Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 182

The DOI matches its Crossref record.

Monitoring road traffic with a UAV-based system

Open access
Elloumi, Mouna, Dhaou, Riadh, Escrig, Benoit, Idoudi, Hanen, Saidane, Leila Azouz·2018·2018 IEEE Wireless Communications and Networking Conference (WCNC)

Abstract

Unmanned Aerial Vehicles (UAVs) are becoming an attractive solution for road traffic monitoring because of their mobility, low cost, and broad view range. Up to now, existing traffic monitoring systems based on UAVs only use one UAV with fixed trajectory to extract information about vehicles. In this paper, we propose a road traffic monitoring system using multiple UAVs. We develop a method to generate adaptive UAVs trajectories, which is based on the tracking of moving points in the UAV field of view. Also we generate UAVs trajectories using mobility models that are usually used to model vehicles mobility. UAVs monitor the traffic on a city road, they are responsible for collecting and sending, in real time, vehicle information to a traffic processing center for traffic regulation purposes. We show that the performance of our system is better than the performance of the fixed UAV trajectory traffic monitoring system in terms of coverage rates and events detection rates.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/wcnc.2018.8377077

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 182

Metadata

Authors
Elloumi, M., Dhaou, R., Escrig, B., Idoudi, H., & Saidane, L. A.
Venue
2018 IEEE Wireless Communications and Networking Conference (WCNC)
Publisher
IEEE
Published
2018-04
Pages
1-6
ISSN
1525-3511 (ISSN-L)
OpenAlex
W2808453126
Cited by
182
References
0
FWCI
26.35
Type
conference paper
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Monitoring road traffic with a UAV-based system. https://app.cochat.ai/papers/10.1109/wcnc.2018.8377077
The paper
Elloumi, M., Dhaou, R., Escrig, B., Idoudi, H., & Saidane, L. A. (2018). Monitoring road traffic with a UAV-based system. 2018 IEEE Wireless Communications and Networking Conference (WCNC). https://doi.org/10.1109/wcnc.2018.8377077

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.