PDF(Pubmed)
Abstract：
Road crashes are a major problem for traffic safety management, which usually causes flash crowd traffic with a profound influence on traffic management and communication systems. In 2020, the sudden outbreak of the novel coronavirus disease (COVID-19) pandemic led to significant changes in road traffic conditions. In this paper, by analyzing crash data from 2016 to 2020 and new COVID-19 case data in 2020, we find that the average crash severity and crash deaths during this period (a rapid increase of new COVID-19 cases in 2020) are higher than those in previous four years. Hence, it is necessary to exploit a novel road crash risk prediction model for such an emergency. We propose a novel data-adaptive fatigue focal loss (DA-FFL) method by fusing fatigue factors to establish a road crash risk prediction model under the scenario of large-scale emergencies. Finally, the experimental results demonstrate that DA-FFL performs better than the other typical methods in terms of area under curve (AUC) and false alarm rate (FAR) for imbalanced data. Furthermore, DA-FFL has better prediction performance in convolutional neural networks-long short-term memory (CNN-LSTM).
摘要：
道路交通事故是交通安全管理的一大难题,这通常会导致快速人群流量，对交通管理和通信系统产生深远的影响。2020年，新型冠状病毒病（COVID-19）大流行的突然爆发导致道路交通状况发生了重大变化。在本文中,通过分析2016年至2020年的撞车数据和2020年新的COVID-19病例数据，我们发现这一时期的平均撞车严重程度和撞车死亡人数（2020年新的COVID-19病例迅速增加）高于前四年。因此，有必要针对此类紧急情况开发一种新颖的道路碰撞风险预测模型。我们提出了一种新颖的数据自适应疲劳聚焦损失（DA-FFL）方法，通过融合疲劳因子来建立大规模紧急情况下的道路碰撞风险预测模型。最后,实验结果表明，在不平衡数据的曲线下面积（AUC）和误报率（FAR）方面，DA-FFL的性能优于其他典型方法。此外，DA-FFL在卷积神经网络-长短期记忆(CNN-LSTM)中具有更好的预测性能。
