Cargando…

Road crash risk prediction during COVID-19 for flash crowd traffic prevention: The case of Los Angeles

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 tra...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Junbo, Yang, Xiusong, Yu, Songcan, Yuan, Qing, Lian, Zhuotao, Yang, Qinglin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9726210/
https://www.ncbi.nlm.nih.gov/pubmed/36506874
http://dx.doi.org/10.1016/j.comcom.2022.12.002
_version_ 1784844724532674560
author Wang, Junbo
Yang, Xiusong
Yu, Songcan
Yuan, Qing
Lian, Zhuotao
Yang, Qinglin
author_facet Wang, Junbo
Yang, Xiusong
Yu, Songcan
Yuan, Qing
Lian, Zhuotao
Yang, Qinglin
author_sort Wang, Junbo
collection PubMed
description 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).
format Online
Article
Text
id pubmed-9726210
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Elsevier B.V.
record_format MEDLINE/PubMed
spelling pubmed-97262102022-12-07 Road crash risk prediction during COVID-19 for flash crowd traffic prevention: The case of Los Angeles Wang, Junbo Yang, Xiusong Yu, Songcan Yuan, Qing Lian, Zhuotao Yang, Qinglin Comput Commun Article 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). Elsevier B.V. 2023-01-15 2022-12-07 /pmc/articles/PMC9726210/ /pubmed/36506874 http://dx.doi.org/10.1016/j.comcom.2022.12.002 Text en © 2022 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Wang, Junbo
Yang, Xiusong
Yu, Songcan
Yuan, Qing
Lian, Zhuotao
Yang, Qinglin
Road crash risk prediction during COVID-19 for flash crowd traffic prevention: The case of Los Angeles
title Road crash risk prediction during COVID-19 for flash crowd traffic prevention: The case of Los Angeles
title_full Road crash risk prediction during COVID-19 for flash crowd traffic prevention: The case of Los Angeles
title_fullStr Road crash risk prediction during COVID-19 for flash crowd traffic prevention: The case of Los Angeles
title_full_unstemmed Road crash risk prediction during COVID-19 for flash crowd traffic prevention: The case of Los Angeles
title_short Road crash risk prediction during COVID-19 for flash crowd traffic prevention: The case of Los Angeles
title_sort road crash risk prediction during covid-19 for flash crowd traffic prevention: the case of los angeles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9726210/
https://www.ncbi.nlm.nih.gov/pubmed/36506874
http://dx.doi.org/10.1016/j.comcom.2022.12.002
work_keys_str_mv AT wangjunbo roadcrashriskpredictionduringcovid19forflashcrowdtrafficpreventionthecaseoflosangeles
AT yangxiusong roadcrashriskpredictionduringcovid19forflashcrowdtrafficpreventionthecaseoflosangeles
AT yusongcan roadcrashriskpredictionduringcovid19forflashcrowdtrafficpreventionthecaseoflosangeles
AT yuanqing roadcrashriskpredictionduringcovid19forflashcrowdtrafficpreventionthecaseoflosangeles
AT lianzhuotao roadcrashriskpredictionduringcovid19forflashcrowdtrafficpreventionthecaseoflosangeles
AT yangqinglin roadcrashriskpredictionduringcovid19forflashcrowdtrafficpreventionthecaseoflosangeles