Sustainable Travel Incentives with Prediction, Optimization and Personalization (TRIPOD)
Transportation accounts for more than 25% of total energy consumed in the United States. Efforts to minimize energy use have predominantly focused on improving vehicle fuel efficiency and expanding use and availability of public transit. However, other factors impact transportation energy use such as traffic congestion, stop-and-go traffic, and limited information on available routes. Now, due to increasing availability of real-time data on traffic flows, transit scheduling, parking, local weather, and activities linked to transportation use, it is additionally possible to empower individual travelers to meet their transportation needs in ways that reduce energy use. To address these opportunities, new methods of modeling real transportation networks, new network optimization approaches and personalized incentive strategies can be used to deliver individuals the information they need to make choices that provide them with quality of service while reducing energy utilization in our transportation systems.
Project Innovation + Advantages:
Massachusetts Institute of Technology (MIT) will develop and test its "Sustainable Travel Incentives with Prediction, Optimization and Personalization" (TRIPOD), a system that could incentivize travelers to pursue specific routes, modes of travel, departure times, vehicle types, and driving styles in order to reduce energy use. TRIPOD relies on an app-based travel incentive tool designed to influence users’ travel choices by offering them real-time information and rewards. MIT researchers will use an open-source simulation platform, SimMobility, and an energy model, TripEnergy, to test TRIPOD. The system model, which will simulate the Greater Boston area, will be able to dynamically measure energy use as changes to the network and travelers’ behavior occur. The team’s system model will be linked with a control architecture that will evaluate energy savings and traveler satisfaction with different incentive structures. The control architecture will present users with personalized options via a smartphone app, and it will include a reward points system to incentivize users to adopt energy-efficient travel options. Reward points, or tokens, could be redeemed for prizes or discounts at participating vendors, or could be transferred amongst users in a social network.
If successful, MIT’s system will demonstrate that energy-efficiency gains in personal transportation can be accomplished through network controls that encourage individual travelers to take specific, energy-relevant actions.
MIT’s system could facilitate a reduction in transportation energy use and help reduce demand for imported oil.
More efficient transportation networks will minimize energy consumption, resulting in improved air quality and lower greenhouse gas emissions.
The team’s system could help reduce congestion in metro areas and increase the efficiency of the transportation network, without requiring investment in new infrastructure.