Treating Noise and Anomalies in Vehicle Trajectories From an Experiment With a Swarm of Drones

Abstract

Unmanned aerial systems, known as "drones," are relatively new in collecting traffic data. Data from drone videography can have potential applications for traffic research. Drones can record the vehicles from their aerial point-of-view and provide their naturalistic driving behavior. Processing raw data from drones to remove noise and anomalies is crucial to ensure that the data are fit for subsequent applications, e.g., the development of traffic flow or crash risk models. This study uses a part of the pNEUMA dataset, a large dataset with almost half a million trajectories captured by a swarm of drones over Athens, Greece. This novel dataset offers an opportunity to analyze the data attributes and treat the noise and outliers in the data. We use a combination of smoothing filters and Extreme Gradient Boosting with adaptive regularization to process the speed and acceleration profiles of the vehicle trajectories in the dataset. Our approach can help prospective data users treat this or similar trajectory datasets alternatively to applying manual thresholds and assist in accelerating research in microscopic traffic analysis.

Vehicle Trajectory Reconstruction Using Lagrange-Interpolation-Based Framework

“…Mahajan et al used smoothing filters and extreme gradient boosting with adaptive regularization to reduce the observation noise between speed and acceleration. The method had better robustness in processing raw data from an unmanned aerial system compared with manual thresholds [32]. Abbas et al proposed a multimodel-based extended Kalman filter (EKF) to reduce the vehicle location noise using the vehicle trajectory.…”

“…With the development of multimedia equipment, many transportation researchers have used high-resolution images to extract vehicle trajectory data, and the high-resolution images are from videos and cameras, which are installed on an unmanned aerial vehicle, a moving car or transportation infrastructures. But the immature computer vision extraction techniques in complex traffic environments (e.g., vehicles are obscured by buildings or bridges in some images) cause some unexpected data in extracted vehicle trajectory data [1,2,32].…”

Risks of Drone Use in Light of Literature Studies

“…[38,[86][87][88][89][90][91][92]] Water environment [93-103] Infrastructure [104-108] Emission of pollution [109-116] Landslides [117-125]…”