CAJ | 학술논문

根据京珠高速公路韶关段4个隧道的交通事故资料,从时间因素、隧道环境因素和交通动态因素3个方面选取9个输入变量,以交通事故严重程度为输出变量,建立高速公路隧道交通事故严重程度预测模型;然后,通过灵敏度分析方法,研究各个输入变量对输出变量的影响程度,并对各个输入变量的灵敏度分析结果进行比较分析.研究结果表明,日交通量与年平均日交通量之比和大型车混入率对交通事故严重程度的影响最大,天气、线形、坡度和事故发生地点在隧道中的位置对交通事故严重程度的影响基本相等,事故发生时段对交通事故严重程度的影响可以忽略不计.
근거경주고속공로소관단4개수도적교통사고자료,종시간인소、수도배경인소화교통동태인소3개방면선취9개수입변량,이교통사고엄중정도위수출변량,건립고속공로수도교통사고엄중정도예측모형;연후,통과령민도분석방법,연구각개수입변량대수출변량적영향정도,병대각개수입변량적령민도분석결과진행비교분석.연구결과표명,일교통량여년평균일교통량지비화대형차혼입솔대교통사고엄중정도적영향최대,천기、선형、파도화사고발생지점재수도중적위치대교통사고엄중정도적영향기본상등,사고발생시단대교통사고엄중정도적영향가이홀략불계.
According to the accident information of Shaoguan tunnels for Beijing-Zhuhai expressway in China, a neural network model was constructed for the purpose of predicting the severity of accidents. In this model,9 input variables were selected from three aspects, which are the time of traffic accident, tunnel environment and traffic dynamic factors, and the output variable was the severity of accident. Then the sensitivity analysis method was selected to study the effects of input variables on output variable. Three conclusions can be obtained. Firstly, the most contribution to the severity of accidents are the ratio of daily traffic volume and the AADT, and the proportion of large vehicles. Secondly, four input variables, which are weather, alignment, grade and accident location, have equal contribution to the severity of accidents. Thirdly, it is negligible that the time of accidents happened contributes to the severity of accidents.