Examining Driver Behaviour at Freeway Ramp Terminals Based on Trajectory Data Collected Using Unmanned Aerial Vehicles and Video Image Processing

This research utilizes Unmanned Aerial Vehicles (UAVs) to examine real-life speeds and behaviour of drivers at freeway entrance and exit ramp terminals. To achieve this goal, a total of 1,127 minutes of high-resolution aerial video data were collected for traffic movements at thirteen single-lane ramp terminals (seven entrances and six exits) using single and multiple UAVs. A complete space-time trajectory was extracted for each vehicle as it moved on the freeway right lane (FRL) or speed-change lane (SCL) and ramp using a combination of computer vision and deep learning tools. The trajectories were processed to extract relevant driver-vehicle behaviour measures (e.g., merging/diverging location, merging/diverging speed, acceleration/deceleration distances, SCL utilization rates, and accepted merging gaps). A descriptive analysis was performed for better understanding of driver behaviour over the entire stretch of the freeway ramp terminal segment, including FRL, SCL, and ramp. The trends of driver behaviour measures and their relationships with the SCL and ramp geometric characteristics were investigated under free-flow and platoon conditions. Results of the descriptive analysis highlighted differences between taper and parallel SCLs in terms of merging/diverging location, merging/diverging speed, and SCL utilization. Observations of data also confirmed the importance of accounting for the effects of ramp controlling features on the behaviour and vehicle acceleration needs, especially at exit ramps. Several statistical models were developed using regression analysis to model drivers' behaviour measures on SCLs and ramps. Moreover, a set of the observed merging accepted gap data were fitted to the models proposed in the literature to check which models provide the best fit. Results revealed that the models developed using trip data from SHRP-2 Naturalistic Driving Study (NDS) database relatively fitted the data better than other models in the literature. This finding is significant in validating the transferability of models developed using the NDS to other study areas in North America. Finally, the research concluded with a demonstration of the practical application of the developed regression models in reliability analysis, considering actual drivers' behaviour and speeds on SCLs and ramps.