Analyzing the Impact of Floods on Vehicular Mobility along Urban Road Networks Using the Multiple Centrality Assessment Approach

Abstract

Rapid urbanization due to fast economic development has put tremendous stress on urban infrastructure and has caused an increase in impervious surface area and runoff, resulting in flash floods in many cities in the world. In developing countries such as India, urban floods have severe impacts due to highly vulnerable population density. The normal functioning of the urban region is disrupted due to the effect of floods on the urban road network. This paper examined vehicular mobility on urban road networks under nonflood and flood conditions and identified an affected region under the flood condition. Multiple centrality assessment (MCA) was used with multiple classical metrics, such as betweenness centrality and closeness centrality, to assess the status of nodes using distance and travel time under nonflood and flood conditions. The road network under floods was generated by the Soil Conservation Service curve number (SCS-CN) and time area histogram methods using flooding depth over road network and betweenness centrality. Weighted overlay centrality layers were created to identify the regions with severely affected mobility under the flood condition by generating a ward-level vulnerability map. The Pearson’s correlation analysis of centrality maps showed that network centrality is correlated strongly with the road network availability at a 95% confidence level. The study also indicates that under flood condition, high betweenness centrality decreases at the city center and closeness centrality exhibits contrasting spatial patterns. According to the ward-level vulnerability map, mobility on road networks was affected severely in 29.35% of the areas, most of which were centrally located. This research suggests that during urban planning, major roads that have greater impedance during floods should be distributed throughout the region so that mobility is less affected by floods.