffic_accidents,
transitCost = total_cost_of_public_transportation,
ownershipCost = total_cost_of_individual_car_ownership,
locationAccess = getting_to_and_from_places_that_are_not_easy_to_access,
timeAvailability = availability_of_my_time_to_do_things_while_traveling_work_sleep_read_etc,
otherCategory = other_please_list,
#Session 2
level2Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_2_automation,
level3Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_3_automation,
level4Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_4_automation,
level5Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_5_automation,
testingTestBeds = i_would_support_testing_of_driverless_vehicles_in_test_beds,
testingTestTrack = i_would_support_testing_of_driverless_vehicles_in_a_local_test_track,
testingLowPop = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_less_populated_areas,
testingCities = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_cities,
testingHW = i_would_support_testing_of_driverless_vehicles_on_highways,
cityDataManagement = it_is_acceptable_for_city_town_transportation_manaers_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataManagement = it_is_acceptable_for_private_companies_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataSelling = it_is_acceptable_for_data_about_passengers_generated_by_automated_mobility_systems_to_be_sold_to_companies,
citizenDataControl = individual_citizens_should_have_control_over_selling_their_data_or_forbidding_its_use_other_than_to_operate_the_system,
anonymizedDataUse = it_is_acceptable_for_the_anonymized_data_generated_by_the_operation_of_vehicles_to_be_freely_available_for_any_use,
#Session 3
currentSystemModel = improving_the_current_system,
individualOwnershipModel = individual_ownership,
rideShareModel = ride_share_model,
publicTransportationModel = public_transportation_model,
#Session 4
#Privacy Protection
nationalgov_PrivacyEffectiveness = national_government,
localgov_PrivacyEffectiveness = local_government,
nonprofit_PrivacyEffectiveness = non_profit_organizations,
transportationIndustry_PrivacyEffectiveness = transportation_industry,
insuranceCompanies_PrivacyEffectiveness = insurance_companies,
#Infrastructure
nationalgov_InfrastructureEffectiveness = national_government_1,
localgov_InfrastructureEffectiveness = local_government_1,
nonprofit_InfrastructureEffectiveness = non_profit_organizations_1,
transportationIndustry_InfrastructureEffectiveness = transportation_industry_1,
insuranceCompanies_InfrastructureEffectiveness = insurance_companies_1,
#Justice/Equity
nationalgov_EquityEffectiveness = national_government_2,
localgov_EquityEffectiveness = local_government_2,
nonprofit_EquityEffectiveness = non_profit_organizations_2,
transportationIndustry_EquityEffectiveness = transportation_industry_2,
insuranceCompanies_EquityEffectiveness = insurance_companies_2,
#Cybersecurity
nationalgov_CyberEffectiveness = national_government_3,
localgov_CyberEffectiveness = local_government_3,
nonprofit_CyberEffectiveness = non_profit_organizations_3,
transportationIndustry_CyberEffectiveness = transportation_industry_3,
insuranceCompanies_CyberEffectiveness = insurance_companies_3,
#Safety
nationalgov_SafetyEffectiveness = national_government_4,
localgov_SafetyEffectiveness = local_government_4,
nonprofit_SafetyEffectiveness = non_profit_organizations_4,
transportationIndustry_SafetyEffectiveness = transportation_industry_4,
insuranceCompanies_SafetyEffectiveness = insurance_companies_4,
#Environment
nationalgov_EnviroEffectiveness = national_government_5,
localgov_EnviroEffectiveness = local_government_5,
nonprofit_EnviroEffectiveness = non_profit_organizations_5,
transportationIndustry_EnviroEffectiveness = transportation_industry_5,
insuranceCompanies_EnviroEffectiveness = insurance_companies_5,
#Privacy Protection
nationalgov_PrivacyTrust = national_government_6,
localgov_PrivacyTrust = local_government_6,
nonprofit_PrivacyTrust = non_profit_organizations_6,
transportationIndustry_PrivacyTrust = transportation_industry_6,
insuranceCompanies_PrivacyTrust = insurance_companies_6,
#Infrastructure
nationalgov_InfrastructureTrust = national_government_7,
localgov_InfrastructureTrust = local_government_7,
nonprofit_InfrastructureTrust = non_profit_organizations_7,
transportationIndustry_InfrastructureTrust = transportation_industry_7,
insuranceCompanies_InfrastructureTrust = insurance_companies_7,
#Justice/Equity
nationalgov_EquityTrust = national_government_8,
localgov_EquityTrust = local_government_8,
nonprofit_EquityTrust = non_profit_organizations_8,
transportationIndustry_EquityTrust = transportation_industry_8,
insuranceCompanies_EquityTrust = insurance_companies_8,
#Cybersecurity
nationalgov_CyberTrust = national_government_9,
localgov_CyberTrust = local_government_9,
nonprofit_CyberTrust = non_profit_organizations_9,
transportationIndustry_CyberTrust = transportation_industry_9,
insuranceCompanies_CyberTrust = insurance_companies_9,
#Safety
nationalgov_SafetyTrust = national_government_10,
localgov_SafetyTrust = local_government_10,
nonprofit_SafetyTrust = non_profit_organizations_10,
transportationIndustry_SafetyTrust = transportation_industry_10,
insuranceCompanies_SafetyTrust = insurance_companies_10,
#Environment
nationalgov_EnviroTrust = national_government_11,
localgov_EnviroTrust = local_government_11,
nonprofit_EnviroTrust = non_profit_organizations_11,
transportationIndustry_EnviroTrust = transportation_industry_11,
insuranceCompanies_EnviroTrust = insurance_companies_11,
rank1 = x1,
rank2 = x2,
rank3 = x3,
rank4 = x4,
rank5 = x5,
rank6 = x6,
gender,
age,
ethnicity,
political_orientation,
residence,
area,
occupation,
education,
household_income
)
### Phoenix formatting
dataPHX <- dfPHX %>%
select(
city = x1_2_location,
ID = cle,
#Session 0
initialAttitude = how_do_you_feel_about_the_idea_of_driverless_mobility_1_very_pessimistic_2_pessimistic_3_neutral_4_optimistic_5_very_optimistic,
#Session 1
accessibilityCar = accessibility_physical_access_of_transportation_by_car,
accessibilityTransit = accessibility_physical_access_of_public_transportation,
travelTimeCar = travel_time_by_car,
travelTimeTransit = travel_time_by_public_transportation,
cityCongestion = traffic_congestion_in_cities,
hwCongestion = traffic_congestion_on_highways,
transportOptions = availability_of_transportation_options,
mobilityAreas = areas_used_for_other_mobility_methods_e_g_biking_walking,
harmTrafficAccidents = harm_occurring_from_traffic_accidents,
transitCost = total_cost_of_public_transportation,
ownershipCost = total_cost_of_individual_car_ownership,
locationAccess = getting_to_and_from_places_that_are_not_easy_to_access,
timeAvailability = availability_of_my_time_to_do_things_while_traveling_work_sleep_read_etc,
otherCategory = other_please_list,
#Session 2
level2Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_2_automation,
level3Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_3_automation,
level4Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_4_automation,
level5Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_5_automation,
testingTestBeds = i_would_support_testing_of_driverless_vehicles_in_test_beds,
testingTestTrack = i_would_support_testing_of_driverless_vehicles_in_a_local_test_track,
testingLowPop = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_less_populated_areas,
testingCities = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_cities,
testingHW = i_would_support_testing_of_driverless_vehicles_on_highways,
cityDataManagement = it_is_acceptable_for_city_town_transportation_manaers_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataManagement = it_is_acceptable_for_private_companies_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataSelling = it_is_acceptable_for_data_about_passengers_generated_by_automated_mobility_systems_to_be_sold_to_companies,
citizenDataControl = individual_citizens_should_have_control_over_selling_their_data_or_forbidding_its_use_other_than_to_operate_the_system,
anonymizedDataUse = it_is_acceptable_for_the_anonymized_data_generated_by_the_operation_of_vehicles_to_be_freely_available_for_any_use,
#Session 3
currentSystemModel = improving_the_current_system,
individualOwnershipModel = individual_ownership,
rideShareModel = ride_share_model,
publicTransportationModel = public_transportation_model,
#Session 4
#Privacy Protection
nationalgov_PrivacyEffectiveness = national_government,
localgov_PrivacyEffectiveness = local_government,
nonprofit_PrivacyEffectiveness = non_profit_organizations,
transportationIndustry_PrivacyEffectiveness = transportation_industry,
insuranceCompanies_PrivacyEffectiveness = insurance_companies,
#Infrastructure
nationalgov_InfrastructureEffectiveness = national_government_1,
localgov_InfrastructureEffectiveness = local_government_1,
nonprofit_InfrastructureEffectiveness = non_profit_organizations_1,
transportationIndustry_InfrastructureEffectiveness = transportation_industry_1,
insuranceCompanies_InfrastructureEffectiveness = insurance_companies_1,
#Justice/Equity
nationalgov_EquityEffectiveness = national_government_2,
localgov_EquityEffectiveness = local_government_2,
nonprofit_EquityEffectiveness = non_profit_organizations_2,
transportationIndustry_EquityEffectiveness = transportation_industry_2,
insuranceCompanies_EquityEffectiveness = insurance_companies_2,
#Cybersecurity
nationalgov_CyberEffectiveness = national_government_3,
localgov_CyberEffectiveness = local_government_3,
nonprofit_CyberEffectiveness = non_profit_organizations_3,
transportationIndustry_CyberEffectiveness = transportation_industry_3,
insuranceCompanies_CyberEffectiveness = insurance_companies_3,
#Safety
nationalgov_SafetyEffectiveness = national_government_4,
localgov_SafetyEffectiveness = local_government_4,
nonprofit_SafetyEffectiveness = non_profit_organizations_4,
transportationIndustry_SafetyEffectiveness = transportation_industry_4,
insuranceCompanies_SafetyEffectiveness = insurance_companies_4,
#Environment
nationalgov_EnviroEffectiveness = national_government_5,
localgov_EnviroEffectiveness = local_government_5,
nonprofit_EnviroEffectiveness = non_profit_organizations_5,
transportationIndustry_EnviroEffectiveness = transportation_industry_5,
insuranceCompanies_EnviroEffectiveness = insurance_companies_5,
#Privacy Protection
nationalgov_PrivacyTrust = national_government_6,
localgov_PrivacyTrust = local_government_6,
nonprofit_PrivacyTrust = non_profit_organizations_6,
transportationIndustry_PrivacyTrust = transportation_industry_6,
insuranceCompanies_PrivacyTrust = insurance_companies_6,
#Infrastructure
nationalgov_InfrastructureTrust = national_government_7,
localgov_InfrastructureTrust = local_government_7,
nonprofit_InfrastructureTrust = non_profit_organizations_7,
transportationIndustry_InfrastructureTrust = transportation_industry_7,
insuranceCompanies_InfrastructureTrust = insurance_companies_7,
#Justice/Equity
nationalgov_EquityTrust = national_government_8,
localgov_EquityTrust = local_government_8,
nonprofit_EquityTrust = non_profit_organizations_8,
transportationIndustry_EquityTrust = transportation_industry_8,
insuranceCompanies_EquityTrust = insurance_companies_8,
#Cybersecurity
nationalgov_CyberTrust = national_government_9,
localgov_CyberTrust = local_government_9,
nonprofit_CyberTrust = non_profit_organizations_9,
transportationIndustry_CyberTrust = transportation_industry_9,
insuranceCompanies_CyberTrust = insurance_companies_9,
#Safety
nationalgov_SafetyTrust = national_government_10,
localgov_SafetyTrust = local_government_10,
nonprofit_SafetyTrust = non_profit_organizations_10,
transportationIndustry_SafetyTrust = transportation_industry_10,
insuranceCompanies_SafetyTrust = insurance_companies_10,
#Environment
nationalgov_EnviroTrust = national_government_11,
localgov_EnviroTrust = local_government_11,
nonprofit_EnviroTrust = non_profit_organizations_11,
transportationIndustry_EnviroTrust = transportation_industry_11,
insuranceCompanies_EnviroTrust = insurance_companies_11,
rank1 = x1,
rank2 = x2,
rank3 = x3,
rank4 = x4,
rank5 = x5,
rank6 = x6,
gender,
age,
ethnicity,
political_orientation,
residence,
area,
occupation,
education,
household_income
)
#### Combining all the data sets
combinedData <- join_all(list(dataBOS, dataBUF, dataPHX, dataDC), by = 'city', type = 'full')
#write_csv(combinedData, path = file.path('data_processed', 'combinedData.csv'))# Load libraries and settings here
library(plyr)
library(readr)
library(tidyverse)
library(janitor)
library(ggplot2)
library(RColorBrewer)
library(devtools)
library(dplyr)
library(likert)
library(here)
library(forcats)
library(cowplot)
knitr::opts_chunk$set(
warning = FALSE,
message = FALSE,
comment = "#>",
fig.path = "figs/", # Folder where rendered plots are saved
fig.width = 7.252, # Default plot width
fig.height = 4, # Default plot height
fig.retina = 3 # For better plot resolution
)
# Put any other "global" settings here, e.g. a ggplot theme:
theme_set(theme_bw(base_size = 20))
dataPath <- here::here('data_processed', 'combinedData.csv')
session1Data <- read_csv(dataPath) %>%
select(
accessibilityCar:timeAvailability
) %>%
filter(
!is.na(accessibilityCar),
!is.na(accessibilityTransit),
!is.na(travelTimeCar),
!is.na(travelTimeTransit),
!is.na(cityCongestion),
!is.na(hwCongestion),
!is.na(transportOptions),
!is.na(mobilityAreas),
!is.na(harmTrafficAccidents),
!is.na(transitCost),
!is.na(ownershipCost),
!is.na(locationAccess),
!is.na(timeAvailability)
)
## Reorder
session1Data$accessibilityCar = factor(session1Data$accessibilityCar,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$accessibilityTransit = factor(session1Data$accessibilityTransit,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$travelTimeCar = factor(session1Data$travelTimeCar,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$travelTimeTransit = factor(session1Data$travelTimeTransit,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$cityCongestion = factor(session1Data$cityCongestion,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$hwCongestion = factor(session1Data$hwCongestion,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$transportOptions = factor(session1Data$transportOptions,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$mobilityAreas = factor(session1Data$mobilityAreas,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$harmTrafficAccidents = factor(session1Data$harmTrafficAccidents,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$transitCost = factor(session1Data$transitCost,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$ownershipCost = factor(session1Data$ownershipCost,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$locationAccess = factor(session1Data$locationAccess,
level = c("-1", "0", "1"),
order = TRUE)
session1Data$timeAvailability = factor(session1Data$timeAvailability,
level = c("-1", "0", "1"),
order = TRUE)
# Adjusting labels and names
levels(session1Data$accessibilityCar) <- c("Make worse", "No effect", "Improve")
levels(session1Data$accessibilityTransit) <- c("Make worse", "No effect", "Improve")
levels(session1Data$travelTimeCar) <- c("Make worse", "No effect", "Improve")
levels(session1Data$travelTimeTransit) <- c("Make worse", "No effect", "Improve")
levels(session1Data$cityCongestion) <- c("Make worse", "No effect", "Improve")
levels(session1Data$hwCongestion) <- c("Make worse", "No effect", "Improve")
levels(session1Data$transportOptions) <- c("Make worse", "No effect", "Improve")
levels(session1Data$mobilityAreas) <- c("Make worse", "No effect", "Improve")
levels(session1Data$harmTrafficAccidents) <- c("Make worse", "No effect", "Improve")
levels(session1Data$transitCost) <- c("Make worse", "No effect", "Improve")
levels(session1Data$ownershipCost) <- c("Make worse", "No effect", "Improve")
levels(session1Data$locationAccess) <- c("Make worse", "No effect", "Improve")
levels(session1Data$timeAvailability) <- c("Make worse", "No effect", "Improve")
names(session1Data) = c("Accessibility (physical access)\nof transportation by car",
"Accessibility (physical access) of public transportation",
"Travel time by car",
"Travel time by public transportation",
"Traffic congestion in cities",
"Traffic congestion on highways",
"Availability of transportation options",
"Areas used for other mobility methods\n(e.g., biking, walking)",
"Harm occurring from traffic accidents",
"Total cost of public transportation",
"Total cost of individual car ownership",
"Getting to and from places that\nare not easy to access",
"Availability of my time to do things\nwhile traveling (work, sleep, read, etc)")
plot1Colors = c('#e78f8e', '#b6b5b0','#b9da8c')
theme_update(legend.text = element_text(size = rel(0.7)))
session1resultAll <- likert(as.data.frame(session1Data))
session1resultGraph <- plot(session1resultAll,
type ="bar",
colors = plot1Colors,
wrap = 35)
session1resultGraph$layers[[2]]$geom_params$width = 0.4
session1resultGraph$layers[[3]]$geom_params$width = 0.4
base = 7
session1resultGraph +
theme_minimal_vgrid(
font_family = 'Helvetica',
font_size = base * 1.5) +
theme(
plot.title = element_text(
family = "Roboto Black",
size = base * 2.2)
) +
labs(
title = "Costs pose greatest concern",
subtitle = "Expected effects of AVs on participants' current transportation experiences",
y = "Percent of Participants"
)
## Loading in data and filtering for Session 2 results
dataPath <- here::here('data_processed', 'combinedData.csv')
session2 <- read_csv(dataPath) %>%
select(
level2Comfort,
level3Comfort,
level4Comfort,
level5Comfort
) %>%
filter(
!is.na(level2Comfort),
!is.na(level3Comfort),
!is.na(level4Comfort),
!is.na(level5Comfort)
)
## Creating a second data frame to allow for grouping by city
session2Cities <- read_csv(dataPath) %>%
select(
city,
level2Comfort,
level3Comfort,
level4Comfort,
level5Comfort
) %>%
filter(
!is.na(level2Comfort),
!is.na(level3Comfort),
!is.na(level4Comfort),
!is.na(level5Comfort)
)
### Change variables to factors and specifying levels
session2$level2Comfort = factor(session2$level2Comfort,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
session2$level3Comfort = factor(session2$level3Comfort,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
session2$level4Comfort = factor(session2$level4Comfort,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
session2$level5Comfort = factor(session2$level5Comfort,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
session2Cities$city = factor(session2Cities$city,
level = c("Boston", "Buffalo", "Phoenix", "Washington"))
### Changing to full question names and adjusting factor labels for clarity
levels(session2$level2Comfort) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
levels(session2$level3Comfort) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
levels(session2$level4Comfort) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
levels(session2$level5Comfort) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
names(session2) = c("Level 2 automation",
"Level 3 automation",
"Level 4 automation",
"Level 5 automation")
# Creating Graph
plotColors = c('sienna','#e2a8a6' , 'grey60','lightsteelblue', 'steelblue')
result <- likert(as.data.frame(session2))
session2Bar <- plot(result,
type = "bar",
colors = plotColors,
ordered = FALSE,
wrap = 40)
session2Bar$layers[[2]]$geom_params$width = 0.4
session2Bar$layers[[3]]$geom_params$width = 0.4
base = 7
session2Bar +
theme_minimal_vgrid(
font_family = 'Helvetica',
font_size = base * 1.5) +
theme(
plot.title = element_text(
family = "Roboto Black",
size = base * 2)
) +
labs(
title = "Participants felt most comfortable with lower levels of automation",
subtitle = '"I would feel comfortable riding in a vehicle with..."',
y = "Percent of Participants")
level2 <- read_csv(here::here('data_processed', 'level2Comfort.csv')) %>%
drop_na() %>%
mutate(
Boston = factor(Boston,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Buffalo = factor(Buffalo,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Phoenix = factor(Phoenix,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Washington = factor(Washington,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
)
level2l <- likert(as.data.frame(level2))
level2Plot <- plot(level2l,
type = "bar",
colors = plotColors)
level2Plot$layers[[2]]$geom_params$width = 0.5
level2Plot$layers[[3]]$geom_params$width = 0.5
base = 10
level3 <- read_csv(here::here('data_processed', 'level3Comfort.csv')) %>%
drop_na() %>%
mutate(
Boston = factor(Boston,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Buffalo = factor(Buffalo,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Phoenix = factor(Phoenix,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Washington = factor(Washington,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
)
level3l <- likert(as.data.frame(level3))
level3Plot <- plot(level3l,
type = "bar",
colors = plotColors)
level3Plot$layers[[2]]$geom_params$width = 0.5
level3Plot$layers[[3]]$geom_params$width = 0.5
level4 <- read_csv(here::here('data_processed', 'level4Comfort.csv')) %>%
drop_na() %>%
mutate(
Boston = factor(Boston,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Buffalo = factor(Buffalo,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Phoenix = factor(Phoenix,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Washington = factor(Washington,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
)
level4l <- likert(as.data.frame(level4))
level4Plot <- plot(level4l,
type = "bar",
colors = plotColors)
level4Plot$layers[[2]]$geom_params$width = 0.5
level4Plot$layers[[3]]$geom_params$width = 0.5
level5 <- read_csv(here::here('data_processed', 'level5Comfort.csv')) %>%
drop_na() %>%
mutate(
Boston = factor(Boston,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Buffalo = factor(Buffalo,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Phoenix = factor(Phoenix,
level = c("1", "2", "3", "4", "5"),
order = TRUE),
Washington = factor(Washington,
level = c("1", "2", "3", "4", "5"),
order = TRUE)
)
# Adjusting labels
levels(level5$Boston) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
levels(level5$Buffalo) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
levels(level5$Phoenix) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
levels(level5$Washington) <- c("Strongly disagree", "Disagree", "Unsure/I don't know",
"Agree", "Strongly agree")
level5l <- likert(as.data.frame(level5))
level5Plot <- plot(level5l,
type = "bar",
colors = plotColors)
level5Plot$layers[[2]]$geom_params$width = 0.5
level5Plot$layers[[3]]$geom_params$width = 0.5
# Creating the plots
l2p <- level2Plot +
theme_minimal_vgrid(
font_family = 'Helvetica',
font_size = base * 1.05) +
theme(
plot.title = element_text(
family = "Roboto Black",
size = base * 1.5),
legend.position = "none"
) +
labs(
title = "Participants in Boston felt most comfortable with\nhigh levels of vehicle automation",
x = "Level 2",
y = NULL)
l3p <- level3Plot +
theme_minimal_vgrid(
font_family = 'Helvetica',
font_size = base * 1.05) +
theme(
plot.title = element_text(
family = "Roboto Black",
size = base * 2),
legend.position = "none"
) +
labs(
x = "Level 3",
y = NULL)
l4p <- level4Plot +
theme_minimal_vgrid(
font_family = 'Helvetica',
font_size = base * 1.05) +
theme(
plot.title = element_text(
family = "Roboto Black",
size = base * 2),
legend.position = "none"
) +
labs(
x = "Level 4",
y = NULL)
l5p <- level5Plot +
theme_minimal_vgrid(
font_family = 'Helvetica',
font_size = base * 1.05) +
theme(
plot.title = element_text(
family = "Roboto Black",
size = base * 2),
legend.position = 'bottom'
) +
labs(
x = "Level 5",
y = "Percent of Participants")
plot_grid(
l2p,
l3p,
l4p,
l5p,
ncol = 1
)
bosPath <- here('data_raw', 'Our Driverless Futures Individual_BOSRaw.csv')
dfBOS <- read_csv(bosPath, skip = 2) %>%
filter(!is.na(Presence0)) %>%
clean_names()
#head(dfBOS)
#tail(dfBOS)
#dim(dfBOS) #Boston had 71 participants, this is correct
bufPath <- here('data_raw', 'Our Driverless Futures Individual_BUFRaw.csv')
dfBUF <- read_csv(bufPath, skip = 2) %>%
filter(!is.na(Presence0)) %>%
clean_names()
#head(dfBUF)
#tail(dfBUF)
#dim(dfBUF) #Buffalo had 96 participants, this is correct
dcPath <- here('data_raw', 'Our Driverless Futures Individual_DCRaw.csv')
dfDC <- read_csv(dcPath, skip = 2) %>%
filter(!is.na(Presence0)) %>%
clean_names()
#head(dfDC)
#tail(dfDC)
#dim(dfDC) #DC had 62 participants, this is correct
phxPath <- here('data_raw', 'Our Driverless Futures Individual_PHXRaw.csv')
dfPHX <- read_csv(phxPath, skip = 2) %>%
filter(!is.na(Presence0)) %>%
clean_names()
#head(dfPHX)
#tail(dfPHX)
#dim(dfPHX) #PHX had 88 participants, this is correct
### Buffalo Formatting
dataBUF <- dfBUF %>%
select(
city = x1_2_location,
ID = cle,
#Session 0
initialAttitude = how_do_you_feel_about_the_idea_of_driverless_mobility_1_very_pessimistic_2_pessimistic_3_neutral_4_optimistic_5_very_optimistic,
#Session 1
accessibilityCar = accessibility_physical_access_of_transportation_by_car,
accessibilityTransit = accessibility_physical_access_of_public_transportation,
travelTimeCar = travel_time_by_car,
travelTimeTransit = travel_time_by_public_transportation,
cityCongestion = traffic_congestion_in_cities,
hwCongestion = traffic_congestion_on_highways,
transportOptions = availability_of_transportation_options,
mobilityAreas = areas_used_for_other_mobility_methods_e_g_biking_walking,
harmTrafficAccidents = harm_occurring_from_traffic_accidents,
transitCost = total_cost_of_public_transportation,
ownershipCost = total_cost_of_individual_car_ownership,
locationAccess = getting_to_and_from_places_that_are_not_easy_to_access,
timeAvailability = availability_of_my_time_to_do_things_while_traveling_work_sleep_read_etc,
otherCategory = other_please_list,
#Session 2
level2Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_2_automation,
level3Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_3_automation,
level4Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_4_automation,
level5Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_5_automation,
testingTestBeds = i_would_support_testing_of_driverless_vehicles_in_test_beds,
testingTestTrack = i_would_support_testing_of_driverless_vehicles_in_a_local_test_track,
testingLowPop = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_less_populated_areas,
testingCities = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_cities,
testingHW = i_would_support_testing_of_driverless_vehicles_on_highways,
cityDataManagement = it_is_acceptable_for_city_town_transportation_manaers_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataManagement = it_is_acceptable_for_private_companies_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataSelling = it_is_acceptable_for_data_about_passengers_generated_by_automated_mobility_systems_to_be_sold_to_companies,
citizenDataControl = individual_citizens_should_have_control_over_selling_their_data_or_forbidding_its_use_other_than_to_operate_the_system,
anonymizedDataUse = it_is_acceptable_for_the_anonymized_data_generated_by_the_operation_of_vehicles_to_be_freely_available_for_any_use,
#Session 3
currentSystemModel = improving_the_current_system,
individualOwnershipModel = individual_ownership,
rideShareModel = ride_share_model,
publicTransportationModel = public_transportation_model,
#Session 4
#Privacy Protection
nationalgov_PrivacyEffectiveness = national_government,
localgov_PrivacyEffectiveness = local_government,
nonprofit_PrivacyEffectiveness = non_profit_organizations,
transportationIndustry_PrivacyEffectiveness = transportation_industry,
insuranceCompanies_PrivacyEffectiveness = insurance_companies,
#Infrastructure
nationalgov_InfrastructureEffectiveness = national_government_1,
localgov_InfrastructureEffectiveness = local_government_1,
nonprofit_InfrastructureEffectiveness = non_profit_organizations_1,
transportationIndustry_InfrastructureEffectiveness = transportation_industry_1,
insuranceCompanies_InfrastructureEffectiveness = insurance_companies_1,
#Justice/Equity
nationalgov_EquityEffectiveness = national_government_2,
localgov_EquityEffectiveness = local_government_2,
nonprofit_EquityEffectiveness = non_profit_organizations_2,
transportationIndustry_EquityEffectiveness = transportation_industry_2,
insuranceCompanies_EquityEffectiveness = insurance_companies_2,
#Cybersecurity
nationalgov_CyberEffectiveness = national_government_3,
localgov_CyberEffectiveness = local_government_3,
nonprofit_CyberEffectiveness = non_profit_organizations_3,
transportationIndustry_CyberEffectiveness = transportation_industry_3,
insuranceCompanies_CyberEffectiveness = insurance_companies_3,
#Safety
nationalgov_SafetyEffectiveness = national_government_4,
localgov_SafetyEffectiveness = local_government_4,
nonprofit_SafetyEffectiveness = non_profit_organizations_4,
transportationIndustry_SafetyEffectiveness = transportation_industry_4,
insuranceCompanies_SafetyEffectiveness = insurance_companies_4,
#Environment
nationalgov_EnviroEffectiveness = national_government_5,
localgov_EnviroEffectiveness = local_government_5,
nonprofit_EnviroEffectiveness = non_profit_organizations_5,
transportationIndustry_EnviroEffectiveness = transportation_industry_5,
insuranceCompanies_EnviroEffectiveness = insurance_companies_5,
#Privacy Protection
nationalgov_PrivacyTrust = national_government_6,
localgov_PrivacyTrust = local_government_6,
nonprofit_PrivacyTrust = non_profit_organizations_6,
transportationIndustry_PrivacyTrust = transportation_industry_6,
insuranceCompanies_PrivacyTrust = insurance_companies_6,
#Infrastructure
nationalgov_InfrastructureTrust = national_government_7,
localgov_InfrastructureTrust = local_government_7,
nonprofit_InfrastructureTrust = non_profit_organizations_7,
transportationIndustry_InfrastructureTrust = transportation_industry_7,
insuranceCompanies_InfrastructureTrust = insurance_companies_7,
#Justice/Equity
nationalgov_EquityTrust = national_government_8,
localgov_EquityTrust = local_government_8,
nonprofit_EquityTrust = non_profit_organizations_8,
transportationIndustry_EquityTrust = transportation_industry_8,
insuranceCompanies_EquityTrust = insurance_companies_8,
#Cybersecurity
nationalgov_CyberTrust = national_government_9,
localgov_CyberTrust = local_government_9,
nonprofit_CyberTrust = non_profit_organizations_9,
transportationIndustry_CyberTrust = transportation_industry_9,
insuranceCompanies_CyberTrust = insurance_companies_9,
#Safety
nationalgov_SafetyTrust = national_government_10,
localgov_SafetyTrust = local_government_10,
nonprofit_SafetyTrust = non_profit_organizations_10,
transportationIndustry_SafetyTrust = transportation_industry_10,
insuranceCompanies_SafetyTrust = insurance_companies_10,
#Environment
nationalgov_EnviroTrust = national_government_11,
localgov_EnviroTrust = local_government_11,
nonprofit_EnviroTrust = non_profit_organizations_11,
transportationIndustry_EnviroTrust = transportation_industry_11,
insuranceCompanies_EnviroTrust = insurance_companies_11,
rank1 = x1,
rank2 = x2,
rank3 = x3,
rank4 = x4,
rank5 = x5,
rank6 = x6,
gender,
age,
ethnicity = what_is_your_ethnicity,
political_orientation,
residence,
area,
occupation,
education,
income = what_is_your_household_income_per_year
)
### Boston formatting
dataBOS <- dfBOS %>%
select(
city = x1_2_location,
ID = cle,
#Session 0
initialAttitude = how_do_you_feel_about_the_idea_of_driverless_mobility_1_very_pessimistic_2_pessimistic_3_neutral_4_optimistic_5_very_optimistic,
#Session 1
accessibilityCar = accessibility_physical_access_of_transportation_by_car,
accessibilityTransit = accessibility_physical_access_of_public_transportation,
travelTimeCar = travel_time_by_car,
travelTimeTransit = travel_time_by_public_transportation,
cityCongestion = traffic_congestion_in_cities,
hwCongestion = traffic_congestion_on_highways,
transportOptions = availability_of_transportation_options,
mobilityAreas = areas_used_for_other_mobility_methods_e_g_biking_walking,
harmTrafficAccidents = harm_occurring_from_traffic_accidents,
transitCost = total_cost_of_public_transportation,
ownershipCost = total_cost_of_individual_car_ownership,
locationAccess = getting_to_and_from_places_that_are_not_easy_to_access,
timeAvailability = availability_of_my_time_to_do_things_while_traveling_work_sleep_read_etc,
otherCategory = other_please_list,
#Session 2
level2Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_2_automation,
level3Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_3_automation,
level4Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_4_automation,
level5Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_5_automation,
testingTestBeds = i_would_support_testing_of_driverless_vehicles_in_test_beds,
testingTestTrack = i_would_support_testing_of_driverless_vehicles_in_a_local_test_track,
testingLowPop = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_less_populated_areas,
testingCities = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_cities,
testingHW = i_would_support_testing_of_driverless_vehicles_on_highways,
cityDataManagement = it_is_acceptable_for_city_town_transportation_manaers_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataManagement = it_is_acceptable_for_private_companies_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataSelling = it_is_acceptable_for_data_about_passengers_generated_by_automated_mobility_systems_to_be_sold_to_companies,
citizenDataControl = individual_citizens_should_have_control_over_selling_their_data_or_forbidding_its_use_other_than_to_operate_the_system,
anonymizedDataUse = it_is_acceptable_for_the_anonymized_data_generated_by_the_operation_of_vehicles_to_be_freely_available_for_any_use,
#Session 3
currentSystemModel = improving_the_current_system,
individualOwnershipModel = individual_ownership,
rideShareModel = ride_share_model,
publicTransportationModel = public_transportation_model,
#Session 4
#Privacy Protection
nationalgov_PrivacyEffectiveness = national_government,
localgov_PrivacyEffectiveness = local_government,
nonprofit_PrivacyEffectiveness = non_profit_organizations,
transportationIndustry_PrivacyEffectiveness = transportation_industry,
insuranceCompanies_PrivacyEffectiveness = insurance_companies,
#Infrastructure
nationalgov_InfrastructureEffectiveness = national_government_1,
localgov_InfrastructureEffectiveness = local_government_1,
nonprofit_InfrastructureEffectiveness = non_profit_organizations_1,
transportationIndustry_InfrastructureEffectiveness = transportation_industry_1,
insuranceCompanies_InfrastructureEffectiveness = insurance_companies_1,
#Justice/Equity
nationalgov_EquityEffectiveness = national_government_2,
localgov_EquityEffectiveness = local_government_2,
nonprofit_EquityEffectiveness = non_profit_organizations_2,
transportationIndustry_EquityEffectiveness = transportation_industry_2,
insuranceCompanies_EquityEffectiveness = insurance_companies_2,
#Cybersecurity
nationalgov_CyberEffectiveness = national_government_3,
localgov_CyberEffectiveness = local_government_3,
nonprofit_CyberEffectiveness = non_profit_organizations_3,
transportationIndustry_CyberEffectiveness = transportation_industry_3,
insuranceCompanies_CyberEffectiveness = insurance_companies_3,
#Safety
nationalgov_SafetyEffectiveness = national_government_4,
localgov_SafetyEffectiveness = local_government_4,
nonprofit_SafetyEffectiveness = non_profit_organizations_4,
transportationIndustry_SafetyEffectiveness = transportation_industry_4,
insuranceCompanies_SafetyEffectiveness = insurance_companies_4,
#Environment
nationalgov_EnviroEffectiveness = national_government_5,
localgov_EnviroEffectiveness = local_government_5,
nonprofit_EnviroEffectiveness = non_profit_organizations_5,
transportationIndustry_EnviroEffectiveness = transportation_industry_5,
insuranceCompanies_EnviroEffectiveness = insurance_companies_5,
#Privacy Protection
nationalgov_PrivacyTrust = national_government_6,
localgov_PrivacyTrust = local_government_6,
nonprofit_PrivacyTrust = non_profit_organizations_6,
transportationIndustry_PrivacyTrust = transportation_industry_6,
insuranceCompanies_PrivacyTrust = insurance_companies_6,
#Infrastructure
nationalgov_InfrastructureTrust = national_government_7,
localgov_InfrastructureTrust = local_government_7,
nonprofit_InfrastructureTrust = non_profit_organizations_7,
transportationIndustry_InfrastructureTrust = transportation_industry_7,
insuranceCompanies_InfrastructureTrust = insurance_companies_7,
#Justice/Equity
nationalgov_EquityTrust = national_government_8,
localgov_EquityTrust = local_government_8,
nonprofit_EquityTrust = non_profit_organizations_8,
transportationIndustry_EquityTrust = transportation_industry_8,
insuranceCompanies_EquityTrust = insurance_companies_8,
#Cybersecurity
nationalgov_CyberTrust = national_government_9,
localgov_CyberTrust = local_government_9,
nonprofit_CyberTrust = non_profit_organizations_9,
transportationIndustry_CyberTrust = transportation_industry_9,
insuranceCompanies_CyberTrust = insurance_companies_9,
#Safety
nationalgov_SafetyTrust = national_government_10,
localgov_SafetyTrust = local_government_10,
nonprofit_SafetyTrust = non_profit_organizations_10,
transportationIndustry_SafetyTrust = transportation_industry_10,
insuranceCompanies_SafetyTrust = insurance_companies_10,
#Environment
nationalgov_EnviroTrust = national_government_11,
localgov_EnviroTrust = local_government_11,
nonprofit_EnviroTrust = non_profit_organizations_11,
transportationIndustry_EnviroTrust = transportation_industry_11,
insuranceCompanies_EnviroTrust = insurance_companies_11,
rank1 = x1,
rank2 = x2,
rank3 = x3,
rank4 = x4,
rank5 = x5,
rank6 = x6,
gender,
age,
ethnicity,
residence,
area,
occupation,
education,
)
dataDC <- dfDC %>%
select(
city = x1_2_location,
ID = cle,
#Session 0
initialAttitude = how_do_you_feel_about_the_idea_of_driverless_mobility_1_very_pessimistic_2_pessimistic_3_neutral_4_optimistic_5_very_optimistic,
#Session 1
accessibilityCar = accessibility_physical_access_of_transportation_by_car,
accessibilityTransit = accessibility_physical_access_of_public_transportation,
travelTimeCar = travel_time_by_car,
travelTimeTransit = travel_time_by_public_transportation,
cityCongestion = traffic_congestion_in_cities,
hwCongestion = traffic_congestion_on_highways,
transportOptions = availability_of_transportation_options,
mobilityAreas = areas_used_for_other_mobility_methods_e_g_biking_walking,
harmTrafficAccidents = harm_occurring_from_traffic_accidents,
transitCost = total_cost_of_public_transportation,
ownershipCost = total_cost_of_individual_car_ownership,
locationAccess = getting_to_and_from_places_that_are_not_easy_to_access,
timeAvailability = availability_of_my_time_to_do_things_while_traveling_work_sleep_read_etc,
otherCategory = other_please_list,
#Session 2
level2Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_2_automation,
level3Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_3_automation,
level4Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_4_automation,
level5Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_5_automation,
testingTestBeds = i_would_support_testing_of_driverless_vehicles_in_test_beds,
testingTestTrack = i_would_support_testing_of_driverless_vehicles_in_a_local_test_track,
testingLowPop = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_less_populated_areas,
testingCities = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_cities,
testingHW = i_would_support_testing_of_driverless_vehicles_on_highways,
cityDataManagement = it_is_acceptable_for_city_town_transportation_manaers_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataManagement = it_is_acceptable_for_private_companies_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataSelling = it_is_acceptable_for_data_about_passengers_generated_by_automated_mobility_systems_to_be_sold_to_companies,
citizenDataControl = individual_citizens_should_have_control_over_selling_their_data_or_forbidding_its_use_other_than_to_operate_the_system,
anonymizedDataUse = it_is_acceptable_for_the_anonymized_data_generated_by_the_operation_of_vehicles_to_be_freely_available_for_any_use,
#Session 3
currentSystemModel = improving_the_current_system,
individualOwnershipModel = individual_ownership,
rideShareModel = ride_share_model,
publicTransportationModel = public_transportation_model,
#Session 4
#Privacy Protection
nationalgov_PrivacyEffectiveness = national_government,
localgov_PrivacyEffectiveness = local_government,
nonprofit_PrivacyEffectiveness = non_profit_organizations,
transportationIndustry_PrivacyEffectiveness = transportation_industry,
insuranceCompanies_PrivacyEffectiveness = insurance_companies,
#Infrastructure
nationalgov_InfrastructureEffectiveness = national_government_1,
localgov_InfrastructureEffectiveness = local_government_1,
nonprofit_InfrastructureEffectiveness = non_profit_organizations_1,
transportationIndustry_InfrastructureEffectiveness = transportation_industry_1,
insuranceCompanies_InfrastructureEffectiveness = insurance_companies_1,
#Justice/Equity
nationalgov_EquityEffectiveness = national_government_2,
localgov_EquityEffectiveness = local_government_2,
nonprofit_EquityEffectiveness = non_profit_organizations_2,
transportationIndustry_EquityEffectiveness = transportation_industry_2,
insuranceCompanies_EquityEffectiveness = insurance_companies_2,
#Cybersecurity
nationalgov_CyberEffectiveness = national_government_3,
localgov_CyberEffectiveness = local_government_3,
nonprofit_CyberEffectiveness = non_profit_organizations_3,
transportationIndustry_CyberEffectiveness = transportation_industry_3,
insuranceCompanies_CyberEffectiveness = insurance_companies_3,
#Safety
nationalgov_SafetyEffectiveness = national_government_4,
localgov_SafetyEffectiveness = local_government_4,
nonprofit_SafetyEffectiveness = non_profit_organizations_4,
transportationIndustry_SafetyEffectiveness = transportation_industry_4,
insuranceCompanies_SafetyEffectiveness = insurance_companies_4,
#Environment
nationalgov_EnviroEffectiveness = national_government_5,
localgov_EnviroEffectiveness = local_government_5,
nonprofit_EnviroEffectiveness = non_profit_organizations_5,
transportationIndustry_EnviroEffectiveness = transportation_industry_5,
insuranceCompanies_EnviroEffectiveness = insurance_companies_5,
#Privacy Protection
nationalgov_PrivacyTrust = national_government_6,
localgov_PrivacyTrust = local_government_6,
nonprofit_PrivacyTrust = non_profit_organizations_6,
transportationIndustry_PrivacyTrust = transportation_industry_6,
insuranceCompanies_PrivacyTrust = insurance_companies_6,
#Infrastructure
nationalgov_InfrastructureTrust = national_government_7,
localgov_InfrastructureTrust = local_government_7,
nonprofit_InfrastructureTrust = non_profit_organizations_7,
transportationIndustry_InfrastructureTrust = transportation_industry_7,
insuranceCompanies_InfrastructureTrust = insurance_companies_7,
#Justice/Equity
nationalgov_EquityTrust = national_government_8,
localgov_EquityTrust = local_government_8,
nonprofit_EquityTrust = non_profit_organizations_8,
transportationIndustry_EquityTrust = transportation_industry_8,
insuranceCompanies_EquityTrust = insurance_companies_8,
#Cybersecurity
nationalgov_CyberTrust = national_government_9,
localgov_CyberTrust = local_government_9,
nonprofit_CyberTrust = non_profit_organizations_9,
transportationIndustry_CyberTrust = transportation_industry_9,
insuranceCompanies_CyberTrust = insurance_companies_9,
#Safety
nationalgov_SafetyTrust = national_government_10,
localgov_SafetyTrust = local_government_10,
nonprofit_SafetyTrust = non_profit_organizations_10,
transportationIndustry_SafetyTrust = transportation_industry_10,
insuranceCompanies_SafetyTrust = insurance_companies_10,
#Environment
nationalgov_EnviroTrust = national_government_11,
localgov_EnviroTrust = local_government_11,
nonprofit_EnviroTrust = non_profit_organizations_11,
transportationIndustry_EnviroTrust = transportation_industry_11,
insuranceCompanies_EnviroTrust = insurance_companies_11,
rank1 = x1,
rank2 = x2,
rank3 = x3,
rank4 = x4,
rank5 = x5,
rank6 = x6,
gender,
age,
ethnicity,
political_orientation,
residence,
area,
occupation,
education,
household_income
)
### Phoenix formatting
dataPHX <- dfPHX %>%
select(
city = x1_2_location,
ID = cle,
#Session 0
initialAttitude = how_do_you_feel_about_the_idea_of_driverless_mobility_1_very_pessimistic_2_pessimistic_3_neutral_4_optimistic_5_very_optimistic,
#Session 1
accessibilityCar = accessibility_physical_access_of_transportation_by_car,
accessibilityTransit = accessibility_physical_access_of_public_transportation,
travelTimeCar = travel_time_by_car,
travelTimeTransit = travel_time_by_public_transportation,
cityCongestion = traffic_congestion_in_cities,
hwCongestion = traffic_congestion_on_highways,
transportOptions = availability_of_transportation_options,
mobilityAreas = areas_used_for_other_mobility_methods_e_g_biking_walking,
harmTrafficAccidents = harm_occurring_from_traffic_accidents,
transitCost = total_cost_of_public_transportation,
ownershipCost = total_cost_of_individual_car_ownership,
locationAccess = getting_to_and_from_places_that_are_not_easy_to_access,
timeAvailability = availability_of_my_time_to_do_things_while_traveling_work_sleep_read_etc,
otherCategory = other_please_list,
#Session 2
level2Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_2_automation,
level3Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_3_automation,
level4Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_4_automation,
level5Comfort = i_would_feel_comfortable_riding_in_a_vehicle_with_level_5_automation,
testingTestBeds = i_would_support_testing_of_driverless_vehicles_in_test_beds,
testingTestTrack = i_would_support_testing_of_driverless_vehicles_in_a_local_test_track,
testingLowPop = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_less_populated_areas,
testingCities = i_would_support_testing_of_driverless_vehicles_in_neighborhoods_in_cities,
testingHW = i_would_support_testing_of_driverless_vehicles_on_highways,
cityDataManagement = it_is_acceptable_for_city_town_transportation_manaers_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataManagement = it_is_acceptable_for_private_companies_to_manage_and_control_data_about_passengers_generated_by_automated_mobility_systems,
companyDataSelling = it_is_acceptable_for_data_about_passengers_generated_by_automated_mobility_systems_to_be_sold_to_companies,
citizenDataControl = individual_citizens_should_have_control_over_selling_their_data_or_forbidding_its_use_other_than_to_operate_the_system,
anonymizedDataUse = it_is_acceptable_for_the_anonymized_data_generated_by_the_operation_of_vehicles_to_be_freely_available_for_any_use,
#Session 3
currentSystemModel = improving_the_current_system,
individualOwnershipModel = individual_ownership,
rideShareModel = ride_share_model,
publicTransportationModel = public_transportation_model,
#Session 4
#Privacy Protection
nationalgov_PrivacyEffectiveness = national_government,
localgov_PrivacyEffectiveness = local_government,
nonprofit_PrivacyEffectiveness = non_profit_organizations,
transportationIndustry_PrivacyEffectiveness = transportation_industry,
insuranceCompanies_PrivacyEffectiveness = insurance_companies,
#Infrastructure
nationalgov_InfrastructureEffectiveness = national_government_1,
localgov_InfrastructureEffectiveness = local_government_1,
nonprofit_InfrastructureEffectiveness = non_profit_organizations_1,
transportationIndustry_InfrastructureEffectiveness = transportation_industry_1,
insuranceCompanies_InfrastructureEffectiveness = insurance_companies_1,
#Justice/Equity
nationalgov_EquityEffectiveness = national_government_2,
localgov_EquityEffectiveness = local_government_2,
nonprofit_EquityEffectiveness = non_profit_organizations_2,
transportationIndustry_EquityEffectiveness = transportation_industry_2,
insuranceCompanies_EquityEffectiveness = insurance_companies_2,
#Cybersecurity
nationalgov_CyberEffectiveness = national_government_3,
localgov_CyberEffectiveness = local_government_3,
nonprofit_CyberEffectiveness = non_profit_organizations_3,
transportationIndustry_CyberEffectiveness = transportation_industry_3,
insuranceCompanies_CyberEffectiveness = insurance_companies_3,
#Safety
nationalgov_SafetyEffectiveness = national_government_4,
localgov_SafetyEffectiveness = local_government_4,
nonprofit_SafetyEffectiveness = non_profit_organizations_4,
transportationIndustry_SafetyEffectiveness = transportation_industry_4,
insuranceCompanies_SafetyEffectiveness = insurance_companies_4,
#Environment
nationalgov_EnviroEffectiveness = national_government_5,
localgov_EnviroEffectiveness = local_government_5,
nonprofit_EnviroEffectiveness = non_profit_organizations_5,
transportationIndustry_EnviroEffectiveness = transportation_industry_5,
insuranceCompanies_EnviroEffectiveness = insurance_companies_5,
#Privacy Protection
nationalgov_PrivacyTrust = national_government_6,
localgov_PrivacyTrust = local_government_6,
nonprofit_PrivacyTrust = non_profit_organizations_6,
transportationIndustry_PrivacyTrust = transportation_industry_6,
insuranceCompanies_PrivacyTrust = insurance_companies_6,
#Infrastructure
nationalgov_InfrastructureTrust = national_government_7,
localgov_InfrastructureTrust = local_government_7,
nonprofit_InfrastructureTrust = non_profit_organizations_7,
transportationIndustry_InfrastructureTrust = transportation_industry_7,
insuranceCompanies_InfrastructureTrust = insurance_companies_7,
#Justice/Equity
nationalgov_EquityTrust = national_government_8,
localgov_EquityTrust = local_government_8,
nonprofit_EquityTrust = non_profit_organizations_8,
transportationIndustry_EquityTrust = transportation_industry_8,
insuranceCompanies_EquityTrust = insurance_companies_8,
#Cybersecurity
nationalgov_CyberTrust = national_government_9,
localgov_CyberTrust = local_government_9,
nonprofit_CyberTrust = non_profit_organizations_9,
transportationIndustry_CyberTrust = transportation_industry_9,
insuranceCompanies_CyberTrust = insurance_companies_9,
#Safety
nationalgov_SafetyTrust = national_government_10,
localgov_SafetyTrust = local_government_10,
nonprofit_SafetyTrust = non_profit_organizations_10,
transportationIndustry_SafetyTrust = transportation_industry_10,
insuranceCompanies_SafetyTrust = insurance_companies_10,
#Environment
nationalgov_EnviroTrust = national_government_11,
localgov_EnviroTrust = local_government_11,
nonprofit_EnviroTrust = non_profit_organizations_11,
transportationIndustry_EnviroTrust = transportation_industry_11,
insuranceCompanies_EnviroTrust = insurance_companies_11,
rank1 = x1,
rank2 = x2,
rank3 = x3,
rank4 = x4,
rank5 = x5,
rank6 = x6,
gender,
age,
ethnicity,
political_orientation,
residence,
area,
occupation,
education,
household_income
)
#### Combining all the data sets
combinedData <- join_all(list(dataBOS, dataBUF, dataPHX, dataDC), by = 'city', type = 'full')
#write_csv(combinedData, path = file.path('data_processed', 'combinedData.csv'))