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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

Researchers with the Georgia Institute of Technology (Georgia Tech) will combine real-time analysis of transportation network data with distributed simulation modeling to provide drivers with information and incentives to reduce energy consumption. The team's system model will use three sources of data to simulate the transportation network of the Atlanta metro area. The Georgia Department of Transportation's intelligent transportation system (ITS) data repository, hosted at Georgia Tech, will provide 20-second, lane-specific operations data while team partner, AirSage, will provide highway speeds and origin-destination patterns obtained from cellular networks. The team will also use real-time speed data collected from 40,000 volunteers using a smartphone application. The researchers will use pattern recognition algorithms to identify traffic accidents and recurrent congestion, predict traffic congestion severity, and user responses to congested conditions. Using this information, the team will develop a control architecture that will signal drivers with options to alter departure times, take specific routes, and/or use alternate modes of transportation to reduce energy use. The team anticipates that users will adopt the suggested guidance because the suggestions identified will not increase the time or cost of the trip, and could ultimately save users money in fuel costs.

Massachusetts Institute of Technology

Sustainable Travel Incentives with Prediction, Optimization and Personalization (TRIPOD)

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.

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

The National Transportation Center at the University of Maryland (UMD) and its partners will develop a technology capable of delivering personalized, real-time travel information to users and incentivizing travelers to adopt more energy-efficient travel plans. The project team will use data from UMD's existing regional integrated transportation information system (RITIS) as well as other available resources to design its system model. This system model will integrate information on individual traveler behavior to simulate the effects of traffic and individual traveler choices on energy use in the Washington/Baltimore metro area. For its control architecture, UMD researchers will apply behavioral research to predict travelers' responses and identify appropriate, personalized incentives to encourage drivers to alter routes, departure times, and driving styles, or to take mass transit or ride-sharing services. The control architecture will incentivize users with monetary and non-monetary rewards, including social influence strategies that leverage social media to generate competition or rewards among social network users.

Utah State University

Feasibility Analysis of Electric Roadways Through Localized Traffic, Cost, Adoption, and Environmental Impact Modeling

Utah State University will develop a technoeconomic analysis to assess the feasibility and environmental and economic impacts of various electric roadway technologies. This project will aggregate, synthesize, and link previously isolated data sets to form a high-resolution, comprehensive assessment of electric roadways at the regional scale. Localized grid and road construction cost estimates are being considered. Targeted outcomes include identification of first adopters, economic and environmental cost/benefit of incremental deployment, and technology gaps that can accelerate adoption. By resolving these key questions, this project endeavors to catalyze public and private investment in electric roadway technology development, pilot projects, infrastructure deployment, and market adoption. The team will also evaluate the technology gaps and the value proposition for broader incremental rollout of electric roadways across the U.S. If successful, this project will provide actionable information regarding guidelines for incremental rollout of electric roadways and quantified metrics that tie technology gaps to their impact on accelerating market adoption.

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