Center for Sustainable Mobility

The Center for Sustainable Mobility (CSM) conducts research relevant to society’s transportation mobility, energy, environmental, and safety needs. The center translates the results of research into realistic and workable applications, creates and provides tools needed to apply developed knowledge and processes, and educates qualified engineers to meet today’s transportation demands and tomorrow’s transportation challenges in the areas of transportation network control, large-scale transportation system modeling, traffic state prediction using large data and artificial intelligence techniques, transit bus real-time routing and scheduling, vehicle energy and environmental modeling, transportation system modeling, and eco-transportation applications. CSM has worked and is currently working on numerous projects funded by the U.S. Department of Transportation, the U.S. Department of Energy, the Virginia Department of Transportation, and the Federal Transit Association. The center is developing eco-routing, eco-cooperative adaptive cruise control systems, and traffic signal control systems that enhance the efficiency, mobility, environmental impacts, and safety impacts of the transportation system.

Hesham Rakha
Center Director

MAUTC Data Quality Needs Assessment

The objective of this Mid-Atlantic Universities Transportation Center (MAUTC)/Virginia Department of Transportation (VDOT)-sponsored effort is to prepare and disseminate accurate medium-term travel-time predictions (i.e., up to 120 minutes in advance) for a major corridor between Richmond and Virginia Beach using probe-based INRIX data. The study section under consideration includes Interstate 64 (I-64) from Interstate 295 (I-295; east of Richmond) to Interstate 264 (I-264). Tasks associated with this project include: 1) Assembling INRIX data and constructing a database of historical data categorized by days of the week and weekends for entire freeway sections from Richmond to Virginia Beach; 2) Developing data imputation techniques and k-Nearest Neighbor (kNN) algorithms to identify similar spatiotemporal conditions for use in travel-time prediction; this work will consider the use of pattern recognition and other statistical techniques to identify comparable conditions; and 3) Testing the algorithm by displaying the travel times on VDOT variable message signs (VMSs).

MAUTC Penn State

CSM leads the Virginia Tech MAUTC team within a consortium that includes Penn State (lead university), University of Maryland, University of Virginia and West Virginia University. For much of its history, MAUTC has functioned as a research-funding clearinghouse for its constituent members. The changing transportation profession dictates that MAUTC also function as a clearinghouse for knowledge creation, knowledge management, and knowledge implementation. MAUTC is a single entity serving multiple non-university stakeholders who are part of the transportation enterprise in the mid-Atlantic region.


TranLIVE is a Tier 1 University Transportation Center (UTC). The theme of TranLIVE is “Transportation for Livability by Integrating Vehicles and the Environment,” with an emphasis on developing technologies that reduce the environmental impact of transportation. The TranLIVE UTC is a consortium of five universities: the University of Idaho (lead), Virginia Tech, Texas Southern University, Syracuse University, and Old Dominion University. The mission of TranLIVE is to help the nation achieve a cleaner environment and greater energy independence through: 1) Eco-traffic signal-system technologies; 2) Eco-routing tools; and 3) Alternative fuels and vehicles. More accurate and reliable vehicle emission and fuel consumption models will be developed by integrating vehicle and environmental data systems. These efforts will lead to improved technology for the industry and better decision-making tools for transportation and land use officials.

Eco-signal Evaluation

As part of a Federal Highway Administration (FHWA)-funded effort, CSM researchers developed an algorithm to compute the fuel-optimal speed profile of a vehicle approaching a signalized intersection that indicates time-to-green (TTG) values. The optimized vehicle trajectory is part of an Eco-Speed Control Model. To predict the most fuel-optimal speed trajectory and advise the driver of optimal actions, the model incorporates information received about future signal changes from an upcoming traffic signal controller using vehicle-to-infrastructure (V2I) communication. As a vehicle equipped with V2I communication capability enters the dedicated short-range communication (DSRC) scope of a particular intersection, the vehicle receives information about lead vehicles and upcoming signal changes. If the vehicle can maintain its current speed and safely pass through the intersection while the light is green, the vehicle is then directed to do so. If the traffic signal indication is yellow, the algorithm calculates whether the vehicle can accelerate to some value below a set limit (usually the speed limit) and pass through the intersection safely before the signal turns red. If this is possible, the vehicle is then controlled to proceed as directed. If the vehicle arrives while the traffic signal is red, the algorithm computes the fuel-optimal speed profile that allows the vehicle to arrive at the intersection stop line when the traffic signal turns green and all queues have been cleared. The research effort will use the eTEXAS model to test the proposed algorithm. The test will consider various intersection geometries, arrival rates, expected times of arrival, DSRC scopes, and different initial queue lengths to quantify the potential benefits of such a system.

Blue Castle Nuclear Plant Evacuation Study

This project entails assessing an evacuation time estimate (ETE) in the vicinity of the Blue Castle nuclear plant in Green River, Utah. Dr. Hesham Rakha is leading a VTTI team that will conduct the modeling study of the evacuation plan. In accordance with draft guidance, a minimum of 10 evacuation scenarios will be modeled to reflect seasonal, day-of-the-week, weather, special events, and roadway effects on ETEs. These scenarios will be developed to identify combinations of variables and events, provide ETEs under varying conditions, and support protective action decisions. The scenarios will include a range of potential evacuation situations dependent on site-specific considerations. The team will also consider the introduction of staged evacuations as alternatives to a keyhole evacuation. Staged evacuations necessitate the evacuation of one area while adjacent areas are ordered to shelter in place until directed to evacuate. For each evacuation scenario, an estimate of the time to complete a staged evacuation will be provided to support protective action decision making. The ETE report will include a discussion of the approaches used during the development of staged evacuations.

Kyoungho Ahn

Research Scientist

Hao Chen

Research Associate

Jianhe Du

Senior Research Associate

Ihab El-Shawarby

Research Scientist