베이지안 다수준 모형을 활용한 고령운전자 사고의 식별 - 대전광역시 사례를 중심으로 -
Bayesian Multilevel Models for Identifying Vehicle Crashes Related to Senior Drivers - A Case Study on Daejeon Metropolitan City -
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참고문헌
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초록
본 연구는 고령운전자에 의한 교통사고의 식별에 영향을 미치는 다양한 인자들을 탐색하기 위한 연구이다. 대전광역시를 연구대상으로, 고령운전자와 관련된 교통사고의 구분에 영향을 미치는 변수로서 운전자 개인의 특성, 사고 유형, 도로의 상태, 및 환경과 같은 요소들과 이외 공간과 시간에 의한 잠재 효과를고려하였다. 여기에서는 설명변수에 의한 고정효과와 공간 종속성 및 시계열의 자기상관의 확률효과를파악하기 위해 베이지안 다수준 로지스틱 회귀 모형을 활용하였다. 연구의 결과에 따르면, 전반적 추세, 법규위반, 교차로와 같은 변수들이 고령운전자에 의한 교통사고 확률의 로그 오즈비와 양의 상관관계가있는 것으로 나타났다. 한편, 눈 또는 결빙의 노면 상태 및 몇몇 사고 유형은 로그 오즈를 낮추는 것으로추정되었다. 그리고 공간의 확률효과가 잔차의 변동성을 대부분 설명하는 것으로 나타나, 채택한 모형의잇점을 시사하였다. 향후, 본 연구에서 활용한 연구 기법을 통해 고령운전자의 안전 정책 수립을 위한객관적 근거를 마련할 수 있을 것이다.
Abstract
This study examines various influential factors aimed at identifying crashes involving senior drivers. We consider variables reflecting the individual characteristics of drivers, crash types, roadway conditions, and environments, along with latent effects related to space and time, as potential identifiers of senior driver-related crashes in Daejeon. To this end, we employ Bayesian multilevel logistic regression models to unveil the random influences arising from spatial dependency and temporal autocorrelation after accounting for the fixed effects by explanatory variables. The result reveals that the overall annual trend, violations, and intersections appear to be positively associated with the log odds of the probability. Conversely, snow and icy roadway surface conditions, along with several crash types, are estimated to lower the log odds. The latent spatial effects dominate overall residual fluctuation, suggesting the benefits of our adopted model. We argue that our approach lays the foundations for designing safety policies for senior drivers.
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