Cargando…

A two-stage simulation analysis of uncertain road damage on the urban emergency delivery network

When a city encounters a natural disaster, the traffic capacity of the road will change uncertainly over time as the disaster spreads. At this time, it will affect the overall distribution of the urban road network. Therefore, in order to ensure the normal operation of the city, evaluate the objecti...

Descripción completa

Detalles Bibliográficos
Autores principales: Song, Yinghua, Wu, Ke, Liu, Dan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9132276/
https://www.ncbi.nlm.nih.gov/pubmed/35613114
http://dx.doi.org/10.1371/journal.pone.0267043
Descripción
Sumario:When a city encounters a natural disaster, the traffic capacity of the road will change uncertainly over time as the disaster spreads. At this time, it will affect the overall distribution of the urban road network. Therefore, in order to ensure the normal operation of the city, evaluate the objective regularities of impact is of great significance and urgency to emergency decision-makers. The extent and scope of road damaged in the disaster-stricken area varies with time due to the impact of natural calamities. To reveal the regularities impact, this paper provides a two-stage analysis method based on the distribution path of the road network, offering basic data analysis and nonlinear fitting regression analysis on distribution costs, spatial accessibility and distribution efficiency. This study uses the degree of road network damage and the double randomness of road damaged to establish a transportation model for dynamic simulation analysis. The research results show that the delivery regularity of costs, spatial accessibility, and efficiency present the s-curve changes obviously. There are obvious inflection points when the damaged road percentage reaches about 10%-15% and 30%-40%. Therefore, the most suitable delivery route and time can be selected to maximize efficiency and reduce losses.