Queue Profile Identification at Signalized Intersections with High-Resolution Data from Drones

• Published in: 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) pp. 1 - 6
• Main Authors: Zhou, Qishen, Mohammadi, Roozbeh, Zhao, Weiming, Zhang, Kaihang, Zhang, Lihui, Wang, Yibing, Roncoli, Claudio, Hu, Simon
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16-06-2021
• Subjects: clustering; Clustering algorithms; Data mining; Heuristic algorithms; high resolution data; Machine learning algorithms; queue length; Timing; traffic management; Trajectory; trajectory data; Vehicle-to-infrastructure
• DOI: 10.1109/MT-ITS49943.2021.9529337

Queue profile is a crucial measure for traffic management in the vicinity of signalized intersections. In this study, we develop a method to identify queue profile using high resolution data, which can be provided from various sources such as drones. Our methodology has three main components which are signal state estimation, queue profile identification, and lane detection. The developed algorithms are tested on the real-world dataset collected by drones as a case study for validation. Remarkably, our method only uses drone data as input and it is independent from any other data source such as geographic information system data. The results demonstrate satisfactory performance of the methodology in extracting queue profile information from raw drone data. The developed algorithm can be also applied on data collected via connected vehicles in future.