연령, 성별, 사고 심각도를 고려한 보행취약계층의 교통사고 위험 탐구 - 데이터베이스 설계 및 계층적 베이지안 접근법을 활용하여 -
Expl oring the Risks of Vulnerable Road Users Based on Age, Gender, and Severity of Pedestrian Crashes - Employing Database Design and Hierarchical Bayesian Approaches -
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
이 연구는 대전광역시 79개 행정동 내 보행취약계층의 연령, 성별, 사고 심각도에 따른 상대위험도를 탐구하고자 한다. 이를 위해, 연구의 분석 단위로 행정동을 선택하고, 사고 심각도-연령-성별에 따른 보행자 사고의 상대위험도 파악을 위해 데이터베이스를 구축하였다. 두 종류의 종속변수(즉, 경상과 중상 이상의 사고건수)를 고려하여, 계층적 베이지안 기법에 기반을 둔 공유 구성요소 모형을 개발하였 다. 이 모형을 통해 우리는 연령, 성별, 사고의 심각도에 의한 각각의 영향을 분해하고 구분할 수 있다. 분석 결과에 의하면, 행정동 간 공유된 성별-사고 심각도 사이의 구조화된 공간효과와 중상 이상 사고만 의 비구조화된 고유효과를 확인할 수 있었다. 또한, 모형을 통해 연령-성별의 고유한 잠재효과를 파악할 수 있었다. 이러한 분석 결과를 종합하여, 사고 심각도, 연령, 성별에 따른 보행자 사고 고위험 지역을 시각화하였다.
Abstract
This research investigates the relative risk faced by vulnerable road users based on age, gender, and severity of crashes across 79 administrative dongs in Daejeon, South Korea. To this end, we employ an administrative dong as the unit of analysis and design a unique database to further examine the relative risks of severity-age-gender-specific pedestrian crashes. We consider two response variables: the number of minor and severe crashes in each dong. A shared component model is developed within a hierarchical Bayesian framework, allowing us to decompose and differentiate the relative influences of age, gender, and crash severity. Our findings confirm the presence of shared spatially structured latent effects between gender and crash severity across dongs along with severe crash-specific unstructured effects. The model also reveals age-gender-specific latent influences. We provide a comprehensive map that delineates these findings, highlighting regions with higher relative pedestrian crash risks concerning age, gender, and crash severity.
참고문헌 (29)
- Besag, Julian, "Bayesian Image Restoration, with Two Applications in Spatial Statistics", Annals of the Institute of Statistical Mathematics, 43(1), pp.1~20 (1991)
- Etxeberria, Jaione, "Using Mortality to Predict Incidence for Rare and Lethal Cancers in Very Small Areas", Biometrical Journal, 65(3), pp.1~17 (2022)
- Goicoa, T., "Age-space-time CAR Models in Bayesian Disease Mapping", Statistics in Medicine, 35(14), pp.2391~2405 (2016)
- Held, Leonhard, "Towards Joint Disease Mapping", Statistical Methods in Medical Research, 14(1), pp.61~82 (2005)
- Hong, Kiman, "Research on Selection of Vulnerable Areas to Walking Traffic Accidents for the Elderly Considering Jaywalking Accidents", Journal of the Society of Disaster Information, 17(2), pp.341~350 (2021)
- Hwang, Junghyun, "Determining the Spatial Ranges and Characteristics of Pedestrian Hot Spots in Seoul Using L-function and KDE", Journal of Korean Society of Transportation, 42(3), pp.313~330 (2024)
- Jeon, Young Eun, "Spatio-temporal Analysis with Risk Factors for Five Major Violent Crimes", The Korean Journal of Applied Statistics, 35(5), pp.619~629 (2022)
- Kang, Ilsuk, "A Study on the Method for Determining Statistical Risk Boundaries for Spatial Data with Application to Traffic Accident Data", The Korean Data Analysis Society, 25(2), pp.563~572 (2023)
- Khazraee, S. Hadi, "Bayesian Poisson Hierarchical Models for Crash Data Analysis : Investigating the Impact of Model Choice on Site-specific Predictions", Accident Analysis and Prevention, 117, pp.181~195 (2018)
- Kim, Seunghoon, "Classifying Severity of Senior Driver Accidents In Capital Regions Based on Machine Learning Algorithms", Journal of Digital Convergence, 19(4), pp.25~31 (2021)
- Kim, Siwon, "Analysis-based Pedestrian Traffic Incident Analysis Based on Logistic Regression", The Journal of the Korea Institute of Intelligent Transport Systems, 23(2), pp.15~31 (2024)
- Knorr-Held, Leonhard, "A Shared Component Model for Detecting Joint and Selective Clustering of Two Diseases", Journal of the Royal Statistical Society. Series a. Statistics in Society, 164(1), pp.73~85 (2001)
- Kwon, Cheol Woo, "Comparative Analysis of Traffic Accident Severity of Two-Wheeled Vehicles Using XGBoost", The Journal of the Korea Institute of Intelligent Transport Systems, 20(4), pp.1~12 (2021)
- Lord, Dominique, "The Statistical Analysis of Crash-frequency Data: A Review and Assessment of Methodological Alternatives", Transportation Research. Part a, Policy and Practice, 44(5), pp.291~305 (2010)
- Lym, Youngbin, "Bayesian Multilevel Models for Identifying Vehicle Crashes Related to Senior Driver : A Case Study on Daejeon Metropolitan City", Crisisonomy, 20(4), pp.81~95 (2024)
- Lym, Youngbin, "Developing a Bayesian Model to Identify Spatial Disparities in Pedestrian Crash Risk : With a Specific Focus on Small Administrative Entities in Daejeon, South Korea", Crisisonomy, 20(7), pp.49~64 (2024)
- Lym, Youngbin, "Does Space Influence on the Frequency and Severity of the Distraction-affected Vehicle Crashes? An Empirical Evidence from the Central Ohio", Accident Analysis and Prevention, 144, pp.105606~ (2020)
- Lym, Youngbin, "Identifying Regions of Excess Injury Risks Associated with Distracted Driving: A Case Study in Central Ohio, USA", SSM - Population Health, 20, pp.101293~ (2022)
- Lym, Youngbin, "Examining Neighborhood-Level Vehicle Crash Risks in Daejeon Metropolitan City Using Spatiotemporal Bayesian Models", Journal of the Korea Academia-Industrial Cooperation Society, 25(5), pp.176~184 (2024)
- MacNab, Ying C., "On Bayesian Shared Component Disease Mapping and Ecological Regression with Errors in Covariates", Statistics in Medicine, 29(11), pp.1239~1249 (2010)
- Mannering, Fred L., "Unobserved Heterogeneity and the Statistical Analysis of Highway Accident Data", Analytic Methods in Accident Research, 11, pp.1~16 (2016)
- Retegui, Garazi, "Estimating LOCP Cancer Mortality Rates in Small Domains in Spain using Its Relationship with Lung Cancer", Scientific Reports, 11(1), pp.22273~ (2021)
- Richardson, Sylvia, "Bayesian Spatio-temporal Analysis of Joint Patterns of Male and Female Lung Cancer Risks in Yorkshire (UK)", Statistical Methods in Medical Research, 15(4), pp.385~407 (2006)
- Riebler, Andrea, "An Intuitive Bayesian Spatial Model for Disease Mapping that Accounts for Scaling", Statistical Methods in Medical Research, 25(4), pp.1145~1165 (2016)
- Rue, Havard, "Approximate Bayesian Inference for Latent Gaussian Models by Using Integrated Nested Laplace Approximations", Journal of the Royal Statistical Society Series B: Statistical Methodology, 71(2), pp.319~392 (2009)
- Savolainen, Peter T., "The Statistical Analysis of Highway Crash-injury Severities : A Review and Assessment of Methodological Alternatives", Accident Analysis and Prevention, 43(5), pp.1666~1676 (2011)
- Simpson, Daniel, "Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors", Statistical Science, 32(1), pp.1~28 (2017)
- "Traffic Accident Analysis System (TAAS)"[URL]
- Washington, Simon, "Statistical and Econometric Methods for Transportation Data Analysis", pp.2020, Chapman and Hall