Transportation Network
Traveler Response Architecture using Novel Signaling for Network Efficiency in Transportation
The projects in ARPA-E's Traveler Response Architecture using Novel Signaling for Network Efficiency in Transportation (TRANSNET) program aim to minimize energy consumption in personal transportation, without having to improve current infrastructure or vehicle efficiency. TRANSNET project teams are developing new network control architectures, coupled with incentive strategies, to encourage individual travelers to take specific energy-relevant actions. These actions could, for example, contribute to reductions in miles traveled and increased occupancy rates for all modes. Project teams will design two interacting computer models: a system model that dynamically simulates the entire transportation network, including roadways, public transit, and other modes of travel, and calculates energy use at an individual level; and a control architecture, which quantifies the impacts of incentives and signals on real-time energy reductions. Operating together, these modules will measure changes to energy use in response to controls. If successful, these systems will allow the optimization of control strategies, which could increase the efficiency in a transportation network.
For a detailed technical overview about this program, please click here.
Georgia Tech Research Corporation
Network Performance Monitoring and Distributed Simulation to ImproveTransportation Energy Efficiency
Massachusetts Institute of Technology
Mobility Electronic Market for Optimized Travel (MeMOT)
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.
National Renewable Energy Laboratory
The Connected Traveler: A Framework to Reduce Energy Use in Transportation
The National Renewable Energy Laboratory (NREL) and its partners will create a network architecture that approaches sustainable transportation as a dynamic system of travelers and decision points, rather than one of vehicles and roads, in order to create personalized energy-saving opportunities. The project will use currently available demographic and transportation data from an urban U.S. city as a test bed for energy reduction. To incentivize travelers to pursue energy-efficient routes, the control architecture will develop algorithms to understand a traveler's preferences, tailor recommendations to the user, and identify personal incentives that will enable transportation system energy benefits. The Connected Traveler framework will provide local transportation authorities and individual travelers with a tool to identify personal travel decisions that balance quality of service with energy efficiency.
Palo Alto Research Center
Collaborative Optimization and Planning for Transportation Energy Reduction (COPTER)
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.
University of Maryland
Integrated, Personalized, Real-Time Traveler Information and Incentive Technology for Optimizing Energy Efficiency in Multimodal Transportation Systems
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