Up to 10% of drivers shifted departure times outside of peak traffic
Learn how information and incentives supported ARPA-E's efforts to reduce peak congestion through Metropia’s behavior platform
The Connected Traveler
The Connected Traveler
ARPA-E leveraged behavioral economics in support of energy-efficient goals
To test the effect information and incentives have on promoting energy-smart travel behavior, the U.S. Department of Energy's Advanced Research Projects Agency-Energy (ARPA-E) launched The Connected Traveler project.
Several key challenges needed to be addressed for program success. Critically, potential energy savings needed to be personalized; each individual needed to understand the specific outcomes and benefits they would see as a result of their behavior changes. Just as important, participant travel choices needed to be validated. Incentives played a prominent role in the experiments, and funding for the project, provided by TRANSNET, had to be safeguarded and preserved.
With these requirements in mind, ARPA-E selected Metropia to serve as the core research platform upon which all data collection, modeling, analytics, and user engagement were developed and conducted.
Published Reports & Papers
The first step of the project entailed the National Renewable Energy Laboratory (NREL) utilizing Metropia collected trajectory data to develop an energy consumption methodology. That methodology along with survey responses from Metropia’s app users were factored into a modeling framework that incorporated Austin’s Dynamic Traffic Assignment (DTA) model, built on Metropia’s DynusT platform, and the MPO’s regional travel demand model to provide an estimate of system-level benefits. Based on those calculations, the energy savings presented to users were established and incentives were calibrated.
Metropia’s app users were targeted to shift toward three energy-saving travel behaviors: alternative mode (carpooling and transit), utilizing more efficient routes, and traveling at off-peak times. Behavior shifts were encouraged through two differing methods: triggers and reinforcement. Triggers came in the form of information or incentives presented at the start of a trip, whereas reinforcements came in the form of information presented at the conclusion of a trip.
Carpooling: Travelers were incentivized with reward points to carpool with other app users through Metropia’s DUO social carpooling feature. Upon completion of a carpool, both driver and passenger earned a random amount points which could be redeemed for gift cards to stores and restaurants.
Transit: Through INDUCE, Metropia’s behavior modification platform, travelers for whom transit options were viable and convenient were targeted through a gradual, personalized information campaign highlighting the corresponding transit mode and its benefits. This incremental ‘baby steps’ approach is influential in raising user interest in the mode presented.
Route Optimization: Metropia app users are always presented with the most efficient route for their trips, however analysis shows that many elect to veer off course based on personal experience and preferences. At the conclusion of a trip, users were shown the increased energy savings to be gained by more closely following the suggested route.
Departure Time: Reward points were offered prior to a trip as an incentive for shifting travel times away from peak congestion periods. The greater impact the shift had on reducing peak congestion, the greater the amount of reward points offered.
The Connected Traveler project was completed in June 2018. A few of the project’s key highlights are summarized below:
Prior to this program, the Metropia app displayed CO2 savings upon completion of a trip. For the purposes of the project, that information was changed to show the energy savings percentage for the trip along with a call-out information box explaining the significance of the energy savings. Based on a before and after analysis, the median adoption rate for the recommended route increased by 8.6%.
Trip activity and socio-demographics proved to be factors in the impact of incentives on departure time shifts. Overall, between 7-10% of users responded to pre-trip incentives and adjusted their departure time outside of the defined rush hours. Users are most responsive to incentives when taking discretionary trips. Work trips showed moderate flexibility, which is a rather encouraging finding. More than 60% of users reported that when they followed the suggested new departure time, they experienced a better or equal crowding experience than before.