On May 1st, University of Maryland’s Maryland Transportation Institute held an open house hosted jointly with the Washington Metropolitan Council of Governments with the purpose of sharing a technology called “incenTrip.” Now available on the Android and iOS app stores, incenTrip had a “soft release” to stakeholders at the open house, where people were able to give it a test drive and provide recommendations to the development team moving forward. But what exactly is incenTrip, and what does it mean for commuting and energy use?
Trip planning, gamified
Right now, when you open up Google Maps, Apple Maps, the Transit app, or any of the other major trip planning applications used by commuters in our region, you can tell your smartphone where you want to go, what your starting location is, and when you want to leave, and given the time of day and the road, transit, bike, or walking routes available, will tell you the shortest way to get from point A to point B, using whatever criteria you have given up (such as if you prefer rail over bus, if you cannot use a bicycle, if you’d like to minimize walking, etc.)
incenTrip utilizes what they call “the latest ARPA-E technology” (which stands for Advanced Research Projects Agency – Energy, a program of the U.S. Department of Energy) to come up with the ideal route for you to take, just like any trip planner would. But it does a few other things at the same time - it determines on the individual level what areas are most prone to traffic, and will also output alternative, slightly more inconvenient travel options for you based on total system demand from others, and what decisions you can make that will save the most energy for both you and the overall transportation network. It will even change the directions it gives you depending on the model and mileage of your personal car, if you are looking at the car-based transportation modes.
All outputted trip plans come tagged with a different amount of “eco-points.” In a nut shell, the trips that are most energy-efficient will award the user more points. For example, driving home from work at your usual time might award you 4 points, but taking a bus at that time will award you 80 points.
Image from incentrip.org
These points help to raise both your “score” (on a fun scale ranging from “Seed Saver” to “Planet Saver”) but also can be redeemed for tangible rewards. At the soft release to stakeholders, this included iTunes and Amazon gift cards. The incenTrip team now plans to take the transportation demand management aspect of this application and sell the idea to stakeholders like local DOTs and Metro, who will hopefully provide some funding for incentives. Funding could even be designated to create what they call “an ecosystem of transportation incentives,” via the awarding of E-Z Pass, Smartrip, and bikeshare system credits.
Transportation demand management, individualized
A promise made by the incenTrip team is that this type of application could be a game-changer for transportation demand management (TDM) efforts. Currently, large-scale programs that aim to nudge commuters to certain behaviors, such as carpooling or biking more often, have already been massively successful.
But what if machine learning and artificial intelligence technologies could learn the behaviors and travel preferences of riders, on the individual level, and then adjust incentives (aka, the amount of points to award) based upon what the application thinks a specific commuter will respond to? Currently, the app is powered by intelligence collected via 2000 surveys to create “diverse behavior models” based on an individual’s age, gender, race, and income level. From there, it attempts to learn how a given user likes to get around, and will try to award points to that person in a way that leads to a change to a more sustainable departure time or mode of travel time. This is a very interesting goal, and it will be exciting to see how far they can develop this technology.
Limitations and opportunities
Currently, the technology is only found on the mobile apps for Android and iOS, and requires that you have location services enabled for the app for the entire duration of your trip, so it can verify that you complete a trip according the route and mode you promised to take before you are awarded points. There are also some equity questions, like how usable the application is for people with disabilities, or whether or not point distribution totals will be looked at as fair. For example, individuals with higher incomes might require a lot more points to be thrown at them for them to change their behavior. This eats up points that can be awarded to riders with lower incomes, or to riders who have already been “Planet Savers” for years, and therefore don’t need to be targeted for behavior change.
Regardless, the concept of individualized, gamified TDM is particularly noteworthy. The team mentioned that they are welcome to any and all feedback, and are open to the project simply being a technology that can be integrated in to existing trip planners, not necessarily another trip planner in itself. If government and transportation stakeholders can take a closer look at their budgets - and continue to see the value that TDM has for transportation infrastructure demand - perhaps we’ll all be trying to earn eco-points in a few years.