Kasem Choocharukul
Portrait from the center’s directory

AI for road safety and sustainable mobility

Kasem Choocharukul

Research connects deep learning for crash-severity prediction and automated traffic surveys with transport planning, travel behaviour and sustainable mobility. Selected work spans Thai highway safety, electric vehicles and public transport.

  • Deep learning
  • Road safety
  • Travel behaviour
  • Electric vehicles

Projects, tools & learning

From road assessment to safer streets2025 · Projects & impact ↗AI for traffic safety: UK–Thai workshop2023 · Teaching & resources ↗

Selected work

Published research, with a short guide to the question and method.

Browse all selected works ↗

8 selected publications

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

Kerkritt Sriroongvikrai; Kasem Choocharukul; Punnarai Siricharoen; Krittiya Phitchakian

Proceedings of the 14th Asia-Pacific Conference on Transportation and the Environment (APTE 2025), 188–198

Research focus

A camera-based traffic survey device detects, counts and classifies vehicles into 13 Department of Highways categories. Image processing and machine learning support automated traffic-volume collection.

Exploring Key Determinants of Traffic Accidents Fatalities in Thailand: A Hybrid Approach Machine Learning and Statistics

Nanon Sonnatthanon; Kasem Choocharukul

Proceedings of the 14th Asia-Pacific Conference on Transportation and the Environment (APTE 2025), 116–126

Research focus

Statistical association measures and a convolutional deep-learning model examine factors linked to fatalities in Thai highway crash records. The study highlights safety equipment, road-user type and lighting conditions.

Factors Influencing Battery Electric Vehicle Adoption in Thailand—Expanding the Unified Theory of Acceptance and Use of Technology’s Variables

Phasiri Manutworakit; Kasem Choocharukul

Sustainability, 14(14), 8482

Research focus

An online survey of 403 participants in Bangkok and nearby areas was analysed using structural equation modelling. The study examined how technology acceptance and environmental concern relate to battery-electric vehicle purchase intentions and use.

Publication details follow the linked sources, checked 8 September 2026. The roster follows the 2022 directory; individual publications do not establish current center project ownership or a PI appointment.

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