Sharlene Chiu, Lawrence Sun, Tricia Shi, and Zachary Collins
Methodology
For our final project, we iterated on the participatory game sketch the “Amazing Race,” changing the name to “Itinerarie.” The data shows that personal vehicles produce a large amount of carbon emissions. Choosing public transportation or other more environmentally friendly modes of transportation can have a major impact on air pollution. Convenience, time and cost are factors that make picking the “greenest” forms of transportation not always practical. There are times, however, in which we can reasonably select between a variety of options. The amount of carbon emissions we can prevent is actually quite shocking. If everyone were to think of the environment when making these decisions, the aggregate results could be huge. Our fully realized sketch is a text-based game that forces our audience, i.e. MIT students, to think of these trade-offs and understand how their decisions impact the environment. We created a web application as the medium for the game to be played, allowing people to play it via their personal computers or mobile devices.
Link to Game: https://itinerarie.herokuapp.com/
At the start of the game, users are presented a list of seven activities such as “Grocery Shopping at Shaws” and “Visiting the Museum of Fine Arts.” Users are told to select three activities they may perform in the upcoming weekend. Then, for each one, the game asks users how they would get there. Users can select between walking, biking (through Hubway), using public transportation, and calling an Uber. Beside each transportation option are the associated estimated prices and travel times. After selecting the mode of transportation for each activity, users are then reminded that the environment is important and are told to repick keeping it in mind as well. The same activities and transportation options are shown again, but this time with the approximate carbon emissions alongside each option as well. Finally, the user is shown the difference and resulting impact changing such decisions would have.
The data used to build the app came from a variety of sources. For each of the destinations, we needed to obtain the cost, time, and carbon emissions associated with each of the given modes of transportation. We also utilized other measurements such as the amount of carbon absorbed by a tree per year in creating our results page.
We obtained, from a report done by the Federal Transit Administration, the average pounds of carbon dioxide per passenger mile released from private auto and public transit. We operated under the assumption that the carbon contribution of walking and biking was zero. We used Hubway, Uber, and MBTA estimated fares when taking trips to find the associated price for each of the possible destinations we incorporated. We operated under the assumption that walking comes with no monetary cost. We used Google Maps to gather the estimated time it would take to walk, bike, use public transportation, or drive to each of the destinations we provided. All of our metrics used the Student Center as the starting point. Finally, using the fact that a mature tree consumes up to 48 pounds of carbon dioxide per year, we were able to convert computed carbon emissions into the number of “trees worth of work” per day. All of our computed numbers and metrics can be found in the following Google Sheets file.
Link to Data File: https://docs.google.com/spreadsheets/d/1rgv9Ryvrx7l6KThole9vDhEYG0xEXWgd2HBasIgi1xE/edit#gid=0
Impact
We wanted our project to change the way users think about how their transportation choices impact the environment. In the short term, we hope to reveal to them how much each of the prescribed options emits, clarifying why always taking an Uber might not be the most environmentally conscious choice, especially over time. In the long term, we hope to convince them to make the more environmentally friendly transportation decision when they can reasonably do so. Just as we bring to light in our project, if everyone made these choices, we would save a considerable amount of pollution from entering the atmosphere.
Our target audience for this project are MIT students. This allows us to focus in on a group that will often go to the same places and make the same transportation decisions. Using the Student Center as a point of reference allowed us to produce numbers that both make sense and are applicable to everyone who we expect to play the game.
To gauge the impact our game could have, we performed two sets of systemized tests. For a more general and loose experiment, we sent the link to our web app and an anonymous survey to many of the dorm mailing lists. This experiment was used to gauge participant reactions to playing the game. Our web app logged over 200 plays and we received over 130 responses to our survey. We used Google Analytics to record the actions and selections users made.
As users played our web app, we recorded the modes of transportation used but not the locations they specified for anonymity. Below is a table tabulating the selections our users made:
| First Run | Second Run | Change | |
| Walk | 310 | 359 | +15.80% |
| Biking (Hubway) | 31 | 70 | +125.80% |
| Public Transport | 201 | 140 | -30.30% |
| Car (Uber) | 63 | 37 | -41.30% |
From the data we can see that once the users are made aware of the impact of their decisions on CO2 emissions, they decrease car and public transportation usage in favor of walking and biking. Public transportation usage decreased less than Uber usage did as it is less harmful to the environment. The data tells us that on their second run, users had on average 35% less carbon emissions than their first run, demonstrating that once users learn more about CO2 emissions they can effectively make use of their knowledge to make better decisions.
Based on the anonymous survey results, we concluded that the game indeed met our short-term goal of improving how well people understood the impacts of their transportation decisions. Prior to playing the game, about 77.8% of participants had at least an okay understanding of transportation carbon emissions. Afterward, that percentage increased to 82.5% of people understanding at least a good amount about the impact of their transportation decisions.
Interestingly, the number of people who marked the two option which indicated the highest knowledge decreased after playing the game. 50.4% of users said they had a solid intuition or knew everything coming into the game, but only 42.6% marked similar options after playing the game. This is most likely a case of where users thought they knew a lot going in, but once they saw the data they realized they knew less than they thought, in which case our game still meets our goal as users are still becoming more knowledgeable about the impact of their transportation decision.
Some survey respondents critiqued the effectiveness of the web app in terms of educating users and inspiring better transportation decisions. However, these respondents tended to walk and bike to different places, so their transportation decisions are already generally environmentally friendly.
For a more controlled experiment, we set up our web app in Lobby 10 and got people to play the game. We first asked them a few questions to get a better understanding of how they make transportation decisions. They then played the game and were asked a few more questions to gauge their reactions to the game and the effect it might have had.
Prior to playing the game, we asked participants what form of transportation would they go to use when trying to visit a local place off campus. After playing, we asked them if they felt their knowledge on the information displayed changed. We also asked the initial question again to see if their answer changed.
We found that when people were flagged down to play the game and answer our questions, they rushed and weren’t really engaged. When people came over voluntarily, the game proved short enough for them to stay focused while completing it. Those who generally would walk or bike did not really change their opinions after playing the game. While they thought they gained a better understanding of the numbers presented, their choice of transportation remained walking or biking. They did not find playing the game very meaningful. One user, who selected walking for every option, said that, “I don’t know how useful this is….. I obviously know that cars are less eco friendly than walking or biking and I don’t feel like the actual numbers changed my opinion all that much. Sorry– it was a beautiful app, though.”
Those that said prior to playing that they tend to Uber, even close by, were more surprised by the numbers presented. They felt that the tree analogy opened their eyes to show how even small changes in transportation actually amount to a lot. Most, however, said that, while they might be more conscious of the effects, they probably won’t change the way they’d get around in the long term. One user remarked that, “It’s kind of scary the numbers if you live this lifestyle, like the number of trees,” but said that after playing they’ll likely still Uber to get around.
Overall we felt that the prototype of our game met our short term goal of making users more conscious of how their transportation decisions impact the environment. The “trees worth of work” analogy rang home with many and seemed to at least make them think about how they’ll make choices in the future. It does seem that it wasn’t very successful at meeting our long term goal of getting users to change the way they’ll make decisions in the future. Many admitted, that although the numbers surprised them, they’ll probably still make the same choices in the long term. We think that making the game provide more personalized tips or using it to help destigmatize the downfalls of “greener” transportation options might help us move toward better achieving our long term aims.
Presentation: https://docs.google.com/presentation/d/1OQkbw-zwaQ3KyszTqNHx0M1SEdtnN6UBe5CZyTPW8kc/edit?usp=sharing