使用不平衡数据集的机器学习模型预测阿拉伯联合酋长国儿童乘员碰撞伤害的严重程度

IF 3.2 Q3 TRANSPORTATION
Muhammad Uba Abdulazeez , Wasif Khan , Kassim Abdulrahman Abdullah
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引用次数: 0

摘要

在高收入国家,道路交通事故多年来有所增加,导致以车辆乘员为主的儿童受到更严重的伤害。这对儿童的健康发展产生不利影响,并可能导致死亡。然而,文献中的研究主要集中在预测成人的碰撞损伤,而儿童与成人相比具有不同的碰撞损伤风险和碰撞运动学。为了解决这一差距,本文提出了一个新的数据集,用于预测阿拉伯联合酋长国(阿联酋)8年来(2012年至2019年)的儿童乘员碰撞伤害严重程度。然后使用建议的数据集评估最先进的机器学习算法的性能。此外,采用特征选择技术和逻辑回归模型提取儿童乘员碰撞损伤严重程度预测的最显著特征。此外,由于数据集高度不平衡,分析了数据平衡方法对预测性能的影响。实验结果表明,Adaboost、Bagging REP、ZeroR、OneR和Decision Table算法预测儿童乘员伤害严重程度的准确率最高。通过特征选择和逻辑回归模型,发现儿童乘员座位位置、酋长国、碰撞位置、碰撞类型和碰撞原因是预测伤害严重程度的重要特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting child occupant crash injury severity in the United Arab Emirates using machine learning models for imbalanced dataset

Road traffic crashes have increased over the years leading to greater injury severity among children who are mostly vehicle occupants in high-income countries. This adversely affects the healthy development of children and might lead to death. However, studies in the literature have focused on predicting crash injuries among adults while children have different crash injury risks as well as crash kinematics compared to adults. To address this gap, this paper presents a new dataset for child occupant crash injury severity prediction collected over 8 years (2012 to 2019) in the United Arab Emirates (UAE). The performance of state-of-the-art machine learning algorithms was then evaluated using the proposed dataset. In addition, feature selection techniques and logistic regression model were employed to extract the most significant features for crash injury severity prediction among child occupants. Furthermore, the impact of data balancing approaches on the prediction performance was analyzed as the dataset is highly imbalanced. The experimental results showed that Adaboost, Bagging REP, ZeroR, OneR, and Decision Table algorithms predicts child occupant injury severity with the highest accuracy. Child occupant seating position, emirate, crash location, crash type and crash cause were observed as significant features that predicts injury severity by both the feature selection and logistic regression models.

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来源期刊
IATSS Research
IATSS Research TRANSPORTATION-
CiteScore
6.40
自引率
6.20%
发文量
44
审稿时长
42 weeks
期刊介绍: First published in 1977 as an international journal sponsored by the International Association of Traffic and Safety Sciences, IATSS Research has contributed to the dissemination of interdisciplinary wisdom on ideal mobility, particularly in Asia. IATSS Research is an international refereed journal providing a platform for the exchange of scientific findings on transportation and safety across a wide range of academic fields, with particular emphasis on the links between scientific findings and practice in society and cultural contexts. IATSS Research welcomes submission of original research articles and reviews that satisfy the following conditions: 1.Relevant to transportation and safety, and the multiple impacts of transportation systems on security, human health, and the environment. 2.Contains important policy and practical implications based on scientific evidence in the applicable academic field. In addition to welcoming general submissions, IATSS Research occasionally plans and publishes special feature sections and special issues composed of invited articles addressing specific topics.
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