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:
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.
If successful, UMD’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.
UMD’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.
UMD’s system could help reduce congestion in metro areas without requiring investment in new infrastructure. A more efficient transportation network could further improve the overall productivity within a regional transportation network.