항만 도시 교통물류 안전 증진을 위한 실시간 기상 변화 및 항만 영향권 특성 별 사고 영향요인 분석에 관한 연구
Advanced computer learning-based accident severity analysis considering weather changes and port influence areas: Focused on South Korea Cases
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초록
항만과 항만 인근 항만의 영향을 받는 도로는 대형 사고를 초래할 수 있는 화물차의 이동이 많기 때문에 교통안전에 각별한 주의가 필요하다. 따라서 사고 심각도를 낮출 수 있는 안전 관리 대책을 마련하기 위해 항만 도시의 도로 구간을 항만의 영향을 받는 정도에 따라 구분하고, 각 항만 영향권에서 사고 심각도에 영향을 미치는 요인을 도출해 안전 관리 전략을 수립할 필요가 있다. 본 연구에서는 항만 영향권에서 사고 심각도에 영향을 미치는 요인을 네 가지 머신러닝 기법을 통해 도출하고자 하였다. 모형 개발 후에는 가장 예측성능이 뛰어난 모형에 대하여 설명가능한 인공지능 기법을 통해 높은 사고 심각도에 영향을 미치는 요인을 도출하였다. 본 연구에서 도출된 결과를 활용하여 항만 지역의 사고 심각도 감소를 위한 정책 수립의 기초자료로 활용될 수 있을 것으로 기대된다.
Abstract
Port safety management should consider a variety of cargo shifting within trucks and containers, occurring at and near port areas. In particular, it is crucial for port safety management to consider not only incidents directly 'at-port' but also those in the surrounding 'near-port' areas, including the port influence area. This is significant because of the potential for high crash severity at near port areas, given the substantial truck traffic that could lead to large-scale crashes. Therefore, developing management strategies for port city safety requires identifying key risk factors that influence crash severity in each port area. During this process, because the key factors influencing crash severity may vary as one gets closer to the port center, it is essential to take into account the size of the port influence area. This study collected and matched both crash and weather data to consider various variables. Additionally, this study developed four machine learning-based crash severity models, including Naive Bayes Classification, Support Vector Machine, Extreme Gradient Boosting, and Light Gradient-Boosting Machine. Furthermore, the identification of key factors influencing high crash severity is determined through the application of an eXplainable Artificial Intelligence technique. It is expected that findings derived from this study can contribute to policy-making efforts aimed at enhancing traffic safety in the port area.
참고문헌 (27)
- Abbasi, E., "Using Classification and Regression Trees(CART)to Identify Factors Contributing to Vehicle Crash Severity in a Port City", International Journal of Transportation Systems, 6(2021), pp.29~38 (2021)
- Baak, M., "A new correlation coefficient between categorical, ordinal and interval variables with Pearson characteristics", Computational Statistics & Data Analysis, 152(2020), pp.107043~ (2020)
- Barman, S., "Analyzing Urban Midblock Crash Severity Outcomes Using Proposed Three-Step Pattern Clustering", Journal of Transportation Engineering, Part A: Systems, 149(7), pp.05023003~ (2023)
- Basso, F., "A deep learning approach for real-time crash prediction using vehicle-by-vehicle data", Accident Analysis & Prevention, 162(2021), pp.106409~ (2021)
- Besag, J., "Bayesian computation and stochastic systems", Statistical science, 10(1), pp.3~66 (1995)
- Boser, B. E., "A training algorithm for optimal margin classifiers", Proceedings of the fifth annual workshop on Computational learning theory, pp.144~152 (1992)
- Budiyanto, M. A., "Risk assessment of work accident in container terminals using the fault tree analysis method", Journal of Marine Science and Engineering, 8(6), pp.466~ (2020)
- Claros, B., "Seasonal crash prediction model for urban signalized intersections : Wisconsin southeast region", Traffic injury prevention, 21(7), pp.447~452 (2020)
- Fabiano, B., "Port safety and the container revolution : A statistical study on human factor and occupational accidents over the long period", Safety science, 48(8), pp.980~990 (2010)
- Gong, L., "Modeling single-vehicle run-off-road crash severity in rural areas : Accounting for unobserved heterogeneity and age difference", Accident Analysis & Prevention, 101(2017), pp.124~134 (2017)
- Jung, H. Y., "A Study on the Influence of Heavy Vehicles at the Port Road", KSCE Journal of Civil and Environmental Engineering Research, 19(3-2), pp.225~234 (1999)
- Lee, H., "Identification of Risk Factors and Evaluation of the Importance of Risk Factors in Container Ports", Korea Logistics Research Association, 31(6), pp.23~33 (2021), Korea Logistics Research Association
- Lee, H., "A study on emissions from drayage trucks in the port city-focusing on the port of incheon", Sustainability, 11(19), pp.5358~ (2019)
- Lei, T., "Crash Prediction on Expressway Incorporating Traffic Flow Continuity Parameters Based on Machine Learning Approach", Journal of Advanced Transportation, 2021, pp.1~13 (2021)
- Li, Y., "Day-of-the-week variations and temporal instability of factors influencing pedestrian injury severity in pedestrian-vehicle crashes : A random parameters logit approach with heterogeneity in means and variances", Analytic methods in accident research, 29(2021), pp.100152~ (2021)
- Lin, L., "A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction", Transportation Research Part C : Emerging Technologies, 55(2015), pp.444~459 (2015)
- Myung-Shin Lee, "A study on Freight-only-lane and Freight-only-highway Policies Implemented in the Incheon City", Journal of Korea Port Economic Association, 25(3), pp.117~138 (2009)
- Park, H. C., "Cross-classified multilevel models for severity of commercial motor vehicle crashes considering heterogeneity among companies and regions", Accident Analysis & Prevention, 106(2017), pp.305~314 (2017)
- Rovai, A. P., "Social science research design and statistics: A practitioner's guide to research methods and IBM SPSS" (2013), Watertree Press LLC
- Shi, Q., "Big data applications in real-time traffic operation and safety monitoring and improvement on urban expressways", Transportation Research Part C : Emerging Technologies, 58, pp.380~394 (2015)
- "Traffic Accident Analysis System (TAAS)" (2023)[URL]
- Ukil, A., "Intelligent Systems and Signal Processing in Power Engineering", pp.161~226 (2007)
- Wang, L., "Real-time crash prediction for expressway weaving segments", Transportation Research Part C : Emerging Technologies, 61(2015), pp.1~10 (2015)
- Xiong, Y., "The analysis of vehicle crash injury-severity data : A Markov switching approach with road-segment heterogeneity", Transportation research part B : methodological, 67(2014), pp.109~128 (2014)
- Xu, P., "Modeling crash spatial heterogeneity : Random parameter versus geographically weighting", Accident Analysis & Prevention, 75(2015), pp.16~25 (2015)
- You, J., "Real-time crash prediction on freeways using data mining and emerging techniques", Journal of modern transportation, 25(2), pp.116~123 (2017)
- Ziemska, M., "Exhaust emissions and fuel consumption analysis on the example of an increasing number of hgvs in the port city", Sustainability, 13(13), pp.7428~ (2021)