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The Analysis for the Traffic Risks in Barcelona before and after the COVID-19 Based on Deep Learning
2nd International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2021 ; : 600-605, 2021.
Article in English | Scopus | ID: covidwho-1788618
ABSTRACT
Big Traffic data [1] is cross-border multi-source data for multiple industries, but traffic roads have brought significant economic and social benefits, the number of traffic accidents and casualties is on the rise. Among them, traffic accidents are related to many factors, such as weather and population density. The data set used in this article is open source in Barcelona. The Random Forest algorithm is used to screen essential risk factors, establish a traffic risk prediction model, and compare traffic risks before and after COVID-19. It is concluded that the outbreak of the new crown virus -19-19 has a great impact on people's travel and transportation. Finally, the R square of the model established by Random Forest is 0.9. The K-means clustering algorithm is used to determine the location of the accident handling centre. Moreover, the scope of each accident risk management centre can cover more than 85 percent of traffic accident sites from 2016 to 2020. © 2021 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study Language: English Journal: 2nd International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study Language: English Journal: 2nd International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2021 Year: 2021 Document Type: Article