Evaluation of small unmanned aerial system highway volume and speed‐sensing applications

Abstract

Small unmanned aerial systems (sUAS) have been utilised in the transportation industry in recent years to decrease the cost of projects and tasks while increasing safety. This is due to their ability to capture aerial images with reduced effort and time. Recently, these devices have begun to be used for traffic monitoring, given their ability to capture video above a roadway. Combined with object‐tracking techniques, vehicle data such as speeds, volumes, and trajectories could be extracted, providing an opportunity to revolutionize traffic data collection techniques. There exists a need to improve upon origin–destination volume and speed data collection procedures through the development of a low‐cost methodology to capture detailed data that would allow for more accurate analysis. This study evaluates a methodology and measures the accuracy of volume and speed data collected through sUAS aerial imagery using object tracking techniques. Using the developed methodology, vehicle volumes were tracked at 93% accuracy, and vehicle speeds were recorded with a 6.6% relative error. While future improvements could be made on this methodology as technology advances, this study reveals a low‐cost solution to collect vehicle data which could improve the efficiency of transportation studies, and in turn, improve safety.

Wireless-Signal-Based Vehicle Counting and Classification in Different Road Environments

“…The researchers also had some experimental results on traffic prediction, which confirm "that the proposed method can provide reasonable estimation not only for traffic states but also, [• • • ], travel time can be effectively estimated and predicted" [12]. Recently, Ryan et al [13] and Byun et al [14] proposed the application of unmanned aerial vehicle (UAV) for estimating road traffic and vehicle speed automatically by analyzing video feed using machine learning approaches like deep neural network.…”

“…The computer vision algorithms were able to detect 95.7% and classify 93.2% of the vehicles. Ryan et al [13] also proposed the computer visionbased approach assisted with small unmanned aerial systems (sUAS) to capture detailed data for collecting vehicle data.…”