School of Civil and Environmental Engineering

Ph.D. Thesis Defense Announcement

Analysis of Drivers' Gap-Acceptance Behavior Using A Drone-Measurement Technique

By Anqi Wei

Advisor:

Dr. Michael Rodgers (CEE)

Committee Members:  Dr. Randall Guensler (CEE), Dr. Michael Hunter (CEE), Dr. Angshuman Guin (CEE), Dr. Franklin Gbologah (Advanced Data Analytics Team, Chick-Fil-A Incorporated), Dr. Abhilasha Saroj (NTRC, ORNL)

Date and Time:  March 25, 2:00 PM

Location: Clough Commons Room 131

 In modern roundabouts, priority is given to circulating vehicles and thus drivers trying to enter must decide to either accept or reject a gap in the circulating stream and these gap acceptance/rejection decisions vary significantly between drivers. A commonly used parameter to characterize these decisions is the “critical gap” that is defined as the minimum gap in the conflicting flow accepted by almost all drivers and serves as an input into most roundabout entry capacity models. Current practice for critical gap estimation requires the data collection to be conducted under saturated conditions when there is a constant queue. Since most roundabouts rarely operate near capacity, this restriction significantly restricts measurement of gap acceptance under a variety of conditions. This study describes a method to obtain reliable and accurate gap acceptance measurements under a wide-range of operational conditions using drone-based video observations coupled with automated data processing using machine-vision techniques.  
 
In this study, a DJI quadcopter drone equipped with a 4k-camera was used collect aerial video recordings of traffic operations at 24 selected roundabouts in Georgia. From these videos, vehicle trajectory data were automatically extracted and analyzed to establish entering drivers’ gap-acceptance behavior. External factors related to geometric design and operation were also measured using the drone-based technique, and a predictive model for roundabout drivers’ critical gap estimation and capacity evaluation was developed accordingly to help identify significant factors that influence field-observed gap-acceptance behavior, and provide insights into future intersection design decisions.