UFTI Director Presents on Driver Behavior & Traffic Modeling at FSU-FAMU Research Seminar

Who says Gators, Rattlers and Cimarrons can’t live in harmony?

This past month, Dr. Lily Elefteriadou traveled up to Tallahassee to present her research on “Driver Behavior and Characteristics and their Use in Traffic Modeling”. The talk was part of the Florida A&M University (FAMU) – Florida State University (FSU) College of Engineering’s Civil and Environmental Engineering Seminar Series. It was also sponsored by the Center for Accessibility and Safety for an Aging Population (ASAP) at FSU.

Dr. Elefteriadou is the Director of the UF Transportation Institute (UFTI) and the Kisinger Campo Professor of Civil Engineering at the University of Florida. Her research focus is traffic operations, traffic flow theory and simulation. She is the principal investigator of the US DOT-funded Regional University Transportation Center (UTC) for Region 4 (the Southeastern Transportation Research Innovation Development and Education, or STRIDE).

“This was the first opportunity I had to visit FSU-FAMU and I enjoyed it very much,” Elefteriadou explained. “I am thankful to Dr. John Sobanjo for the invitation.  I look forward to increased interactions and collaboration with FSU-FAMU.”

Dr. John, Sobanjo, Director of ASAP at the FAMU-FSU College of Engineering, extended the invitation and was pleased to have Dr. Elefteriadou as a seminar speaker. The ASAP is a U.S.DOT Tier-1 UTC at FSU with a primary focus on the aging population.

“Despite the research topic being in a challenging area she was able to communicate the methodology and results of her traffic research in an efficient manner,” he said. “People in the audience with non-transportation backgrounds were able to understand and ask questions, so everyone walked away learning something new.”

Check out the presentation’s abstract below:

Traffic modeling has frequently considered and accounted for variability in driver behavior and characteristics.  For example, microscopic traffic simulators have the capability to replicate vehicular movements (such as lane changing) considering driver characteristics to a significant level of detail.  Such traffic simulators can typically replicate traffic streams with several different driver types which are based on driver aggressiveness.  Vehicular movements (such as car following) are then determined based on the respective action of the particular driver type.  However, a limited amount of research has been reported to categorize driver types or to link particular driving actions with a set of driver types and their characteristics.  Car-following, lane changing, and gap acceptance algorithms have rarely been calibrated to match various driver types, and it is not always clear how micro-simulators incorporate driver behavior aspects into these algorithms.  The presentation described two approaches to collecting driver behavior and characteristics-related data so that they can be used to improve traffic micro-simulators.  The first approach is based on focus groups, while the second is based on in-vehicle field data collection with an instrumented vehicle.  The presentation described these two data collection approaches and provided three example applications related to freeway merging, car-following, and arterial lane changing.