摘要
为定量化识别、预测交叉口区域内的非机动车冲突易发点,利用视域分析方法及随机森林模型,构建针对交叉口非机动车冲突易发点的空间预测模型,预测交叉口区域内的具体冲突点位置及严重程度;通过改进既有的冲突量化指标,量化识别非机动车-机动车的交叉冲突、侧向刮擦及事故,并依据非机动车调整机动车的躲避特性参数;以南京市的4处典型交叉口作为案例进行分析验证。研究结果表明:改进后的指标能补充既有冲突指标进行非机动车冲突量化的部分缺失值;基于视域分析及随机森林预测模型,可较好实现对交叉口冲突易发点的量化预测与误差评估;案例的预测冲突易发区域与实际区域的总体误差为6.76%,预测准确性较好。
In order to quantitatively identify and predict traffic risk points of non-motor vehicle conflict in intersections,visibility graph analysis method and random forest model were adopted to establish a spatial model to predict conflict points with their severity. Then,existing quantitative conflict indexes were improved so that cross conflicts,lateral scraping conflicts and accidents could be identified quantitatively,and avoidance characteristic parameter was adjusted according to avoidance trend of non-motor vehicles.Finally,verification was conducted with four typical intersections in Nanjing city as cases. The results show that the improved index can effectively complement missing values of current index in non-motor vehicle conflict quantization. And the prediction model based on visibility graph analysis and random forest model can better realize quantitative prediction and error evaluation of conflict risk points. The overall error between prediction area and actual conflict area of cases is 6. 76%,which indicates a great prediction accuracy.
作者
巫诚诚
陈大伟
WU Chengcheng;CHEN Dawei(School of Transportation,Southeast University,Nanjing Jiangsu 211189,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2021年第8期165-171,共7页
China Safety Science Journal
基金
河南省交通运输厅科技项目(2020G-2-2)。
关键词
城市交叉口
非机动车
冲突易发点
空间预测模型
空间句法
urban intersection
non-motor vehicle
traffic risk points
spatial prediction model
space syntax
