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

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Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2021-06-16
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
Series
2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
Abstract
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.
Description
| openaire: EC/H2020/856602/EU//FINEST TWINS
Keywords
queue length, traffic management, high resolution data, trajectory data, clustering
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Citation
Zhou , Q , Mohammadi , R , Zhao , W , Zhang , K , Zhang , L , Wang , Y , Roncoli , C & Hu , S 2021 , Queue Profile Identification at Signalized Intersections with High-Resolution Data from Drones . in 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021 . IEEE , IEEE International Conference on Models and Technologies for Intelligent Transportation Systems , Heraklion , Greece , 16/06/2021 . https://doi.org/10.1109/MT-ITS49943.2021.9529337