Data Governance in Traffic Data: Anomaly Detection with Generalized Additive Models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00604019" target="_blank" >RIV/67985807:_____/24:00604019 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21260/24:00379705

Result on the web

<a href="https://doi.org/10.14311/NNW.2024.34.011" target="_blank" >https://doi.org/10.14311/NNW.2024.34.011</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2024.34.011" target="_blank" >10.14311/NNW.2024.34.011</a>

Result language

angličtina

Original language name

Data Governance in Traffic Data: Anomaly Detection with Generalized Additive Models

Original language description

The primary objective of the presented research is to enhance an existing data quality control application by integrating advanced anomaly detection mechanisms based on generalized additive models. This approach targets time- series traffic data, where traditional methods may fall short in identifying complex, non-linear patterns of anomalies. In collaboration with Simplity s.r.o., we are extending their current data quality assessment tool to incorporate generalized additive models, providing a more robust and dynamic solution for monitoring and ensuring the reliability of traffic datasets. The integration of these models aims to improve the accuracy of anomaly detection, leading to more effective data management in transport systems and contributing to higher standards of data quality in the field of traffic informatics.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

<a href="/en/project/CK04000189" target="_blank" >CK04000189: Data quality tools for ensuring system reliability of transport information centres</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neural Network World

ISSN

1210-0552

e-ISSN

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Volume of the periodical

34

Issue of the periodical within the volume

4

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

16

Pages from-to

203-218

UT code for WoS article

001414975800001

EID of the result in the Scopus database

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