摘要
高速公路大桥路段事故频发,引发各界广泛关注.本文以高速公路大桥路段为研究对象,首先基于鄂东长江公路大桥2016—2019年事故数据,从时空角度对路段事故特征进行分析;其次以鄂东长江公路大桥2018年217起事故为研究对象,利用同质法与出入口分段结合的思想对路段单元进行划分,以交通流量及道路线形2类指标6个自变量构建零膨胀负二项回归模型,对交通事故数据进行拟合.研究结果表明,鄂东长江公路大桥路段事故存在一定的时空分布特征,流量、路段长度、线形(曲线vs直线)对高速公路大桥路段事故影响显著.本文有助于高速公路管理公司及交通管理部门直观了解高速公路大桥路段事故分布特征,拟为提高高速公路大桥路段运营安全水平,创新精细化交通管理提供理论支撑及参考.
The frequent occurrence of crashes on the bridge segment of freeway has attracted the attention of many researchers.This study takes the freeway bridge road segment as the research object and analyzes the crash characteristics of the road segment from a spatial and temporal perspective based on the crash data of the Edong Yangtze River Highway Bridge from 2016 to 2019.The 217 crashes of the Erdong Yangtze River Highway Bridge in 2018 were studied to classify the section units using the idea of combining the homogeneous method with the entrance and exit segments.A zero-inflated negative binomial regression model was developed with siXindependent variables of two types of indicators,including traffic flow and road alignment,to fit the traffic crash data.The results show that there are certain spatial and temporal distribution characteristics of crashes in the Edong Yangtze River Highway Bridge segment,and traffic flow,segment length,and horizontal alignment(curve vs.straight)have significant effects on crashes in the freeway bridge segment.This study helps freeway management companies and traffic management departments to understand the crash distribution characteristics of freeway bridge segments intuitively,and is intended to provide theoretical support and reference for improving the operation safety level of freeway bridge segments and innovating refined traffic management.
作者
邹晓芳
张俊杰
杨培东
ZOU Xiaofang;ZHANG Junjie;YANG Peidong(China Merchants New Intelligence Technology Co.,Ltd.,Beijing 100070,China;College of Transportation,Southeast University,Nanjing 211189,China)
出处
《交通工程》
2023年第6期27-34,共8页
Journal of Transportation Engineering
基金
国家自然科学基金(52102402).
关键词
交通安全
高速公路大桥路段
事故特征
事故预测
零膨胀负二项回归模型
traffic safety
freeway bridge segment
crash characteristics
crash prediction
zero-inflated negative binomial regression model