Enhancing Highway Traffic Volume Surveys in Thailand with Image Processing and Machine Learning

Kerkritt Sriroongvikrai; Kasem Choocharukul; Punnarai Siricharoen; Krittiya Phitchakian

doi:10.2991/978-94-6463-972-8_18

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Enhancing Highway Traffic Volume Surveys in Thailand with Image Processing and Machine Learning

Authors

Kerkritt Sriroongvikrai¹^{, *}, Kasem Choocharukul¹, Punnarai Siricharoen², Krittiya Phitchakian³

¹Chulalongkorn University, Faculty of Engineering, Department of Civil Engineering, Transportation division, Bangkok, Thailand

²Chulalongkorn University, Faculty of Engineering, Department of Computer Engineering, Bangkok, Thailand

³iNFRA-Corporation, Bangkok, Thailand

^*Corresponding author. Email: kerkritt.s@student.chula.ac.th

Corresponding Author

Kerkritt Sriroongvikrai

Available Online 29 December 2025.

DOI: 10.2991/978-94-6463-972-8_18 How to use a DOI?
Keywords: Traffic Monitoring; Vehicle detection; Vehicle classification
Abstract: This research presents the development of an efficient and accurate traffic count device for the Department of Highways (DOH). Its objective is to reduce the burden of traditional traffic volume surveys, requiring staff to count each vehicle manually. The developed device utilizes video cameras in conjunction with image processing analysis to detect, count, and classify up to 13 types of vehicles. Image processing techniques enable accurate and rapid analysis of video footage, allowing for diverse vehicle classification according to the DOH’s standards. Test results demonstrate that this device exhibits high accuracy and can operate in various environments. The algorithm for counting vehicles using the developed model has a Weighted Mean Absolute Percentage Error (Weighted MAPE) of 15.95%. Implementing this device will enable the DOH to collect traffic volume data more quickly and efficiently, leading to improved traffic planning and management. Furthermore, DOH can utilize the device and developed model to expand the traffic count on highways nationwide annually.
Copyright: © 2025 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 14th Asia-Pacific Conference on Transportation and the Environment (APTE 2025)
Series: Atlantis Highlights in Engineering
Publication Date: 29 December 2025
ISBN: 978-94-6463-972-8
ISSN: 2589-4943
DOI: 10.2991/978-94-6463-972-8_18 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Kerkritt Sriroongvikrai
AU  - Kasem Choocharukul
AU  - Punnarai Siricharoen
AU  - Krittiya Phitchakian
PY  - 2025
DA  - 2025/12/29
TI  - Enhancing Highway Traffic Volume Surveys in Thailand with Image Processing and Machine Learning
BT  - Proceedings of the 14th Asia-Pacific Conference on Transportation and the Environment (APTE 2025)
PB  - Atlantis Press
SP  - 188
EP  - 198
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-972-8_18
DO  - 10.2991/978-94-6463-972-8_18
ID  - Sriroongvikrai2025
ER  -

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