Georgia Tech Research Corporation

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.

Researchers with the Georgia Tech Research Corporation 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.

If successful, the Georgia Tech 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.