The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Hunan 410082, China.
Hopkins Extreme Materials Institute, The Johns Hopkins University, USA.
Accid Anal Prev. 2021 Sep;160:106305. doi: 10.1016/j.aap.2021.106305. Epub 2021 Jul 28.
Due to the high frequent traffic accidents involving electric bicycles (E-bike), it urgently needs improved protection of cyclists, especially their heads. In this study, by adjusting the initial impact velocities of E-bike and car, initial impact angle between E-bike and car, initial E-bike impact location, and body size of cyclist, 1512 different accident conditions were constructed and simulated using a verified E-bike-to-car impact multi-body model. The cyclist's head kinematic responses including the head relative impact velocity, WAD (Wrap around distance) of head impact location and HIC (15 ms Head Injury Criterion) were collected from simulation results to make up a dataset for data mining. The decision tree models of cyclist's head kinematic responses were then created from this dataset and verified accordingly. Based on simulated results obtained from decision tree models, it can be found as follows. 1. In the E-bike-to-car accidents, the average head impact relative velocity and WAD of head impact location are higher than those in the car-to-pedestrian accidents. 2. Increasing the initial impact velocity of car can increase the cyclist's head relative impact velocity, WAD of head impact location, and HIC. 3. The WAD of cyclist's head impact location is also significantly affected by the initial impact angle between E-bike and car and body size of cyclist: the WAD of head impact location becomes higher with increasing initial impact angle between E-bike and car and body size of cyclist. 4. The effects of initial E-bike impact location on the WAD of cyclist's head impact location is not significant when initial E-bike impact location is concentrated in the region of 0.25 m around the centerline of the car.
由于涉及电动自行车（E-bike）的高频交通事故，迫切需要提高对自行车骑手，尤其是头部的保护。在这项研究中，通过调整 E-bike 和汽车的初始碰撞速度、E-bike 和汽车之间的初始碰撞角度、E-bike 的初始碰撞位置和骑手的体型，使用经过验证的 E-bike 与汽车碰撞多体模型构建并模拟了 1512 种不同的事故条件。从模拟结果中收集了骑手头部运动学响应，包括头部相对碰撞速度、头部碰撞位置的 WAD（环绕距离）和 HIC（15ms 头部损伤准则），以构成数据集进行数据挖掘。然后从该数据集创建并相应验证了骑手头部运动学响应的决策树模型。基于从决策树模型获得的模拟结果，可以发现以下几点。1. 在 E-bike 与汽车的事故中，头部的平均碰撞相对速度和头部碰撞位置的 WAD 高于汽车与行人事故中的相应值。2. 增加汽车的初始碰撞速度会增加骑手头部的相对碰撞速度、头部碰撞位置的 WAD 和 HIC。3. 骑手头部碰撞位置的 WAD 还受到 E-bike 和汽车之间的初始碰撞角度和骑手体型的显著影响：随着 E-bike 和汽车之间的初始碰撞角度和骑手体型的增加，头部碰撞位置的 WAD 也会升高。4. 当 E-bike 的初始碰撞位置集中在汽车中心线周围 0.25m 的区域内时，E-bike 的初始碰撞位置对骑手头部碰撞位置的 WAD 的影响不显著。
