TUMDOT–MUC: Data Collection and Processing of Multimodal Trajectories Collected by Aerial Drones

Abstract

Currently available trajectory data sets undoubtedly provide valuable insights into traffic events, the behavior of road users and traffic flow theory, thus enabling a wide range of applications. However, there are still shortcomings that need to be addressed: (i) the continuous temporal recording (ii) of a coherent area covering several intersections (iii) with the detection of all road users, including pedestrians and cyclists. Therefore, this study focuses on the design of a large-scale aerial drone observation in the city of Munich, Germany, as well as the processing steps and the description of the resulting data set. Using twelve camera-equipped, unmanned aerial drones, the observation monitored an inner urban road section with a length of 700 m continuously for several hours during the afternoon peak hours on two working days. The trajectories of all road users were then extracted from the videos and post-processed in order to obtain a coherent and accurate data set. The resulting trajectories contain information on the category, dimensions, location, velocity, acceleration and orientation of each road user at each frame, merged continuously in time and space across several drone observation areas and subsequent time slots. The data, therefore, includes various interactions between different modes of motorized traffic and active mobility users like pedestrians and cyclists. The whole data set and the supporting data are available open source for research purposes to ensure global accessibility.