School of Transportation and Logisitics, Southwest Jiaotong University, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, School of Transportation and Logistics, No. 999, Xi'an Road, Chengdu, Sichuan, PR China.
Department of Civil Engineering, Suit 217, Heshanheng Bldg, Tsinghua University, Beijing 10084, PR China.
Accid Anal Prev. 2021 Feb;150:105936. doi: 10.1016/j.aap.2020.105936. Epub 2020 Dec 17.
The crash data are often predominantly imbalanced, among which the fatal injury (or minority) crashes are significantly underrepresented relative to the non-fatal injury (or majority) ones. This unbalanced phenomenon poses a huge challenge to most of the statistical learning methods and needs to be addressed in the data preprocessing. To this end, we comparatively apply three data balance methods, i.e., the Synthetic Minority Oversampling Technique (SMOTE), the Borderline SMOTE (BL-SMOTE), and the Majority Weighted Minority Oversampling (MWMOTE). Then, we examine different Bayesian networks (BNs) to explore the contributing factors of fatal injury crashes. The 2016 highway crash data of Ghana are retrieved for the case study. The results show that the accuracy of the injury severity classification is improved by using the preprocessed data. Highest improvement is observed on the data preprocessed by the MWMOTE technique. Statistical verification is done by the Wilcoxon signed-rank test. The inference results of the best BNs show the significant factors of fatal crashes which include off-peak time, non-intersection area, pedestrian involved collisions, rural road environment, good tarred road, roads without shoulders, and multiple vehicles involved crash.
碰撞数据通常存在严重的不平衡现象，其中致命伤害（少数）事故相对于非致命伤害（多数）事故明显代表性不足。这种不平衡现象给大多数统计学习方法带来了巨大挑战，需要在数据预处理中加以解决。为此，我们比较应用了三种数据平衡方法，即合成少数过采样技术（SMOTE）、边界 SMOTE（BL-SMOTE）和多数加权少数过采样（MWMOTE）。然后，我们研究了不同的贝叶斯网络（BN），以探讨致命伤害事故的影响因素。该案例研究采用了加纳 2016 年高速公路碰撞数据。结果表明，使用预处理数据可提高伤害严重程度分类的准确性。使用 MWMOTE 技术预处理的数据改进幅度最大。通过 Wilcoxon 符号秩检验进行了统计验证。最佳 BN 的推理结果表明，致命碰撞的显著因素包括非高峰时间、非交叉区域、涉及行人的碰撞、农村道路环境、良好的柏油路面、无路肩道路以及多车碰撞。
