CoChat
Sign Up
Cited by 6Traffic Prediction and Management TechniquesVehicular Ad Hoc Networks (VANETs)Internet Traffic Analysis and Secure E-voting

The DOI matches its Crossref record; the references it cites are not checked yet.

Machine learning applied to network traffic for vehicular applications to predict the vehicle context

do Vale Saraiva, Tiago, Girardi, Rosario, Campos, Carlos Alberto Vieira·2021·2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)
DOI 10.1109/vtc2021-fall52928.2021.9625538
Open this paper in CoChat — free account

Abstract

The analysis of vehicular traffic and the precise forecast of the user demands enable the development of intelligent vehicular networks. It is usually necessary a considerable effort to obtain, process, and store the data from sensors in vehicles or city infrastructure before using it in benefit of meeting vehicular application's communication requirements. This work deals with the problem of providing information about the vehicular context for the network infrastructure supporting modern vehicular applications. We want to determine whether machine learning (ML) models could be trained on the network measurements of vehicular applications and used to predict events in the vehicular context, such as traffic jams, accidents, among others. We conduct a proof of concept with a typical scenario of an urban traffic jam consisting of vehicles associated with different vehicular applications using the same network infrastructure. The data generated from the network measurements is used to train and test different supervised ML algorithms. The results show that it is possible to use ML applied to the communication network to obtain the vehicular context. The main gain of our proposal is the evaluation of ML applied to the network communication data to predict the vehicular context. This novel approach has the advantage of using data that can be easily obtained in the network core, providing resources that can be used to meet the requirements of modern vehicular applications of Intelligent Transport Systems efficiently.

Overview

Integrity signals

Registry

Crossref · 10.1109/vtc2021-fall52928.2021.9625538

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

The citation lists haven’t been fetched yet

References 35 · Cited by 6

Research works printed but not linked: 33

  1. tan, 2019, Wireless sensor networks intrusion detection based on SMOTE and the random forest algorithm, SENSORS, 19, 10.3390/s19010203 · DOI
  2. 10.1109/CNSM.2015.7367387 · DOI
  3. 10.1016/j.comcom.2020.01.005 · DOI
  4. 10.1109/TCCN.2021.3101239 · DOI
  5. ahmad, 2019, Location-Aware, Context-Driven QoS for IoT Applications, IEEE Syst J, 1
  6. 10.1109/MWC.2017.1600304WC · DOI · Paper page
  7. 10.1109/MCOM.2017.1601160 · DOI
  8. 10.1109/MWC.2016.1500356WC · DOI · Paper page
  9. 10.1109/GCWCN.2018.8668649 · DOI
  10. 10.1109/JIOT.2020.3034156 · DOI
  11. 10.1109/LWC.2021.3077946 · DOI
  12. 10.1109/CCNC.2019.8651823 · DOI
  13. 10.1109/WiMOB.2016.7763223 · DOI
  14. 10.1016/j.adhoc.2019.101913 · DOI
  15. 10.1109/TITS.2021.3086064 · DOI
  16. 10.1109/PIMRC.2018.8580840 · DOI
  17. 10.1109/TITS.2018.2890619 · DOI
  18. 10.1109/PIMRC.2018.8580987 · DOI
  19. 10.1109/PIMRC.2019.8904273 · DOI
  20. 10.1109/TVT.2016.2563164 · DOI
  21. 10.1109/IWCMC51323.2021.9498622 · DOI
  22. 10.1109/PIMRC.2018.8581000 · DOI
  23. 10.1109/PIMRC.2019.8904153 · DOI
  24. seçinti, 0, Software defined architecture for VANET: A testbed implementation with Wireless Access Management, IEEE Communications Magazine, 55, 135, 10.1109/MCOM.2017.1601186 · DOI
  25. 10.1016/j.vehcom.2016.01.002 · DOI
  26. 10.1109/MCOM.2017.1601183 · DOI
  27. chavhan, 2019, IoT-based Context-Aware Intelligent Public Transport System in a metropolitan area, IEEE Internet of Things Journal, 4662, 1
  28. 10.1109/MVT.2017.2752798 · DOI
  29. 10.1016/j.inffus.2018.09.013 · DOI
  30. krajzewicz, 2012, Recent Development and Applications of SUMO - Simulation of Urban MObility, Int J Adv Syst Meas, 5, 128
  31. 10.1016/j.adhoc.2014.09.007 · DOI
  32. 10.1109/MCOM.2017.1601150 · DOI
  33. 10.1109/TITS.2016.2546555 · DOI

Metadata

Authors
do Vale Saraiva, T., Girardi, R., & Campos, C. A. V.
Affiliations
  • Federal University of State of Rio de Janeiro (UNIRIO),Department of Applied Informatics,Rio de Janeiro,RJ,Brazil
  • Universidade Federal do Estado do Rio de Janeiro (BR)
ORCID
Campos, Carlos Alberto Vieira
Venue
2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)
Publisher
IEEE
Published
2021-09
Pages
1-5
ISSN
1090-3038 (ISSN-L)
DOI
10.1109/vtc2021-fall52928.2021.9625538
OpenAlex
W4200024452
Cited by
6
References
35
Citations by year
2026: 1 · 2025: 4 · 2024: 1
FWCI
0.51
Type
conference paper
Language
en
Open access
closed
Licence
Locations
IEEE Vehicular Technology Conference (publishedVersion)
Topics
  • Social Sciences › Transportation › Traffic Prediction and Management Techniques
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
  • Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
Keywords
vehicular networks, network traffic analysis, intelligent transportation systems, traffic jam detection, accident prediction, network measurement data, supervised learning, urban traffic

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: Machine learning applied to network traffic for vehicular applications to predict the vehicle context. https://app.cochat.ai/papers/10.1109/vtc2021-fall52928.2021.9625538
The paper
do Vale Saraiva, T., Girardi, R., & Campos, C. A. V. (2021). Machine learning applied to network traffic for vehicular applications to predict the vehicle context. 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall). https://doi.org/10.1109/vtc2021-fall52928.2021.9625538