To construct an accurate crash prediction model, the road safety performance function (SPF), which provides a safety guide for the management department, is often used. In traditional parametric SPFs, the importance of traffic features is calculated using analytic expression, but the model is inaccurate and low in generalization. This article proposes a machine learning-based method to replace parametric SPFs, this framework is built based on integrated visual feature importance, global model training, and a structure self-organizing scheme. From the analysis, this model can not only predict multiregional car crashes accurately but can also provide a feature importance and selection guide for the management department to better understand it. At last, experiments using real-world data collected from Highway 401 Ontario Canada and several highways in the U.S. show that the proposed framework outperformed other State-of-the-Art models in terms of interpretability, accuracy, generalizability, and model conciseness.

中文翻译：

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利用深度神经网络自适应特征选择开发自动全局碰撞预测模型


为了构建准确的碰撞预测模型，经常使用为管理部门提供安全指导的道路安全性能函数（SPF）。传统的参数SPF中，通过解析表达式来计算流量特征的重要性，但模型不准确且泛化性较低。本文提出了一种基于机器学习的方法来替代参数化 SPF，该框架是基于集成视觉特征重要性、全局模型训练和结构自组织方案构建的。从分析来看，该模型不仅可以准确预测多区域车祸，而且可以为管理部门更好地理解它提供特征重要性和选择指南。最后，使用从加拿大安大略省 401 号高速公路和美国几条高速公路收集的真实世界数据进行的实验表明，所提出的框架在可解释性、准确性、概括性和模型简洁性方面优于其他最先进的模型。