The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Road Traffic Volume Survey Using Vehicle Specification DB

자동차 제원 DB를 활용한 도로교통량 조사방안 연구

  • 김지민 (한국지능형교통체계협회 R&BD센터 ) ;
  • 오동섭 (한국지능형교통체계협회 R&BD센터 )
  • Received : 2022.09.07
  • Accepted : 2023.02.05
  • Published : 2023.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Currently, the permanent road traffic volume surveys under Road Act are conducted using a intrusive Automatic Vehicle Classification (AVC) equipments to classify 12 categories of vehicles. However, intrusive AVC equipment inevitably have friction with vehicles, and physical damage to sensors due to cracks in roads, plastic deformation, and road construction decreases the operation rate. As a result, accuracy and reliability in actual operation are deteriorated, and maintenance costs are also increasing. With the recent development of ITS technology, research to replace the intrusive AVC equipment is being conducted. However multiple equipments or self-built DB operations were required to classify 12 categories of vehicles. Therefore, this study attempted to prepare a method for classifying 12 categories of vehicles using vehicle specification information of the Vehicle Management Information System(VMIS), which is collected and managed in accordance with Motor Vehicle Management Act. In the future, it is expected to be used to upgrade and diversify road traffic statistics using vehicle specifications such as the introduction of a road traffic survey system using Automatic Number Plate Recognition(ANPR) and classification of eco-friendly vehicles.

도로법에 의거한 도로교통량 상시조사는 매설식 AVC를 통해 12종 차종분류가 이루어지고 있다. 하지만 매설식 AVC 장비는 차량과의 마찰, 도로 균열, 소성변형, 도로공사로 인한 센서의 물리적 파손 등으로 인해 장비 가동률이 낮고, 수집 정보의 정확도와 신뢰도 저하 문제가 발생하고 있다. 이로인해 장비보수 등 유지비용 또한 증가하고 있다. 이러한 문제를 해결하고자 비매설식 AVC 장비 도입을 위한 연구가 진행되고 있으나, 차종을 분류하기 위해 복수의 장비 또는 교통량 정보 매칭을 위한 별도의 DB 구축·운영이 필요하였다. 이에 본 연구에서는 자동차 관리법에 근거하여 운영 중인 자동차관리정보시스템(VMIS)의 차량 제원 정보와 번호판 자동인식 기술(ANPR)을 활용한 12종 차종분류 방안을 마련하고자 하였다. 이를 통해 기존 도로교통량 조사체계를 개선하고 자동차 제원 정보를 활용하여 친환경 차량 분류 등 도로교통량 통계 고도화, 다변화에 기여할 수 있을 것으로 기대된다.

Keywords

Acknowledgement

본 연구는 국토교통부의 「도로교통량 조사 고도화 방안 연구」의 지원을 받아 수행되었으며, 내용의 일부를 요약 및 활용하여 작성되었습니다.

References

  1. Ahn, H. Y. and Lee, J. T.(2018), "Classification of vehicles based on Faster R-CNN suitable for use in actual road environments", Journal of Korean Institute of Intelligent Systems, vol. 28, no. 3, pp.210-218. https://doi.org/10.5391/JKIIS.2018.28.3.210
  2. Kim, Y. I. and Choi, J. S.(2005), "A Study on Modifying Vehicle Classification Categories for Road Design", Proceedings of Korean Society of Road Engineers Conference, vol. 2005, pp.401-406.
  3. Korea Expressway Corporation(2021), A Study on the Improvement of Highway Traffic Survey System, pp.1-4.
  4. Ministry of Land, Infrastructure and Transport Director for Audit and Inspection Office(2020), Request for disposition of agency audit results of Korea Institute of Civil Engineering and Building Technology, pp.9-14.
  5. Ministry of Land, Infrastructure and Transport(2016), Traffic Volume Survey Guideline.
  6. Ministry of Land, Infrastructure and Transport(2020), Motor Vehicle Management Act.
  7. Ministry of Land, Infrastructure and Transport(2022), Enforcement Rule of the Motor Vehicle Management Act.
  8. Oh, J. S., Jang, K. C. and Kim, M. S.(2010), "Improvement of Vehicle Classification Method using Vehicle Height Measurement", Journal of the Korean Society of Road Engineers, vol. 12, no. 4, pp.47-51.
  9. Oh, J. S., Jang, K. C. and Kim, M. S.(2011), "Vehicle Classification Scheme of Two-Axle Unit Vehicle Based on the Laser Measurement of Height Profiles", Journal of Korean Institute of Intelligent Systems, vol. 10, no. 5, pp.47-52.
  10. Orun, A.(2022), Automatic Real time Vehicle Classification by Image Colour Component Based Template Matching, arXiv preprint arXiv:2210.06586.
  11. Park, H. S., Kim, B. K., Moon, H. R., Sin, S. P., Jo, Y. S., Jung, Y. S. and Byeon, K. S.(2019), Developing technology to extract detailed specification of vehicle for smart city implementation, Korea Institute of Civil Engineering and Building Technology, pp.10-19.
  12. Som, Y. T., Do, M. S. and Yoon, Y. H.(2001), "Modifying Vehicle Classification Categories for Enhancing Utillization of Traffic Volume by Vehicle types", Journal of Korean Society of Transportation, vol. 19, no. 3, pp.153-165.
  13. Srividhya, S. R., Kavitha, C., Wen-Cheng, L., Vinodhini Mani, M. and Khalaf, O. I.(2022), "A Machine Leaning Algorithm to Automate Vehicle Classification and License Plate Detection", Wireless Communications and Mobile Computing, 9273233.
  14. Yoo, B. G., Park, J. C. and Park, D. H.(2016), Automatic vehicle classification using vehicle DB, Korea Expressway Corporation, pp.55-59.