Highlights Image traffic data are collected from the UAV and trajectories are extracted through object-based detection and tracking. The Safety Space boundary in proximity of the vehicle is observed from data. This Safety Space forms the boundary of the comfort area drivers perceive as safe when traveling. Invasions of the Safety Space by other vehicles potentially reveal traffic conflicts.
Abstract The mixed traffic flow has complex dynamics by nature. The kinematic differences between automobiles and motorcycles result to distinct driving behaviors. Traditional automobile-based traffic flow theory is not always suitable for mixed traffic streams. The purpose of this study is to observe from actual data a clearance boundary, called Safety Space, drivers maintain from other vehicles, and use it as a spatial filter to determine conflicts in mixed traffic flows. Image data are collected from an Unmanned Aerial Vehicle (UAV), and microscopic characteristics such as vehicle type, position, velocity, and trajectory are extracted through computer vision techniques. The Histogram of Oriented Gradients (HOG) feature and the Support Vector Machine (SVM) classifier are utilized for the vehicle detection, while the Kalman Filter is employed for the derivation of vehicle trajectories. The Safety Space is then determined based on those trajectories. Validation data are collected at intersections in Taipei, Taiwan; Bangkok, Thailand; and Mumbai, India. The vehicle detection and tracking are satisfactory, and the Safety Space surrogate reveals risk zones caused by spatial proximity between vehicles.
Conflict analytics through the vehicle safety space in mixed traffic flows using UAV image sequences
2020-08-02
Article (Journal)
Electronic Resource
English
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