Analysis of Factors Influencing the Severity of Rental Cars Crashes in Tourist Areas Using Machine Learning Algorithms

doi:10.15683/kosdi.2025.3.31.260

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2025 Vol.21, Issue 1 Preview Page Next Page

Original Article

Analysis of Factors Influencing the Severity of Rental Cars Crashes in Tourist Areas Using Machine Learning Algorithms 기계학습 알고리즘을 활용한 관광지역 렌터카 교통사고 심각도에 미치는 영향요인 분석: 윤병조¹*ㆍ이선민²ㆍ박형근³
Byoung-Jo Yoon¹*, Sun-min Lee², Hyung-Geun Park³; ¹Professor, College of Urban Science, Incheon National University, Incheon, Republic of Korea
²Researcher, College of Urban Science, Incheon National University, Incheon, Republic of Korea
³Researcher, College of Urban Science, Incheon National University, Incheon, Republic of Korea

31 March 2025. pp. 260-272

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Abstract

Purpose: This study aims to analyze the key factors influencing the severity of rental car crashes in Jeju Special Self-Governing Province, where the rental car usage rate is high, and propose systematic measures to reduce crash severity. To achieve this, a crash severity analysis model was developed using machine learning techniques. Method: Data on rental car crashes in Jeju Special Self-Governing Province from 2018 to 2022 were analyzed using XGBoost, Decision Tree, and Random Forest models. Random Forest was identified as the optimal model, and SHAP analysis was employed to derive the feature importance of variables. Result: The Random Forest model demonstrated the highest performance. The feature importance analysis revealed that the most significant factors influencing crash severity were crash type, with “vehicle-pedestrian collisions”(0.2132169) and “vehicle-to-vehicle collisions”(0.150534) having the highest impact. Among traffic violations, “failure to drive safely”(0.145027) and “center line violations”(0.097202) were identified as critical factors. Additionally, the “intersection” road type (0.049608) was a significant factor. Environmental conditions such as rainy weather, nighttime, specific days(Sunday and Thursday), and seasons (autumn and summer) also increased crash severity. Conclusion: To reduce rental car crash severity, implementing a tailored vehicle rental system, expanding traffic safety infrastructure, and strengthening measures to prevent traffic violations are essential. Future research should expand the analysis scope and evaluate the effectiveness of policies to derive actionable and effective improvement strategies.

Keywords

Rental Car Crash

Crash Severity Analysis

Machine Learning

Random Forest Regression

SHAP

연구목적: 본 연구는 렌터카 이용률이 높은 제주특별자치도를 대상으로 렌터카 교통사고 심각도에 영향을 미치는 주요 요인을 분석하고, 사고감소를 위한 체계적인 방안을 제시하는 것을 목적으로 한다. 이를 위해 기계학습 기법을 활용하여 사고심각도 분석모델을 개발하였다. 연구방법: 2018년부터 2022년까지의 제주특별자치도 렌터카 교통사고 데이터를 바탕으로 XGBoost, Decision Tree, Random Forest를 비교 분석하였다. 최적의 모델로 Random Forest를 선정하고, SHAP 분석을 통해 변수 중요도를 도출하였다. 연구결과: 랜덤 포레스트 모형이 가장 우수한 성능을 나타냈으며 변수 중요도 분석 결과, 사고심각도에 큰 영향을 미치는 요인은 사고유형이 ‘차대 보행자’(0.2132169), ‘차대 차’(0.150534) 사고인 경우로 나타났다. 법규 위반 중 ‘안전운전 불이행’(0.145027), ‘중앙선 침범’(0.097202), 도로 형태 ‘교차로’(0.049608)인 경우에도 높은 변수 중요도를 보였다. 또한, 기상 상태_비, 야간, 특정요일(일요일, 목요일), 계절(가을, 여름)에 사고심각도가 높게 나타났다. 결론: 렌터카 사고감소를 위해 맞춤형 차량 대여 시스템, 교통안전 시설물 확충, 법규 위반 예방조치를 강화해야 하며, 향후 연구에서는 분석 범위를 확대하고 정책의 효과성을 평가하여 실효성 있는 개선 방안 도출이 필요하다.

키워드

렌터카 교통사고

사고심각도 분석

기계학습

랜덤포레스트 회귀분석

SHAP 분석

References

Almamlook, R.E., Kwayu, K.M., Alkasisbeh, M.R., Frefer, A.A. (2019). "Comparison of machine learning algorithms for predicting traffic accident severity." IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), Amman, Jordan, pp. 272-276. 10.1109/JEEIT.2019.8717393
Jeju Tourism Organization (2023). 2023 Jeju Special Self-Governing Province Visitor Survey Report: A Decade-Long Integrated Analysis. Jeju.
Jeong, H., Kim, H., Park, S., Han, E., Kim, K.H., Yun, I. (2017). "Prediction of severities of rental car traffic accidents using naive bayes big data classifier." Journal of Korean Institute of Intelligent Transportation Systems, Vol. 16, No. 4, pp. 1-12. 10.12815/kits.2017.16.4.01
Kim, S.-H., Lym, Y.-B., Kim, K.-J. (2021). "Classifying severity of senior driver accidents in capital regions based on machine learning algorithms." Journal of Digital Policy, Vol. 19, No. 4, pp. 25-31.
Koopmans, J.M., Friedman, L., Kwon, S., Sheehan, K. (2015). "Urban crash-related child pedestrian injury incidence and characteristics associated with injury severity." Accident Analysis & Prevention, Vol. 77, pp.127-136. 10.1016/j.aap.2015.02.005 25703351
Korea National Police Agency (2023). Traffic Accidents Statistics. Seoul.
KOROAD (2022). OECD, Road Safety Annual Report 2022. Wonju.
KOROAD (2023). Traffic Safety Index by Local Governments in 2022. Wonju.
Kwon, C.-W., Chang, H.-H. (2021). "Comparative analysis of traffic accident severity of tow-wheeled vehicles using XGBoost." Journal of Information Technology Services, Vol. 20, No. 4, pp. 1-12. 10.12815/kits.2021.20.4.1
Lee, H.S., Kang, J.H., Kim, W.J., Kang, Y.J., Kang, K.W., Park, H.S., Song, S.W., Park, C.B., Bu, J.H., Lee, S.K., Ko, S.Y. (2022). "Are rental car accidents more fatal on Jeju Island?." Journal of The Korean Society of Emergency Medicine, Vol. 33, No. 4, pp. 406-415.
Lee, J.E., Kim, Y.-B., Kim, J.-N. (2020). "Hyperparameter optimization for image classification in convolutional neural network." The Journal of Korea Institute of Convergence Signal Processing, Vol. 21, No. 3, pp. 148-153.
Megnidio-Tchoukouegno, M., Adedeji, J.A. (2023). "Machine learning for road traffic accident improvement and environmental resource management in the transportation sector." Sustainability, Vol. 15, No. 3, 2014. 10.3390/su15032014
Richard, T., Choi. J. (2017). "Differences in rental and nonrental car crashes." Journal of Advanced Transportation, Vol. 2017, No. 1 8757891. 10.1155/2017/8757891
Yoon, B.-J., Lee, S.-Y., Jung, S.-Y. (2017). "A study on the factors of highway traffic accidents affecting the EPDO." 2017 Annual Conference and Special Seminar of the Korean Society of Disaster Information, The Korean Society of Disaster Information, pp. 251-252.
Yun, I., Kim, K.-H., Heo, T.-Y., Geom, K.-H., Song, H. (2016). "A study on analysis of severities of rental car traffic accidents: Case of major sightseeing cities including Busan, Gangneung and Jeju Island." Journal of The Korean Data Analysis Society, Vol. 18, No. 2, pp. 755-769.

Information

Publisher :The Korean Society of Disaster Information
Publisher(Ko) :한국재난정보학회
Journal Title :Journal of the Society of Disaster Information
Journal Title(Ko) :한국재난정보학회논문집
Volume : 21
No :1
Pages :260-272
DOI :https://doi.org/10.15683/kosdi.2025.3.31.260

[1] Almamlook, R.E., Kwayu, K.M., Alkasisbeh, M.R., Frefer, A.A. (2019). "Comparison of machine learning algorithms for predicting traffic accident severity." IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), Amman, Jordan, pp. 272-276. 10.1109/JEEIT.2019.8717393

[2] Jeju Tourism Organization (2023). 2023 Jeju Special Self-Governing Province Visitor Survey Report: A Decade-Long Integrated Analysis. Jeju.

[3] Jeong, H., Kim, H., Park, S., Han, E., Kim, K.H., Yun, I. (2017). "Prediction of severities of rental car traffic accidents using naive bayes big data classifier." Journal of Korean Institute of Intelligent Transportation Systems, Vol. 16, No. 4, pp. 1-12. 10.12815/kits.2017.16.4.01

[4] Kim, S.-H., Lym, Y.-B., Kim, K.-J. (2021). "Classifying severity of senior driver accidents in capital regions based on machine learning algorithms." Journal of Digital Policy, Vol. 19, No. 4, pp. 25-31.

[5] Koopmans, J.M., Friedman, L., Kwon, S., Sheehan, K. (2015). "Urban crash-related child pedestrian injury incidence and characteristics associated with injury severity." Accident Analysis & Prevention, Vol. 77, pp.127-136. 10.1016/j.aap.2015.02.005 25703351

[6] Korea National Police Agency (2023). Traffic Accidents Statistics. Seoul.

[7] KOROAD (2022). OECD, Road Safety Annual Report 2022. Wonju.

[8] KOROAD (2023). Traffic Safety Index by Local Governments in 2022. Wonju.

[9] Kwon, C.-W., Chang, H.-H. (2021). "Comparative analysis of traffic accident severity of tow-wheeled vehicles using XGBoost." Journal of Information Technology Services, Vol. 20, No. 4, pp. 1-12. 10.12815/kits.2021.20.4.1

[10] Lee, H.S., Kang, J.H., Kim, W.J., Kang, Y.J., Kang, K.W., Park, H.S., Song, S.W., Park, C.B., Bu, J.H., Lee, S.K., Ko, S.Y. (2022). "Are rental car accidents more fatal on Jeju Island?." Journal of The Korean Society of Emergency Medicine, Vol. 33, No. 4, pp. 406-415.

[11] Lee, J.E., Kim, Y.-B., Kim, J.-N. (2020). "Hyperparameter optimization for image classification in convolutional neural network." The Journal of Korea Institute of Convergence Signal Processing, Vol. 21, No. 3, pp. 148-153.

[12] Megnidio-Tchoukouegno, M., Adedeji, J.A. (2023). "Machine learning for road traffic accident improvement and environmental resource management in the transportation sector." Sustainability, Vol. 15, No. 3, 2014. 10.3390/su15032014

[13] Richard, T., Choi. J. (2017). "Differences in rental and nonrental car crashes." Journal of Advanced Transportation, Vol. 2017, No. 1 8757891. 10.1155/2017/8757891

[14] Yoon, B.-J., Lee, S.-Y., Jung, S.-Y. (2017). "A study on the factors of highway traffic accidents affecting the EPDO." 2017 Annual Conference and Special Seminar of the Korean Society of Disaster Information, The Korean Society of Disaster Information, pp. 251-252.

[15] Yun, I., Kim, K.-H., Heo, T.-Y., Geom, K.-H., Song, H. (2016). "A study on analysis of severities of rental car traffic accidents: Case of major sightseeing cities including Busan, Gangneung and Jeju Island." Journal of The Korean Data Analysis Society, Vol. 18, No. 2, pp. 755-769.

Journal of the Society of Disaster Information ISSN:1976-2208(Print) 2671-5287(Online) 한국재난정보학회논문집

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