Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/46341
Title: Automating Composition of Origin-Destination Flows of Intersections Based on UAV Data
Authors: Betru, Abel
TRAN, Thi 
ECTORS, Wim 
Advisors: Ectors, Wim
Issue Date: 2025
Publisher: 
Source: Proceedingsbook of the 16th International Conference on Ambient Systems, Networks and Technologies (ANT), , p. 233 -240
Abstract: With the exponential development rate of UAV and computer version technologies, vast and sophisticated data on traffic is now available. Spatial and temporal data, including speed and other parameters of trajectory data, can be captured. Likewise, there are unsupervised clustering algorithms in the domain of machine learning that can group data points into clusters based on their inherent similarities without using labeled data. Algorithms such as GMM, DBSCAN, and HDBSCAN identify patterns and structures within the dataset, allowing for the discovery of natural groupings. Leveraging data from UAVs and these clustering algorithms, this paper aims to develop an effective and efficient methodology to automate the extraction of Origin-Destination (OD) flows of different types of intersections. A new custom-made intersection OD flow automation method called IODF is introduced, along with the deployment of DBSCAN, HDBSCAN, and GMM algorithms for clustering intersection trajectories, leading to the automatic extraction of OD flows. The results demonstrate that all four methods performed effectively in extracting OD flow for various intersection types.
Keywords: OD flow;UAV;DBSCAN;HDBSCAN;GMM
Document URI: http://hdl.handle.net/1942/46341
DOI: 10.1016/j.procs.2025.03.032
Rights: 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer review under the responsibility of the scientific committee of the Program Chairs
Category: C1
Type: Proceedings Paper
Appears in Collections:Research publications

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