Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China.
National Engineering Research Center for Water Transport Safety, Wuhan 430063, China.
Int J Environ Res Public Health. 2022 May 11;19(10):5867. doi: 10.3390/ijerph19105867.
Real-time regional risk prediction can play a crucial role in preventing traffic accidents. Thus, this study established a lane-level real-time regional risk prediction model. Based on observed data, the least squares-support vector machines (LS-SVM) algorithm was used to identify each lane region of the mainline, and the initial traffic parameters and surrogate safety measures (SSMs) were extracted and aggregated. The negative samples that characterized normal traffic and the positive samples that characterized regional risk were identified. Mutual information (MI) was used to determine the information gain of various feature variables in the samples, and the key feature variables affecting the regional conditions were tested and screened by means of binary logit regression analysis. Upon screening the variables and corresponding labels, the construction and verification of a lane-level regional risk prediction model was completed using the catastrophe theory. The results showed that lane difference is an important parameter to reduce the uncertainty of regional risk, and its odds ratio (OR) was 16.30 at the 95% confidence level. The 10%-quantile modified time to collision (MTTC) inverse, the speed difference between lanes, and 10%-quantile headway (DHW) had an obvious influence on regional status. The model achieved an overall accuracy of 86.50%, predicting 84.78% of regional risks with a false positive rate of 13.37% and 86.63% of normal traffic with a false positive rate of 15.22%. The proposed model can provide a basis for formulating individualized active traffic control strategies for different lanes.
实时区域风险预测在预防交通事故方面起着至关重要的作用。因此，本研究建立了一个车道级实时区域风险预测模型。基于观测数据，采用最小二乘支持向量机（LS-SVM）算法识别主线的各个车道区域，并提取和聚合初始交通参数和替代安全措施（SSMs）。识别出表示正常交通的负样本和表示区域风险的正样本。互信息（MI）用于确定样本中各种特征变量的信息增益，通过二元逻辑回归分析测试和筛选影响区域条件的关键特征变量。在筛选变量和相应标签后，使用突变理论完成车道级区域风险预测模型的构建和验证。结果表明，车道差异是降低区域风险不确定性的重要参数，其优势比（OR）在 95%置信水平下为 16.30。10%分位数反向碰撞时间（MTTC）、车道速度差和 10%分位数车头时距（DHW）对区域状态有明显影响。该模型的整体准确率为 86.50%，预测区域风险的准确率为 84.78%，假阳性率为 13.37%，预测正常交通的准确率为 86.63%，假阳性率为 15.22%。所提出的模型可以为制定针对不同车道的个性化主动交通控制策略提供依据。
