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

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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A Study of Classification Analysis about Traffic Conditions Using Factor Analysis and Cluster Analysis

요인분석 및 군집분석을 활용한 교통상황 유형 분류분석

  • 정수환 (아주대학교 교통공학과) ;
  • 한경희 (아주대학교 교통공학과 ) ;
  • 소재현 (아주대학교 교통시스템공학과) ;
  • 이철기 (아주대학교 교통시스템공학과)
  • Received : 2022.11.04
  • Accepted : 2022.12.15
  • Published : 2023.02.28
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Abstract

In this study, a classification analysis was performed based on the type of traffic situation. The purpose was to derive the major variable factors that could represent the traffic situation. The TTI(Travel Time Index) was used as a criterion for determining traffic conditions, and analysis was performed using data generally detected by the Vehicle Detecting System(VDS). First, the major factors influencing the traffic situation were selected through factor analysis, and traffic conditions were clustered through a cluster analysis of the major factors. After that, variance analysis for each cluster was performed based on the TTI, and similar clusters were merged to categorize the type of traffic situation. The analysis derived, the maximum queue length and occupancy as major factors that could represent the traffic situation. Through this study, it is expected that efficient management of traffic congestion would be possible by just concentrating on the main variable factors that affect the traffic situation.

본 연구에서는 교통상황을 대변할 수 있는 주요 요인변수 도출을 목적으로 교통상황 유형에 대해 분류분석을 수행하였다. TTI(Travel Time Index)를 교통상황 판단 기준으로 사용하였고 VDS에서 일반적으로 검지되는 데이터를 활용하여 분석을 수행하였다. 먼저 요인분석을 통해 교통상황에 영향을 주는 주요인을 선정하였고, 주요인에 대하여 군집분석을 통해 교통상황을 군집화하였다. 그 후 TTI를 기준으로 각 군집별 분산분석을 실시하고 유사한 군집을 병합하여, 교통상황 유형을 분류하였다. 분석 결과 교통상황을 대변할 수 있는 주요 요인변수로 최대대기행렬길이와 점유율을 도출하였다. 본 연구 방법론을 통해 교통상황에 영향을 미치는 주요 요인변수만을 활용하여 효율적인 교통혼잡 관리가 가능할 것으로 기대된다.

Keywords

Acknowledgement

이 논문은 2022년도 정부(경찰청)의 재원으로 과학치안진흥센터의 지원을 받아 수행된 연구임 (092021C29S01000, 네트워크 제어를 위한 교통정체 및 혼잡 운영관리 기술 개발). 본 논문은 2022년 한국ITS학회 춘계학술대회에 게재되었던 논문을 수정·보완하여 작성하였습니다.

References

  1. Cho, E. H.(2017), A study on estimation and application of travel time index for comparison and analysis in Seoul, Master's Thesis, University of Seoul, https://dl.nanet.go.kr/
  2. Cho, Y. J. and Kim, Y. H.(2007), "A strategy through segmentation using factor and cluster analysis: Focusing on corporations having a special status", The Korean Journal of Applied Statistics, vol. 20, no. 1, pp.23-38. https://doi.org/10.5351/KJAS.2007.20.1.023
  3. Choi, C. H. and You, Y. Y.(2017), "The study on the comparative analysis of EFA and CFA", Journal of Digital Convergence, vol. 15, no. 10, pp.103-111.
  4. Han, Y. H. and Kim, Y. C.(2017), "A study of measuring traffic congestion for urban network using average link travel time based on DTG big data", The Journal of The Korea Institute of Intelligent Transport System, vol. 16, no. 5, pp.72-84. https://doi.org/10.12815/kits.2017.16.5.72
  5. Hong, J. H., Oh, M. J., Cho, Y. B., Lee, K. H. and Cho, W. S.(2020), "A dimensional reduction method in cluster analysis for multidimensional data: Principal component analysis and factor analysis comparison", The Korea Journal of BigData, vol. 5, no. 2, pp.135-143. https://doi.org/10.36498/KBIGDT.2020.5.2.135
  6. Kim, S. G., Yun, I. S., Oh, Y. T., Ahn, H. K., Kwon, K. A. and Hong, D. P.(2014), "Feasibility evaluation of lane grouping methods for signalized intersection performance index analysis in KHCM", The Journal of the Korea Institute of Intelligent Transport System, vol. 10, no. 1, pp.109-126.
  7. Kong, X., Yang, J. W. and Yang, Z. G.(2015), "Measuring traffic congestion with taxi GPS data and travel time index", COTA International Conference of Transportation Professionals (CICTP) 2015, pp.3751-3762.
  8. Korea Road Traffic Authority(KoROAD)(2014), A study on the establishment data quality diagnosis and evaluation system for traffic information management system, pp.14-16.
  9. Lee, I. M., Min, J. H., Kim, K. T. and Kho, S. Y.(2020), "Generating travel patterns of public transportation users using a K-means clustering based on smart card data", Journal of the Korean Society for Railway, vol. 23, no. 3, pp.204-215. https://doi.org/10.7782/JKSR.2020.23.3.204
  10. Lee, S. J., Kim, T. Y., Ko, H. G. and Bok, K. C.(2008), "The evaluation of existing congestion indices applicability for development of traffic condition index", Korean Society of Road Engineers, vol. 10, no. 3, pp.119-128.
  11. Sim, S. W. and Chou, K. C.(2009), "Classification of freeway traffic condition by the impacts of road weather factors", Journal of the Korean Society of Civil Engineers, vol. 29, no. 6, pp.685-691.
  12. The Korea Transport Institute(2021a), Traffic policy evaluation index survey project brief 2020, pp.6-8.
  13. The Korea Transport Institute(2021b), 2021 National transportation cost forecast-congestion cost, logistics cost, accident cost, pp.2-6.
  14. The Korea Transport Institute(2021c), A basic study on the revision of the Highway Capacity Manual, pp.5-15.
  15. Yuan, Q., Li, X., Wang, C., Li, Y. and Gao, Y.(2017), "Cluster and factor analysis on data of fatal traffic crashes in China", 2017 4th International Conference on Transportation Information and Safety (ICTIS), pp.211-224