School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile; Instituto Sistemas Complejos de Ingeniería, Chile.
Escuela de Ingeniería Industrial, Universidad Diego Portales, Chile.
Accid Anal Prev. 2021 Nov;162:106409. doi: 10.1016/j.aap.2021.106409. Epub 2021 Sep 30.
In road safety, real-time crash prediction may play a crucial role in preventing such traffic events. However, much of the research in this line generally uses data aggregated every five or ten minutes. This article proposes a new image-inspired data architecture capable of capturing the microscopic scene of vehicular behavior. In order to achieve this, an accident-prediction model is built for a section of the Autopista Central urban highway in Santiago, Chile, based on the concatenation of multiple-input Convolutional Neural Networks, using both the aggregated standard traffic data and the proposed architecture. Different oversampling methodologies are analyzed to balance the training data, finding that the Deep Convolutional Generative Adversarial Networks technique with random undersampling presents better results when generating synthetic instances that permit maximizing the predictive performance. Computational experiments suggest that this model outperforms other traditional prediction methodologies in terms of AUC and sensitivity values over a range of false positives with greater applicability in real life.
在道路安全领域，实时事故预测可能在预防此类交通事件中发挥关键作用。然而，该领域的大多数研究通常使用每五到十分钟聚合的数据。本文提出了一种新的基于图像的启发式数据架构，能够捕捉车辆行为的微观场景。为此，本文基于串联多个输入卷积神经网络，利用聚合的标准交通数据和所提出的架构，为智利圣地亚哥的 Autopista Central 城市高速公路的一段路构建了一个事故预测模型。分析了不同的过采样方法来平衡训练数据，发现使用随机欠采样的深度卷积生成对抗网络技术在生成允许最大限度提高预测性能的合成实例时，效果更好。计算实验表明，该模型在 AUC 和灵敏度值方面优于其他传统预测方法，并且在具有更大实际适用性的一系列假阳性值范围内具有更好的性能。
