MAGIC Dataset: Multiple Conditions Unmanned Aerial Vehicle Group-Based High-Fidelity Comprehensive Vehicle Trajectory Dataset

Abstract

High-fidelity vehicle trajectory data contain rich spatiotemporal characteristics and play a major role in the field of transportation research, for example, driving behavior models, traffic flow models, traffic state identification, and driver assistance strategies. There are some coverage issues with the existing datasets recorded in continuous traffic flow facilities, which may limit the further development of research on the safety and efficiency of continuous traffic flow facilities. For example, the observation areas of these datasets lack different traffic states and section types. Therefore, it is necessary to collect a new trajectory dataset. This paper proposes a detailed plan for aerial photography by unmanned aerial vehicle (UAV) group. An experiment was conducted from 7:40 to 10:40 a.m. on a section of the Shanghai Inner Ring, Shanghai, China, with a total length of 4,000 m in both directions including a large radius curve and six ramps. The trajectory dataset, named MAGIC, is extracted and compared with NGSIM US-101 and HIGH-SIM from the aspects of experiment field, traffic states, and so on. Further, to illustrate the advantages of the MAGIC dataset incorporating different traffic states and section types, the MAGIC dataset is evaluated from the three aspects of traffic congestion state, fundamental diagram, and traffic conflict. Overall, there are significant differences in macroscopic traffic flow and traffic safety characteristics between road sections with different section types or traffic states. Therefore, the proposed method presents some unique advantages and may perform effectively in many fields.