DOI QR코드

DOI QR Code

머신러닝 기반의 수도권 지역 고령운전자 차대사람 사고심각도 분류 연구

Classifying Severity of Senior Driver Accidents In Capital Regions Based on Machine Learning Algorithms

  • Kim, Seunghoon (City and Regional Planning, The Ohio State University) ;
  • Lym, Youngbin (Center for Innovation Strategy and Policy, KAIST) ;
  • Kim, Ki-Jung (Department of Smart Car Engineering, Doowon Technical University)
  • 투고 : 2021.01.26
  • 심사 : 2021.04.20
  • 발행 : 2021.04.28

초록

고령화 시대에 따라 고령운전자 역시 증가하고 있으며, 이들에 의한 교통사고 심각성에 대한 관심이 높아지고 있다. 이에 고령운전자에 의한 사고심각도 예측 모형의 필요성이 점차 요구됨에 따라, 본 연구에서는 기계학습 기법을 활용하여 고령운전자에 의한 차대사람 사고심각도 예측을 위한 모형 정립 및 분석을 수행하고자 한다. 이를 위해 4개의 기계학습 알고리즘 (Logistic Model, KNN, RF, SVM)을 활용, 예측 모형을 개발하고 각 결과를 비교하였다. 연구 결과에 따르면 Logistic과 SVM 모형이 상대적으로 높은 예측력을 보였으며, 정확도 측면에서는 RF가 높은 것으로 나타났다. 추가적으로 각 중요 변수들을 이용하여 교차분석을 수행한 후 그 결과를 제시하였다. 본 연구의 결과들은 고령화시대에 고령운전자에 의한 사고심각성을 예방하기 위한 안전정책 및 인프라 개발에 활용될 것으로 판단된다.

Moving toward an aged society, traffic accidents involving elderly drivers have also attracted broader public attention. A rapid increase of senior involvement in crashes calls for developing appropriate crash-severity prediction models specific to senior drivers. In that regard, this study leverages machine learning (ML) algorithms so as to predict the severity of vehicle-pedestrian collisions induced by elderly drivers. Specifically, four ML algorithms (i.e., Logistic model, K-nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM)) have been developed and compared. Our results show that Logistic model and SVM have outperformed their rivals in terms of the overall prediction accuracy, while precision measure exhibits in favor of RF. We also clarify that driver education and technology development would be effective countermeasures against severity risks of senior driver-induced collisions. These allow us to support informed decision making for policymakers to enhance public safety.

키워드

참고문헌

  1. Statistics Korea. (2020). Statistics on the Aged
  2. J. J. Choi, J. S. Gim & T. H. Kim. (2017) Analyzing Driving Environment Effects on Severity of Elderly Driver's Traffic Accidents. Journal of Transport Research 24(1), 79-94. DOI : 10.34143/jtr.2017.24.1.79
  3. Gyeonggi Research Institute. (2020). A study on the improvement of road traffic facilities for elderly drivers in a super-aged society
  4. The Seoul Institute. (2019). A study on risk assessment techniques for prevention of road traffic accidents.
  5. Korea Road Traffic Authority. (2012). Analysis of traffic accident characteristics and accident prevention measures for elderly drivers.
  6. J. H. Kim. J. S. Oh & S. C. Lee. (2006). The Influences of Driving Behavior Determinants on Traffic Violations and Accidents. Korean Society for Industrial and Organizational Psychology. 19(3). 349-369.
  7. J. S. Oh, E. Y. Lee, J. B. Ryu & W. Y. Lee. (2015). An Analysis for Main Vulnerable Situations and Human Errors of Elderly Drivers' Traffic Accidents. Journal of Transport Research. 22(4). 57-75. DOI : 10.34143/jtr.2015.22.4.57
  8. S. C. Lee. (2006). Psychological effects on elderly driver's traffic accidents. Korean Psychological Journal of Culture and Social Issues. 12(5). 149-167.
  9. J. M. Jang, J. S. Choi & T. H. Gim. (2017). Analyzing Driving Environment Effects on Severity of Elderly Driver's Traffic Accidents. Journal of Transport Research. 24(1). 79-94. DOI : 10.34143/jtr.2017.24.1.79
  10. S. H. Lee, W. D. Jeung & Y. H. Woo. (2012). Comparative Analysis of Elderly's and Non-elderly's Human Traffic Accident Severity. The Korea Institute Of Intelligent Transport Systems. 11(6). 133-144. https://doi.org/10.12815/kits.2012.11.6.133
  11. S. G. Shin & M. S. Cho. (2010). A Study on Traffic Accident Prevention through Older Driver's Characteristics Analysis. Journal of Korean Public Police and Security Studies. 7(2). 157-185 DOI : 10.25023/kapsa.7.2.201008.157
  12. S. J. Lim, J. T. Park, Y. I. Kim & T. H. Kim. (2012). Analysis of Elderly Drivers' Accident Models Considering Operations and Physical Characteristics. Korean Society of Transportation. 30(6). 37-46. DOI : 10.7470/jkst.2012.30.6.037
  13. M. J. lEE & M. S. Lee. (2014). Elderly Driver's Perceived Driving Ability and Driving Behavior Associated with Traffic Accident Risk. Crisis and Emergency Management: Theory and Praxis. 10(12). 279-304.
  14. L. Dominique & M. Fred. (2010). The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transportation Research Part A 44 291-305. https://doi.org/10.1016/j.tra.2010.02.001
  15. T. S. Peter, L. M. Fred, L. Dominique & A. Q. Mohammed. (2011). The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accident Analysis and Prevention. 43(6). 1666-1676. https://doi.org/10.1016/j.aap.2011.03.025
  16. Y. O. Kang, S. R. Son & N. H. Cho. (2017) Analysis of Traffic Accidents Injury Severity in Seoul using Decision Trees and Spatiotemporal Data Visualization. Korea Land and Geospatial InformatiX Corporation. 47(2). 233-254 https://doi.org/10.22640/lxsiri.2017.47.2.223
  17. S. B. LEE, D. H. HAN & Y. I. LEE. (2015). Development of Freeway Traffic Incident Clearance Time Prediction Model by Accident Level. Journal of Korean Society of Transportation. 33(5). 497-507 DOI : 10.7470/jkst.2015.33.5.497
  18. G. Chen, Z. Zhang, R. Qian, R. A. Tarefder, and Z. Tian. (2016). Investigating Driver Injury Severity Patterns in Rollover Crashes Using Support Vector Machine Models. Accident Analysis and Prevention. 90. 128-139. https://doi.org/10.1016/j.aap.2016.02.011
  19. M. Rezapour, A. Mehrara Molan, & K. Ksaibati. (2020). Analyzing injury severity of motorcycle at-fault crashes using machine learning techniques, decision tree and logistic regression models. International Journal of Transportation Science and Technology, 9(2), 89-99. https://doi.org/10.1016/j.ijtst.2019.10.002
  20. R. E. Mamlook et al. (2020). Utilizing Machine Learning Models to Predict the Car Crash Injury Severity among Elderly Drivers. IEEE International Conference on Electro Information Technology. July. 105-111. DOI : 10.1109/EIT48999.2020.9208259
  21. S. Alkheder, M. Taamneh, & S. Taamneh. (2017). Severity Prediction of Traffic Accident Using an Artificial Neural Network. Journal of Forecasting, 36(1), 100-108. https://doi.org/10.1002/for.2425
  22. J. H. Rho. (2012). Transportation planning : Travel demand theory and modeling
  23. Zhang, J., Li, Z., Pu, Z., & Xu, C. (2018). Comparing prediction performance for crash injury severity among various machine learning and statistical methods. IEEE Access, 6(c), 60079-60087. https://doi.org/10.1109/ACCESS.2018.2874979
  24. Mafi, S., AbdelRazig, Y., & Doczy, R. (2018). Machine Learning Methods to Analyze Injury Severity of Drivers from Different Age and Gender Groups. Transportation Research Record, 2672(38), 171-183. https://doi.org/10.1177/0361198118794292
  25. Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324