Shunchao Wang

Vehicle trajectories offer vehicular-level information for traffic control. However, it is difficult to obtain fully-sampled trajectories from field-based sensors. By incorporating driving behaviours, this study concentrates on vehicle trajectory reconstruction on different arterial roads. First, a combined approach is tailored to capture driving dynamics and recognise driving behaviour patterns. Second, according to three shape-based metrics, the optimal pattern numbers are determined.

ABSTRACT Vehicle trajectory data provides critical information for traffic flow modeling and analysis during different traffic states and state transitions. This paper introduces a trajectory dataset based on aerial videos called Ubiquitous Traffic Eyes (UTE). It contains high-resolution vehicle trajectories automatically extracted from UAV videos, addressing issues such as pixel shake, vehicle detection, and trajectory fragment reconstruction.

Conflict of Interest The authors declare no conflict of interest. Supporting Information Filename Description adfm202500165-sup-0001-SuppMat.docx74.1 MB Supporting Information References 1a) , , , , , , , , , , Angew. Chem., Int. Ed. 2023, 62, 202214258; b) , , Adv. Energy Mater. 2020, 10, 2000288; c) , , , , , , , , , Adv. Energy Mater. 2020, 10,1903006; d) , , , , , , , , , , , , , , , , Adv. Funct. Mater. 2023, 33, 2213215; e) , , , , , Tungsten 2024, 6, 574.