Massachusetts Institute of Technology (MIT) will develop and test its "Mobility Electronic Market for Optimized Travel" (MeMOT), 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. MeMOT 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 MeMOT. 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.

The Palo Alto Research Center (PARC) will develop its COPTER system to identify the energy-efficient routes most likely to be adopted by a traveler. PARC's system model will use currently available data from navigation tools, public transit, and intelligent transportation systems to simulate the Los Angeles transportation network and its energy use. For its control architecture, PARC will leverage its expertise in behavioral modeling and use machine-learning algorithms to predict the near-time travel needs of users, their constraints, and how likely they are to respond to suggested travel options. The system would send users recommendations for energy-efficient trips before departure, and could provide real-time guidance to users if adjustments in a trip need to be made to account for traffic or other unexpected interruptions. Unlike existing platforms, PARC's technology will be able to optimize for multiple travelers at the same time, organized by their most likely corridors of travel. This would prevent travelers from all pursuing the same alternative, which could cause additional traffic, and would also create dynamic ride-sharing options. By improving travelers' quality of service, PARC believes no further incentives are needed to encourage users to adopt the suggestions pushed to their smartphone.