Evaluating a Signalized Intersection Performance Using Unmanned Aerial Data

Simple item page
dc.contributor.authorAshqer, Mujahid I.
dc.contributor.authorAshqar, Huthaifa
dc.contributor.authorElhenawy, Mohammed
dc.contributor.authorAlmannaa, Mohammed
dc.contributor.authorAljamal, Mohammad A.
dc.contributor.authorRakha, Hesham A.
dc.contributor.authorBikdash, Marwan
dc.date.accessioned2022-10-20T16:21:27Z
dc.date.available2022-10-20T16:21:27Z
dc.date.issued2023-04-20
dc.description.abstractThis paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from real-time videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, and fuel consumption. The results of the various MOEs were found to be promising. We also demonstrated that estimating MOEs in real-time is achievable using drone data. Such models can track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions.en_US
dc.description.sponsorshipAuthors would like to thank pNEUMA project for providing the dataset that was used in this study.en_US
dc.description.urihttps://www.tandfonline.com/doi/abs/10.1080/19427867.2023.2204249
dc.format.extent11 pagesen_US
dc.genrejournal articlesen_US
dc.genrepreprintsen_US
dc.identifierdoi:10.13016/m2nqhb-ulhj
dc.identifier.citationMujahid I. Ashqer, Huthaifa I. Ashqar, Mohammed Elhenawy, Mohammed Almannaa, Mohammad A. Aljamal, Hesham A. Rakha & Marwan Bikdash (2023) Evaluating a signalized intersection performance using unmanned aerial Data, Transportation Letters, DOI: 10.1080/19427867.2023.2204249
dc.identifier.urihttps://doi.org/10.1080/19427867.2023.2204249
dc.identifier.urihttp://hdl.handle.net/11603/26204
dc.language.isoen_USen_US
dc.publisherTaylor & Francis
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Data Science Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis is the submitted manuscript of an article published by Taylor & Francis in Transportation Letters: The International Journal of Transportation Research on 20 Apr 2023, available online: http://www.tandfonline.com/10.1080/19427867.2023.2204249.en_US
dc.titleEvaluating a Signalized Intersection Performance Using Unmanned Aerial Dataen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-6835-8338en_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
2207.08025.pdf
Size:
5.48 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UMBC Data Science
UMBC Faculty Collection