Vision Based Traffic Conflict Analytics of Mixed Traffic Flow

Safety issues of motorcycles are mainly caused by the complicated interactions among various vehicle types in the mixed traffic flow. The structural differences between automobiles and motorcycles results to distinct driving behavior. Moreover, most of the research in traffic flow analytics is carried out in the context of developed countries where vehicle follows lane markings. However, motorcycles have very different behaviors, and the traditional automobile-based traffic theory and transportation management cannot be applied to mixed traffic streams with automobiles and motorcycles. The purpose of this study is to observe the features of mixed traffic flow, driver behavior and traffic conflict between automobiles and motorcycles. The data was collected by using an unmanned aerial vehicle (UAV) at urban intersections in Taipei. The microscopic characteristics of mixed traffic flow such as vehicle types, velocity, acceleration, trajectories are observed through computer vision and image processing methods. The Histogram of Oriented Gradients (HOG) descriptor is adapted for the detection of vehicles utilizing a Support Vector Machine (SVM), and the Kalman Filter is employed for the tracking of the vehicles’ trajectory. The traffic conflict severity was conducted by calculating the time-to-collision (TTC) and invasion of safety space for vehicles. The detection results show superior stability and performance with the precision of 98.3% and 98.1% for automobiles and motorcycles, respectively. In addition, even under highly dense urban traffic conditions, vehicle classification and tracking are successful. The results of this study can serve as a reference for roadway safety guidance.