A novel approach to investigate effects of front-end structures on injury response of e-bike riders: Combining Monte Carlo sampling, automatic operation, and data mining. (April 2022)

• Record Type: Journal Article
• Authors: Liu, Yu; Wan, Xinming; Xu, Wei; Shi, Liangliang; Bai, Zhonghao; Wang, Fang
• Is Part Of: Accident analysis and prevention. Volume 168(2022)
• Publication Date: 2022-04
• Subjects: e-Bike accidents -- Front-end structures -- Injury response -- Automatic operation -- Data mining; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2022.106599
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: A novel approach including all processes of modeling, sampling, and analyzing. Parameterized model for front-end structures and standardization of car to e-bike collision simulations. Stochastical generation of vehicle shapes and automatic operation of collision models. Based on decision tree models, not only the effects of front-end structural parameters on injuries but also the interaction mechanism between various variables can be interpreted. High potentials to develop the virtual test system for the evaluation of vehicle performance and the optimization of front-end shapes for rider's protection. Abstract: Transportation safety related to e-bikes is becoming more problematic with the growing popularity in recent decade years, however, rare studies focused on the protection for e-bike riders in traffic accidents. This paper aimed to investigate the relationship between vehicle front-end structures and rider's injury based on a novel approach including modeling, sampling, and analyzing. Firstly, a parametrized model for front-end structures of the vehicle was developed with nine parameters to realize the standardization of multi-body models of car to e-bike collision considering three stature riders and different impacting velocities. Secondly, a framework, combining Monte Carlo sampling for twelve initial variables and automatic operation for 1000 impact simulations, was built to obtain valid results automatically and then to construct a big dataset. Finally, according to the sensitive variables to riders' vulnerable regions, the decision tree algorithm was further adopted to develop the decision or prediction model on injuries. The novel approach achieved the stochastical generation of vehicle shapes and the automatic operation of multi-body models. The results showed that the rider's head, pelvis, and thighs were more vulnerable to being injured in the car to e-bike perpendicular accidents. The three decision tree models (HIC15, lateral force of pelvis, bending moment of upper leg) were validated to be accurate and reliable according to the confusion matrix with the precision of more than 80% and the receiver operating characteristic curves (ROC) with the under area more than 85%. Based on decision tree models, not only the effects of front-end structural parameters on the corresponding injury but also the interaction mechanism between various variables can be clearly interpreted. Each route from the same root node to hierarchical middle nodes then to various leaf nodes represented a decision-making process. And the different branches under the same decision node directly illustrated the correlation between variables, which is highly readable and comprehensible. During the safety performance design of front-end structures, the rational value of variables could be decided according to decision routes that resulted in lower injury levels; Even if the accident was inevitable, the collision parameters could be controlled within a certain range for the least injury according to the prediction rules. Based on the novel framework coupling Monte Carlo sampling and automatic operation, it's foreseeable to apply the parametric and standard car-to-e-bike collision models to develop the virtual test system and to optimize front-end shapes for rider's protection.