摘要
The prevention and treatment of epidemic is always an urgent problem faced by the human being. Due to the special space structure, huge passenger flow and great people mobility, the subway lines have become the areas with high epidemic transmission risks. However, there is no recent study related to epidemic transmission in the subway network on urban-scale. In this article, from the perspective of big data, we study the transmission risk of epidemic in Beijing subway network by using urban subway mobility data. By reintegrating and mining the urban subway mobility data, we preliminary assess the transmission risk in the subway lines from the passenger behaviors, station features, route features and individual case on the basis of subway network structure. This study has certain practical significance for the early stage of epidemic tracking and prevention.
The prevention and treatment of epidemic is always an urgent problem faced by the human being. Due to the special space structure, huge passenger flow and great people mobility, the subway lines have become the areas with high epidemic transmission risks. However, there is no recent study related to epidemic transmission in the subway network on urban-scale. In this article, from the perspective of big data, we study the transmission risk of epidemic in Beijing subway network by using urban subway mobility data. By reintegrating and mining the urban subway mobility data, we preliminary assess the transmission risk in the subway lines from the passenger behaviors, station features, route features and individual case on the basis of subway network structure. This study has certain practical significance for the early stage of epidemic tracking and prevention.
