一种新的深度学习方法预测农村山区高速公路恶劣天气下的碰撞严重程度

IF 2.4 3区 工程技术 Q3 TRANSPORTATION
Md Nasim Khan, Mohamed M. Ahmed
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引用次数: 5

摘要

本研究的主要重点是开发一个基于深度学习的鲁棒预测模型,该模型能够及时预测农村山区高速公路恶劣天气下的伤害和致命事故。这项研究利用了一种名为ResNet18的有前途的深度学习技术。为了应用所提出的深度学习模型,使用一种称为DeepInsight的尖端方法将数字碰撞数据转换为图像。此外,考虑到碰撞数据的不平衡性,本研究利用了两种数据平衡技术,即随机欠采样(RUS)和合成少数过采样技术(SMOTE);并尝试了几种数据采样比率。使用1:2:2(致命:伤害:PDO)的比例结合RUS和SMOTE,发现预测效果最好,对致命和伤害碰撞的总体预测准确率分别为99.3%和80.5%。该研究还调查了影响碰撞严重程度的变量的重要性,结果表明驾驶员居住、车辆损坏程度、安全气囊是否打开、驾驶员状况、天气和路面状况是影响碰撞严重程度的最重要变量。所提出的深度学习框架可以提供致命和伤害碰撞的准确预测,这对于确保有效的交通碰撞管理至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel deep learning approach to predict crash severity in adverse weather on rural mountainous freeway
Abstract The main focus of this study was to develop a robust prediction model based on deep learning capable of providing timely predictions of injury and fatal crashes in adverse weather on rural mountainous freeways. This study leveraged a promising deep learning technique named ResNet18. To apply the proposed deep learning model, the numeric crash data were converted to images utilizing a cutting-edge method, called DeepInsight. In addition, considering the imbalanced nature of the crash data, this study leveraged two data balancing techniques, namely Random Under Sampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE); and experimented with several data sampling ratios. The best prediction performance was found using a ratio of 1:2:2 (Fatal:Injury:PDO) coupled with both RUS and SMOTE, which produced an overall prediction accuracy of 99.3% and 80.5% for fatal and injury crashes, respectively. This study also investigated the importance of variables on crash severity, which revealed that driver residency, vehicle damage extent, airbag deployment, driver conditions, weather, and road surface conditions were the most important variables contributing to the severity of crashes. The proposed deep learning framework can provide an accurate prediction of fatal and injury crashes, which is crucial to ensuring effective traffic collision management.
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来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
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