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Boosting Machine Learning Algorithms with Grid-Search for Transport IoT Data Prediction
University of Jinan, CHN.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-5824-425X
University of Jinan, CHN.
University of Jinan, CHN.
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2022 (English)In: Proceedings of the 12th International Conference on Computer Engineering and Networks / [ed] Liu Q., Liu X., Cheng J., Shen T., Tian Y., Springer Science+Business Media B.V., 2022, p. 903-910Conference paper, Published paper (Refereed)
Abstract [en]

IoT (internet of things) data is a topic we have discussed a lot in recent years. Traffic data is an important part of IoT data. Traffic flow prediction not only facilitates people’s travel and saves our time, but also provides effective technical support for highway traffic control and scheduling. To achieve accurate traffic flow prediction, this study aims to build a machine learning-based traffic flow prediction model. We first screen out the features that have a greater impact on traffic flow. On this basis, this work establishes a traffic flow prediction model based on CatBoost. By comparing with other machine learning models, conclusions can be drawn: CatBoost model can accurately predict traffic flow; CatBoost outperforms traditional machine learning models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2022. p. 903-910
Series
Lecture Notes in Electrical Engineering, ISSN 1876-1100, E-ISSN 1876-1119 ; 961
Keywords [en]
CatBoost, Machine learning, Predictive models, Traffic flow, Adaptive boosting, Forecasting, Data prediction, Grid search, Machine learning algorithms, Machine learning models, Machine-learning, Prediction modelling, Traffic flow prediction, Internet of things
National Category
Transport Systems and Logistics Computer Sciences
Identifiers
URN: urn:nbn:se:bth-24180DOI: 10.1007/978-981-19-6901-0_93Scopus ID: 2-s2.0-85144540165ISBN: 9789811969003 (print)OAI: oai:DiVA.org:bth-24180DiVA, id: diva2:1725276
Conference
12th International Conference on Computer Engineering and Networks, CENet 2022, Haikou, 4 November through 7 November 2022
Available from: 2023-01-10 Created: 2023-01-10 Last updated: 2023-01-10Bibliographically approved

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Sun, Bin

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