Motorised two-wheelers (TW) have the highest proportion among vehicles in Chennai district of Tamil Nadu, and they are involved in a large number of fatal traffic crashes every year. We develop a machine learning model to predict injury severity of TW drivers involved in crashes at mid-block road sections, and thereby identify factors contributing to the severity. We used 7654 TW crash cases that occurred in Chennai from 2016 to 2018. We study the performance of two machine learning models random forest (RF) and Conditional inference forest (Cforest), in injury severity prediction and compared their performance with ordered probit (OP) model. Cforest outperforms both RF and OP models in predicting injury severity. We identify significant variables based on variable importance factor measure. Out of considered variables, type of colliding vehicle has the highest influence on crash severity followed by collision type, driver age, and visibility of the road. The Cforest model captures interaction effects that are missed by the other two models.

电动两轮车（TW）在泰米尔纳德邦钦奈地区的车辆中所占比例最高，并且每年涉及大量致命交通事故。我们开发了一种机器学习模型，以预测在路障中间路段发生撞车的TW驾驶员的伤害严重程度，从而确定造成严重程度的因素。我们使用了2016年至2018年在钦奈发生的7654 TW崩溃案例。我们研究了两种机器学习模型随机森林（RF）和条件推理森林（Cforest）在伤害严重性预测中的性能，并将其性能与有序概率（OP） ）模型。Cforest在预测伤害严重性方面优于RF和OP模型。我们基于变量重要性因子度量来识别重要变量。出于考虑的变量，碰撞类型，碰撞年龄，驾驶员年龄和道路可见度对碰撞严重程度的影响最大。Cforest模型捕获其他两个模型遗漏的交互效果。