Motor vehicle crashes are one of the most common causes of fatalities on the roads. Real-time severity prediction of such crashes may contribute towards reducing the rate of fatality. In this study, the fundamental goal is to develop machine learning models that predict whether the outcome of a collision will be fatal or not. A Canadian road crash dataset containing 5.8 million records is utilized in this research. In this study, ensemble models have been developed using majority and soft voting to address the class imbalance in the dataset. The prediction accuracy of approximately 75% is achieved using Convolutional Neural Networks. Moreover, a comprehensive analysis of the attributes that are important in distinguishing between fatal vs. non-fatal motor vehicle collisions has been presented in this paper. In-depth information content analysis reveals the factors that contribute the most in the prediction model. These include roadway characteristics and weather conditions at the time of the crash, vehicle type, time when the collision happen, road user class and their position, any safety device used, and the status of traffic control. With real-time data based on weather and road conditions, an automated warning system can potentially be developed utilizing the prediction model employed in this study.

中文翻译：

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机动车碰撞死亡率预测：使用深度学习和集成方法挖掘大数据

机动车事故是道路上最常见的死亡原因之一。对此类事故的实时严重程度预测可能有助于降低死亡率。在这项研究中，基本目标是开发预测碰撞结果是否致命的机器学习模型。本研究使用了一个包含 580 万条记录的加拿大道路交通事故数据集。在这项研究中，使用多数和软投票开发了集成模型，以解决数据集中的类别不平衡问题。使用卷积神经网络可以实现大约 75% 的预测精度。此外，本文还对区分致命与非致命机动车辆碰撞的重要属性进行了全面分析。深入的信息内容分析揭示了在预测模型中贡献最大的因素。这些包括碰撞时的道路特征和天气条件、车辆类型、碰撞发生的时间、道路使用者类别及其位置、使用的任何安全装置以及交通控制的状态。借助基于天气和道路状况的实时数据，可以利用本研究中使用的预测模型开发自动预警系统。