The emergency accessibility analysis based on traffic big data and flood scenario simulation in the context of Shanghai hotel industry

This paper presents a novel methodology for assessing emergency response capabilities in coastal cities in China amidst the challenges posed by global warming, rapid tourism industry growth, and increasing flood occurrences. Our approach integrates flood simulation, traffic big data, and web-based path navigation to evaluate the emergency response of the Fire & Rescue Service (FRS) to tourist hotels in Shanghai. The empirical study highlights the significant impact of transportation conditions, hotel locations, flood inundation intensity, and urban FRS distribution on emergency response effectiveness. It further demonstrates that existing traffic conditions heavily influence flood-induced emergency accessibility, with severe congestion adversely affecting spatial accessibility. The study also reveals that flooding events and real-time traffic can cause delays in emergency responses by altering optimal routes. Consequently, selecting the most efficient routes becomes crucial for enhancing a city's emergency response capabilities. The results validate the efficacy of our proposed approach, which holds significant promise for improving emergency response capabilities in urban tourism settings when faced with disasters.