Panicker Anju K, Ramadurai Gitakrishnan
Civil Engineering Department, Indian Institute of Technology, Madras, Chennai, India.
Int J Inj Contr Saf Promot. 2025 Jun;32(2):290-302. doi: 10.1080/17457300.2025.2501573. Epub 2025 May 10.
In India, motorized two-wheeler (TW) riders account for 44.5% of fatal road crashes. While factors affecting drivers have been studied, research on pillion riders' injury severity remains limited. The study aims to identify factors causing severe injuries to pillion riders by developing an accurate prediction model. The study includes machine learning (ML) models, such as conditional inference tree, random forest (RF), gradient boosting, support vector machine, and a statistical model ordered probit for comparison. The study accounts for the imbalance in injury severity crash data by adopting data balancing techniques. Also, it recommends a combination of ML techniques, variable importance charts, and individual conditional expectation plots for identifying key variables and their effects. The finding suggests that RF trained in up-sampled data performs better than the remaining models. The presence of a central divider on the road reduces fatal injuries to pillion riders. The likelihood of getting severe injury is higher during nighttime crashes, TW-HMV (truck or bus) collisions, and hit-and-run crash cases where the colliding vehicle is unidentified. Older pillion riders are more vulnerable to sustaining fatal injuries in a crash. Crashes involving TWs hitting stationary objects and skidding are more fatal for pillion riders than other collision types.
在印度，机动两轮车（TW）骑手占致命道路交通事故的44.5%。虽然已经对影响驾驶员的因素进行了研究，但关于后座乘客受伤严重程度的研究仍然有限。该研究旨在通过开发一个准确的预测模型来确定导致后座乘客严重受伤的因素。该研究包括机器学习（ML）模型，如条件推断树、随机森林（RF）、梯度提升、支持向量机，以及用于比较的统计模型有序概率单位模型。该研究通过采用数据平衡技术来解决受伤严重程度碰撞数据中的不平衡问题。此外，它还推荐结合ML技术、变量重要性图表和个体条件期望图来识别关键变量及其影响。研究结果表明，在上采样数据中训练的RF比其他模型表现更好。道路上设有中央分隔带可减少后座乘客的致命伤害。在夜间碰撞、TW与重型机动车（卡车或公共汽车）碰撞以及肇事车辆不明的肇事逃逸碰撞事故中，后座乘客受重伤的可能性更高。年龄较大的后座乘客在碰撞中更容易遭受致命伤害。与其他碰撞类型相比，涉及TW撞上静止物体和打滑的碰撞对后座乘客来说更致命。
