LAUSR

Abstract This study provides a new methodology to uncover the topology and dynamic evolution of road network vulnerability, and understand the interacted effects of the traffic conditions and the users/passengers’ traveling behaviors on it. In this paper, by using a data set included the traffic flows during the morning peak period (07:00 a.m. to 09:00 a.m.) in the normal state and the flood-hit state, we simulated and compared the dynamic characterisitics of the normal traffic condition with that of the flood-hit traffic condition. We identified and visualized 51 flood-prone areas to predict the geographical distribution of hot spots in the road network to floods. We built a conceptual framework to define and measure the vulnerability as a function of exposure and importance. We measured and mapped the flood-prone areas’ vulnerability scores in the normal state and the flood-hit state, respectively, and used statistical analysis to compare their dynamic characteristics. We investigated the influence of the traffic conditions and the users/passengers’ traveling behaviors on the dynamic evolution of road network vulnerability. Our findings helped transport planners and decision-makers better derive the dynamic evolution of road network vulnerability affected by the users/passengers’ traveling behaviors, and they can also be used to guide the users/passengers to choose the optimum routes for improving the overall performance of the road network effectively when a flood occurs.