用深度神经网络预测交通事故严重程度，准确率达92%
Predicting Traffic Accident Severity with Deep Neural Networks
- 通过自编码器降维并分析特征共线性，提升数据质量
- 在交叉验证中实现最高92%的事故严重程度分类准确率
- 适合交通安全管理与智能预警系统研究者参考
交通事故可为降低后续事件风险提供研究依据。近年来机器学习的发展为分析交通肇事数据提供了新途径，新模型在不平衡数据上表现出良好的泛化能力和高预测性能。本研究基于神经网络模型对交通事故数据进行分析，首先通过自编码器进行无监督降维并评估特征共线性，随后构建密集神经网络。所提方法以事故特征为输入，目标是分类事故严重程度。实验结果显示，在交叉验证下，该深度神经网络对事故严重程度的分类准确率最高达到92%。
原文摘要 · Abstract (English)
Traffic accidents can be studied to mitigate the risk of further events. Recent advances in machine learning have provided an alternative way to study data associated with traffic accidents. New models achieve good generalization and high predictive power over imbalanced data. In this research, we study neural network-based models on data related to traffic accidents. We begin analyzing relative feature colinearity and unsupervised dimensionality reduction through autoencoders, followed by a dense network. The features are related to traffic accident data and the target is to classify accident severity. Our experiments show cross-validated results of up to 92% accuracy when classifying accident severity using the proposed deep neural network.
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