@article{ref1,
title="Road crash risk prediction during COVID-19 for flash crowd traffic prevention: the case of Los Angeles",
journal="Computer communications",
year="2023",
author="Wang, Junbo and Yang, Xiusong and Yu, Songcan and Yuan, Qing and Lian, Zhuotao and Yang, Qinglin",
volume="198",
number="",
pages="195-205",
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).<p /> <p>Language: en</p>",
language="en",
issn="0140-3664",
doi="10.1016/j.comcom.2022.12.002",
url="http://dx.doi.org/10.1016/j.comcom.2022.12.002"
}